EGU General Assembly 2024  

This study introduces an approach for land heatwave forecasting, using spatiotemporal machine learning models trained with ERA5 reanalysis data. We focused on key environmental variables like soil moisture, vegetation, and meteorological factors for modelling. The study utilized linear regression as a base model, augmented by more complex algorithms such as Random Forest (RF), XGBoost, and Graph Neural Networks (GNN). We defined heatwaves using temperature data from 1970-2000, and the training phase involved data from 2000 to 2020, focusing on predictive accuracy for 2021-2023. This methodology enabled a detailed exploration of heatwave trends and dynamics over an extended period. Finally, we used explainable AI methods to further deepen our understanding of the complex interplay between environmental variables and heatwave occurrences.

How to cite: Mazumder, S., Hahn, S., and Wagner, W.: Monitoring The Development Of Land Heatwaves Using Spatiotemporal Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1101, https://doi.org/10.5194/egusphere-egu24-1101, 2024.

Deep learning methods have emerged as a potential alternative for the complex problem of climate data downscaling. Precipitation downscaling is challenging due to its stochasticity, skewness, and sparse extreme values. Also, the extreme values are essential to preserve during downscaling and extrapolating future climate projections, as they serve as trivial signals for impact assessments. This research looks into the usefulness of a deep learning method designed for gridded precipitation downscaling, focusing on how well it can generalize and transfer what it learns. This study configures and evaluates a deep learning-based super-resolution neural network called the Super-Resolution Deep Residual Network (SRDRN). Several synthetic experiments are designed to assess its performance over four geographically and climatologically distinct domain boxes over the Indian subcontinent. Domain boxes over Central India (CI), Southern Peninsula (SP), Northwest (NW), and Northeast (NE), exhibiting diverse geographical and climatological characteristics, are chosen to assess the generalization and transferability of SRDRN. Following the training on a set of samples from CI, SP and NW, the performance of the models is evaluated in comparison to the Bias Correction and Spatial Disaggregation (BCSD), a renowned statistical downscaling method. NE is a transfer domain where the trained SRDRN models are directly applied without additional training or fine-tuning. Several objective evaluation metrics, like the Kling-Gupta Efficiency (KGE) score, root mean squared error, mean absolute relative error, and percentage bias, are chosen for the evaluation of SRDRN. The systematic assessment of SRDRN models (KGE~0.9) across these distinct regions reveals a substantial superiority of SRDRN over the BCSD method (KGE~0.7) in downscaling and reconstructing precipitation rates during the test period, along with preserving extreme values with high precision. In conclusion, SRDRN proves to be a promising alternative for the statistical downscaling of gridded precipitation.

Keywords: Precipitation, Statistical downscaling, Deep learning, Transfer learning, SRDRN

How to cite: Murukesh, M. and Kumar, P.: Downscaling and reconstruction of high-resolution precipitation fields using a deep residual neural network: An assessment over Indian subcontinent, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2552, https://doi.org/10.5194/egusphere-egu24-2552, 2024.

The exchange of water and carbon between the land-surface and the atmosphere is regulated by meteorological conditions as well as plant physiological processes. Accurate modeling of the coupled system is not only crucial for understanding local feedback loops but also for global-scale carbon and water cycle interactions. Traditional mechanistic modeling approaches, e.g., the Earth system model ICON-ESM with the land component JSBACH4, have long been used to study the land-atmosphere coupling. However, these models are hampered by relatively rigid functional representations of terrestrial biospheric processes, e.g., semi-empirical parametrizations for stomatal conductance.

Here, we develop data-driven, flexible parametrizations controlling terrestrial carbon-water coupling based on eddy-covariance flux measurements using machine learning (ML). Specifically, we introduce a hybrid modeling approach (integration of data-driven and mechanistic modeling), that aims to replace specific empirical parametrizations of the coupled photosynthesis (GPP ) and transpiration (E_tr ) modules with ML models pre-trained on observations. First, as a proof-of-concept, we train parametrizations based on original JSBACH4 output to showcase that our approach succeeds in reconstructing the original parametrizations, namely latent dynamic features for stomatal (g_s) and aerodynamic (g_a) conductance, the carboxylation rate of RuBisCO (V_cmax), and the photosynthetic electron transport rate for RuBisCO regeneration (J_max). Second, we replace JSBACH4’s original parametrizations by dynamically calling the emulator parameterizations trained on the original JSBACH4 output using a Python-FORTRAN bridge. This allows us to assess the impact of data-driven parametrizations on the output in the coupled land-surface model. In the last step, we adopt the approach to infer these parametrizations from FLUXNET observations to construct an observation-informed model of water and carbon fluxes in JSBACH4.

Preliminary results in emulating JSBACH4 parametrizations reveal R² ranging between 0.91-0.99 and 0.92-0.97 for GPP, E_tr, and the sensible heat flux Q_H  at half-hourly scale for forest and grassland sites, respectively. JSBACH4 with the plugged-in ML-emulator parametrizations provides very similar, but not identical predictions as the original JSBACH4. For example, R² for E_tr (g_s) amounts to 0.91 (0.84) and 0.93 (0.86) at grassland and forest sites, respectively. These differences in the transpiration flux between original predictions and JSBACH4 with emulating parametrizations only result in minor changes in the system, e.g., the soil-water budget in the two models is almost the same (R² of ~0.99). Based on these promising results of our proof-of-concept, we are now preparing the hybrid JSBACH4 model with parametrizations trained on FLUXNET observations.

This modeling framework will then serve as the foundation for coupled land-atmosphere simulations using ICON-ESM, where key biospheric processes are represented by our hybrid observation-informed land-surface model.

How to cite: ElGhawi, R., Reimers, C., Schnur, R., Reichstein, M., Körner, M., Carvalhais, N., and Winkler, A. J.: Hybrid-Modeling of Land-Atmosphere Fluxes Using Integrated Machine Learning in the ICON-ESM Modeling Framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2819, https://doi.org/10.5194/egusphere-egu24-2819, 2024.

Understanding long-term total water storage (TWS) changes in the Yangtze River Basin (YRB) is essential for optimizing water resource management and mitigating hydrological extremes. While the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission have provided valuable observations for investigating global or regional TWS changes, the approximately one-year data gap between these missions and their relatively short 20-year data record limits our ability to study the continuous and long-term variability of YRB's TWS. In this study, two deep learning models are employed to bridge the data gap and reconstruct the historical TWS changes within YRB, respectively. For the data gap filling task, a noise-augmented u-shaped network (NA-UNet) is presented to address UNet's overfitting issues associated with training on limited GRACE observations. Results show that NA-UNet can accurately bridge the data gap, exhibiting favourable and stable performance at both the basin and grid scales. Subsequently, we introduce another deep learning model named RecNet, specifically designed to reconstruct the climate-driven TWS changes in YRB from 1923 to 2022. RecNet is trained on precipitation, temperature, and GRACE observations using a weighted mean square error (WMSE) loss function. We show that RecNet can successfully reconstruct the historical TWS changes, achieving strong correlations with GRACE, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. We also observe superior performance in RecNet when trained with WMSE compared to its non-weighted counterpart. In addition, the reconstructed datasets reveal a recurring occurrence of diverse hydrological extremes over the past century within YRB, influenced by major climate patterns. Together, NA-UNet and RecNet provide valuable observations for studying long-term climate variability and projecting future hydrological extremes in YRB, which can inform effective water resource management and contribute to the development of adaptive strategies for climate change.

How to cite: Wang, J., Shen, Y., Awange, J., Yang, L., and Chen, Q.: Reconstructing total water storage changes in the Yangtze River Basin based on deep learning models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3272, https://doi.org/10.5194/egusphere-egu24-3272, 2024.

This study explores the transformative potential of supervised machine learning algorithms in improving rainfall prediction models for Indonesia. Leveraging the NEX-GDDP-CMIP6 dataset's high-resolution, global, and bias-corrected data, we compare various machine learning regression algorithms. Focusing on the EC Earth3 model, our approach involves an in-depth analysis of five weather variables closely tied to daily rainfall. We employed a diverse set of algorithms, including linear regression, K-nearest neighbor regression (KNN), random forest regression, decision tree regression, AdaBoost, extra tree regression, extreme gradient boosting regression (XGBoost), support vector regression (SVR), gradient boosting decision tree regression (GBDT), and multi-layer perceptron. Performance evaluation highlights the superior predictive capabilities of Gradient Boosting Decision Tree and KNN, achieving an impressive RMSE score of 0.04 and an accuracy score of 0.99. In contrast, XGBoost exhibits lower performance metrics, with an RMSE score of 5.1 and an accuracy score of 0.49, indicating poor rainfall prediction. This study contributes in advancing rainfall prediction models, hence emphasizing the improvement of methodological choices in harnessing machine learning for climate research.

How to cite: Prasetya, R., Harmoko Saputro, A., Sukma Permana, D., and Florida Riama, N.: Comparative Study of Supervised Learning Algorithms on Rainfall Prediction using NEX-GDDP-CMIP6 Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3307, https://doi.org/10.5194/egusphere-egu24-3307, 2024.

Underrepresentation of cloud formation is a known failing in current climate simulations. The coarse grid resolution required by the computational constraint of integrating over long time scales does not permit the inclusion of underlying cloud generating physical processes. This work employs a multi-output Gaussian Process (MOGP) trained on high resolution Unified Model (UM) simulation data to predict the variability of temperature and specific humidity fields within the climate model. A proof-of-concept study has been carried out where a trained MOGP model is coupled in-situ with a simplified Atmospheric General Circulation Model (AGCM) named SPEEDY. The temperature and specific humidity profiles of the SPEEDY model outputs are perturbed at each timestep according to the predicted high resolution informed variability. 10-year forecasts are generated for both default SPEEDY and ML-hybrid SPEEDY models and output fields are compared ensuring hybrid model predictions remain representative of Earth's atmosphere. Some changes in the precipitation, outgoing longwave and shortwave radiation patterns are observed indicating modelling improvements in the complex region surrounding India and the Indian sea.

How to cite: Briant, J., Giles, D., Morcrette, C., and Guillas, S.: A Hybrid Machine Learning Climate Simulation Using High Resolution Convection Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3499, https://doi.org/10.5194/egusphere-egu24-3499, 2024.

Understanding atmospheric variability is essential for adapting to future climate extremes. Key ways to do this are through analysing climate field reconstructions and reanalyses. However, producing such reconstructions can be limited by high production costs, unrealistic linearity assumptions, or uneven distribution of local climate records. 

Here, we present a machine learning-based non-linear climate variability reconstruction method using a Recurrent Neural Network that is able to learn from existing model outputs and reanalysis data. As a proof-of-concept, we reconstructed more than 400 years of global, monthly temperature anomalies based on sparse, realistically distributed pseudo-station data.

Our reconstructions show realistic temperature patterns and magnitude reproduction costing about 1 hour on a middle-class laptop. We highlight the method’s capability in terms of mean statistics compared to more established methods and find that it is also suited to reconstruct specific climate events. This approach can easily be adapted for a wide range of regions, periods and variables. As additional work-in-progress we show output of this approach for reconstructing European weather in 1807, including the extreme summer heatwave of that year.

How to cite: Wegmann, M. and Jaume-Santero, F.: From climate to weather reconstruction with inexpensive neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3614, https://doi.org/10.5194/egusphere-egu24-3614, 2024.

Shales are important rocks that store a significant amount of Organic Content. In this work, we present applications of realistic synthetic simulations using real-scaled geological sections. The case of the study is Santos Sedimentary Basin, a well-known and well-studied Geologic Basin. This synthetic data improves the performance of our IA for TOC estimators. Besides, it reduces costs and resources concerning data acquisition for IA simulations. The work consists of reconstructing a pseudo-well formed in a fracture zone modelled through an accurate 2D geological section. To simulate the effects of a fracture zone on geophysical logging data, we present the law of mixtures based on well-drilling concepts, whose objective is to impose geometric conditions on the set of subsurface rock packages. We generated four rock packs belonging to two mixed classes. Tests with noisy synthetic data produced by an accurate geological section were developed and classified using the proposed method (Carreira et al., 2024). Firstly, we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section. We then define two specific training data sets composed of density (RHOB), sonic (DT), spontaneous potential (SP) and gamma-ray (GR) logs,  and  Total Organic Carbon (TOC), spontaneous potential (SP), density (RHOB) and photoelectric effect (PE) all simulated through a Gaussian distribution function per lithology. Acquiring the sonic profile is essential not only for estimating the porosity of the rocks but also for in-depth simulations of the Total Organic Content (TOC) with the geological units cut by the synthetic wells. Since most wells Exploitation does not have this profile well and it is not economically viable to make a new acquisition, resorting to the nonlinear regression models to estimate the sonic profile showed that it is an important feature. We estimate the observed Total Organic Carbon (TOC) measurements using Passey and Wang's (2016) methodology to input data into the k-means classification model. The synthetic model proposed showed promissory results indicating that linear dependency may underscore k-means shale classification. 

How to cite: Carreira, V., Silva, M., Venancio, I., Belem, A., Viegas, I., Spigolon, A., Albuquerque, A. L., and Vitor, P.: Exploiting Pseudo Wells in a Synthetic Sedimentary Basin: a simulation in the Santos Off-Shore Basin in the Southeast Atlantic portion of Brazil, using synthetic TOC for k-means classification., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3640, https://doi.org/10.5194/egusphere-egu24-3640, 2024.

Studying low-likelihood high-impact extreme weather and climate events in a warming world requires massive
ensembles to capture long tails of multi-variate distributions. In combination, it is simply impossible to generate
massive ensembles, of say 10,000 members, using traditional numerical simulations of climate models at high
resolution. We describe how to bring the power of machine learning (ML) to replace traditional numerical
simulations for short week-long hindcasts of massive ensembles, where ML has proven to be successful in terms of
accuracy and fidelity, at five orders-of-magnitude lower computational cost than numerical methods. Because
the ensembles are reproducible to machine precision, ML also provides a data compression mechanism to
avoid storing the data produced from massive ensembles. The machine learning algorithm FourCastNet (FCN) is
based on Fourier Neural Operators and Transformers, proven to be efficient and powerful in modeling a wide
range of chaotic dynamical systems, including turbulent flows and atmospheric dynamics. FCN has already been
proven to be highly scalable on GPU-based HPC systems. 

We discuss our progress using statistics metrics for extremes adopted from operational NWP centers to show
that FCN is sufficiently accurate as an emulator of these phenomena. We also show how to construct huge
ensembles through a combination of perturbed-parameter techniques and a variant of bred vectors to generate a
large suite of initial conditions that maximize growth rates of ensemble spread. We demonstrate that these
ensembles exhibit a ratio of ensemble spread relative to RMSE that is nearly identical to one, a key metric of
successful near-term NWP systems. We conclude by applying FCN to severe heat waves in the recent climate
record.

How to cite: Collins, W., Pritchard, M., Brenowitz, N., Cohen, Y., Harrington, P., Kashinath, K., Mahesh, A., and Subramanian, S.: Huge Ensembles of Weather Extremes using the Fourier Forecasting Neural Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4460, https://doi.org/10.5194/egusphere-egu24-4460, 2024.

As communities observe recurring regional weather patterns they will often ascribe colloquial names to them such as the Meiyu in East Asia or the Santa Ana winds of California. However, attaching quantitative characterizations to these same names often proves challenging. Classically heuristics have been developed for particular locations and climate phenomena, but their inherent subjectivity undermine the robustness of any subsequent quantitative analysis. To develop a neutral universal mesoscale metric we start by observing that the spatial distribution of rain in a given region is controlled by the interplay between the meteorological parameters (humidity, wind, pressure etc.) and the Earth’s topography. As a result, each recurring climactic phenomena exhibits a unique regional signature/distribution. Unlike at the synoptic scale, mesoscale climate patterns are largely stationary and an accumulation of two decades of high resolution satellite observations means that these patterns can now be reliably numerically extracted. The key additional observation is that at the mesoscale climate phenomena typically have either one or two non-co-occurring stationary states. This allows us to isolate patterns by a simple bifurcating of the subspace of the first two singular vectors. The end result behaves like a trivial Empirical Orthogonal Function (EOF) rotation that has a clear interpretation. It isolates the climate patterns as basis vectors and allows us to subsequently estimate the presence of the climate phenomena at arbitrary timescales. As a case study we use gridded precipitation data from NASA’s Global Precipitation Measurement (GPM) mission (compiled in to the IMERG dataset) in several regions and timescales of particular interest

How to cite: Kontsevich, G. and Löwemark, L.: Using IMERG precipitation patterns to index climate at the mesoscale: A basis rotation method based on climate bistability - an update, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4507, https://doi.org/10.5194/egusphere-egu24-4507, 2024.

Land ecosystems play an important role in the carbon cycle, and hence the climate system. The engine of this cycle is Gross Primary Production (GPP), the assimilation of CO₂ via photosynthesis at the ecosystem scale. Photosynthesis is directly affected by rising CO₂ levels which, in turn, is expected to increase GPP and alter the dynamics of the carbon cycle. However, there is substantial uncertainty about the magnitude and geographical variability of the CO₂ fertilisation effect (CFE) on GPP.

We use a large collection of eddy covariance measurements (317 sites, 2226 site-years), paired with remotely sensed information of vegetation greenness to estimate the effect of rising CO₂ levels on GPP. We propose a hybrid modelling architecture, combining a physically-grounded process model based on eco-evolutionary optimality theory and a deep learning model. The intuition is that the process model represents the current understanding of the CFE, whereas the deep learning model does not implement explicit physical relations but has a higher capacity to learn effects of large and fast variations in the light, temperature, and moisture environment. The hybrid model is set up to learn a correction on the theoretically expected CFE. This makes it more effective in distilling the relatively small and gradual CFE. 

Our study investigates inherent limitations of different models when it comes to drawing conclusions about the CO₂ fertilisation effect. Often, these limitations are due to the presence of latent confounders that give rise to spurious correlations. A promising avenue to address them is therefore the use of causal inference techniques. We show that one way to investigate causality is to test whether the trained hybrid model and its estimate of the CFE is stable across different ecosystems, as expected for a causal physical relation. 

In summary, we study how causal inference, based on a combination of physics-informed and statistical modelling, can contribute to more reliable estimates of the CO₂ fertilisation effect, derived from ecosystem flux measurements.

How to cite: Biegel, S., Schindler, K., and Stocker, B.: Causal inference of the CO2 fertilisation effect from ecosystem flux measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5033, https://doi.org/10.5194/egusphere-egu24-5033, 2024.

Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach we are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Niño and La Niña with very little given information. Our method outperforms the widely used statistical kriging method as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

How to cite: Bochow, N., Poltronieri, A., Rypdal, M., and Boers, N.: Reconstructing Historical Climate Fields With Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5103, https://doi.org/10.5194/egusphere-egu24-5103, 2024.

In recent years, deep learning models have rapidly emerged as a standalone alternative to physics-based numerical models for medium-range weather forecasting. Several independent research groups claim to have developed deep learning weather forecasts which outperform those from state-of-the-art physics-basics models, and operational implementation of data-driven forecasts appears to be drawing near. Yet, questions remain about the capabilities of deep learning models to provide robust forecasts of extreme weather.

Our current work aims to provide an overview of recent developments in the field of deep learning weather forecasting, and highlight the challenges that extreme weather events pose to leading deep learning models. Specifically, we problematise the fact that predictions generated by many deep learning models appear to be oversmooth, tending to underestimate the magnitude of wind and temperature extremes. To address these challenges, we argue for the need to tailor data-driven models to forecast extreme events, and develop models aiming to maximise the skill in the tails rather than in the mean of the distribution. Lastly, we propose a foundational workflow to develop robust models for extreme weather, which may function as a blueprint for future research on the topic.

How to cite: Olivetti, L. and Messori, G.: Advances and Prospects of Deep Learning for Medium-Range Extreme Weather Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5611, https://doi.org/10.5194/egusphere-egu24-5611, 2024.

Tropical Cyclones (TCs) are synoptic-scale, rapidly rotating storm systems primarily driven by air-sea heat and moisture exchanges. They are among the deadliest geophysical hazards, causing substantial economic losses and several fatalities due to their associated strong winds, heavy precipitation, and storm surges, leading to coastal and inland flooding. Because of the severe consequences of their impacts, accurately predicting the occurrence, intensity, and trajectory of TCs is of crucial socio-economic importance. Over the past few decades, advancements in Numerical Weather Prediction models, coupled with the availability of high-quality observational data from past events, have increased the accuracy of short-term forecasts of TC tracks and intensities. However, this level of improvement has not yet been mirrored in long-term climate predictions and projections. This can be attributed to the substantial computational resources required for running high-resolution climate models with numerous ensemble members over long periods. Additionally, the physical processes underlying TC formation are still poorly understood. To overcome these challenges, the future occurrence of TCs can instead be studied using indices, known as Genesis Potential Indices (GPIs), which correlate the likelihood of Tropical Cyclone Genesis (TCG) with large-scale environmental factors instrumental in their formation. GPIs are generally constructed as a product of atmospheric and oceanic variables accounting both for dynamic and thermodynamic processes. The variables are combined with coefficients and exponents numerically determined from past TC observations. Despite reproducing the spatial pattern and the seasonal cycle of observed TCs, GPIs fail to capture the inter-annual variability and exhibit inconsistent long-term trends.

In this work, we propose a new way to formulate these indices by using Machine Learning. Specifically, we forego all previously empirically determined coefficients and exponents and consider all the dynamic and thermodynamic factors incorporated into various indices documented in the literature. Then, using feature selection algorithms, we identify the most significant variables to explain TCG. Our analysis incorporates atmospheric variables as candidate factors to discern whether they inherently possess predictive signals for TCG. Furthermore, we also consider several climate indices that have been demonstrated to be related to TCG at the ocean basin scale. Recognizing that each factor and teleconnection has a distinct impact on TCG, we tailored our analysis to individual ocean basins. Consequently, our final model comprises a series of sub-models, each corresponding to a different tropical region. These sub-models estimate the distribution of TCG using distinct inputs, which are determined based on the outcomes of the basin-specific feature selection process. Preliminary findings indicate that the feature selection process yields distinct inputs for each ocean basin.

How to cite: Dainelli, F., Ascenso, G., Scoccimarro, E., Giuliani, M., and Castelletti, A.: Rethinking Tropical Cyclone Genesis Potential Indices via Feature Selection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5616, https://doi.org/10.5194/egusphere-egu24-5616, 2024.

In the context of global warming, changes in extreme weather events may pose a larger threat to society. Therefore, it is particularly important to improve our climatological understanding of high impact precipitation types (PTs), and how their frequency may change under warming. In this study, we use MIDAS (the Met Office Integrated Data Archive System) observational data to provide our best estimate of historical PTs (e.g. liquid rain, freezing rain, snow etc.) over China. We use machine learning (ML) techniques and meteorological analysis methods applied to data from the ERA5 historical climate reanalysis data to find the best variables for diagnosing PTs, and formed training and testing sets, which were input into ML training. We evaluate the diagnostic ability of the Random Forest Classifier (RFC) for different PTs. The results show that using meteorological variables such as temperature, relative humidity, and winds to determine different PTs, ERA5 grid data and MIDAS station data have good matching ability. Comparing the feature selection results with Kernel Density Estimation, it was found that the two methods have consistent results in evaluating the ability of variables to distinguish different PTs. RFC shows strong robustness in predicting different PTs by learning the differences in meteorological variables between 1990 and 2014. It can capture the frequency and spatial distribution of different PTs well, but this capture ability is sensitive to the training methods of the algorithm. In addition, the algorithm finds it difficult to identify events such as hail that are very low frequency in observations. According to the results of testing for different regions and seasons in China, models trained using seasonal data samples have relatively good performance, especially in winter. These results show the potential for combining a RFC with state-of-the-art climate models to effectively project the possible response of different PT frequencies to climate warming in the future. However, the training method of ML algorithm should be selected with caution.

How to cite: Wang, Y.: Identifying precipitation types over China using a machine learning algorithm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6282, https://doi.org/10.5194/egusphere-egu24-6282, 2024.

Heatwaves over western Europe are increasing faster than elsewhere, which recent studies have attributed at least partly to changes in atmospheric dynamics. To increase our understanding of the dynamical drivers of western European heatwaves, we developed a heatwave classification method taking into account the spatio-temporal atmospheric dynamics. Our deep learning approach consists of several steps: 1) heatwave detection using the Generalized Density-based Spatial Clustering of Applications with Noise (GDBSCAN) algorithm; 2) dimensionality reduction of the spatio-temporal heatwave samples using a 3D Variational Autoencoder (VAE); and 3) a clustering of heatwaves using K-means, a Gaussian Mixture Model, and opt-SNE. We show that a VAE can extract meaningful features from high-dimensional climate data. Furthermore, we find four physically distinct clusters of heatwaves that are interpretable with known circulation patterns, i.e. UK High, Scandinavian High, Atlantic High, and Atlantic Low. Our results indicate that the heatwave phase space, as found with opt-SNE, is continuous with soft boundaries between these circulation regimes, indicating that heatwaves are best categorized in a probabilistic way.

How to cite: Happe, T., Wijnands, J., Fernández-Torres, M. Á., Scussolini, P., Muntjewerf, L., and Coumou, D.: Detecting spatio-temporal dynamics of western European heatwaves using deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6655, https://doi.org/10.5194/egusphere-egu24-6655, 2024.

The tropical Pacific experienced triple La Nina conditions during 2020-22, and the future evolution of the climate condition in the region has received extensive attention. Recent observations and studies indicate that an El Nino condition is developing with its peak stage in late 2023, but large uncertainties still exist. Here, a transformer-based deep learning model is adopted to make predictions of the 2023-24 climate condition in the tropical Pacific. This purely data driven model is configured in such a way that upper-ocean temperature at seven depths and zonal and meridional wind stress fields are used as input predictors and output predictands, representing ocean-atmosphere interactions that participate in the form of the Bjerknes feedback and providing physical basis for predictability. In the same way as dynamical models, the prediction procedure is executed in a rolling manner; multi-month 3D temperature fields as well as surface winds are simultaneously preconditioned as input predictors in the prediction. This transformer model has been demonstrated to outperform other state-of-the-art dynamical models in retrospective prediction cases. Real-time predictions indicate that El Nino conditions in the tropical Pacific peak in late 2023. The underlying processes are further analyzed by conducting sensitivity experiments using this transformer model, in which initial fields of surface winds and upper-ocean temperature fields can be purposely adjusted to illustrate the changes to prediction skills. A comparison with other dynamical coupled model is also made.

How to cite: Zhang, R.: A purely data-driven transformer model for real-time predictions of the 2023-24 climate condition in the tropical Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6924, https://doi.org/10.5194/egusphere-egu24-6924, 2024.

Predicting the weather in urban environments is a complex task because of the highly heterogeneous nature of the urban structure. However, there are many issues inherent in urban meteorology, such as thermal comfort and building’s energy consumption. Those stakes are linked to highly heterogeneous meteorological variables within the city such as temperature, humidity, wind, net radiative flux and city characteristics such as building uses and characteristics. State-of-the-art meteorological models with hectometric resolution, such as the Meso-NH (Lac et al. 2018) research model, can provide accurate forecasts of urban meteorology. However, they require too much computing power to be deployed operationally. Statistical downscaling techniques are machine learning methods enabling the estimation of a fine resolution field based on one or several lower resolution fields. ARPEGE is the operational planetary model of Météo-France and operates at a resolution of 5km on France. Using Meso-NH simulations covering Paris and the Île-de-France region, a statistical downscaling has been carried out to obtain a temperature field at 300m resolution using simulation outputs from the ARPEGE planetary model at 5km. The deduced temperature reproduces the urban heat island and the temperature heterogeneity simulated in Meso-NH. The estimated temperature field is able to represent the links between temperature and topography as well as the sharp gradients between the city and the urban parks.

Lac et al. 2018 : https://doi.org/10.5194/gmd-11-1929-2018

How to cite: Garcia Cristobal, J., Wurtz, J., and Masson, V.: Statistical Downscaling for urban meteorology at hectometric scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8010, https://doi.org/10.5194/egusphere-egu24-8010, 2024.

Tropical cyclones (TCs) are among the costliest and deadliest natural disasters worldwide. The destructive potential of a TC is usually modelled as a power of its maximum sustained wind speed, making the estimation of the intensity of TCs (TCIE) an active area of research. Indeed, TCIE has improved steadily in recent years, especially as researchers moved from subjective methods based on hand-crafted features to methods based on deep learning, which are now solidly established as the state of the art.

However, the datasets used for TCIE, which are typically collections of satellite images of TCs, often have two major issues: they are relatively small (usually ≤ 40,000 samples), and they are highly imbalanced, with orders of magnitude more samples for weak TCs than for intense ones. Together, these issues make it hard for deep learning models to estimate the intensity of the strongest TCs. To mitigate these issues, researchers often use a family of Computer Vision techniques known as “data augmentation”—transformations (e.g., rotations) applied to the images in the dataset that create similar, synthetic samples. The way these techniques have been used in TCIE studies has been largely unexamined and potentially problematic. For instance, some authors flip images horizontally to generate new samples, while others avoid doing so because it would cause images from the Northern Hemisphere to look like images from the Southern Hemisphere, which they argue would confuse the model. The effectiveness or potentially detrimental effects of this and other data augmentation techniques for TCIE have never been examined, as authors typically borrow their data augmentation strategies from established fields of Computer Vision. However, data augmentation techniques are highly sensitive to the task for which they are used and should be optimized accordingly. Furthermore, it remains unclear how to properly use data augmentation for TCIE to alleviate the imbalance of the datasets.

In our work, we explore how best to perform data augmentation for TCIE using an off-the-shelf deep learning model, focusing on two objectives:

• Determining how much augmentation is needed and how to distribute it across the various classes of TC intensity. To do so, we use a modified Gini coefficient to guide the amount of augmentation to be done. Specifically, we aim to augment the dataset more for more intense (and therefore less represented) TCs. Our goal is to obtain a dataset that, when binned according to the Saffir Simpson scale, is as close to a normal distribution as possible (i.e., all classes of intensity are equally represented). 
• Evaluating which augmentation techniques are best for deep learning-based TCIE. To achieve this, we use a simple feature selection algorithm called backwards elimination, which leads us to find an optimal set of data augmentations to be used. Furthermore, we explore the optimal parameter space for each augmentation technique (e.g., by what angles images should be rotated).

Overall, our work provides the first in-depth analysis of the effects of data augmentation for deep learning-based TCIE, establishing a framework to use these techniques in a way that directly addresses highly imbalanced datasets.

How to cite: Ascenso, G., Palcic, G., Scoccimarro, E., Giuliani, M., and Castelletti, A.: A Systematic Framework for Data Augmentation for Tropical Cyclone Intensity Estimation Using Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8955, https://doi.org/10.5194/egusphere-egu24-8955, 2024.

To account for uncertainties in future projections associated with the level of greenhouse gas emissions, most climate models are run using different forcing scenarios, like the Shared Socioeconomic Pathways (SSPs). Although it is possible to compare real-world greenhouse gas concentrations with these hypothetical scenarios, it is less clear how to determine whether observed patterns of weather and climate anomalies align with individual scenarios, especially at the interannual timescale. As a result, this study designs a data-driven approach utilizing artificial neural networks (ANNs) that learn to classify global maps of annual-mean temperature or precipitation with a matching emission scenario using a high-resolution, single model initial-condition large ensemble. Here we construct our ANN framework to consider whether a climate map is from SSP1-1.9, SSP2-4.5, SSP5-8.5, a historical forcing scenario, or a natural forcing scenario using the Seamless System for Prediction and EArth System Research (SPEAR) by the NOAA Geophysical Fluid Dynamics Laboratory. A local attribution technique from explainable AI is then applied to identify the most relevant temperature and precipitation patterns used for each ANN prediction. The explainability results reveal that some of the most important geographic regions for distinguishing each climate scenario include anomalies over the subpolar North Atlantic, Central Africa, and East Asia. Lastly, we evaluate data from two overshoot simulations that begin in either 2031 or 2040, which are a set of future simulations that were excluded from the ANN training process. For the rapid mitigation experiment that starts a decade earlier, we find that the ANN links its climate maps to the lowest emission scenario by the end of the 21^st century (SSP1-1.9) in comparison to the more moderate scenario (SSP2-4.5) that is selected for the later mitigation experiment. Overall, this framework suggests that explainable machine learning could provide one possible strategy for assessing observations with future climate change pathways.

How to cite: Labe, Z., Delworth, T., Johnson, N., and Cooke, W.: Explainable AI for distinguishing future climate change scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9110, https://doi.org/10.5194/egusphere-egu24-9110, 2024.

Subseasonal to seasonal (S2S) weather forecasts play an important role as a decision making tool in several sectors of modern society. However, the time scale on which these forecasts are skillful is strongly dependent on atmospheric and oceanic background conditions. While deep learning-based weather prediction models have shown impressive results in the short- to medium range, S2S forecasts from such models are currently limited, partly due to fewer available training data and larger fluctuations in predictability. In order to develop more reliable S2S predictions we leverage Masked Autoencoders, a state-of-the-art deep learning framework, to extract large-scale representations of tropical precipitation and sea-surface temperature data.  We show that the learned representations are highly predictive for the El Niño Southern Oscillation and the Madden-Julian Oscillation, and can thus serve as a foundation for identifying windows of opportunity and generating skillful S2S forecasts.

How to cite: Thümmel, J., Schlör, J., Strnad, F., and Goswami, B.: Subseasonal to seasonal forecasts using Masked Autoencoders, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10129, https://doi.org/10.5194/egusphere-egu24-10129, 2024.

Maps of land surface temperature of the area of Naples (Southern Italy) show large spatial variation of temperature anomalies. In particular, the metropolitan area of Naples is generally characterized by higher temperatures than the rest of the area considered.

Since heat waves have become more frequent in the last decade, the creation of heat maps helps to understand the location where a town’s population may be more affected by them. Ideally, this kind of maps would provide residents with accurate information about the health problems they may face.

Large temperature anomalies variations are caused by multiple or competing factors, leaving uncertainty in identifying vulnerable areas at this time.

To overcome this limitation and identify areas more vulnerable to the effects of heat waves, not only in the city of Naples but also in its suburbs, we combine the use of Landsat data with unsupervised machine learning algorithms to provide detailed heat wave vulnerability maps. In particular, we develop a procedure based on a combined use of hierarchical and partitional cluster analyses that allows us to better identify areas characterized by temperature anomalies that are more similar to each other than to any other all over the year. This has important implications allowing discrimination between locations that potentially would be impacted higher or lower energy consumption.

How to cite: Flocco, D., Piegari, E., and Scafetta, N.: Heat wave vulnerability maps of Naples (Italy) from Landsat images and machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10156, https://doi.org/10.5194/egusphere-egu24-10156, 2024.

High resolution projections are useful for planning climate change adaptation [1] but are expensive to produce using physical simulations. We make use of a state-of-the-art generative machine learning (ML) method, a diffusion model [2], to predict variables from a km-scale model over England and Wales. This is trained to emulate daily mean output from the Met Office 2.2km UK convection-permitting model (CPM) [3], averaged to 8.8km scale for initial testing, given coarse-scale (60km) weather states from the Met Office HadGEM3 general circulation model. This achieves downscaling at much lower computational cost than is required to run the CPM and when trained to predict precipitation the emulator produces samples with realistic spatial structure [4, 5]. We show the emulator learns to represent climate change over the 21^st century. We present some diagnostics indicating that there is skill for extreme events with ~100 year return periods, as is necessary to inform decision-making. This is made possible by training the model on ~500 years of CPM data (48 years from each of 12 ensemble members). We also show the method can be useful in scenarios with limited high-resolution data. The method is stochastic and we find that it produces a well-calibrated spread of high resolution precipitation samples for given large-scale conditions, which is highly important for correctly representing extreme events.

Furthermore, we are extending this method to generate coherent multivariate samples including other impact-relevant variables (e.g. 2m temperature, 2m humidity and 10m wind). We will show the model’s performance at producing samples with coherent structure across all the different variables and its ability to represent extremes in multivariate climate impact indices.

References

[1] Kendon, E. J. et al. (2021). Update to the UKCP Local (2.2km) projections. Science report, Met Office Hadley Centre, Exeter, UK. [Online]. Available: https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/ukcp18_local_update_report_2021.pdf

[2] Song, Y. et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR.

[3] Kendon EJ, E Fischer, CJ Short (2023) Variability conceals emerging trend in 100yr projections of UK local hourly rainfall extremes, Nature Comms, doi: 10.1038/s41467-023-36499-9

[4] Addison, Henry, Elizabeth Kendon, Suman Ravuri, Laurence Aitchison, and Peter AG Watson. (2022). Machine learning emulation of a local-scale UK climate model. arXiv preprint arXiv:2211.16116.

[5] Addison, H., Kendon, E., Ravuri, S., Aitchison, L., and Watson, P. (2023). Downscaling with a machine learning-based emulator of a local-scale UK climate model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14253, https://doi.org/10.5194/egusphere-egu23-14253

How to cite: Addison, H., Kendon, E., Ravuri, S., Aitchison, L., and Watson, P.: Machine learning-based emulation of a km-scale UK climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10262, https://doi.org/10.5194/egusphere-egu24-10262, 2024.

Accurately assessing precipitation impacts due to anthropogenic global warming relies on numerical Earth system model (ESM) simulations. However, the discretized formulation of ESMs, where unresolved small-scale processes are included as semi-empirical parameterizations, can introduce systematic errors in the simulations. These can, for example, lead to an underestimation of spatial intermittency and extreme events.
 Generative deep learning has recently been shown to skillfully bias-correct and downscale precipitation fields from numerical simulations [1,2]. Using spatial context, these methods can jointly correct spatial patterns and summary statistics, outperforming established statistical approaches.
However, these approaches require separate training for each Earth system model individually, making corrections of large ESM ensembles computationally costly. Moreover, they only allow for limited control over the spatial scale at which biases are corrected and may suffer from training instabilities.
Here, we follow a novel diffusion-based generative approach [3, 4] by training an unconditional foundation model on the high-resolution target ERA5 dataset only. Using fully coupled ESM simulations of precipitation, we investigate the controllability of the generative process during inference to preserve spatial patterns of a given ESM field on different spatial scales.

[1] Hess, P., Drüke, M., Petri, S., Strnad, F. M., & Boers, N. (2022). Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nature Machine Intelligence, 4(10), 828-839.

[2] Harris, L., McRae, A. T., Chantry, M., Dueben, P. D., & Palmer, T. N. (2022).A generative deep learning approach to stochastic downscaling of precipitation forecasts. Journal of Advances in Modeling Earth Systems, 14(10), e2022MS003120.

[3] Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J. Y., & Ermon, S. (2021).  Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073.

[4] Bischoff, T., & Deck, K. (2023). Unpaired Downscaling of Fluid Flows with Diffusion Bridges. arXiv preprint arXiv:2305.01822.

How to cite: Hess, P., Gelbrecht, M., Aich, M., Pan, B., Bathiany, S., and Boers, N.: Downscaling precipitation simulations from Earth system models with generative deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10298, https://doi.org/10.5194/egusphere-egu24-10298, 2024.

In order to improve climate projections, machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computations by emulating existent parameterizations. These data-driven models have shown success in approximating subgrid-scale processes based on high-resolution storm-resolving simulations. However, most studies have used a particular machine learning method such as simple Multilayer Perceptrons (MLPs) or Random Forest (RFs) to parameterize the subgrid tendencies or fluxes originating from the compound effect of various small-scale processes (e.g., turbulence, radiation, convection, gravity waves). Here, we use a filtering technique to explicitly separate convection from these processes in data produced by the Icosahedral Non-hydrostatic modelling framework (ICON) in a realistic setting. We use a method improved by incorporating density fluctuations for computing the subgrid fluxes and compare a variety of different machine learning algorithms on their ability to predict the subgrid fluxes. We further examine the predictions of the best performing non-deep learning model (Gradient Boosted Tree regression) and the U-Net. We discover that the U-Net can learn non-causal relations between convective precipitation and convective subgrid fluxes and develop an ablated model excluding precipitating tracer species. We connect the learned relations of the U-Net to physical processes in contrast to non-deep learning-based algorithms. Our results suggest that architectures such as a U-Net are particularly well suited to parameterize multiscale problems like convection, paying attention to the plausibility of the learned relations, thus providing a significant advance upon existing ML subgrid representation in ESMs.

How to cite: Heuer, H., Schwabe, M., Gentine, P., Giorgetta, M. A., and Eyring, V.: Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10325, https://doi.org/10.5194/egusphere-egu24-10325, 2024.

Recent advances in climate model emulation have been shown to accurately represent atmospheric variables from large general circulation models, but little investigation has been done into emulating land-related variables. The land-carbon sink absorbs around a third of the fossil fuel anthropogenic emissions every year, yet there is significant uncertainty around this prediction. We aim to reduce this uncertainty by first investigating the predictability of several land-related variables that drive land-atmospheric carbon exchange. We use data from the IPSL-CM6A-LR submission to the Decadal Climate Prediction Project (DCPP). The DCPP is initialized from observed data and explores decadal trends in relationships between various climatic variables. The land-component of the IPSL-CM6A-LR, ORCHIDEE, represents various land-carbon interactions and we target these processes for emulation. As a first step, we attempt to predict the target land variables from ORCHIDEE using a vision transformer. We then investigate the impacts of different feature selection on the target variables - by including atmospheric and oceanic variables, how does this improve the short and medium term predictions of land-related processes? In a second step, we apply generative modeling (with diffusion models) to emulate land processes. The diffusion model can be used to generate several unseen scenarios based on the DCPP and provides a tool to investigate a wider range of climatic scenarios that would be otherwise computationally expensive. 

How to cite: Clyne, G.: Emulating Land-Processes in Climate Models Using Generative Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10328, https://doi.org/10.5194/egusphere-egu24-10328, 2024.

How to cite: Aich, M., Pan, B., Hess, P., Bathiany, S., Huang, Y., and Boers, N.: Down-scaling and bias correction of precipitation with generative machine learning models , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10692, https://doi.org/10.5194/egusphere-egu24-10692, 2024.

How to cite: Lütjens, B., Selin, N., Souza, A., Geogdzhayev, G., Newman, D., Giani, P., Tebaldi, C., Watson-Parris, D., and Ferrari, R.: Is linear regression all you need? Clarifying use-cases for deep learning in climate emulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10759, https://doi.org/10.5194/egusphere-egu24-10759, 2024.

Glacier ice thickness is a fundamental variable required for modelling flow and mass balance. However, direct measurements of ice thickness are scarce. Physics-based and data-driven approaches aim to reconstruct glacier ice thicknesses from the limited in-situ data. Farinotti et al. compared 17 models and found that their ice thickness estimates differ considerably on test glaciers.[1] Following these results, Farinotti et al. created an ensemble of models to develop the so-called consensus estimate of the ice thickness for the world’s glaciers in 2019.[2] Later, Millan et al. derived ice thickness estimates for the world’s glaciers using ice motion as the primary constraint. However, these results differ considerably from existing estimates and the 2019 consensus estimates.[3] It is evident, therefore, that significant uncertainty remains in ice thickness estimates.

Deep learning approaches are flexible and adapt well to complex structures and non-linear behaviour. However, they do not guarantee physical correctness of the predicted quantities. Therefore, we employ a physics-informed neural network (PINN), which integrates physical laws into their training process and is not purely data-driven. We include, for example, the conservation of mass in the loss function and estimate the depth-averaged flow velocity. Teisberg et al. also employed a mass-conserving PINN to interpolate the ice thickness of the well-studied Byrd glacier in Antarctica.[4] In this work, we extend the methodology by integrating the ratio between slope and surface flow velocities in estimating the depth-averaged flow velocity and mapping the coordinate variables to higher dimensional Fourier Features.[5] This allows to encompass glaciers in western Svalbard, addressing challenges posed by basal sliding, surface melting, and complex glacier geometries. Using surface velocity data from Millan et al. and topographical data from Copernicus DEM GLO-90[6] gathered through OGGM[7],  the model predicts ice thickness on glaciers with limited measurements. We are extending it to perform as a predictor of thickness for glaciers with no observations. Here, we present the machine learning pipeline, including the physical constraints employed and preliminary results for glaciers in western Svalbard.

How to cite: Steidl, V., Bamber, J., and Zhu, X. X.: Physics-aware Machine Learning to Estimate Ice Thickness of Glaciers in West Svalbard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10876, https://doi.org/10.5194/egusphere-egu24-10876, 2024.

Large deep neural network emulators are poised to revolutionize numerical weather prediction (NWP). Recent models like GraphCast or NeuralGCM can now compete and sometimes outperform traditional NWP systems, all at much lower computational cost. Yet to be explored is the applicability of large deep neural network emulators to other dense prediction tasks such as the modeling of 3D atmospheric composition. For instance the inverse modeling of carbon fluxes essential for estimating carbon budgets relies on fast CO₂ transport models.

Here, we present a novel approach to atmospheric transport modeling of CO₂ and other inert trace gases. Existing Eulerian transport modeling approaches rely on numerical solvers applied to the continuity equation, which are expensive: short time steps are required for numerical stability at the poles, and the loading of driving meteorological fields is IO-intensive. We learn high-fidelity transport in latent space by training graph neural networks, analogous to approaches used in weather forecasting, including an approach that conserves the CO₂ mass. For this, we prepare the CarbonBench dataset, a deep learning ready dataset based on Jena Carboscope CO₂ inversion data and NCEP NCAR meteorological reanalysis data together with ObsPack station observations for model evaluation.

Qualitative and quantitative experiments demonstrate the superior performance of our approach over a baseline U-Net for short-term (<40 days) atmospheric transport modeling of carbon dioxide. While the original GraphCast architecture achieves a similar speed to the TM3 transport model used to generate the training data, we show how various architectural changes introduced by us contribute to a reduced IO load (>4x) of our model, thereby speeding up forward runs. This is especially useful when applied multiple times with the same driving wind fields, e.g. in an inverse modeling framework. Thus, we pave the way towards integrating not only atmospheric observations (as is done in current CO₂ inversions), but also ecosystem surface fluxes (not yet done) into carbon cycle inversions. The latter requires backpropagating through a transport operator to optimize a flux model with many more parameters (e.g. a deep neural network) than those currently used in CO₂ inversions – which becomes feasible if the transport operator is fast enough. To the best of our knowledge, this work presents the first emulator of global Eulerian atmospheric transport, thereby providing an initial step towards next-gen inverse modeling of the carbon cycle with deep learning.

How to cite: Benson, V., Bastos, A., Reimers, C., Winkler, A. J., Yang, F., and Reichstein, M.: Graph Neural Networks for Atmospheric Transport Modeling of CO2 , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10922, https://doi.org/10.5194/egusphere-egu24-10922, 2024.

Analyzing climate scenarios is crucial for quantifying uncertainties, identifying trends, and validating models. Objective statistical methods provide decision support for policymakers, optimize resource allocation, and enhance our understanding of complex climate dynamics. These tools offer a systematic and quantitative framework for effective decision-making and policy formulation amid climate change, including accurate projections of extreme events—a fundamental requirement for Earth system modeling and actionable future predictions. 

This study applies dynamic mode decomposition with control (DMDc) to assess temperature and precipitation variability in climate model projections under various future shared socioeconomic pathways (SSPs). We leverage global greenhouse gas emissions and local aerosol emissions as control parameters to unveil nuanced insights into climate dynamics.Our approach involves fitting distinct DMDc models over a high-ambition/low-forcing scenario (SSP126), a medium-forcing scenario (SSP245) and a high-forcing scenario (SSP585). By scrutinizing the eigenvalues and dynamic modes of each DMDc model, we uncover crucial patterns and trends that extend beyond traditional climate analysis methods. Preliminary findings reveal that temporal modes effectively highlight variations in global warming trends under different emissions scenarios. Moreover, the spatial modes generated by DMDc offer a refined understanding of temperature disparities across latitudes, effectively capturing large-scale oscillations such as the El Niño Southern Oscillation. 

The proposed data-driven analytical framework not only enriches our comprehension of climate dynamics but also enhances our ability to anticipate and adapt to the multifaceted impacts of climate change. Integrating DMDc into climate scenario analysis may help formulate more effective strategies for mitigation and adaptation.

References

Allen, Myles R., et al. "Warming caused by cumulative carbon emissions towards the trillionth tonne." Nature 458.7242 (2009): 1163-1166.

Zelinka, Mark D., et al. "Causes of higher climate sensitivity in CMIP6 models." Geophysical Research Letters 47.1 (2020): e2019GL085782.

Proctor, Joshua L., Steven L. Brunton, and J. Nathan Kutz. "Dynamic mode decomposition with control." SIAM Journal on Applied Dynamical Systems 15.1 (2016): 142-161.

How to cite: Mankovich, N., Bouabid, S., and Camps-Valls, G.: Analyzing Climate Scenarios Using Dynamic Mode Decomposition With Control, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11831, https://doi.org/10.5194/egusphere-egu24-11831, 2024.

This study focuses on the application of machine learning techniques to better characterize predictability of the spatiotemporal variability of sea surface temperature (SST) on the basin scale. Both, sub-seasonal variability including extreme events (cf. marine heatwaves) and interannual variability are considered. 

We rely on dimensionality reduction techniques---linear principal component analysis (PCA)  and nonlinear autoencoders and their variants---to then perform the actual prediction tasks in the corresponding latent space using disparate methodologies ranging from linear inverse modeling (LIM) to reservoir computing (RC), and attention-based transformers. 

After comparing performance, we examine various issues including the role of generalized synchronization in RC and implicit memory of RC vs. explicit long-term memory of transformers with the broad aim of shedding light on the effectiveness of these techniques in the context of data-driven climate prediction.

How to cite: Nadiga, B. and Srinivasan, K.: Climate Prediction in Reduced Dimensions: A Comparative Analysis of Linear Inverse Modeling, Reservoir Computing and Attention-based Transformers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12141, https://doi.org/10.5194/egusphere-egu24-12141, 2024.

Combining process-based models in Earth system science with data-driven machine learning methods holds tremendous promise. Can we harness the best of both approaches? In our study, we integrate components of atmospheric models into artificial neural networks (ANN). The resulting hybrid atmospheric model can learn atmospheric dynamics from short trajectories while ensuring robust generalization and stability. We achieve this using the neural differential equations framework, combining ANNs with a differentiable, GPU-enabled version of the well-studied Marshall Molteni Quasigeostrophic Model (QG3). Similar to the approach of many atmospheric models, part of the model is computed in the spherical harmonics domain, and other parts in the grid domain. In our model, ANNs are used as parametrizations in both domains, and form together with the components of the QG3 model the right-hand side of our hybrid model. We showcase the capabilities of our model by demonstrating how it generalizes from the QG3 model to the significantly more complex primitive equation model of SpeedyWeather.jl. 

How to cite: Gelbrecht, M. and Boers, N.: Hybrid neural differential equation models for atmospheric dynamics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12495, https://doi.org/10.5194/egusphere-egu24-12495, 2024.

The Analogue Method (AM) is a classical statistical downscaling technique applied to field reconstruction. It is widely used for prediction and attribution tasks. The method is based on the principle that two similar atmospheric states cause similar local effects. The core of the AM method is a K-nearest neighbor methodology. Thus, two different states have similarities according to the analogy criterion. The method has remained unchanged since its definition, although some attempts have been made to improve its performance. Machine learning (ML) techniques have recently been used to improve AM performance, however, it remains very similar. An ML-based hybrid approach for heatwave (HW) analysis based on the AM is presented here. It is based on a two-step procedure: in the first step, a non-supervised task is developed, where an autoencoder (AE) model is trained to reconstruct the predictor variable, i.e. the pressure field. Second, an HW event is selected, and then the AM method is applied to the latent space of the trained AE. Thus, the analogy between the fields is searched in the encoded data of the input variable, instead of on the original field. Experiments show that the meaningful features extracted by the AE lead to a better reconstruction of the target field when pressure variables are used as input. In addition, the analysis of the latent space allows for interpreting the results, since HW occurrence can be easily distinguished. Further research can be done on including multiple input variables. 

How to cite: Pérez-Aracil, J., Marina, C. M., Gutiérrez, P., Barriopedro, D., García-Herrera, R., Giuliani, M., McAdam, R., Scoccimarro, E., Zorita, E., Castelletti, A., and Salcedo-Sanz, S.: Autoencoder-based model for improving  reconstruction of heat waves using the analogue method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12600, https://doi.org/10.5194/egusphere-egu24-12600, 2024.

We explore the task of learning the dynamics of a system from observed data without prior knowledge of the laws governing the system. Our extensive simulation study focuses on ordinary differential equation (ODE) problems that are specifically designed to reflect key aspects of various machine learning tasks for dynamical systems - namely, chaos, complexity, measurement uncertainty, and variability in measurement intervals. The study evaluates a variety of methods, including neural ODEs, transformers, Gaussian processes, echo state networks, and spline-based estimators. Our results show that the relative performance of the methods tested varies widely depending on the specific task, highlighting that no single method is universally superior. Although our research is predominantly in low-dimensional settings, in contrast to the high-dimensional nature of many climate science challenges, it provides insightful comparisons and understanding of how different approaches perform in learning the dynamics of complex systems.

How to cite: Schötz, C., White, A., and Boers, N.: Comparing Machine Learning Methods for Dynamical Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12826, https://doi.org/10.5194/egusphere-egu24-12826, 2024.

Enhancing flood prediction is imperative given the profound socio-economic impacts of flooding and the projected increase in its frequency due to the impacts of climate change. In this context, artificial intelligence (AI) models have emerged as valuable tools, offering enhanced accuracy and cost-effective solutions to simulate physical flood processes. This study addresses the development of an early warning system for groundwater flooding in the lowland karst area of south Galway, Ireland, employing neural network models with Bayesian regularization and scaled conjugate gradient training algorithms. The lowland karst area is characterised by several groundwater fed, intermittent lakes, known as turloughs that fill when the underlying karst system becomes surcharged during periods of high rainfall. The training datasets incorporate several years of field data from the study area and outputs from a highly calibrated semi-distributed hydraulic/hydrological model of the karst network. Inputs for training the models include flood volume data from the past 5 days, rainfall data, and tidal amplitude data over the preceding 4 days. Both daily and hourly models were developed to facilitate real-time flood predictions. Results indicate strong performance by both Bayesian and Scaled Conjugate Gradient models in real-time flood forecasting. The Bayesian model shows forecasting capabilities extending up to 45 days into the future, with a Nash-Sutcliffe Efficiency (NSE) of 1.00 up to 7 days ahead and 0.95 for predictions up to 45 days ahead. The Scaled Conjugate Gradient model offers the best performance up to 60 days into the future with NSE of 0.98 up to 20 days ahead and 0.95 for predictions up to 60 days ahead, coupled with the advantage of significantly reduced training time compared to the Bayesian model. Additionally, both models exhibit a Co-efficient of Correlation (r) value of 0.98 up to 60 days ahead. Evaluation measures such as Kling Gupta Efficiency reveal high performance, with values of 0.96 up to 15 days ahead for both Bayesian and Scaled Conjugate Gradient models, and 0.90 up to 45 days ahead in the future. The integration of diverse data sources and consideration of both daily and hourly models enhance the resilience and reliability of such an early warning system. In particular, the Scaled Conjugate Gradient model emerges as a versatile tool. It balances predictive accuracy with reduced computational demands, thereby offering practical insights for real-time flood prediction, and aiding in proactive flood management and response efforts.

How to cite: Tabbussum, R., Basu, B., and Gill, L.: Neural Network Driven Early Warning System for Groundwater Flooding: A Comprehensive Approach in Lowland Karst Areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13138, https://doi.org/10.5194/egusphere-egu24-13138, 2024.

Inverse modelling systems relying on Lagrangian Particle Dispersion Models (LPDMs) are a popular way to quantify greenhouse gas emissions using atmospheric observations, providing independent evaluation of countries' self-reported emissions. For each GHG measurement, the LPDM performs backward-running simulations of particle transport in the atmosphere, calculating source-receptor relationships (“footprints”). These reflect the upwind areas where emissions would contribute to the measurement. However, the increased volume of satellite measurements from high-resolution instruments like TROPOMI cause computational bottlenecks, limiting the amount of data that can be processed for inference. Previous approaches to speed up footprint generation revolve around interpolation, therefore still requiring expensive new runs. In this work, we present the first machine learning-driven LPDM emulator that once trained, can approximate satellite footprints using only meteorology and topography. The emulator uses Graph Neural Networks in an Encode-Process-Decode structure, similar to Google’s Graphcast [1], representing latitude-longitude coordinates as nodes in a graph. We apply the model for GOSAT measurements over Brazil to emulate footprints produced by the UK Met Office’s NAME LPDM, training on data for 2014 and 2015 on a domain of size approximately 1600x1200km at a resolution of 0.352x0.234 degrees. Once trained, the emulator can produce footprints for a domain of up to approximately 6500x5000km, leveraging the flexibility of GNNs. We evaluate the emulator for footprints produced across 2016 on the 6500x5000km domain size, achieving intersection-over-union scores of over 40% and normalised mean absolute errors of under 30% for simulated CH4 concentrations. As well as demonstrating the emulator as a standalone AI application, we show how to integrate it with the full GHG emissions pipeline to quantify Brazil’s emissions. This method demonstrates the potential of GNNs for atmospheric dispersion applications and paves the way for large-scale near-real time emissions emulation.

 [1] Remi Lam et al.,Learning skillful medium-range global weather forecasting. Science 382,1416-1421 (2023). DOI:10.1126/science.adi2336

How to cite: Fillola, E., Santos-Rodriguez, R., and Rigby, M.: A Graph Neural Network emulator for greenhouse gas emissions inference, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15144, https://doi.org/10.5194/egusphere-egu24-15144, 2024.

Decision making and efficient early warning systems for extreme weather rely on subseasonal-to-seasonal (S2S) forecasts. However, the chaotic nature of the atmosphere impedes predictions by dynamical forecast systems on the S2S time scale. Improved predictability may arise due to remote drivers and corresponding teleconnections in so-called windows of opportunities, but using knowledge of such drivers to boost S2S forecast skill is challenging. Here, we present a spatio-temporal deep neural network (DNN), predicting a time series of weekly North Atlantic European (NAE) weather regimes on lead-times of one to six weeks during boreal winter. The spatio-temporal architecture combines a convolutional Long-short-term-memory (convLSTM) encoder with an Long-short-term-memory (LSTM) decoder and was built to consider both short and medium-range variability as information. As predictors it uses 2D (image) time series input data of expected drivers of European winter weather, including the stratospheric polar vortex  and tropical sea surface temperatures, alongside the 1D time series of NAE regimes. Our results indicate that additional information provided in the image time series yield a skill score improvement for longer lead times. In addition, by analysing periods of enhanced or decreased predictability of the DNN, we can infer further information regarding prevalent teleconnections.

How to cite: Bommer, P. L., Kretschmer, M., Boehnke, P., and Hoehne née Vidovic, M. M.-C.: Using spatio-temporal neural networks to investigating teleconnections and enhance S2S forecasts of european extreme weather , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15174, https://doi.org/10.5194/egusphere-egu24-15174, 2024.

Deep learning-based weather prediction models have gained popularity in recent years and are effective in forecasting weather over short to medium time scales with models such as FourCastNet being competitive with Numerical Weather Prediction models. 
However, on longer timescales, the complexity and interplay of different weather and climate variables leads to increasingly inaccurate predictions. 

Large-scale climate phenomena, such as the active periods of the Madden-Julian Oscillation (MJO), are known to provide higher predictability for longer forecast times.
These so called Windows of Opportunity thus hold promise as strategic tools for enhancing S2S forecasts.

In this work, we evaluate the capability of FourCastNet to represent and utilize the presence of (active) MJO phases.
First, we analyze the correlation between the feature space of FourCastNet and different MJO indices.
We further conduct a comparative analysis of prediction accuracy within the South East Asia region during active and inactive MJO phases.

How to cite: Banciu, D., Thuemmel, J., and Goswami, B.: Identifying Windows of Opportunity in Deep Learning Weather Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15586, https://doi.org/10.5194/egusphere-egu24-15586, 2024.

The application of automatic differentiation and deep learning approaches to tackle current challenges is now a widespread practice. The biogeosciences community is no stranger to this trend; however, quite often, previously known physical model abstractions are discarded.

In this study, we model the ecosystem dynamics of vegetation, water, and carbon cycles adopting a hybrid approach. This methodology involves preserving the physical model representations for simulating the targeted processes while utilizing neural networks to learn the spatial variability of their parameters. These models have historically posed challenges due to their complex process representations, varied spatial scales, and parametrizations.

We show that a hybrid approach effectively predicts model parameters with a single neural network, compared with the site-level optimized set of parameters. This approach demonstrates its capability to generate predictions consistent with in-situ parameter calibrations across various spatial locations, showcasing its versatility and reliability in modelling coupled systems.
Here, the physics-based process models undergo evaluation across several FLUXNET sites. Various observations—such as gross primary productivity, net ecosystem exchange, evapotranspiration, transpiration, the normalized difference vegetation index, above-ground biomass, and ecosystem respiration—are utilized as targets to assess the model's performance. Simultaneously, a neural network (NN) is trained to predict the model parameters, using input features(to the NN) such as plant functional types, climate types, bioclimatic variables, atmospheric nitrogen and phosphorus deposition, and soil properties. The model simulation is executed within our internal framework Sindbad.jl (to be open-sourced), designed to ensure compatibility with gradient-based optimization methods.

This work serves as a stepping stone, demonstrating that incorporating neural networks into a broad collection of physics-based models holds significant promise and has the potential to leverage the abundance of current Earth observations, enabling the application of these methods on a larger scale.

How to cite: Alonso, L., Koirala, S., Carvalhais, N., Gans, F., Ahrens, B., Cremer, F., Wutzler, T., Ayoub Chettouh, M., and Reichstein, M.: Hybrid Modelling: Bridging Neural Networks and Physics-Based Approaches in Terrestrial Biogeochemical Ecosystems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16513, https://doi.org/10.5194/egusphere-egu24-16513, 2024.

Deep learning weather prediction (DLWP) models have recently proven to be a viable alternative to classical numerical integration. Often, the skill of these models can be improved further by providing additional exogenous fields such as time of day, orography, or sea surface temperatures stemming from an independent ocean model. These merely serve as information sources and are not predicted by the model.

In this study, we explore how such exogenous fields can be utilized by DLWP models most optimally and find that the de facto standard way of concatenating them to the input is suboptimal. Instead, we suggest leveraging existing conditioning techniques from the broader deep learning community that modulate the mean and variance of normalized feature vectors in latent space. These, so called, style-based techniques lead to consistently smaller forecast errors and, at the same time, can be integrated with relative ease into existing forecasting architectures. This makes them an attractive avenue to improve deep learning weather prediction in the future.

How to cite: Hoffmann, S., Thümmel, J., and Goswami, B.: Conditioning Deep Learning Weather Prediction Models On Exogenous Fields, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17165, https://doi.org/10.5194/egusphere-egu24-17165, 2024.

The biogeoscience community has increasingly embraced the application of machine learning models across various domains from fire prediction to vegetation forecasting. Yet, as these models become more widely used, there is sometimes a gap in understanding between what we assume the model learns and what the model actually learns. For example, Long-short Term Memory (LSTM) models are applied to long time series, hoping they benefit from access to more information, despite their tendency to rapidly forget information. This can lead to erroneous conclusions, misinterpretation of results, and an overestimation of the models, ultimately eroding trust in their reliability. 

To address this issue, we employ an explainable artificial intelligence (XAI) post hoc perturbation technique that is task-agnostic and model-agnostic. We aim to examine the extent to which the model leverages information for its predictions, both in terms of time and space. In other words, we want to observe the actual receptive field utilized by the model. We introduce a methodology designed to quantify both the spatial impact of neighboring pixels on predicting a specific pixel and the temporal periods contributing to predictions in time series models. The experiments take place after training the model, during inference. In the spatial domain, we define ground-truth pixels to predict, then examine the increase in prediction error, caused by shuffling their neighboring pixels at various distances from the selection. In the temporal domain, we investigate how shuffling a sequence of frames within the context period at different intervals relative to the target period affects the increase in prediction loss. This method can be applied across a broad spectrum of spatio-temporal tasks. Importantly, the method is easy-to-implement, as it only relies on the inference of predictions at test time and the shuffling of the perturbation area. 

For our experiments, we focus on the vegetation forecasting task, i.e., forecasting the evolution of the Vegetation Index (VI) based on Sentinel-2 imagery using previous Sentinel-2 sequences and weather information to guide the prediction. This task involves both spatial non-linear dependencies arising from the spatial context (e.g., the surrounding area, such as a river or a slope, directly influencing the VI) and non-linear temporal dependencies such as the gradual onset of drought conditions and the rapid influence of precipitation events. We compare several models for spatio-temporal tasks, including ConvLSTM and transformer-based architectures on their usage of neighboring pixels in space, and context period in time. We demonstrate that the ConvLSTM relies on a  restricted spatial area in its predictions, indicating a limited utilization of the spatial context up to 50m (5 pixels). Furthermore, it utilizes the global order of the time series sequence to capture the seasonal cycle but loses sensitivity to the local order after 15 days (3 frames). The introduced XAI method allows us to quantify spatial and temporal behavior exhibited by machine learning methods.

How to cite: Robin, C., Benson, V., Requena-Mesa, C., Alonso, L., Poehls, J., Russwurm, M., Carvalhais, N., and Reichstein, M.: Analyzing Spatio-Temporal Machine Learning Models through Input Perturbation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17389, https://doi.org/10.5194/egusphere-egu24-17389, 2024.

Central to understanding climate change impacts and mitigation strategies is the generation of high-resolution, local-scale projections from global climate models. This study focuses on Danish hydrology, developing models finely tuned to generate essential climate fields such as temperature, precipitation, evaporation, and water vapor flux.

Employing advancements in computer science and deep learning, we introduce a pioneering Cascaded Diffusion Model for high-resolution image generation. This model utilizes our understanding of climate dynamics in a hydrological context by integrating multiple climate variable fields across an expanded North Atlantic domain to produce a model for stable and realistic generation. In our approach, 30 years of low-resolution daily conditioning data (ERA5) are re-gridded to match the 2.5x2.5 km 'ground truth' data (30 years of DANRA), and preprocessed by shifting a 128x128 image within a larger 180x180 pixel area, ensuring varied geographic coverage. This data, along with land-sea masks and topography, is fed as channels into the model. A novel aspect of our model is its specialized loss function, weighted by a signed distance function to reduce the emphasis on errors over sea areas, aligning with our focus on land-based hydrological modeling.

This research is part of a larger project aimed at bridging the gap between CMIP data models and ERA5 and DANRA analysis. It represents the first phase in a three-step process, with future stages focusing on downscaling from CMIP6 to CORDEX-EUROPE models, and ultimately integrating model and analysis work to form a complete pipeline from global projections to localized daily climate statistics.

How to cite: Quistgaard, T., Langen, P. L., Denager, T., Schneider, R., and Stisen, S.: Using Cascaded Diffusion Models and Multi-Channel Data Integration for High-Resolution Statistical Downscaling of ERA5 over Denmark, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17554, https://doi.org/10.5194/egusphere-egu24-17554, 2024.

Here we develop a machine learning emulator based on the Norwegian Earth System Model (NorESM) to predict regional climate responses to aerosol emissions and use it to study the sensitivity of surface temperature to anthropogenic emission changes in key policy regions. Aerosol emissions have both an immediate local effect on air quality, and regional effects on climate in terms of changes to temperature and precipitation distributions via direct radiative impacts and indirect cloud-aerosol interactions. Regional climate change depends on a balance between aerosol and greenhouse gas forcing, and in particular extreme events are very sensitive to changes in aerosol emissions. Our goal is to provide a tool which can be used to test the impacts of policy-driven emission changes efficiently and accurately, while retaining the spatio-temporal complexity of the larger physics-based Earth System Model.

How to cite: Dewey, M., Ekman, A., Watson-Parris, D., and Hansson, H. C.: Machine learning aerosol impacts on regional climate change., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17601, https://doi.org/10.5194/egusphere-egu24-17601, 2024.

In recent years, the intersection of machine learning (ML) and climate science has yielded profound insights into understanding and predicting extreme climate events, particularly heatwaves and droughts. Various approaches have been suggested to define and model extreme events, including extreme value theory (Sura, 2011), random forests (e.g., (Weirich-Benet et al., 2023) and, more recently, deep learning (e.g., (Jacques-Dumas et al., 2022)). Within this context, quantile regression (QR) is valuable for modelling the relationship between variables by estimating the conditional quantiles of the response variable. This provides insights into the entire distribution rather than just the mean but also aids in unravelling the complex relationships among climate variables (Barbosa et al., 2011; Franzke, 2015). QR has been extended in many ways to address critical issues such as nonlinear relations, nonstationary processes, compound events, and the complexities of handling spatio-temporal data. 

This study presents a novel approach for predicting and better understanding heatwaves. We introduce an interpretable, nonlinear, non-parametric, and structured Spatio-Temporal Quantile Regression (STQR) method that incorporates the QR check function, commonly known as pinball loss, into machine learning models. We focus on analysing how the importance of predictors changes as the quantile being modelled increases. This allows us to circumvent arbitrary definitions of what constitutes a heatwave and instead observe if a natural definition of a heatwave emerges in predictor space. By analysing European heatwaves over recent decades using reanalysis and weather data, we demonstrate the advantages of our methodology over traditional extreme event modelling methods.

References

Barbosa, S.M., Scotto, M.G., Alonso, A.M., 2011. Summarising changes in air temperature over Central Europe by quantile regression and clustering. Nat. Hazards Earth Syst. Sci. 11, 3227–3233. https://doi.org/10.5194/nhess-11-3227-2011

Franzke, C.L.E., 2015. Local trend disparities of European minimum and maximum temperature extremes. Geophys. Res. Lett. 42, 6479–6484. https://doi.org/10.1002/2015GL065011

Jacques-Dumas, V., Ragone, F., Borgnat, P., Abry, P., Bouchet, F., 2022. Deep Learning-based Extreme Heatwave Forecast. Front. Clim. 4, 789641. https://doi.org/10.3389/fclim.2022.789641

Sura, P., 2011. A general perspective of extreme events in weather and climate. Atmospheric Res. 101, 1–21. https://doi.org/10.1016/j.atmosres.2011.01.012

Weirich-Benet, E., Pyrina, M., Jiménez-Esteve, B., Fraenkel, E., Cohen, J., Domeisen, D.I.V., 2023. Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models. Artif. Intell. Earth Syst. 2. https://doi.org/10.1175/AIES-D-22-0038.1

How to cite: Bassotto, D., Diaz, E., and Camps-Valls, G.: Spatio-temporal Nonlinear Quantile Regression for Heatwave Prediction and Understanding, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17694, https://doi.org/10.5194/egusphere-egu24-17694, 2024.

Earth observation (EO) repositories comprise Petabytes of data. Due to their widespread use, these repositories experience extremely large volumes of data transfers. For example, users of the Sentinel Data Access System downloaded 78.6 PiB of data in 2022 alone. The transfer of such data volumes between data producers and consumers causes substantial latency and requires significant amounts of energy and vast storage capacities. This work introduces Neural Embedding Compression (NEC), a method that transmits compressed embeddings to users instead of raw data, greatly reducing transfer and storage costs. The approach uses general purpose embeddings from Foundation Models (FM), which can serve multiple downstream tasks and neural compression, which balances between compression rate and the utility of the embeddings. We implemented the method by updating a minor portion of the FM’s parameters (approximately 10%) for a short training period of about 1% of the original pre-training iterations. NEC’s effectiveness is assessed through two EO tasks: scene classification and semantic segmentation. When compared to traditional compression methods applied to raw data, NEC maintains similar accuracy levels while reducing data by 75% to 90%. Notably, even with a compression rate of 99.7%, there’s only a 5% decrease in accuracy for scene classification. In summary, NEC offers a resource-efficient yet effective solution for multi-task EO modeling with minimal transfer of data volumes.

How to cite: Gomes, C. and Brunschwiler, T.: Earth Observation Applications through Neural Embedding Compression from Foundation Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19460, https://doi.org/10.5194/egusphere-egu24-19460, 2024.

Pests significantly impact crop yields, leading to food insecurity. Pesticides are substances, or a mixture of substances, made to eliminate or control pests, or to regulate the growth of crops.
Currently, more than 1000 pesticides are available in the market. However, their long-lasting environmental impact necessitates strict regulation, especially regarding their presence in food (FAO, 2022). Pesticides play also a role in the atmosphere as their volatilization can produce oxidized products through photolysis or OH reactions and they can be transported over large distances.
The fundamental properties and behaviours of these compounds are still not well understood. Because of their complex structure, even low DFT level computations can be extremely expensive. 
This project applies machine learning (ML) tools to chemical ionization mass spectra to ultimately develop a technique capable of predicting spectra’s peak intensities and the chemical ionization mass spectrometry (CIMS) sensitivity to pesticides. The primary challenge is to develop a ML model that comprehensively explains ion-molecule interactions while minimizing computational costs.

Our data set comprises different standard mixtures containing, in total, 716 pesticides measured with an orbitrap atmospheric pressure CIMS, with a multi-scheme chemical ionization inlet (MION) and five different concentrations (Rissanen et al, 2019; Partovi et al, 2023). The reagents of the ionization methods are CH₂Br₂, H₂O, O₂ and (CH₃)₂CO, generating respectively Br^- , H₃O⁺, O^2- and [(CH₃)₂ CO + H]⁺ ions.

The project follows a general ML workflow: after an exploratory analysis, the data are preprocessed and fed to the ML algorithm, which classifies the ionization method able to detect the molecule, and, therefore, predicts the peak intensity of each pesticide; the accuracy of the prediction can be retrieved after measuring the performance of the model.
A random forest classifier was chosen to perform the classification of the ionization methods, to predict which one was able to detect each pesticide. The regression was performed with a kernel ridge regressor. Each algorithm was run with different types of molecular descriptors (topological fingerprint, MACCS keys and many-body tensor representation), to test which one was able to represent the molecular structure most accurately.

The results of the exploratory analysis highlight different trends between the positive and negative ionization methods, suggesting that different ion-molecule mechanisms are involved (Figure 1). The classification reaches around 80% accuracy for each ionization method with all four molecular descriptors tested, while the regression can predict fairly well the logarithm of the intensities of each ionization method, reaching 0.5 of error with MACCS keys for (CH₃)₂CO reagent (Figure 2).

Figure 1: Distribution of pesticide peak intensities for each reagent ion at five different concentrations.

Figure 2: Comparison of the KRR performance on (CH3)2CO reagent data with four different molecular descriptors.

How to cite: Bortolussi, F., Sandström, H., Partovi, F., Mikkilä, J., Rinke, P., and Rissanen, M.: Building A Machine Learning Model To Predict Sample Pesticide Content Utilizing Thermal Desorption MION-CIMS Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20342, https://doi.org/10.5194/egusphere-egu24-20342, 2024.

Leveraging Machine Learning (ML) models, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) like Long-Short Term Memory (LSTM), and Artificial Neural Networks (ANN), has become pivotal in addressing the escalating frequency and severity of extreme events such as heatwaves, hurricanes, floods, and droughts. In climate modeling, ML proves invaluable for analyzing diverse datasets, including climate data and satellite imagery, outperforming traditional methods by adeptly handling vast information and identifying intricate patterns. Focusing on the study's emphasis on extreme precipitation events, the urgency arises from climate change, demanding more accurate and timely methods to predict and manage the impacts of these events.

In this study, we completed two main experiments to understand if ML algorithms can detect the extreme events. In both experiment the predictors that have been used are eastern and northern wind (u,v), geopotential height (z), specific humidity (q) and temperature (t) at four pressure levels, which are 1000hpa, 850hpa, 700hpa, and 500hpa. The frequency for the predictors is 3 hours, while the predictand being the precipitation accumulated over 3 hours. The data used in this study are the Re-Analysis -5th generation- (ERA5) produced by European Center for Medium-Range Weather Forecast (ECMWF), which provides global hourly estimates of large number of atmospheric, land and oceanic climate variables with a resolution of 25 km at different pressure levels and for the surface (precipitation in our case).

In this study, two main architectures have been applied. The first emulator, ERA-Emulator, contains 14 layers, divided in 4 blocks (input, convolutional, dense, output). In the convolutional block we have 6 convolutional layers, one layer of type ConvLSTM2D, that combines a 2D Convolutional layer and an LSTM layer, and five simple 2D convolutional layers, with two of them followed by a MaxPooling layer. In the Dense block there are three fully connected Dense layers followed by one Flatten layer and one Dropout layer. Then, we have the output layer, also a Dense layer. We used the same architecture for the second emulator, GRIPHO-Emulator, with one extra MaxPooling in the convolutional block, for a total of 15 layers. The first emulator uses variables from ERA5 both as input and output at 25 km resolution, while the second one uses variables from ERA5 as input, and the Gridded Italian Precipitation Hourly Observations dataset (GRIPHO) as output at 3 km resolution.

The ERA-Emulator is designed to approximate the downscaling function by utilizing low-resolution simulations to generate equivalent low resolution precipitation fields. ERA-Emulator resulted in a viable approach to address this challenge. The emulator demonstrates the capability to derive precipitation fields that align with ERA5 low-resolution simulations.  GRIPHO-emulator aims to downscale high resolution precipitation from low-resolution large-scale predictors. The emulator aims to estimate the downscaling function. GRIPHO-Emulator is able to create realistic high-resolution precipitation fields that well represent the observed precipitation distribution from the high resolution GRIPHO dataset.

How to cite: Abed, W. and Coppola, E.: Detection of High Convective Precipitation Events Using Machine Learning Methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21760, https://doi.org/10.5194/egusphere-egu24-21760, 2024.

Ocean regional climate variability is a part of the Earth's complex system that can influence the occurrence and intensity of extreme weather events. Variability in ocean temperature can either amplify or mitigate the impact of these events. For example, the El Niño phenomena affect weather conditions in various parts of the world, leading to droughts, floods, and altered precipitation patterns. Furthermore, regional climate variability is also linked to changes in sea level. Understanding regional variability is crucial for predicting how sea level changes will vary in different parts of the world, which has profound implications for coastal communities and infrastructure. To contribute to this understanding, we have developed a novel method that combines K-means clustering and Principal Component Analysis to extract ocean climate modes at a regional scale worldwide. This integrated approach automatically identifies regions of variability, allowing for the emulation of coastal and regional sea level variations across multiple timescales. It also has the potential to offer valuable insights into the significance of temperature across multiple depth layers extending up to 700 meters. The produced set of regional sea-level emulators are a complementary source of information in coastal areas, especially in situations where satellite altimetry encounters challenges and/or tide-gauge sensor records are incomplete, thereby supporting well-informed decision-making.

How to cite: Radin, C. and Nieves, V.: Exploring Regional Ocean Climate Variability: Insights from Integrated Clustering and Principal Component Analysis., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-120, https://doi.org/10.5194/egusphere-egu24-120, 2024.

How to cite: Irie, R., Stewart, H., Kura, T., Hisada, M., and Yaguchi, T.: Parameterizing ocean vertical mixing using deep learning trained from high-resolution simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2297, https://doi.org/10.5194/egusphere-egu24-2297, 2024.

Uncrewed Aerial Vehicles (UAVs) can be a cost-effective solution for capturing a comprehensive view of surface ocean phenomena to study marine population dynamics and ecology. UAVs have several advantages, such as quick deployment from shore, low operational costs, and the ability to be equipped with various sensors, including visual imaging systems and thermal imaging sensors. However, analyzing high-resolution images captured from UAVs can be challenging and time-consuming, especially when identifying small objects or anomalies. Therefore, we developed a method to quickly identify a diverse range of targets in UAV images.

We will discuss our workflow for accelerating the analysis of high-resolution visual images captured from a Trinity F90+ Vertical Take-Off and Landing (VTOL) drone in near-shore habitats around the Monterey Bay region in California at approximately 60 meters altitude. Our approach uses a state-of-the-art self-distillation with knowledge (DINO) transformer foundation model and multi-scale, sliced object detection (SAHI) methods to locate a wide range of objects, from small to large, such as schools or individual jellyfish, flocks of birds, kelp forests or kelp fragments, small debris, occasional cetaceans, and pinnipeds. To make the data analysis more efficient, we create clusters of similar objects based on visual similarity, which can be quickly examined through a web-based interface. This approach eliminates the need for previously labeled objects to train a model, optimizing limited human resources. Our work demonstrates the useful application of state-of-the-art techniques to assist in the rapid analysis of images and how this can be used to develop a recognition system based upon machine-learning for the rapid detection and classification of UAV images. All of our work is freely available as open-source code.

How to cite: Edgington, D. R., Cline, D. E., O'Reilly, T., Haddock, S. H. D., Ryan, J. P., Touryan-Schaefer, B., Kirkwood, W. J., McGill, P. R., and McEwen, R. S.: Accelerating Marine UAV Drone Image Analysis with Sliced Detection and Clustering (MBARI SDCAT), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2934, https://doi.org/10.5194/egusphere-egu24-2934, 2024.

El Niño-Southern Oscillation (ENSO) events have significant impacts on global climate change, and the research on their accurate forecasting and dynamic predictability holds remarkable scientific and engineering values. Recent years, we have constructed two ENSO deep learning forecasting models, ENSO-ASC and ENSO-GTC, which are both incorporated with prior ENSO dynamic mechanisms. Specifically, the former possesses the multivariate air-sea coupler (ASC), which can simulate the occurrence and decay of ENSO events, accompanied by concurrent energy interactions among multiple physical variables in the Pacific Ocean. The latter possesses the global teleconnection coupler (GTC), which can modulate the significant teleconnections of global ocean basins rather than the isolated interactions in the Pacific Ocean. From the perspective of forecasting skill, the Niño 3.4 index correlation skills of these two models can reach 0.78/0.65/0.50 (0.79/0.66/0.51) in 6/12/18 lead-month prediction, which means they exhibit an effective forecasting lead month of more than 18, outperforming the Ham et al.'s Nature-published ENSO forecasting model. The test of the past year's (2022) forecast results shows that the average forecast error of these two models is 0.156, which is less than 10% of the actual ENSO amplitudes. It is worth noting that these two models also encounter the spring presistence barrier (SPB), but indicates a profound improvement compared to the numerical models. From the perspective of ENSO predictability, zonal and meridional winds are two sensitive predictors for ENSO forecasting besides sea surface temperature (SST), which greatly contribute to the Bjerknes positive feedback mechanism and WES mechanism. Walker circulation, acting as the "atmpsphric bridge", induces the teleconnections of the three oceans, which can derive the easterly wind anomalies in the equatorial western Pacific from the Indian Ocean and North Pacific meridional mode in the northeastern Pacific from the Atlantic Ocean, promoting ENSO event development and decay.

How to cite: Qin, B.: Two Physics-informed Enso Deep Learning Forecasting Models: ENSO-ASC and ENSO-GTC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3372, https://doi.org/10.5194/egusphere-egu24-3372, 2024.

The assessment and monitoring of microbial plankton biodiversity are essential to obtain a robust evaluation of the health status of marine environments. The PETRI-MED project addresses this imperative by developing novel strategies to monitor the microbial plankton community composition and function, based on satellite observations. PETRI-MED will focus on the Mediterranean Sea as a global biodiversity hotspot with profound ecological and cultural importance. The primary objectives of PETRI-MED project encompass (i) the development of innovative satellite-based indicators to determine the biodiversity status and trends of microbial plankton community, (ii) the identification of spatio-temporal patterns in microbial plankton distribution and diversity, and (iii) the elucidation of key controls of biodiversity patterns, including ecological connectivity, natural and human-related forcings, by focusing on key indicators of ocean’s health and/or biogeochemical state. To do so, PETRI-MED will largely rely on satellite optical radiometric measurements (i.e, Ocean Colour, OC), exploiting the combined temporal and spatial characteristics of latest OC European datasets (i.e., Copernicus Sentinel-3 and European Space Agency OC-CCI) with state-of-the-art remote sensing observations and biogeochemical models (as provided by Copernicus Marine), marine currents modelling, and genomic techniques. To achieve the ambitious goal of merging remote sensing, biogeochemical/physical modelling, and in situ omics measurements, PETRI-MED will rely on Artificial Intelligence (AI). The overarching goal of PETRI-MED is to empower policymakers and stakeholders with the necessary knowledge to adopt prioritization approaches for ecosystem management based on quantitative, real-time metrics. This includes the design and implementation of protection strategies and policies to safeguard biodiversity, quantifying the impact of implemented actions at various levels, and enabling systematic, fact-supported management of Marine Protected Areas (MPAs), Key Biodiversity Areas, and Ecologically or Biologically Significant Marine Areas. Furthermore, PETRI-MED seeks to evaluate the viability of MPA management in response to climate change, ensuring adaptive strategies for the conservation of marine ecosystems in the face of environmental challenges. In summary, PETRI-MED represents a comprehensive and innovative approach to advancing our understanding of microbial plankton biodiversity in the Mediterranean Sea. Through the integration of satellite technology, omics techniques and AI, the project contributes valuable insights and tools for effective marine ecosystem management and conservation strategies.

How to cite: Tinta, T. and the PETRI-MED: PETRI-MED: Advancing Satellite-Based Monitoring for Microbial Plankton Biodiversity in the Mediterranean Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3523, https://doi.org/10.5194/egusphere-egu24-3523, 2024.

The development of the world economy in recent years has been accompanied by a significant increase in maritime traffic. Accordingly, numerous ship collision incidents, especially in dense maritime traffic zones, have been reported with damage, including oil spills, transportation interruption, etc. To improve maritime surveillance and minimize incidents over the seas, satellite imagery provided by synthetic aperture radar (SAR) and optical sensors has become one of the most effective and economical solutions in recent years. Indeed, both SAR and optical images can be used to detect vessels of different sizes and categories, thanks to their high spatial resolutions and wide swath.

To process a mass of satellite data, Deep Learning (DL) has become an indispensable solution to detect ships with a high accuracy rate. However, the DL models require time and effort for implementation, especially for training, validating, and testing with big datasets. This issue is more significant if we use different satellite imagery datasets for ship detection because data preparation tasks will be multiplied. Therefore, this paper aims to investigate various approaches for applying the DL models trained and tested on different datasets with various spatial resolution and radiometric features. Concretely, we focus on two aspects of ship detection from multi-source satellite imagery that have not been attentively discussed in the literature. First, we compare the performance of DL models trained on one HR or MR dataset and those trained on the combined HR and MR datasets. Second, we compare the performance of DL models trained on an optical or SAR dataset and tested on another. Likewise, we evaluate the performance of DL models trained on the combined SAR and optical dataset. The objective of this work is to answer a practical question of ship detection in maritime surveillance, especially for emergency cases if we can directly apply the DL models trained on one dataset to others having differences in spatial resolution and radiometric features without the supplementary steps such as data preparation and DL models retraining.

When dealing with a limited number of training images, the performance of DL models via the approaches proposed in this study was satisfactory. They could improve 5–20% of average precision, depending on the optical images tested. Likewise, DL models trained on the combined optical and radar dataset could be applied to both optical and radar images. Our experiments showed that the models trained on an optical dataset could be used for radar images, while those trained on a radar dataset offered very poor scores when applied to optical images.

How to cite: La, T.-V., Pham, M.-T., and Chini, M.: Collocation of multi-source satellite imagery for ship detection based on Deep Learning models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3954, https://doi.org/10.5194/egusphere-egu24-3954, 2024.

Seismic hazard assessment requires a detailed understanding of the evolution of fault systems, rupture processes, and linkage between segments. Identifying and characterizing Quaternary surface faulting features, such as fault scarps, provide valuable morphotectonic data on cumulative displacement over time, especially in regions with moderate to low seismic activity. Although fault cumulative vertical surface offsets have been traditionally measured using topographic profiles, these profiles are unevenly spread along the faults and may not reflect all the morphological changes along them. To address this situation, expanding the analysis to encompass a larger number of profiles is a viable option; nevertheless, manually executing this task would prove significantly time-consuming. Machine Learning (ML) has shown unprecedented capacities to evaluate large datasets in reduced time and provide a wealth valuable information with their related uncertainties. With this in mind, we propose a ML algorithm called ScarpLearn based on Convolutional Neural Networks (CNN) to compute the vertical cumulative displacement and its uncertainty for normal fault scarps. Despite ScarpLearn being initially developed to characterize simple scarps in onshore areas, we have enhanced its capabilities so that it can also be used in offshore areas subject to oceanic processes. This includes, among others, more intense diffusion, or the presence of seabed features such as pockmarks. Additionally, we have improved the code's versatility by providing a method modification that allows it to better characterization of scarps in more complex areas where multiple faults offset the seafloor. To this, we have trained the algorithm using a large database of realistic synthetic bathymetric profiles, including different parameters such as fault dip, slip velocity, scarp spread, scarp diffusion coefficient, and variable resolutions to ensure adaptability to all datasets. These modifications have resulted in the improvement of the ScarpLearn algorithm’s adaptability, elevating its accuracy and reliability in capturing the complexity of marine fault systems, but also applicable to terrestrial systems. We have applied the new ScarpLearn version to the North-South faults of the northern Alboran Sea, contributing to the accurate analysis of this Plio-Quaternary transtensional system and its complex geological structures. This innovative approach has allowed us to refine the morphotectonic analysis of the area and to understand better the geodynamics of the North-South faults system. In this research, we have explored the advances of the CNN method achieved in oceanic environments, considering intensive data compilation, computational time, accuracy, uncertainties, and current limitations. Our advances demonstrate the ScarpLearn ML potential, specifically tailored to analyze marine environments and multiple fault segments both onshore and offshore. Our research results contribute to the progress of marine geosciences by improving morphotectonic analysis using ML algorithms.

Keywords: Convolutional Neural Networks (CNN), Oceanic processes, Normal faults, Multiple scarps.

How to cite: Canari, A., Pousse-Beltran, L., Giffard-Roisin, S., Perea, H., and Martínez – Loriente, S.: Revealing Machine Learning's potential for morphotectonic analysis of marine faults: Application to the North-South faults in the Alboran Sea (Westernmost Mediterranean), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4126, https://doi.org/10.5194/egusphere-egu24-4126, 2024.

Prediction of sea surface current is essential for various marine activities, such as tourist industry, commercial transportation, fishing industries, search and rescue operations, and so on. Numerical forecast models make it possible to predict a realistic ocean with the help of data-assimilation and fine spatial resolution. Nevertheless, complicated numerical prediction model requires heavy power and time for computation, which initiated development of novel approaches with efficient computational costs. In that sense, artificial neural networks could be one of the solutions because they need low computational power for prediction thanks to pre-trained networks. Here, we present a prediction framework applicable to the surface current prediction in the seas around the Korean peninsula using three-dimensional (3-D) convolutional neural networks. The network is based on the 3-D U-net structure and modified to predict sea surface currents using oceanic and atmospheric variables. In the forecast procedure, it is optimized to minimize the error of the next day’s sea surface current field and its recursively predicting structure allows more days to be predicted. The network’s performance is evaluated by changing input days and variables to find the optimal surface-current-prediction artificial neural network model, which demonstrates its strong potential for practical uses near future.

How to cite: Park, J.-H., Chae, J.-Y., and Kim, Y. T.: Surface current prediction in the seas around the Korean peninsula using a CNN-based deep-learning model , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4488, https://doi.org/10.5194/egusphere-egu24-4488, 2024.

The Indonesian ThroughfFow (ITF) plays a vital role in the global ocean circulation and climate system. The intricate labyrinth of passages around the Indonesian Seas poses a grand challenge in monitoring and understanding the throughflow in the region. In this study, we employ the deep-learning approach to examine to what degree known sea level variations can determine main in- and outflows through the Indonesian Seas. The approach is first validated using the simulated environment from a regional circulation model. Our results show that the Recurrent Neural Network (RNN) models can well represent the temporal variations of throughflows across the Indonesian Seas. Moreover, the skills can be significantly improved if aided by time series of transport from a small number of passages. We also apply the trained model to the satellite derived sea surface height in design of more effective allocations of observation assets.

How to cite: Xue, H., Wang, Z., and Wang, Y.: Applying Deep-learning Models in Observation Simulation Experiments of Throughflows Across the Indonesian Seas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4587, https://doi.org/10.5194/egusphere-egu24-4587, 2024.

Coral reefs, which host more than a third of the ocean’s biodiversity on less than 0.1% of its surface, are existentially threatened by climate change and other human activities. This necessitates methods for evaluating the state of coral reefs that are efficient, scalable, and low-cost. Current digital reef monitoring tools typically rely on conventional Structure-from-Motion photogrammetry, which can limit the scalability, and current datasets for training semantic segmentation systems are either sparsely labeled, domain-specific, or very small. We describe the first deep-learning-based 3D semantic mapping approach, which enables rapid mapping of coral reef transects by leveraging the synergy between self-supervised deep learning SLAM systems and neural network-based semantic segmentation, even when using low-cost underwater cameras. The 3D mapping component learns to tackle the challenging lighting effects of underwater environments from a large dataset of reef videos. The transnational data-collection initiative was carried out in Djibouti, Sudan, Jordan, and Israel, with over 150 hours of collected video footage for training the neural network for 3D reconstruction. The semantic segmentation component is a neural network trained on a dataset of video frames with over 80’000 annotated polygons from 36 benthic classes, down to the resolution of prominent visually identifiable genera found in the shallow reefs of the Red Sea. This research paves the way for affordable and widespread deployment of the method in analysis of video transects in conservation and ecology, highlighting a promising intersection with machine learning for tangible impact in understanding these oceanic ecosystems. 

How to cite: Sauder, J., Banc-Prandi, G., Perna, G., Meibom, A., and Tuia, D.: Scalable 3D Semantic Mapping of Coral Reefs with Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5552, https://doi.org/10.5194/egusphere-egu24-5552, 2024.

Oxygen is fundamentally essential for all life. Unfortunately, recent research has shown that global ocean deoxygenation has significantly increased over the past 50 years, and the stock of dissolved oxygen (DO) in the ocean has been continuously decreasing. Breathless ocean has led to large-scale death of fish, seriously affecting the marine ecosystem. Moreover, global warming and human activities have further intensified the expansion of dead zones (low-oxygen area) in the ocean.

Hence, it is of vital importance to quantitatively understand and predict the trend of global ocean deoxygenation. However, despite of the accumulation of in-situ DO observation in recent years, global and long-term observation data is still severely sparse, leading to a critical challenge in reconstructing global ocean deoxygenation over a century. Existing works can be categorized into two ways: (1) Physics-informed numerical models. These methods simulate the DO concentration based on climate models without utilizing in-situ observations, e.g., Coupled Model Intercomparison Project Phase 6 (CMIP6). However, these models fail to adjust biased simulation results based on temporal DO observations and cause error propagation. (2) Spatial interpolation methods. These methods reconstruct the global deoxygenation through available observations by geostatistical regression, Kriging, etc. But these ways are unable to capture the complex spatiotemporal heterogeneity and physical-biogeochemical properties, showing inconsistent performance in different areas.

To this end, we propose a knowledge-infused deep graph learning method called 4D Spatio-Temporal Graph HyperNetwork (4D-STGHN) to reconstruct four-dimensional (including time, latitude, longitude, and depth) global ocean deoxygenation from 1920 till now. To capture the spatio-temporal heterogeneity in different regions, 4D-STGHN utilize hypernetwork to generate non-shared parameters by fusing 4D geographic information and observations. Moreover, we design a chemistry-informed gradient norm mechanism as the loss function by integrating the observation of nitrate and phosphate, hereby further improving the performance of DO reconstruction. 4D-STGHN shows promising reconstruction with mean absolute percentage error (MAPE) of only 5.39%, largely outperforming three CMIP6 experiments (CESM2-omip1, CESM2-omip2 and GFDL-ESM4-historical) on dissolved oxygen and other machine learning methods. Further analysis on the global oxygen minimum zones, as well as regional analysis are conducted to evaluate the effectiveness of our proposed methods.

How to cite: Lu, B., Zhao, Z., Han, L., Gan, X., Zhou, Y., Zhou, L., Fu, L., Wang, X., Zhang, J., and Zhou, C.: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5926, https://doi.org/10.5194/egusphere-egu24-5926, 2024.

To elucidate the complex dynamics of zooplankton grazing and its impact on the organic carbon pump, we leveraged machine learning algorithms to analyze extensive datasets encompassing zooplankton behavior, environmental variables, and carbon flux measurements. Specifically, we employed regression models to establish predictive relationships between zooplankton grazing rates and key environmental factors, such as Potential Temperature, Sea Ice extension and iron availability.

The results demonstrate the potential of machine learning in discerning patterns and nonlinear relationships within the data, offering insights into the factors influencing zooplankton grazing dynamics. Additionally, the models provide a predictive framework to estimate the contribution of zooplankton to the organic carbon pump under varying environmental conditions. We have further analyzed the results by using two explainable AI methods, the Layer Wise Relevance Propagation and Integrated Gradients that informs which physical variables contribute to the prediction.

This research contributes to our understanding of the intricate processes governing carbon sequestration in the ocean, with implications for climate change mitigation and marine ecosystem management. Machine learning techniques assists to unravel the complexities of zooplankton-mediated carbon flux, to unravel the complexities of zooplankton-mediated carbon flux, paving the way for more accurate predictions and proactive conservation strategies in the face of global environmental changes.

How to cite: Navarra, G. G., Sane, A., and Deutsch, C.: Analyzing Zooplankton grazing spatial variability in the Southern Ocean using deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6735, https://doi.org/10.5194/egusphere-egu24-6735, 2024.

Tropical cyclones (TCs) are natural disasters originating over tropical or subtropical oceans. Their landfall is generally accompanied by extensive high winds and persistent precipitation, causing severe economic losses and human casualties yearly. Consequently, conducting effective TC landfall intensity forecasts for disaster risk reduction is imperative. The calm center of a TC, known as the TC eye, serves as a vital indicator of its intensity. Hence, precisely locating TC centers is crucial for determining TC intensity. In this study, a deep-learning model was developed to extract TC centers from satellite remote-sensing images automatically.
Space-borne synthetic aperture radar (SAR) imagery plays a critical role in monitoring natural hazards owing to its high spatial resolution, wide coverage, and day-night imaging capabilities. A total of 110 Sentinel SAR images spanning from 2016 to 2019 were used for TC center localization in this paper. They were acquired in interferometric-wide (IW) mode with a pixel size of 10 m and extra-wide (EW) mode with a pixel size of 40 m. They were resampled by spatial averaging to maintain the same pixel size of 80 m. Additionally, we manually annotated the central area of tropical cyclone images as ground truth data.
For the dataset, we initially divided 110 SAR images and the corresponding truth data into training, validation, and testing sets in an 8:1:1 ratio. Subsequently, we partitioned the SAR images into 256 × 256 pixel-sized slices as the model inputs. 32151/4611/3900 samples were extracted as the training/validation/testing dataset. Considering the target samples containing the center position are far less than compared background samples in TCs, we retained all center-containing samples and randomly selected 1.2 times the number of background samples for each image. Consequently, we obtained a final dataset of 2388/257/245 samples for training, validation, and testing.
As is known, deep learning technology excels in learning non-linear relationships and is good at automatically extracting intricate patterns from SAR imagery. The Res-UNet, a U-Net-like model with the weighted attention mechanism and the skip connection scheme that has been proven effective in solving the problem of contrast reduction caused by signal interference, was ultimately determined as the deep learning model for the automatic positioning of tropical cyclone centers in our study.
We calculated the centroid of the central region and compared the model results with ground truth. Our model outputs agreed well with the visually located TC center with a mean intersection over union (IOU) and a mean TC center location error of 0.71/0.70/0.67 and 3.59/2.24/2.20 km for the training/validation/testing dataset. Moreover, our model greatly simplifies the complexity of traditional methods such as using spiral rainbands and background wind fields for center positioning. At the same time, our method can not only obtain the position of the TC center but also extract the central area, thereby obtaining the morphological characteristics of TCs, which is conducive to better monitoring and intensity determination of TC.

How to cite: Mu, S., Wang, H., and Li, X.: A Deep Learning Model for Tropical Cyclone Center Localization Based on SAR Imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6927, https://doi.org/10.5194/egusphere-egu24-6927, 2024.

Surface currents influence ship navigation, coastal heat transfer and sediment transport, and thus necessitate robust models that can reliably predict surface current behaviour. However, our ability to make predictions over long time scales are commonly hampered by a lack of long observational datasets. Remote sensing technologies, which include satellite altimetry and high-frequency radar, are often used to measure global surface currents. However, their ability to reveal insights on ocean dynamics at a regional scale remain limited by restrictions related to space-time sampling. Here, we explore the use of AIS data as a means to derive surface currents in the Sunda Shelf Region in equatorial southeast Asia. Firstly, we apply nearest-neighbour interpolation to map relevant AIS information, that includes the ship’s speed over ground, course over ground and heading, onto a grid with a spatial resolution of 100m and an hourly temporal resolution. Next, we applied a gradient descent approach to derive surface currents at the positions of the ships. We then implement a generative model on PyTorch to reconstruct surface currents in the region. The model performance is evaluated by comparing to observational data from drifters and drifting buoys. Lastly, we employed wavelet analysis, a type of nonstationary spectral analysis, to examine the dominant frequencies or periods where surface currents are strong. Our pilot study highlights the potential of AIS data as a credible alternative to traditional methods of measuring surface currents in data scarce areas.

How to cite: Puah, J. Y., Haigh, I. D., Lallemant, D., Fablet, R., Morgan, K., and Switzer, A. D.: Unveiling the Ocean’s Rhythms: Blending Deep Learning and Spectral Analysis Together to Gain Insights into Sunda Shelf Surface Currents using AIS Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8207, https://doi.org/10.5194/egusphere-egu24-8207, 2024.

Phytoplankton sustains marine ecosystems and influences global carbon dioxide levels through photosynthesis. To grow, phytoplankton rely on nutrient availability in the upper sunlit layer, closely related to ocean dynamics and specifically ocean stratification. Human-caused climate change is responsible, among others, for an increase in global temperature and regional modifications of winds, thus affecting the stratification of the ocean's surface. Consequently, phytoplankton biomass is expected to be impacted by these environmental changes. While most existing studies focus on one or two satellite products to investigate phytoplankton trends in the global ocean, in this study, we analyze surface chlorophyll-a concentration (chl-a), a proxy for phytoplankton biomass, using six merged satellite products from January 1998 to December 2020. Significant regional discrepancies are observed among the different products, displaying opposing trends. To distinguish trends arising from changes in the physical ocean from those potentially resulting from sensor biases, a convolutional neural network is employed to examine the relationship between chl-a and physical ocean variables (sea surface temperature, sea surface height, sea surface currents, wind, and solar radiation). The training is conducted over 2002-2009 when the number of merged sensors is constant, and chl-a is reconstructed over 2010-2020. Our results suggest that the merging algorithm of the Globcolour Garver, Siegel, Maritorena (GSM) bio-optical model is not reliable for trend detection. Specifically, changes in chl-a after 2016 are not supported by changes in the physical ocean but rather by the introduction of the VIIRS sensor. These results emphasize the need for a careful interpretation of chl-a trends and highlight the potential of machine learning to study the evolution of marine ecosystems.

How to cite: Pauthenet, E., Martinez, E., Gorgues, T., Roussillon, J., Drumetz, L., Fablet, R., and Roux, M.: Chlorophyll-a satellite climate time series: How machine learning can help distinguish between bias and consistency, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8942, https://doi.org/10.5194/egusphere-egu24-8942, 2024.

The Federal Maritime and Hydrographic Agency of Germany performs weekly analyses of sea surface temperatures (SST) for the North Sea and Baltic Sea on an operational basis. The analysis is based on in-situ observations and satellite retrievals. Existing procedures require manual quality control and subjective decisions on plausibility of measurements combined with simple interpolation techniques. This study presents ongoing work to develop new procedures based on a machine learning approach, designed to fill in gaps in observational data fields. The employed inpainting technique makes use of a convolutional neural network (CNN) that is trained with complete SST-fields from high-resolution (~3 km) ocean model simulations and masks derived from satellite retrievals to ignore regions covered by clouds on respective days.

First validation efforts for the North Sea based on reconstructing modelled fields that were excluded from training data indicate very promising results, that is an RMSE of ~ 0.5 K or less for most regions of the North Sea. However, areas with high variance such as waters very close to the coast and the Norwegian Channel exhibit larger errors up to 1 K. Additionally, we can show that errors tend to be larger when less observational data are available, e.g. on days with lots of clouds.

It will be tested if optional features of the algorithm may help to improve results in these cases. Especially the possibility to use “memory” of preceding days – potentially featuring less clouds – seems promising in this respect. Furthermore, it will be elaborated if the option of overwriting existing observational data with values better fitting the patterns learned by the CNN is suitable to improve the overall results and hence may be an alternative to external (manual) quality control and plausibility checking.

The final aim of this study is to establish an approach suitable for the operational analysis of daily SSTs with a horizontal resolution of approx. 5 km and the production of an SST reanalysis of North Sea and Baltic Sea SSTs starting in 1990.

How to cite: Kruschke, T., Kadow, C., Meuer, J., and Hinrichs, C.: Machine-learning-based analysis and reconstruction of high-resolution sea-surface temperatures for the North Sea and Baltic Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11061, https://doi.org/10.5194/egusphere-egu24-11061, 2024.

Retrievals of ocean color (OC) properties from space are important for better understanding the ocean ecosystem and carbon cycle. The launch of atmospheric hyperspectral instruments such as the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) and GEMS, provide a unique opportunity to examine the diurnal variability in ocean ecology across various waters in North America and prepare for the future suite of hyperspectral OC sensors. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO OC instrument (OCX), it provides hourly coverage of US coastal regions and great lakes, such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. We will apply our newly developed machine learning (ML) based atmospheric correction approach for OC retrievals to TEMPO data. Our approach begins by decomposing measured hyperspectral radiances into spectral features that explain the variability in atmospheric scattering and absorption as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict OC properties such as chlorophyll concentration derived from collocated MODIS/VIIRS physically-based retrievals. This ML approach compliments the standard radiative transfer-based OC retrievals by providing gap-filling over cloudy regions where the standard algorithms are limited. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate OC properties in less-than-ideal conditions such as lightly to moderately cloudy conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it provides extended spectral measurements at green and red wavelengths which are important particularly for coastal waters. Additionally, our ML technique can be applied to provisional data early in the mission and has potential to demonstrate the value of near real time OC products that are important for monitoring of harmful algae blooms and transient oceanic phenomena.   

How to cite: Fasnacht, Z., Joiner, J., Bandel, M., Haffner, D., Vassilkov, A., Castellanos, P., and Krotkov, N.: Harnessing Machine Learning and Principal Components Techniques for Atmospheric and Glint Correction to Retrieve Ocean Color from Geostationary Satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12271, https://doi.org/10.5194/egusphere-egu24-12271, 2024.

The Peruvian coastal upwelling system (PCUS) is one of the most productive in the world ocean. The Peruvian Marine Research Institute (IMARPE) has been monitoring the PCUS  since the 1960’s with an increase in the frequency and spatial distribution of measurements since the early 2000’s focusing on temperature, salinity and oxygen. In recent years, autonomous gliders have started to be routinely deployed by IMARPE, collecting a large amount of profiles. However, there is still a gap for the high-resolution  sampling of biogeochemical parameters such as nutrients (nitrate, phosphate and silicate).

New  methods using machine learning to reconstruct missing data have been developed recently with promising results (Sauzède et al, 2017; Bittig et al., 2018; Fourrier et al., 2020). In particular, a recent global approach using neural networks (NN) named CANYON-B (CArbonate system and Nutrientes concentration from hYdrological properties and Oxygen using a Neural network) was developed in order to fill those gaps and infer nutrients’ concentrations from the more sampled variables of temperature, salinity and oxygen (Bittig et al., 2018).

In this work we show the application of this global CANYON-B algorithm to the PCUS using all the historical IMARPE’s CTD casts. Moreover, we trained a new NN and compared its outputs with the ones from CANYON-B, demonstrating the benefits of training the NN with the extensive regional data set collected by IMARPE.

How to cite: Asto, C., Bosse, A., Pietri, A., Colas, F., Sauzède, R., and Gutiérrez, D.: Application of a Neural Network Algorithm to Estimate the Nutrients Concentration in the Peruvian Upwelling System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13571, https://doi.org/10.5194/egusphere-egu24-13571, 2024.

The ocean plays a critical role in modulating climate change by absorbing atmospheric CO2. Timely and geographically detailed estimates of the global ocean-atmosphere CO2 flux provide an important constraint on the global carbon budget, offering insights into temporal changes and regional variations in the global carbon cycle. However, previous estimates of this flux have a 1 year delay and cannot monitor the very recent changes in the global ocean carbon sink. Here we present a near-real-time, monthly grid-based dataset of global surface ocean fugacity of CO2 and ocean-atmosphere CO2 flux data from January 2022 to July 2023, which is called Carbon Monitor Ocean (CMO-NRT). The data have been derived by updating the estimates from 10 Global Ocean Biogeochemical Models and 8 data products in the Global Carbon Budget 2022 to a near-real-time framework. This is achieved by employing Convolutional Neural Networks and semi-supervised learning methods to learn the non-linear relationship between the estimates from models or products and the observed predictors. The goal of this dataset is to offer a more immediate, precise, and comprehensive understanding of the global ocean-atmosphere CO2 flux. This advancement enhances the capacity of scientists and policymakers to monitor and respond effectively to alterations in the ocean's CO2 absorption, thereby contributing significantly to climate change management.

How to cite: Ke, P., Gui, X., Cao, W., Wang, D., Hou, C., Wang, L., Song, X., Li, Y., Zhu, B., Bian, J., Sitch, S., Ciais, P., Friedlingstein, P., and Liu, Z.: Near-real-time monitoring of global ocean carbon sink based on CNN, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14839, https://doi.org/10.5194/egusphere-egu24-14839, 2024.

Short-term forecasting (several days in advance) of underwater visibility range is needed for marine and maritime operations involving divers or optical sensors, as well as for recreational activities such as scuba diving (e.g. Chang et al 2013). Underwater visibility mainly depends on water turbidity, which is caused by small suspended particles of organic and mineral origin (Preisendorfer 1986). Modelling the fate of these particles can be complex, encouraging the development of machine learning methods based on satellite data and hydrodynamic simulations (e.g. Jourdin et al 2020). In the field of forecasting visibility, deep learning methods are emerging (Prypeshniuk 2023). Here, in continuation of Vient et al (2022) on the interpolation purpose, this work deals with forecasting subsurface mineral turbidity levels over the French continental shelf of the Bay of Biscay using the deep learning method entitled 4DVarNet (Fablet et al 2021) applied to ocean colour satellite data, with additional data such as bathymetry (ocean depths) and time series of main forcing statistical parameters like wave significant heights and tidal coefficients. Using satellite data alone, results show that 2-day forecasts are accurate enough. When adding bathymetry and forcing parameters in the process, forecasts can go up to 6 days in advance.

References

Chang, G., Jones, C., and Twardowski, M. (2013), Prediction of optical variability in dynamic nearshore environments, Methods in Oceanography, 7, 63-78, https://doi.org/10.1016/j.mio.2013.12.002

Fablet, R., Chapron, B., Drumetz, L., Mémin, E., Pannekoucke, O., and Rousseau, F. (2021), Learning variational data assimilation models and solvers, Journal of Advances in Modeling Earth Systems, 13, e2021MS002572, https://doi.org/10.1029/2021MS002572

Jourdin, F., Renosh, P.R., Charantonis, A.A., Guillou, N., Thiria, S., Badran, F. and Garlan, T. (2021), An Observing System Simulation Experiment (OSSE) in Deriving Suspended Sediment Concentrations in the Ocean From MTG/FCI Satellite Sensor, IEEE Transactions on Geoscience and Remote Sensing, 59(7), 5423-5433, https://doi.org/10.1109/TGRS.2020.3011742

Preisendorfer, R. W. (1986), Secchi disk science: Visual optics of natural waters, Limnology and Oceanography, 31(5), 909-926, https://doi.org/10.4319/lo.1986.31.5.0909

Prypeshniuk, V. (2023), Ocean surface visibility prediction, Master thesis, Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences, Lviv, Ukraine, 39 pp, https://er.ucu.edu.ua/handle/1/3948?locale-attribute=en

Vient, J.-M., Fablet, R.;, Jourdin, F. and Delacourt, C. (2022), End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations, Remote Sens., 14(16), 4024, https://doi.org/10.3390/rs14164024

How to cite: Vient, J.-M., Jourdin, F., Fablet, R., and Delacourt, C.: Data-driven short-term forecast of suspended inorganic matter as seen by ocean colour remote sensing., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15508, https://doi.org/10.5194/egusphere-egu24-15508, 2024.

Rising sea levels are one of many consequences of anthropogenic climate
change. Over the past few decades, several global observational records have
become available that give a more detailed picture of the increasing
impacts. Nevertheless, there continue to be data challenges, such as
sparsity or signal to noise ratio, that need to be dealt with. Machine Learning (ML)
and specifically, Deep Learning (DL) approaches have presented themselves as valuable
tools for such large-scale and complex data sources. To this end, the OceanBench
Benchmark suite was recently developed to provide a
standardized pre-processing and evaluation framework for Sea Surface Height
(SSH) interpolation tasks involving nadir and Surface Water and Ocean Topography
(SWAT) Altimetry Tracks. From the methodological perspective, a reoccurring
issue is the lack of uncertainty quantification for DL applications in Earth
Observation. Therefore, we extend the suite of metrics provided by OceanBench
to probabilistic evaluation metrics and test state-of-the-art uncertainty
quantification models from the DL community. Specifically, we focus on
Conditional Convolutional Neural Processes (ConvCNP) and
Inpainting Diffusion models as methodologies to quantify
uncertainty for the interpolation task and demonstrate their viability and
advantages over other ML methods for both accuracy and probabilistic metrics.

How to cite: Lehmann, N., Bamber, J., and Zhu, X.: Conditional Generative Models for OceanBench Sea Surface Height Interpolation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15594, https://doi.org/10.5194/egusphere-egu24-15594, 2024.

Ocean color data are essential for developing our understanding of biological and ecological phenomena and processes and also of important sources of input for physical and biogeochemical ocean models. Chlorophyll-a (Chl-a) is a critical variable of ocean color in the marine environment. Quantitative retrieval from satellite remote sensing is a main way to obtain large-scale oceanic Chl-a. However, missing data are a major limitation in satellite remote-sensing-based Chl-a products due mostly to the influence of cloud, sun glint contamination, and high satellite viewing angles. The common methods to reconstruct (gap fill) missing data often consider spatiotemporal information of initial images alone, such as Data Interpolating Empirical Orthogonal Functions, optimal interpolation, Kriging interpolation, and the extended Kalman filter. However, these methods do not perform well in the presence of large-scale missing values in the image and overlook the valuable information available from other datasets for data reconstruction. Here, we developed a convolutional neural network (CNN) named Ocean Chlorophyll-a concentration reconstruction by convolutional neural NETwork (OCNET) for Chl-a concentration data reconstruction in open-ocean areas, considering environmental variables that are associated with ocean phytoplankton growth and distribution. Sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) from reanalysis data and satellite observations were selected as the input of OCNET to correlate with the environment and phytoplankton biomass. The developed OCNET model achieves good performance in the reconstruction of global open ocean Chl-a concentration data and captures spatiotemporal variations of these features. The reconstructed Chl-a data are available online at https://doi.org/10.5281/zenodo.10011908. This study also shows the potential of machine learning in large-scale ocean color data reconstruction and offers the possibility of predicting Chl-a concentration trends in a changing environment.

How to cite: Hong, Z., Long, D., Li, X., Wang, Y., Zhang, J., Hamouda, M., and Mohamed, M.: A global daily gap-filled chlorophyll-a dataset in open oceans during 2001–2021 from multisource information using convolutional neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16166, https://doi.org/10.5194/egusphere-egu24-16166, 2024.

Traditional physics-based numerical models have served and are serving as reliable tools to gain insights into spatiotemporal behavior of ocean states such as water levels and currents. However, they have significant computational demand that often translates to slower forecasting capabilities. Additionally, these models can encounter difficulties in capturing certain physical processes and struggle to effectively bridge various spatial and temporal scales.

Considering these challenges, machine learning-based surrogate models emerge as a promising alternative. Physical surrogate models that learn multiple physics (on different spatial and temporal scales) from large datasets during extensive pretraining (Multiple physics pretraining, MPP) can enable later applications to poorly observed data domains which are common in ocean sciences. Hence, transfer learning capabilities can help improve the oceanographic forecasting, especially in data-limited regimes.

In this work, we explore the capabilities of pretrained transformer models for prediction on a test case for the North Sea. The results from two-dimensional simulations are used for training and fine-tuning. We utilize 2D datasets from publicly available PDEBench together with domain-specific datasets from DHI’s historical records of simulated 2D metocean data. We forecast water levels and currents with pretrained models and evaluate MPP forecast results against in-situ point observations and numerical model results.

Initial findings suggest that pretraining poses potential for generalizing and transferring knowledge to novel regions and relevance in practical application. A challenge is posed by model interpretability, highlighting an area for further development.

How to cite: Cremer, C., Anderson, H., and Mariegaard, J.: Exploring Pretrained Transformers for Ocean State Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17159, https://doi.org/10.5194/egusphere-egu24-17159, 2024.

One of the disadvantages of oceanographic models is that they can be very computationally expensive. When combined with data assimilation, dynamical approaches such as the EnKF become expensive as they need a large number of ensemble members and thus model runs. In this work we investigate the use of a Multi-Fidelity Ensemble Kalman Filter (MF-EnKF), where the lower fidelity model is a machine learned surrogate model and the high fidelity model is the original full model. The idea behind this is to use an ensemble of a few but expensive full model runs, combined with an ensemble of many cheap but less accurate surrogate model runs. In this way we can reach similar or increased accuracy with less full model runs and thus less computational time. We investigate the performance by testing the approach on a simple atmospheric model, namely the Lorenz-96 model, and an oceanographic model, namely the Quasi-Geostrophic model. Results show that the MF-EnKF outperforms the EnKF for the same number of full model runs and that the MF-EnKF can reach similar or improved accuracy with less full model runs.

How to cite: van der Voort, J., Verlaan, M., and Kekkonen, H.: A Multi-Fidelity Ensemble Kalman Filter with a machine learned surrogate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17199, https://doi.org/10.5194/egusphere-egu24-17199, 2024.

Mesoscale eddies significantly influence ocean circulation, nutrient distribution, and climate patterns globally.  A thorough reconstruction of the eddy field is therefore important, yet classical eddy detection algorithms based on sea level anomaly (SLA) suffer from the low coverage of the current altimetry network.

In this work, we evaluate the efficacy of deep learning techniques in enhancing the oceanic eddy field reconstruction of an operational ocean forecasting system. We use two ocean models from an Observing System Simulation Experiments (OSSE): a free-run high-resolution ocean circulation model representing the ‘truth’ and a second one constrained by synthetic observations mimicking the altimetry network through assimilation techniques to approximate the state of the ’truth’ model. 

We train a neural network model that takes sea surface temperature, sea surface height, and ocean surface currents inputs from the data-assimilation model to recover eddies identified in the ‘truth’ model, which are generated with py-eddy-tracker, a sea surface height-based eddy detection algorithm.

Our investigation centers on a semantic segmentation problem using the U- Net architecture to classify pixels for a given map into non-eddy, cyclonic eddy, and anticyclonic eddy. Our study focuses on the Gulf Stream region, an area renowned for its dynamic oceanic conditions. We find a higher detection rate of eddies and reduced inter-class misclassification when compared to eddy fields reconstructed from the data-assimilated model using the traditional SLA-based algorithm. 

Our data-driven method improves the detection of ‘true’ eddies from degraded data in an OSSE framework, and shows potential for application in operational analysis and forecasting systems.

How to cite: El Kadiri, I., Van Gennip, S., Drevillon, M., El Aouni, A., Botvinko, D., and Fablet, R.: Assessing data assimilation techniques with deep learning-based eddy detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17320, https://doi.org/10.5194/egusphere-egu24-17320, 2024.

The Sea Surface Height (SSH) is an important variable of the ocean state. It is currently estimated by satellites measuring the return time of a radar pulse. Due to this remote sensing technology, nadir-pointing altimeters take measures vertically, only along their ground tracks. Recovering fully gridded SSH fields involves a challenging spatiotemporal interpolation. The most widely used operational product, the Data Unification and Altimeter Combination System (DUACS), combines data from several satellites through linear optimal interpolation to estimate the SSH field. However several studies demonstrate that DUACS does not resolve mesoscale structures, motivating our interest in improving interpolation methods. Recently, Deep Learning has emerged as one of the leading methods to solve ill-posed inverse imaging problems. Deep Neural Networks can use multi-variate information to constrain the interpolation. Among them, Sea Surface Temperature (SST) data is based on a different remote-sensing technology, which leads to higher data coverage and resolution. Deep Learning methods have been proposed to interpolate SSH from track measurements, efficiently using SST contextual information. However, training neural networks usually requires either a realistic simulation of the problem on which we have access to SSH ground truth or a loss function that does not require it. Both solutions present limitations: the first is likely to suffer from domain gap issues once applied to real-world data, and training on observations only leads to lower performance than supervision on complete fields. We propose a hybrid method: a supervised pretraining on a realistic simulation, and an unsupervised fine-tuning on real-world observations. This approach was performed using a deep Attention-based Encoder-Decoder architecture. We compare the performances of the same neural network architecture trained in the three described settings: simulation-based training, observation-based training, and our hybrid approach. Preliminary results show an improvement of approximately 25% over DUACS in the interpolation task on the Ocean Data Challenge 2021 dataset. We further explore the ability of the architecture proposed to produce near real-time forecasts of SSH.

How to cite: Archambault, T., Garcia, P., Charantonis, A. A., and Béréziat, D.: Deep Sea Surface Height Multivariate Interpolation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17465, https://doi.org/10.5194/egusphere-egu24-17465, 2024.

Global ocean numerical models typically have their first vertical level about 0.5m below the sea surface. However, a key physical quantity like the sea surface temperature (SST) can be retrieved from satellites at a reference depth of a few microns or millimeters below the sea surface. Assimilating such temperatures can lead to bias in the ocean models and it is thus necessary to project the satellite retrievals to the first model level to safely use them in the assimilation process. This projection is non-trivial, since it depends on several factors (e.g., daily cycle, winds, latitude) and it is usually performed either with computationally expensive numerical models or with too simple statistical methods.  

In this work we present an attempt to construct the projection operator with machine learning techniques. We consider three different networks: a convolutional neural network architecture called U-Net, which was first introduced in the field of computer vision and image segmentation, and it is thus optimal to process satellite retrievals; a pix2pix network, which is a U-Net trained in an adversarial way against a patch-classifier discriminator; a random forest model, which is a more traditional machine learning technique. We train the networks with L3 global subskin SST from AVHRR’s infrared channels on MetOp satellites produced by OSISAF and wind speed analysis at 10m by ECMWF to reproduce the ESA SST CCI and C3S global SST reprocessed product by CMEMS, that we take as ground truth during training and validation. The pix2pix network is the most effective in the projection and we thus choose it to shape an observation operator for the CMCC’s OceanVar assimilation system.

Finally, we compare several one-year-long reanalysis-like experiments, based on the CMCC reanalysis system, that assimilate the SST in different ways, e.g. nudging, unbiased approach, as observation operator. We discuss the potential impact of such new scheme in providing the best surface ocean state estimate.

How to cite: Broccoli, M., Cipollone, A., and Masina, S.: Towards an Observation Operator for Satellite Retrievals of Sea Surface Temperature with Convolutional Neural Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17731, https://doi.org/10.5194/egusphere-egu24-17731, 2024.

Large Language Models (LLMs) have made significant strides in language understanding, including natural language processing, summarization, and translation, and they have the potential to be applied to a range of climate-related challenges. For instance, LLMs can be leveraged for data cleaning and transformation, and also assisting scientists/engineers in their daily work tasks.

For the machine learning community, the year 2023 was arguably the year of breakthroughts in LLM use in production. I present in this work the exciting potential for recent advances in LLMs to revolutionize how the ocean science community can interact with computer code, information gathering, dataset finding, etc. Specifically, I will present simple applications of how these advancements in Natural Language Processing (NLP) can assist the NEMO ocean model community. Examples range from using question answering systems for browsing efficiently NEMO documentation to creating conversational agents or chatbots that can assist not only new members wanting to learn about the NEMO model but also confirmed users. 

An important aspect of this work is relying only on open source LLMs, evaluating the performances of several models and discussing the ethical implications of these tools. I also discuss the question of whether using these LLMs blindly without domain knowledge is a good idea, as an important chunk of this work can arguably be easily done by anyone with good computer science skills thanks to the democratization of data science tools and learning materials.

How to cite: Lguensat, R.: Leveraging recent advances in Large Language Models for the ocean science community, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18493, https://doi.org/10.5194/egusphere-egu24-18493, 2024.

The water in the basin of sill fjords is renewed occasionally. In some fjords, this renewal occurs irregularly while in others it has a more regular character. Independently of the renewal period, the renewal mechanism is thought to be common to all sill fjords: subsurface water outside of the fjord mouth lifted above the sill depth will trigger a renewal, provided that the lifted water mass is denser than the water in the basin. In Western Norway, the northerly, upwelling favorable winds that occur during Spring/Summer, provide a forcing for the uplifting of the isopycnals and bring dense, subsurface water to the upper water column, thereby creating the conditions for renewals to occur. The renewal of sill fjord basins is an important aspect of the fjord ecological condition because it supplies oxygen rich water to the fjord basin, whose oxygen is consumed by the degradation of organic matter during the stagnant periods. Byfjorden is the urban fjord in Bergen, Norway. It is heavily urbanized and has been consistently showing lower oxygen levels in its basin, which has ecological implications.

Byfjorden’s basin water is regularly renewed between the months of March and August and a strong link to coastal and atmospheric variability is well known, which makes it an attractive choice for the application of Deep Learning to predict basin water renewal in sill fjords, in the context of the atmospheric and hydrographic setting of the Norwegian coast.

In this work, the prediction of deep water renewal in Byfjorden and the basin’s oxygen levels is investigated with deep learning techniques. After a statistical study of oxygen variability correlation with wind forcing along the Norwegian coast, we develop and test a model to predict renewals and fill gaps in Byfjorden’s oxygenatio record.

How to cite: Bettencourt, J.: Prediction of sill fjord basin water renewals and oxygen levels, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18627, https://doi.org/10.5194/egusphere-egu24-18627, 2024.

Understanding Phytoplankton community dynamics in response to environmental shifts is crucial for assessing the impact of climate change on marine biology. To this end, satellite observations offer a dataset spanning two decades, capturing diverse sea surface parameters, including temperature, ocean color, and surface height. Notably, ocean color data is processed to derive sea surface chlorophyll-a concentration, widely acknowledged as a reliable proxy for phytoplankton biomass. 

Lately, advances in ocean color observation allow us to describe the phytoplankton community structure in terms of groups (broad functional or taxonomic groups) or size classes. Although these advances provide more detailed information on phytoplankton diversity and structure, these datasets suffer from spatial and temporal coverage limitations due to strict quality control in the presence of atmospheric aerosols, clouds, sea ice, etc... As a result, studies examining phytoplankton trends over the past two decades and future projections rely on incomplete chlorophyll-a and ocean color data. Therefore this compromises the identification of consistent trends within phytoplankton datasets.

In this study, we address this issue using a deep-learning approach. Our method constructs an attention network that learns from the available satellite dataset of Chla and phytoplankton size classes images (weekly and one-degree-degraded spatial resolution) while assimilating information from gap-free sea surface physics data originating from satellite observations and assimilated numerical models). The primary objective is to estimate the phytoplankton dataset based on the knowledge of physical factors, while filling the gaps within this dataset

The trained deep-learning model allows us to discern patterns and correlations between chlorophyll concentration and the phytoplankton size classes on one hand, and the physics-based data on the other hand. From a phytoplankton weekly database spanning from 1997 to 2020, with 50% missing pixels, our approach demonstrates promising results in replicating chlorophyll concentration and accurately inferring phytoplankton size classes.

The methodology shows the potential of deep-learning for robust ecological applications but mainly lays the groundwork for future trend studies on phytoplankton communities.

How to cite: Ollier, L., El Hourany, R., and Levy, M.: Linking Satellite and physics-informed Data with Phytoplankton communities Using Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18663, https://doi.org/10.5194/egusphere-egu24-18663, 2024.

4DVarNet algorithm is an AI based variational approach that performs spatiotemporal time-series interpolation. It has been used with success on Ocean Color satellite images to fill in the blank of missing data due to e.g., the satellites trajectories or the clouds covering. 4DVarNet has shown impressive interpolation performances compare to other classical approaches such as DInEOF.
We propose to show that 4DVarNet is a flexible model that learns global dynamics instead of local patterns, thus enabling it to interpolate different type of data, i.e., data from different spatio-temporal domain and/or representing different variables, using the same pre-trained model.

The core of our technique involves extrapolating the learned models to other, somewhat larger geographical areas, including the entire Mediterranean and other regions like the North Sea. We achieve this by segmenting larger areas into smaller and manageable sections, and then choosing a section to train the model. Finally the trained model is applied to each segment and seamlessly integrating the prediction results. This method ensures detailed and accurate coverage over extensive areas, significantly enhancing the predictive power of our models while maintaining low computational costs.

Our results demonstrate that this approach not only outperforms traditional methods in terms of accuracy but also provides a scalable solution, adaptable to various geographical contexts. By leveraging localized training and strategic extrapolation, we offer a robust framework for ocean monitoring, paving the way for advanced satellite image applications in diverse settings.

How to cite: Dorffer, C., Nguyen, T. T. N., Jourdin, F., and Fablet, R.: Spatial Generalization of 4DVarNet in ocean colour Remote Sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18688, https://doi.org/10.5194/egusphere-egu24-18688, 2024.

The ICT+ project:” Transforming ocean surveying by the power of DL and statistical methods” hosted by UiT The Artic University of Norway, aims at employing machine learning techniques in improving and streamlining methods currently used in ocean surveying by private sector partners to the project, MultiConsult and Argeo. The tasks include detection and identification of µm (e.g. foraminifera, microplastics) to m (e.g. boulders, shipwrecks) sized objects and elements at and in the seabed in data that presently is processed manually by skilled workers, but ideally could be wholly or partially processed using an automated approach.

Here we present preliminary work and results related to application of the YOLO (You Only Look Once) algorithms in detection and identification of meio fauna (foraminifera) in - and macro (mollusc) fauna at the seabed. Both proxies are used in evaluation of the environmental state of the seabed. YOLO is a real-time object detection deep learning algorithm that efficiently identifies and locates objects in images or videos in a single pass through the neural network.

Presently the year on year growth or shrinkage of protected mollusc banks in northern Norwegian fjords is manually evaluated via video observation in seabed video sequences annually captured via remotely operated vehicles. The preliminary results suggest that upon moderate training the YOLO algorithm can identify presence/absence of mollusc bank formations in set video sequences, thus supporting and eventually minimizing the task of inspecting the video footage manually.      

Foraminifera are abundant marine meiofauna living in the water column or at and in the seabed. Foraminifera are utilized in research into both modern and past marine environments as they have high turnover rates and individual shells have high preservation potential. Foraminiferal shells are accumulated in the sediments and after sample processing, they subsequently can be manually detected and identified via microscope. This work is very labour-intensive and demands skilled expertise but suffers from errors by and bias of the individual expert.

Preliminary results show that a YOLO network, trained on ca 4100 individuals (20 subgroups; benthic calcareous foraminifera (n=19), Planktic foraminifera (n=1)) in 346 images have model performances of up to 0.96 mAP (mean average precision) when trained, validated and tested on the training set. These promising results will be tested on real world samples. This testing is complicated by real world samples containing many more foraminiferal species/groups that were not trained upon, overlapping or closely set specimens and presence of non-foraminiferal material (e.g. sediment grains, other meio-fauna or –flora, etc.). Thus, additional training with focus on set complicating aspects will likely be necessary and most recent result will be presented.

How to cite: Aagaard Sørensen, S., Myrvoll-Nielsen, E., Martinsen, I., Godtliebsen, F., Galata, S., Junttila, J., and Vassdal, T.: Detection and identification of environmental faunal proxies in digital images and video footage from northern Norwegian fjords and coastal waters using deep learning object detection algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18759, https://doi.org/10.5194/egusphere-egu24-18759, 2024.

The Coupled Model Intercomparison Project, now in its sixth phase (CMIP6), is a global effort to project future climate scenarios on following certain shared socioeconomic pathways (SSP). For the period 1950-2014, CMIP6 provides a historical model output. From 2015 future projections with four different SSP scenarios, viz. SSP126, 245, 370 and 585 are available. From 2015-2023, we also have reanalysis of the actual ocean and atmosphere variables in these years. From this data, it is observed that CMIP6 future projections of ocean variables have a root mean square error (RMSE) of 1.22 psu in sea surface salinity, 1.24 °C in sea surface temperature, 2.23 m/s in the zonal ocean velocity component, 1.34 m/s in the meridional ocean velocity component. Similarly, the atmospheric variables have a RMSE of 1.34 °C in temperature at 2-meter height, 2.12 m/s in the zonal, and 1.321 m/s meridional wind component. Our goal is to develop an accurate method to correct this bias and provide updated future projections for scientific analysis. To this end, we developed a two phase deep neural network model that accepts monthly fields from the CMIP6 projections (all four SSP scenarios), and outputs a bias corrected field. In the first phase, a deep neural model, which we call as Atmospheric-Ocean Network 1 (AONet1) is used to obtain bias corrected fields for each of the four SSP separately. The AONet1 is trained and validated using the historical CMIP6 data (1950-2014) as input and ORAS5 and ERA5 data as the output (the bias corrected field). In the second phase, the four bias-corrected SSP fields are fed to AONet2 and the final bias corrected single field is produced. The AONet2 is trained and validated using future projection data from 2015-2021 as input and ORAS5 and ERA5 from the same period as output. The testing of the two phase model is performed for years 2022 and 2023, before bias corrected future fields are produced. Results are compared to the statistical EDCDF method using different Image Quality Assessment metrics such as Data structural similarity index measure (DSSIM), Multi-Scale SSIM, and Visual information fidelity. On test data, the RMSE after bias reduction using the two phase AONet model is 40% lower. Image assessment metric values surpassed the EDCDF approach as well.

How to cite: Pasula, A. and Subramani, D.: A two-phase Neural Model for CMIP6 bias correction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18857, https://doi.org/10.5194/egusphere-egu24-18857, 2024.

Physics-based coastal ocean models provide vital insights into local and regional coastal dynamics but require significant computational resources to solve numerically. In this work, we develop data-driven reduced order models (ROMs) using machine learning techniques to emulate a 2D flexible mesh hydrodynamic model of Øresund, the Straight between Denmark and Sweden, achieving orders of magnitude speedup while retaining good accuracy. This Øresund model has complex spatio-temporal dynamics driven by time-varying boundary conditions. Two different approaches to generate ROMs offline are developed and compared. Our objective is to assess the advantage of generating such models offline to enable real-time analysis in the online setting.

The first approach extracts patterns in space and time using principal component analysis and learn mappings from previous states and boundary conditions to future states using gradient boosting. The second approach employs Dynamic Mode Decomposition with control (DMDc) to account for boundary forcing. The reduced models are trained offline on a part of the available 12 months of 30-minute resolution snapshots of surface elevation, and u- and v-components of the depth-averaged currents. In both cases a very low number O(100) of latent space dimensions are necessary to get accurate results at the order of 2-4 cm RMSE compared to the full high-fidelity model.

The emulators provide state estimates online in seconds rather than hours, enabling new applications like uncertainty quantification, data assimilation and parameter optimization that require fast model evaluations. Further developments could look to condition the ROMs on a wider range of potential boundary forcings for scenario exploration. This demonstrates machine learning's potential for accelerating coastal simulations for real-time decision support and planning systems facing long-term change and uncertainty.

How to cite: Mariegaard, J. S., Larsen, E. S., and Engsig-Karup, A. P.: Fast data-driven reduced order models for emulating physics-based flexible mesh coastal-ocean models , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19104, https://doi.org/10.5194/egusphere-egu24-19104, 2024.

The ocean biological carbon pump, a significant set of processes in the global carbon cycle, drives the sinking of particulate organic carbon (POC) towards the deep ocean. Global estimates of POC fluxes and an improved understanding of how environmental factors influence organic ocean carbon transport can help quantify how much carbon is sequestered in the ocean and how this can change in different environmental conditions, in addition to improving global carbon and marine ecosystem models. POC fluxes can be derived from observations taken by a variety of in situ instruments such as sediment traps, 234-Thorium tracers and Underwater Vision Profilers. However, the manual and time-consuming nature of data collection leads to limitations of spatial data sparsity on a global scale, resulting in large estimate uncertainties in under-sampled regions.

This research takes an observation-driven approach with machine learning and statistical models trained to estimate POC fluxes on a global scale using the in situ observations and well-sampled environmental driver datasets, such as temperature and nutrient concentrations. This approach holds two main benefits: 1) the ability to fill observational gaps on both a spatial and temporal scale and 2) the opportunity to interpret the importance of each environmental factor for estimating POC fluxes, and therefore exposing their relationship to organic carbon transport processes. The models built include random forests, neural networks and Bayesian hierarchical models, where their global POC flux estimates, feature importance and model performances are studied and compared. Additionally, this research explores the use of data fusion methods to combine all three heterogeneous in situ POC flux data sources to achieve improved accuracy and better-informed inferences about organic carbon transport than what is possible using a single data source. By treating the heterogeneous data sources differently, accounting for their biases, and introducing domain knowledge into the models, our data fusion method can not only harness the information from all three data sources, but also gives insights into their key differences.

How to cite: Navaneethanathan, A., Cael, B., Luo, C., Challenor, P., Martin, A., and Leonelli, S.: Estimating global POC fluxes using ML and data fusion on heterogeneous and sparse in situ observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19157, https://doi.org/10.5194/egusphere-egu24-19157, 2024.

The Arctic region is undergoing unprecedented transformations due to Arctic amplification, warming at twice the global average rate. This warming has led to a drastic reduction in sea ice, with predictions of ice-free Arctic summers before 2050. Such profound changes signal a shift to a new climatic regime, posing significant risks to regional communities, industries, and ecosystems.

This research addresses the urgent need to understand the evolving Arctic environment by harnessing machine learning (ML) to analyse sparse oceanic data. Utilising nearly two decades of Ice Tethered Profilers (ITP) data, complemented by ship-based (U-DASH), and ARGO profiles, this study aims to investigate the structure and dynamics of the Arctic Ocean.

We fit a Gaussian Mixture Model (GMM) to our observations, assigning each data point into a different cluster or class. Despite no spatial information being provided to the model, we find coherent classes emerge. We analyse the properties of each class, compare them with standard water masses from the literature, and look at decadal trends in properties such as oxygen saturation. This approach promises to enhance our understanding of Arctic water masses and their evolving role in a changing environment.

How to cite: Allemang, O.: Arctic Processes Under Ice: Structures in a Changing Climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20454, https://doi.org/10.5194/egusphere-egu24-20454, 2024.

Biogeochemical ocean models are usually based on two size classes for particulate organic matter: small classes (1-100 𝜇m) and large classes (100-5000 𝜇m). Based on the measurements of particulate organic carbon (POC) concentration from UVP5 profiles and observations of environmental and ecosystem conditions we estimated an optimal number of size classes for POC that can be introduced in biogeochemical ocean models. 

We identified four size classes based on the correlation between POC concentration and environmental and ecosystem variables. It gives us information on the relationship between POC and surrounding temperature, chlorophyll-a concentration, nitrate, phosphate and oxygen levels as well as plankton functional types (PFTs). 

Further, we applied Machine Learning methods to reconstruct size classes of POC concentration and identify the most important drivers for each class. We showed that the concentration of POC smaller than 0.3 mm mostly depends on environmental characteristics while concentration of POC bigger than 0.3 mm strongly depends on PFTs.  

How to cite: Denvil-Sommer, A., Le Quere, C., Kiko, R., Buitenhuis, E., Racault, M.-F., and Lombard, F.: Size classification of particulate organic carbon concentration and its link to the ecosystem based on Machine Learning techniques., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20799, https://doi.org/10.5194/egusphere-egu24-20799, 2024.

Microfossils are important in climate analysis and in exploration of subsea energy resources. The abundance and distribution of species found in sediment cores provide valuable information, but the analysis is difficult and time consuming as it is based on manual work by human experts. It is also a challenge to have enough labelled data to train a standard deep learning classifier on microfossil images. We propose an efficient pipeline for processing and grouping fossils by species from microscope slides using self-supervised learning. First we show how to efficiently extract crops from whole slide images by adapting previously trained object detection algorithms. Second, we provide a comparison of a range of contrastive self-supervised learning methods to classify and identify microfossil from very few labels. We obtain excellent results with convolutional neural networks and vision transformers fine-tuned by self-supervision.

How to cite: Martinsen, I., Wade, D., Ricaud, B., and Godtliebsen, F.: A deep learning pipeline for automatic microfossil analysis and classification, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21554, https://doi.org/10.5194/egusphere-egu24-21554, 2024.

The Southern Ocean closes the global overturning circulation and is key to the regulation of carbon, heat, biological production, and sea level. However, the dynamics of the general circulation and its leading order controls remain poorly understood, in part because of the challenge of characterizing and tracking changes in ocean physics in complex models. This gap in understanding is especially problematic in the face of climate change. Here, we wish to understand changes in the dynamics of the Southern Ocean under climate change, specifically how bathymetric controls on the general circulation could impact the location of major currents and impact upwelling. We use a suite of CMIP models for our analysis. A physics-informed equation discovery framework guided by machine learning is used to partition and interpret dynamics is used to understand spatial structures, and a supervised learning framework that quantifies its uncertainty and provides explanations of its predictions is leveraged to track change. The method, called Tracking global Heating with Ocean Regimes (THOR). A region undergoing a profound shift is where the Antarctic Circumpolar Current intersects with bathymetry, for example, the Pacific-Antarctic Ridge. We see major changes in areas associated with upwelling between the CMIP models, suggesting the changes in wind stress allow the control bathymetry has in the historical scenario to change. For example, we find that as the Antarctic Circumpolar Current shifts north under intensifying wind stress, when meeting the Pacific-Antarctic Ridge. We note associated change in the regions where gyre circulation favors upwelling, with spatial distributions varying between models. Our efforts go towards a better understanding of what dynamics are driving changes, and could allow reduction of bias between models and decrease uncertainties in future projections.

How to cite: Sonnewald, M., Yik, W., Clare, M. C., and Lguensat, R.: Discovering Dominant Controls on Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21905, https://doi.org/10.5194/egusphere-egu24-21905, 2024.

Conventional studies of subseasonal-to-seasonal sea ice variability across scales have relied upon computationally expensive physics-based models solving systems of differential equations. IceNet, a deep learning-based sea ice forecasting model under development since 2021, has proven competitive to such state-of-the-art physics-based models, capable of generating daily 25 km resolution forecasts of sea ice concentration across the Arctic and Antarctic at a fraction of the computational cost once trained. Yet, these IceNet forecasts leave room for improvement through three main weaknesses. First, the forecasts exhibit physically unrealistic spatial and temporal blurring characteristic of deep learning methods trained under mean loss objectives. Second, the use of 25 km scale OSISAF data renders local forecasts along coastal regions and in regions surrounding maritime vessels inconclusive. Third, the sole provision of sea ice concentration in forecasts leaves questions about other critical ice properties such as thickness unanswered. We present preliminary results addressing these three challenges, turning to deep generative models to capture forecast uncertainty and improve spatial sharpness; leveraging 3 and 6 km scale AMSR-2 sea ice products to improve spatial resolution; and incorporating auxiliary datasets, chiefly thickness, into the training and inference pipeline to produce multivariate forecasts of sea ice properties beyond simple sea ice concentration. We seek feedback for improvement and hope continued development of IceNet can help answer key scientific questions surrounding the state of sea ice in our changing polar climates.

How to cite: McDonald, A., Smith, J., Yatsyshin, P., Andersson, T., Bowler, E., van Zeeland, L., Ubald, B., Byrne, J., Pérez-Ortiz, M., Turner, R. E., and Hosking, J. S.: Pushing the Limits of Subseasonal-to-Seasonal Sea Ice Forecasting with Deep Generative Modelling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22070, https://doi.org/10.5194/egusphere-egu24-22070, 2024.

Estimating the seismic hazard in earthquake-prone regions, in order to assess the risk associated to nuclear facilities, must take into account a large number of uncertainties, and in particular our limited knowledge of the geology. And yet, we know that certain geological features can create site effects that considerably amplify earthquake ground motion. In this work, we provide a quantitative assessment of how largely can earthquake ground motion simulation benefit from deep learning approaches, quantifying the influence of geological heterogeneities on the spatio-temporal nature of the earthquake-induced site response. Two main frameworks are addressed: conditional generative approaches with diffusion models and neural operators. On one hand, generative adversarial learning and diffusions models are compared in a time-series super-resolution context [1]. The main task is to improve the outcome of 3D fault-to-site earthquake numerical simulations (accurate up to 5 Hz [2, 3]) at higher frequencies (5-30 Hz), by learning the low-to-high frequency mapping from seismograms recorded worldwide [1]. The generation is conditioned by the numerical simulation synthetic time-histories, in a one-to-many setup that enables site-specific probabilistic hazard assessment. On the other hand, the successful use of Factorized Fourier Neural Operator (F-FNO) to entirely replace cumbersome 3D elastodynamic numerical simulations is described [4], showing how this approach can pave the way to real-time large-scale digital twins of earthquake prone regions. The trained neural operator learns the relationship between 3D heterogeneous geologies and surface ground motions generated by the propagation of seismic wave through these geologies. The F-FNO is trained on the HEMEW-3D (https://github.com/lehmannfa/HEMEW3D/releases) database, comprising 30000 high-fidelity numerical simulations of earthquake ground motion through generic geologies, performed by employing the high-performance code SEM3D [4]. Next, a smaller database was built specifically for the Teil region (Ardèche, France), where a MW 4.9 moderate shallow earthquake occurred in November 2019 [4]. The F-FNO is then specialized on this database database with just 250 examples. Transfer learning improved the prediction error by 22 %. According to seismological Goodness-of-Fit (GoF) metrics, 91% of predictions have an excellent GoF for the phase (and 62% for the envelope). Ground motion intensity measurements are, on average, slightly underestimated.

[1] Gatti, F.; Clouteau, D. Towards Blending Physics-Based Numerical Simulations and Seismic Databases Using Generative Adversarial Network. Computer Methods in Applied Mechanics and Engineering 2020, 372, 113421.
https://doi.org/10.1016/j.cma.2020.113421.

[2] Touhami, S.; Gatti, F.; Lopez-Caballero, F.; Cottereau, R.; de Abreu Corrêa, L.;Aubry, L.; Clouteau, D. SEM3D: A 3D High-Fidelity Numerical Earthquake Sim-ulator for Broadband (0–10 Hz) Seismic Response Prediction at a Regional Scale.Geosciences 2022, 12 (3), 112. https://doi.org/10.3390/geosciences12030112. https://github.com/sem3d/SEM

[3] Gatti, F.; Carvalho Paludo, L. D.; Svay, A.; Lopez-Caballero, F.-; Cottereau, R.;Clouteau, D. Investigation of the Earthquake Ground Motion Coherence in Het-erogeneous Non-Linear Soil Deposits. Procedia Engineering 2017, 199, 2354–2359.https://doi.org/10.1016/j.proeng.2017.09.232.[4] Lehmann, F.; Gatti, F.; Bertin, M.; Clouteau, D. Machine Learning Opportunities to Conduct High-Fidelity Earthquake Simulations in Multi-Scale Heterogeneous Geology.Front. Earth Sci. 2022, 10, 1029160. https://doi.org/10.3389/feart.2022.1029160.

[4] Lehmann, F.; Gatti, F.; Bertin, M.; Clouteau, D. Fourier Neural Operator Sur-rogate Model to Predict 3D Seismic Waves Propagation. arXiv April 20, 2023.http://arxiv.org/abs/2304.10242 (accessed 2023-04-21).

How to cite: Gatti, F., Lehmann, F., Gabrielidis, H., Bertin, M., Clouteau, D., and Vialle, S.: Deep learning generative strategies to enhance 3D physics-based seismic wave propagation: from diffusive super-resolution to 3D Fourier Neural Operators., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2443, https://doi.org/10.5194/egusphere-egu24-2443, 2024.

How to cite: Edwards, T., Turner, F., Rougier, J., and Rohmer, J. and the EU PROTECT project: Grand designs: quantifying many kinds of model uncertainty to improve projections of sea level rise , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2691, https://doi.org/10.5194/egusphere-egu24-2691, 2024.

Use of machine learning algorithms in climate simulations requires such algorithms to replicate certain aspects of the physics in general circulation models.  In this study, a neural network is used to mimic the behavior of one of the subgrid parameterization schemes used in global climate models, the nonorographic gravity wave scheme.  Use of a one-dimensional mechanistic model is advocated, allowing neural network hyperparameters to be chosen based on emergent features of the coupled system with minimal computational cost, and providing a testbed prior to coupling to a climate model. A climate model simulation, using the neural network in place of the existing parameterization scheme, is found to accurately generate a quasi-biennial oscillation of the tropical stratospheric winds, and correctly simulate the nonorographic gravity wave variability associated with the El Niño–Southern Oscillation and stratospheric polar vortex variability. These internal sources of variability are essential for providing seasonal forecast skill, and the gravity wave forcing associated with them is reproduced without explicit training for these patterns.

How to cite: Hardiman, S., Scaife, A., van Niekerk, A., Prudden, R., Owen, A., Adams, S., Dunstan, T., Dunstone, N., and Madge, S.: Machine Learning for Nonorographic Gravity Waves in a Climate Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3520, https://doi.org/10.5194/egusphere-egu24-3520, 2024.

Predicting coastal tsunami impact requires the computation of inundation metrics such as maximum inundation height or momentum flux at all locations of interest. The high computational cost of inundation modelling, in both long term tsunami hazard assessment and urgent tsunami computing, comes from two major factors: (1) the high number of simulations needed to capture the source uncertainty and (2) the need to solve the nonlinear shallow water equations on high-resolution grids. We seek to mitigate the second of these factors using machine learning. The offshore tsunami wave is far cheaper to calculate than the full inundation map, and an emulator able to predict an inundation map with acceptable accuracy from simulated offshore wave height time-series would allow both more rapid hazard estimates and the processing of greater numbers of scenarios. The procedure would necessarily be specific to one stretch of coastline and a complete numerical simulation is needed for each member of the training set. Success of an inundation emulator would demand an acceptable reduction in time-to-solution, a modest number of training scenarios, an acceptable accuracy in inundation predictions, and good performance for high impact, low probability, scenarios. We have developed a convolutional encoder-decoder based neural network and applied it to a dataset of high-resolution inundation simulations for the Bay of Catania in Sicily, calculated for almost 28000 subduction earthquake scenarios in the Mediterranean Sea. We demonstrate encouraging performance in this case study for relatively small training sets (of the order of several hundred scenarios) provided that appropriate choices are made in the setting of model parameters, the loss function, and training sets. Scenarios with severe inundation need to be very well represented in the training sets for the ML-models to perform sufficiently well for the most tsunamigenic earthquakes. The importance of regularization and model parameter choices increases as the size of the training sets decrease.

How to cite: Gibbons, S. J., Briseid Storrøsten, E., Ramalingam, N., Lorito, S., Volpe, M., Sánchez-Linares, C., and Løvholt, F.: Emulators for Predicting Tsunami Inundation Maps at High Resolution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5048, https://doi.org/10.5194/egusphere-egu24-5048, 2024.

Global gridded crop models (GGCMs) have become state-of-the-art tools in large-scale climate impact and adaptation assessments. Yet, these combinations of large-scale spatial data frameworks and plant growth models have limitations in the volume of scenarios they can address due to computational demand and complex software structures. Emulators mimicking such models have therefore become an attractive option to produce reasonable predictions of GGCMs’ crop productivity estimates at much lower computational costs. However, such emulators’ flexibility is thus far typically limited in terms of crop management flexibility and spatial resolutions among others. Here we present a new emulator pipeline CROp model Machine learning Emulator Suite (CROMES) that serves for processing climate features from netCDF input files, combining these with site-specific features (soil, topography), and crop management specifications (planting dates, cultivars, irrigation) to train machine learning emulators and subsequently produce predictions. Presently built around the GGCM EPIC-IIASA and employing a boosting algorithm, CROMES is capable of producing predictions for EPIC-IIASA’s crop yield estimates with high accuracy and very high computational efficiency. Predictions require for a first used climate dataset about 45 min and 10 min for any subsequent scenario based on the same climate forcing in a single thread compared to approx. 14h for a GGCM simulation on the same system.

Prediction accuracy is highest if modeling the case when crops receive sufficient nutrients and are consequently most sensitive to climate. When training an emulator on crop model simulations for rainfed maize and a single global climate model (GCM), the yield prediction accuracy for out-of-bag GCMs is R2=0.93-0.97, RMSE=0.5-0.7, and rRMSE=8-10% in space and time. Globally, the best agreement between predictions and crop model simulations occurs in (sub-)tropical regions, the poorest is in cold, arid climates where both growing season length and water availability limit crop growth. The performance slightly deteriorates if fertilizer supply is considered, more so at low levels of nutrient inputs than at the higher end.

Importantly, emulators produced by CROMES are virtually scale-free as all training samples, i.e., pixels, are pooled and hence treated as individual locations solely based on features provided without geo-referencing. This allows for applications on increasingly available high-resolution climate datasets or in regional studies for which more granular data may be available than at global scales. Using climate features based on crop growing seasons and cardinal growth stages enables also adaptation studies including growing season and cultivar shifts. We expect CROMES to facilitate explorations of comprehensive climate projection ensembles, studies of dynamic climate adaptation scenarios, and cross-scale impact and adaptation assessments.

How to cite: Folberth, C., Baklanov, A., Khabarov, N., Oberleitner, T., Balkovic, J., and Skalsky, R.: CROMES - A fast and efficient machine learning emulator pipeline for gridded crop models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5852, https://doi.org/10.5194/egusphere-egu24-5852, 2024.

Simulating nitrate fate and transport in freshwater is an essential part in water quality management. Numerical and data-driven models have been used for it. The numerical model SWAT simulates daily nitrate loads using simulated flow rate. Data-driven models are more flexible compared to SWAT as they can simulate nitrate load and flow rate independently. The objective of this work was evaluating the performance of SWAT and a deep learning model in terms of nutrient loads in cases when deep learning model is used in (a) simulating flow rate and nitrate concentration independently and (b) simulating both flow rate and nitrate concentration. The deep learning model was built using long-short-term-memory and three-dimensional convolutional networks. The input data, weather data and image data including leaf area index and land use, were acquired at the Tuckahoe Creek watershed in Maryland, United States. The SWAT model was calibrated with data over the training period (2014-2017) and validated with data over the testing period (2019) to simulate flow rate and nitrate load. The Nash-Sutcliffe efficiency was 0.31 and 0.40 for flow rate and -0.26 and -0.18 for the nitrate load over training and testing periods, respectively. Three data-driven modeling scenarios were generated for nitrate load. Scenario 1 included the flow rate observation and nitrate concentration simulation, scenario 2 included the flow rate simulation and nitrate concentration observation, and scenario 3 included the flow rate and nitrate concentration simulations. The deep learning model outperformed SWAT in all three scenarios with NSE from 0.49 to 0.58 over the training period and from 0.28 to 0.80 over the testing period. Scenario 1 showed the best results for nitrate load. The performance difference between SWAT and the deep learning model was most noticeable in fall and winter seasons. The deep learning modeling can be an efficient alternative to numerical watershed-scale models when the regular high frequency data collection is provided.

How to cite: Lee, J., Kim, D., Hong, S., Yun, D., Kwon, D., Hill, R., Pachepsky, Y., Gao, F., Zhang, X., Lee, S., and Cho, K.: Comparison of SWAT and a deep learning model in nitrate load simulation at the Tuckahoe creek watershed in the United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6622, https://doi.org/10.5194/egusphere-egu24-6622, 2024.

Interfacing challenges continue to impede the implementation of neural network-based parameterizations into numerical models of the atmosphere, particularly those written in Fortran. In this study, we leverage a specialized interfacing tool to successfully implement a neural network-based parameterization for both deep and shallow convection within the General Circulation Model, ARPEGE-Climat. Our primary objective is to not only evaluate the performance of this data-driven parameterization but also assess the numerical stability of ARPEGE-Climat when coupled with a convection parameterization trained on data from a different high-resolution model, namely SPCAM 5. 

The performance evaluation encompasses both offline and online assessments of the data-driven parameterization within this framework. The data-driven parameterization for convection is designed using a multi-fidelity approach and is adaptable for use in a stochastic configuration. Challenges associated with this approach include ensuring consistency between variables in ARPEGE-Climat and the parameterization based on data from SPCAM 5, as well as managing disparities in geometry (e.g., horizontal and vertical resolutions), which are crucial factors affecting the intermodel parameterization transferability.

How to cite: Balogh, B., Saint-Martin, D., Geoffroy, O., Bhouri, M. A., and Gentine, P.: Assessment of ARPEGE-Climat using a neural network convection parameterization based upon data from SPCAM 5, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7455, https://doi.org/10.5194/egusphere-egu24-7455, 2024.

Urban warming in cities is increasingly exacerbated by the escalation of more frequent and severe heat extremes. Effectively mitigating overheating necessitates the adoption of a comprehensive, whole-system approach that integrates various heat mitigation measures to generate rapid and sustained efficacy in mitigation efforts. However, there remains a significant gap in the exploration of how to quantify the efficacy of mitigation strategies at the city-scale.

We address this research question by leveraging mesoscale Weather Research Forecasting (WRF) models alongside machine-learning (ML) techniques. As a showcase, ML models have been established for Zurich and Basel, Switzerland, utilizing seven WRF-output-based features, including shortwave downward radiation (SWDNB), hour of the day (HOUR), zenith angle (COSZEN), rain mix ratio (QRAIN), longwave downward radiation (LWDNB), canopy water content (CANWAT), and planetary boundary layer height (PBLH). Impressively, the resultant median R² values for T2 (2m temperature) predictions during heatwave and non-heatwave periods are notably high at 0.94 and 0.91 respectively.

Within the perspective of the whole-system approach, we quantify the impacts of reducing shortwave radiation absorption at ground surfaces, a potential result of a combination of both shading and reflective coating-based mitigation measures, through the utilization of ML models. Remarkably, a 5% reduction in the absorption of radiation at ground surfaces in Zurich could lead to a reduction in T2 by as much as 3.5 °C in the city center. During a heatwave in Basel, the potential for cooling is even more pronounced, with temperature decreases of up to 5 °C. These case studies in Zurich and Basel underscore the efficacy of utilizing WRF feature-trained ML models to quantify heat mitigation strategies at the city-scale.

How to cite: Zhao, Y., Wang, Z., Strebel, D., and Carmeliet, J.: Blending machine-learning and mesoscale numerical weather prediction models to quantify city-scale heat mitigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7581, https://doi.org/10.5194/egusphere-egu24-7581, 2024.

Deep learning models emerged as viable alternatives to rapid and accurate flood mapping, overcoming the computational burden of numerical methods. In particular, hydraulic-based graph neural networks present a promising avenue, offering enhanced transferability to domains not used for the model training. These models exploit the analogy between finite-volume methods and graph neural networks to describe how water moves in space and time across neighbouring cells. However, existing models face limitations, having been exclusively tested on regular meshes and necessitating initial conditions from numerical solvers. This study proposes an extension of hydraulic-based graph neural networks to accommodate time-varying boundary conditions, showcasing its efficacy on irregular meshes. For this, we employ multi-scale methods that jointly model the flood at different scales. To remove the necessity of initial conditions, we leverage ghost cells that enforce the solutions at the boundaries. Our approach is validated on a dataset featuring irregular meshes, diverse topographies, and varying input hydrograph discharges. Results highlight the model's capacity to replicate flood dynamics across unseen scenarios, without any input from the numerical model, emphasizing its potential for realistic case studies.

How to cite: Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.: Multi-scale hydraulic-based graph neural networks: generalizing spatial flood mapping to irregular meshes and time-varying boundary condition, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7681, https://doi.org/10.5194/egusphere-egu24-7681, 2024.

Atmospheric general circulation models include two main distinct components: the dynamical one solves the Navier-Stokes equations to provide a mathematical representation of atmospheric movements while the physical one includes parameterizations representing small-scale phenomena such as turbulence and convection (Balaji et al., 2022). However, computational demands of the parameterizations limit the numerical efficiency of the models. The burgeoning field of machine learning techniques opens new horizons by producing accurate, robust and fast emulators of parts of a climate model. In particular, they can reliably reproduce physical processes, thus providing an efficient alternative to traditional process representation. Indeed, some pioneering studies (Gentine et al., 2018; Rasp et al., 2018) have shown that these emulators can replace one or more parameterizations that are computationally expensive and so, have the potential to enhance numerical efficiency.

Our research work aligns with these perspectives, since it involves exploiting the potential of developing an emulator of the physical parameterizations of the IPSL climate model, and more specifically of the ICOLMDZOR atmospheric model (for DYNAMICO, the dynamic solver using an icosahedral grid - LMDZ, the atmospheric component - ORCHIDEE, the surface component). The emulator could improve performance, as currently almost half of the total computing time is given to the physical part of the model.

We have developed two initial offline emulators of the physical parameterizations of our standard model, in an idealized aquaplanet configuration, to reproduce profiles of tendencies of the key variables - zonal wind, meridional wind, temperature, humidity and water tracers - for each atmospheric column. The results of these emulators, based on a dense neural network or a convolutional neural network, have begun to show their potential for use, since we easily obtain good performances in terms of the mean of the predicted tendencies. Nevertheless, their variability is not well captured, and the variance is underestimated, posing challenges for our application. A study of physical processes has revealed that turbulence was at the root of the problem. Knowing how turbulence is parameterized in the model, we show that incorporating physical knowledge through latent variables as predictors into the learning process, leading to a significant improvement of the variability.

Future plans involve an online physics emulator, coupled with the atmospheric model to provide a better assessment of the learning process (Yuval et al., 2021).

How to cite: Crossouard, S., Kageyama, M., Vrac, M., Dubos, T., Thao, S., and Meurdesoif, Y.: Contribution of latent variables to emulate the physics of the IPSL model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10087, https://doi.org/10.5194/egusphere-egu24-10087, 2024.

Sea ice plays an essential role in global ocean circulation and in regulating Earth's climate and weather, and melt ponds that form on the ice have a profound impact on the Arctic's climate by altering the ice albedo. Melt pond evolution is complex, sub grid scale and poorly understood - and melt ponds are represented in sea ice models as parametrisations. Parametrisations of these physical processes are based on a number of assumptions and can include many uncertain parameters that have a substantial effect on the simulated evolution of the melt ponds. 

We have shown, using Sobol sensitivity analysis and through investigating perturbed parameter ensembles (PPEs), that a state-of-the-art sea ice column model, Icepack, demonstrates substantial sensitivity to its uncertain melt pond parameters. These PPEs demonstrate that perturbing melt pond parameters (within known ranges of uncertainty) cause predicted sea ice thickness over the Arctic Ocean to differ by many metres after only a decade of simulation. Understanding the sources of uncertainty, improving parametrisations and fine tuning the parameters is a paramount, but usually very complex and difficult task. Given this uncertainty, we propose to replace the sub grid scale melt pond parametrisation (MPP) in Icepack with a machine learning emulator. 

Building and replacing the MPP with a machine learning emulator has been done in two broad steps that contain multiple computational challenges. The first is generating a melt pond emulator using 'perfect' or 'model' data. Here we demonstrate a proof of concept and show how we achieve numerically stable simulations of Icepack when embedding an emulator in place of the MPP - with Icepack running stably for the whole length of the simulations (over a decade) across the Arctic. 

Secondly, we develop offline an emulator from observational data that faithfully predicts observed sea ice albedo and melt pond fraction given climatological input variables. Embedding an observational emulator can require different challenges as compared to using model data, such as not all variables needed by the host model being observable/observed for an emulator to predict. We discuss how we achieve online simulations interfacing this emulator with the Icepack model.

Our focus on using column models ensures that our observational emulator of sea ice albedo and melt pond fraction can readily be used in sea ice models around the world, irrespective of grid resolutions and mesh specifications, and offers one approach for creating general emulators that can be used by many climate models. 

How to cite: Driscoll, S., Carrassi, A., Brajard, J., Bertino, L., Bocquet, M., Olason, E., and Lawless, A.: Replacing parametrisations of melt ponds on sea ice with machine learning emulators, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10749, https://doi.org/10.5194/egusphere-egu24-10749, 2024.

Machine learning (ML) models have become popular in the Earth Sciences for improving predictions based on observations. Beyond pure prediction, though, ML has a large potential to create surrogates that emulate complex numerical simulation models, considerably reducing run time, hence facilitating their analysis.

The behaviour of eco-geomorphological systems is often examined using minimal models, simple equation-based expressions derived from expert knowledge. From them, one can identify complex system characteristics such as equilibria, tipping points, and transients. However, model formulation is largely subjective, thus disputable. Here, we propose an alternative approach where a ML surrogate of a high-fidelity numerical model is used instead, conserving suitability for analysis while incorporating the higher-order physics of its parent model. The complexities of developing such an ML surrogate for understanding the co-evolution of vegetation, hydrology, and geomorphology on a geological time scale are presented, highlighting the potential of this approach to capture novel, data-driven scientific insights.

To obtain the surrogate, the ML models were trained on a data set simulating a coupled hydrological-vegetation-soil system. The rate of change of the two variables describing the system, soil depth and biomass, was used as output, taking their value at the previous time step and the pre-defined grazing pressure as inputs. Two popular ML methods, random forest (RF) and fully connected neural network (NN), were used. As proof of concept and to configure the model setup, we first trained the ML models on the output of the minimal model described in [1], comparing the ML responses at gridded inputs with the derivative values predicted by the minimal model. While RF required less tuning to achieve competitive results, a relative root mean squared error (rRMSE) of 5.8% and 0.04% for biomass and soil depth respectively, NN produced better-behaved outcome, reaching a rRMSE of 2.2% and 0.01%. Using the same setup, the ML surrogates were trained on a high-resolution numerical model describing the same system. The study of the response from this surrogate provided a more accurate description of the dynamics and equilibria of the hillslope ecosystem, depicting, for example, a much more complex process of hillslope desertification than captured by the minimal model.

It is thus concluded that the use of ML models instead of expert-based minimal models may lead to considerably different findings, where ML models have the advantage that they directly rely on system functioning embedded in their parent numerical simulation model.

How to cite: Pomarol Moya, O., Karssenberg, D., Immerzeel, W., Nussbaum, M., and Mehrkanoon, S.: Understanding geoscientific system behaviour from machine learning surrogates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11880, https://doi.org/10.5194/egusphere-egu24-11880, 2024.

Artificial intelligence and deep learning are currently reshaping numerical simulation frameworks by introducing new modeling capabilities. These frameworks are extensively investigated in the context of model correction and parameterization where they demonstrate great potential and often outperform traditional physical models. Most of these efforts in defining hybrid dynamical systems follow offline learning strategies in which the neural parameterization (called here sub-model) is trained to output an ideal correction. Yet, these hybrid models can face hard limitations when defining what should be a relevant sub-model response that would translate into a good forecasting performance. End-to-end learning schemes, also referred to as online learning, could address such a shortcoming by allowing the deep learning sub-models to train on historical data. However, defining end-to-end training schemes for the calibration of neural sub-models in hybrid systems requires working with an optimization problem that involves the solver of the physical equations. Online learning methodologies thus require the numerical model to be differentiable, which is not the case for most modeling systems. To overcome this difficulty and bypass the differentiability challenge of physical models, we present an efficient and practical online learning approach for hybrid systems. The method, called EGA for Euler Gradient Approximation, assumes an additive neural correction to the physical model, and an explicit Euler approximation of the gradients. We demonstrate that the EGA converges to the exact gradients in the limit of infinitely small time steps. Numerical experiments are performed on various case studies, including prototypical ocean-atmosphere dynamics. Results show significant improvements over offline learning, highlighting the potential of end-to-end online learning for hybrid modeling.

How to cite: Ouala, S., Chapron, B., Collard, F., Gaultier, L., and Fablet, R.: End-to-end Learning in Hybrid Modeling Systems: How to Deal with Backpropagation Through Numerical Solvers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14744, https://doi.org/10.5194/egusphere-egu24-14744, 2024.

How to cite: Prasad, A., Merkouriadi, I., and Nummelin, A.: Exploring data-driven emulators for snow on sea ice , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14957, https://doi.org/10.5194/egusphere-egu24-14957, 2024.

The Machine Learning Estimator for Ground Shaking Maps (MLESmaps) harnesses the ground shaking inference capability of Machine Learning (ML) models trained on physics-informed earthquake simulations. It infers intensity measures, such as RotD50, seconds after a significant earthquake has occurred given its magnitude and location. 

Our methodology incorporates both offline and online phases in a comprehensive workflow. It begins with the generation of a synthetic training data set, progresses through the extraction of predictor characteristics, proceeds to the validation and learning stages, and yields a learned inference model. 

MLESmap results can complement empirical Ground Motion Models (GMMs), in particular in data-poor areas, to assess post-earthquake hazards rapidly and accurately, potentially improving disaster response in earthquake-prone regions. Learned models incorporate physical features such as directivity, topography, or resonance at a speed comparable to that of the empirical GMMs. 

In this work, we present an overview of the MLESmap methodology and its application to two distinct study areas: southern California and southern Iceland

How to cite: Monterrubio-Velasco, M., Blanco, R., Callaghan, S., Bhihe, C., Pienkowska, M., Ejarque, J., and de la Puente, J.: Machine Learning Estimator for Ground-Shaking maps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15914, https://doi.org/10.5194/egusphere-egu24-15914, 2024.

The combination of Machine Learning (ML) with geoscientific models is an active area of research with a wide variety of applications. A key practical question for those models is to define how high level languages ML components can be encoded and maintained into pre-existing legacy solvers, written in low level abstraction languages (as Fortran). We address this question through the strategy of creating pipes between a geoscientific code and ML components executed in their own separate scripts. The main advantage of this approach is the possibility to easily share the inference models within the community without keeping them bound to one code with its specific numerical methods. Here, we chose to focus on OASIS (https://oasis.cerfacs.fr/en/), which is a Fortran coupling library that performs field exchanges between coupled executables. It is commonly used in the numerical geoscientific community to couple different codes and assemble earth-system models. Last releases of OASIS provided C and Python APIs, which enable coupling between non-homogeneously written codes. We seek to take advantage of those new features and the presence of OASIS in the community codes, and propose a Python library (named Eophis) that facilitates the deployment of inference models for coupled execution. Basically, Eophis allows to: (i) wrap an OASIS interface to exchange data with a coupled earth-system code, (ii) wrap inference models into a simple in/out interface, and (iii) emulate time evolution to synchronize connexions between earth-system and models. We set up a demonstration case with the European numerical code NEMO in which the pre-existing OASIS interface has been slightly modified. A forced global ocean model simulation is performed with regular exchanges of 2D and 3D fields with Eophis. Received data are then sent to inference models that are not implemented in NEMO. Performances of the solution will finally be assessed with references.

How to cite: Barge, A. and Le Sommer, J.: Online deployment of pre-trained machine learning components within Earth System models via OASIS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16148, https://doi.org/10.5194/egusphere-egu24-16148, 2024.

Neural differential equations (NDEs) provide a powerful and general framework for interfacing machine learning with numerical modeling. However, constraining NDE solutions to obey known physical priors, such as conservation laws or restrictions on the allowed state of the system, has been a challenging problem in general. We present stabilized NDEs (SNDEs) [1], the first method for imposing arbitrary explicit constraints in NDE models. Alongside robust theoretical guarantees, we demonstrate the effectiveness of SNDEs across a variety of settings and using diverse classes of constraints. In particular, SNDEs exhibit vastly improved generalization and stability compared to unconstrained baselines. Building on this work, we also present constrained NDEs (CNDEs), a novel and complementary method with fewer hyperparameters and stricter constraints. We compare and contrast the two methods, highlighting their relative merits and offering an intuitive guide to choosing the best method for a given application.

[1] Alistair White, Niki Kilbertus, Maximilian Gelbrecht, Niklas Boers. Stabilized neural differential equations for learning dynamics with explicit constraints. In Advances in Neural Information Processing Systems, 2023.

How to cite: White, A., Kilbertus, N., Gelbrecht, M., and Boers, N.: Two Methods for Constraining Neural Differential Equations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16149, https://doi.org/10.5194/egusphere-egu24-16149, 2024.

Across geoscience, numerical models are used for understanding, experimentation, and prediction of complex systems. Many of these models are computationally intensive and involve sub-models for certain processes, often known as parameterisations. Such parameterisations may capture unresolved sub-grid processes, such as turbulence, or represent fast-moving dynamics, such as gravity waves, or provide a combination of the two, such as microphysics schemes.

Recently there has been significant interest in incorporating machine learning (ML) methods
into these parameterisations. Two of the main drivers are the emulation of computationally intensive processes, thereby reducing computational resources required, and the development of data-driven parameterisation schemes that could improve accuracy through capturing ‘additional physics’.

Integrating ML sub-models in the context of numerical modelling brings a number of challenges, some of which are scientific, others computational. For example, many numerical models are written in Fortran, whilst the majority of machine learning is conducted using Python-based frameworks such as PyTorch that provide advanced ML modelling capabilities. As such there is a need to leverage ML models developed externally to Fortran, rather than the error-prone approach of writing neural networks directly in Fortran, missing the benefits of highly-developed libraries.

Interoperation of the two languages requires care, and increases the burden on researchers and developers. To reduce these barriers we have developed the open-source FTorch library [1] for coupling PyTorch models to Fortran. The library is designed to streamline the development process, offering a Fortran interface mimicking the style of the Python library whilst abstracting away the complex details of interoperability to provide a computationally efficient interface.

A significant benefit of this approach is that it enables inference to be performed on either CPU or GPU, enabling deployment on a variety of architectures with low programmer effort. We will report on the performance characteristics of our approach, both in the CPU and GPU settings and include a comparison with alternative approaches.

This approach has been deployed on two relevant case studies in the geoscience context: a gravity-wave parameterisation in an intermediate complexity atmospheric model (MiMA) based on Espinosa et al. [2], and a convection parameterisation in a GCM (CAM/CESM) based on Yuval et al. [3]. We will report on these applications and lessons learned from their development. 

[1] FTorch https://github.com/Cambridge-ICCS/FTorch
[2] Espinosa et al., Machine Learning Gravity Wave Parameterization Generalizes to Capture the QBO and Response to Increased CO2, GRL 2022 https://doi.org/10.1029/2022GL098174
[3] Yuval et al., Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision, GRL 2021 https://doi.org/10.1029/2020GL091363

How to cite: Orchard, D., Kasoar, E., Atkinson, J., Meltzer, T., Clifford, S., and Elafrou, A.: FTorch - lowering the technical barrier of incorporating ML into Fortran models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17852, https://doi.org/10.5194/egusphere-egu24-17852, 2024.

Physics-based simulations of earthquake ground motions prove invaluable, particularly in regions where strong ground motion recordings remain scarce. However, the computational demands associated with these simulations limit their applicability in tasks that necessitate large-scale computations of a wide range of possible earthquake scenarios, such as those required in physics-based probabilistic seismic hazard analyses. To address this challenge, we propose a neural-network approach that enables the rapid computation of earthquake ground motions in the spectral domain, alleviating a significant portion of the computational burden. To illustrate our approach, we generate a database of ground motion simulations in the San Francisco Bay Area using AxiSEM3D, a 3D seismic wave simulator. The database includes 30 double-couple sources with varying depths and horizontal locations. Our simulations explicitly incorporate the effects of topography and viscoelastic attenuation and are accurate up to frequencies of 0.5 Hz. Preliminary results demonstrate that the trained neural network almost instantaneously produces estimates of peak ground displacements as well as displacement waveforms in the spectral domain that align closely with those obtained from the wave propagation simulations. Our approach also extends to predicting ground motions for ‘unsimulated’ source locations, ultimately providing a comprehensive resolution of the source space in our chosen physical domain. This advancement paves the way for a cost-effective simulation of numerous seismic sources, and enhances the feasibility of physics-based probabilistic seismic hazard analyses. 

How to cite: Ramadan, F., Fry, B., and Nissen-Meyer, T.: Rapid Computation of Physics-Based Ground Motions in the Spectral Domain using Neural Networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18444, https://doi.org/10.5194/egusphere-egu24-18444, 2024.

How to cite: Montagna, C. P., Garg, D., Allegra, M., Cannavò, F., Currenti, G., Bruni, R., and Papale, P.: A digital twin for volcanic deformation merging 3D numerical simulations and AI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19255, https://doi.org/10.5194/egusphere-egu24-19255, 2024.

Non-linear, dynamic patterns are the rule rather than the exception in ecosystems. Predicting such patterns would allow an improved understanding of energy and nutrient flows in such systems. The Scientific Machine Learning approach Universal Differential Equation (UDE) by Rackauckas et al. (2020) tries to extract the underlying dynamical relations of state variables directly from their time series in combination with some knowledge on the dynamics of the system. This approach makes this kind of tool a promising approach to support classical modeling when precise knowledge of dynamical relationships is lacking, but measurement data of the phenomenon to be modeled is available.

We applied the UDE approach to a 22-year data set of the southern Baltic Sea coast, which constituted six different phytoplankton bloom types. The data set contained the state variables chlorophyll and different dissolved and total nutrients. We learned the chlorophyll:nutrient interactions from the data with additional forcing of external temperature, salinity and light attenuation dynamics as drivers. We used a neural network as a universal function approximator that provided time series of the state variables and their derivatives.

Finally, we recovered algebraic relationships between the variables chlorophyll, dissolved and total nutrients and the external drivers temperature, salinity and light attenuation using Sparse Identification of nonlinear Dynamics (SinDy) by Brunton et al. (2016).

The gained algebraic relationships differed in their importance of the different state variables and drivers for the six phytoplankton bloom types in accordance with general mechanisms reported in literature for the southern Baltic Sea coast. Our approach may be a viable option to guide ecosystem management decisions based on those algebraic relationships.

Rackauckas et al. (2020), arXiv preprint arXiv:2001.04385.

Brunton et al. (2016), PNAS 113.15: 3932-3937.

How to cite: Nieters, P., Berthold, M., and Vortmeyer-Kley, R.: Learning phytoplankton bloom patterns - A long and rocky road from data to equations , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19352, https://doi.org/10.5194/egusphere-egu24-19352, 2024.

Seismology has witnessed significant advancements in recent years with the application of deep
learning methods to address a broad range of problems. These techniques have demonstrated their
remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the
capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open-source
Python package specifically developed for fast data processing by implementing deep learning.
SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitude
estimation, seismic phase picking, and polarity identification. We introduce upgraded versions
of previously published models such as CREIME_RT capable of identifying earthquakes with an
accuracy above 99.8% and a root mean squared error of 0.38 unit in magnitude estimation. These
upgraded models outperform state-of-the-art approaches like the Vision Transformer network. SAIPy
provides an API that simplifies the integration of these advanced models, including CREIME_RT,
DynaPicker_v2, and PolarCAP, along with benchmark datasets. The package has the potential to be
used for real-time earthquake monitoring to enable timely actions to mitigate the impact of seismic
events.

How to cite: Srivastava, N., Li, W., Chakraborty, M., Cartaya, C. Q., Köhler, J., Faber, J., and Rümpker, G.: SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19502, https://doi.org/10.5194/egusphere-egu24-19502, 2024.

While forecasting of climate and earth system processes has long been a task for numerical models, the rapid development of deep learning applications has recently brought forth competitive AI systems for weather prediction. Earth system models (ESMs), even though being an integral part of numerical weather prediction have not yet caught that same attention. ESMs forecast water, carbon and energy fluxes and in the coupling with an atmospheric model, provide boundary and initial conditions. We set up a comparison of different deep learning approaches for improving short-term forecasts of land surface and ecosystem states on a regional scale. Using simulations from the numerical model and combining them with observations, we will partially emulate an existing land surface scheme, conduct a probabilistic forecasts of core ecosystem processes and determine forecast horizons for all variables.

How to cite: Wesselkamp, M., Chantry, M., Kalweit, M., Pinnington, E., Choulga, M., Boedecker, J., Dormann, C., Pappenberger, F., and Balsamo, G.: Partial land surface emulator forecasts ecosystem states at verified horizons, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20863, https://doi.org/10.5194/egusphere-egu24-20863, 2024.

Thanks to the recent progress in numerical methods, the application fields of artificial intelligence (AI) and machine learning methods (ML) are growing at a very fast pace. The EURAD (European Joint Programme on Radioactive Waste Management) community has recently started using ML for a) acceleration of numerical simulations, b) improvement of multiscale and multiphysics couplings efficiency, c) uncertainty quantification and sensitivity analysis. A number of case studies indicate that use of ML based approaches leads to overall acceleration of geochemical and reactive transport simulations from one to four orders of magnitude. The achieved speed-up depends on the chemical system, simulation code, problem formulation and the research question to be answered. Within EURAD-DONUT (Development and Improvement Of Numerical methods and Tools for modelling coupled processes), a benchmark is on-going to coordinate the relevant activities and to test a variety of ML techniques for geochemistry and reactive transport simulations in the framework of radioactive waste disposal. It aims at benchmarking several widely used geochemical codes, at generating high-quality geochemical data for training/validation of existing/new methodologies, and at providing basic guidelines about the benefits, drawbacks, and current limitations of using ML techniques.

A joint effort has resulted in the definition of benchmarks of which one is presented here. The benchmark system is relevant to the sorption of U in claystone formations (e.g. Callovo-Oxfordian, Opalinus or Boom clay). Regarding the chemical complexity, a system containing Na-Cl-U-H-O is considered as the base case, and a more complex system with the addition of calcium and carbonate (CO2) to change aqueous speciation of U. Parameters of interest, among others, are the resulting concentrations of U sorbed on edges (surface complexes), of U on ion exchange sites, and the amount of metaSchoepite, with the resulting Kd’s. Following aspects are discussed: (i) Streamline the production of high-quality consistent training datasets, using the most popular geochemical solvers (PHREEQC, ORCHESTRA and GEMS). (ii) The use of different methods (e.g. Deep Neural Networks, Polynomial Chaos Expansion, Gaussian Processes, Active Learning, and other techniques to learn from the generated data. (iii) Setup appropriate metrics for the critical evaluation of the accuracy of ML models. (iv) Testing the accuracy of predictions for geochemical and reactive transport calculations. 

How to cite: Laloy, E. and Montoya, V. and the EURAD-DONUT Team: Machine learning based metamodels for geochemical calculations in reactive transport models: Benchmark within the EURAD Joint Project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21545, https://doi.org/10.5194/egusphere-egu24-21545, 2024.

Despite efforts to obtain consistent results, the prediction of patterns in the behavior of geophysical fields still faces many uncertainties. However, these analyses are important for studying the effects of human action on the environment and the effects reflected in climate change. There is much evidence that Multifractals are capable of describing intermittent behavior and statistical data of all orders and over a wide range of scales. Therefore, this work consists of using the multifractal framework to analyze recent precipitation projection data in France, verifying the evolution of its parameters over a relatively long period of time (from 1951 to 2100) and over space, using 12 points on French territory with a resolution of 2.8º x 2.8º. For this, the Double Trace Moment technique was applied to determine the mean intermittency codimensions, the multifractality indexes and the maximum probability singularities. These results were compared to the article by J.-F. Royer et al., C. R. Geoscience 340 (2008) to verify if projections remained consistent with changes in data and economic scenarios. Despite the differences found in the range of parameter values ​​and scaling behavior, recent data also indicated an increase in intermittency over time and presented spatial behavior similar to old projections, which reinforces the expectation of an increase in precipitation extremes in the coming decades.

How to cite: Dias Kovalczuk, P. H., Schertzer, D., and Tchiguirinskaia, I.: Multifractal analysis of recent precipitation projections in the context of climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-444, https://doi.org/10.5194/egusphere-egu24-444, 2024.

Precipitation nowcasting, referring to short-term forecasting ahead for 0 to 6 hours, is an important aspect of many urban meteorological and hydrological studies. This is due to the fact that reliable nowcasting can serve as an early warning of massive flooding and a guide for water-related risk management, making it highly significant in urban areas from a socio-economic perspective. Precipitation exhibits extreme variability over a wide range of space-time scales, so nowcasting is essentially a spatiotemporal sequence forecasting. Convolutional long short-term memory (ConvLSTM) models are frequently used to capture the spatiotemporal correlation, but they often struggle with an issue that produces blurry predictions. Therefore, generative adversarial network (GAN) architecture is employed to achieve more detailed and realistic predictions. The framework of universal multifractal (UM) with only three scale-independent parameters (α, C₁, H) is also introduced in the deep learning model to characterize the extreme variability of precipitation. The developed hybrid approach using stochastic models physically based on the cascade paradigm ensures that intermittency is directly taken into account, including in the generation of uncertainty. In addition to the common evaluating metrics, like mean absolute error (MAE), root mean squared error (RMSE), critical success index (CSI), probability of detection (POD), power spectral density (PSD) and UM are also introduced to evaluate nowcasting performance in the spectrum space. This ongoing work is based on the previous research about combining recurrent neural networks with variational mode decomposition and multifractals to predict rainfall time series in Paris area.

How to cite: Zhou, H., Schertzer, D., and Tchiguirinskaia, I.: Combining Generative Adversarial Networks with Multifractals for Urban Precipitation Nowcasting , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-531, https://doi.org/10.5194/egusphere-egu24-531, 2024.

Urbanization induced carbon dioxide (CO2) emissions have attracted widespread attention.

A comprehensive attribution analysis model is designed to understand the inherent uncertainties in diagnosing the effects of urban expansion dynamics and modes on carbon dioxide (CO2) emissions. First, 68 selected cities across China are categorized into three types, including expanding, contracting, and staying cities, through developing an evaluation indicator system by integrating population, economy, construction, and social information. Next, the carbon dioxide (CO2) emissions of the cities were quantified. The Lasso method was employed to select the factors influencing CO2 emissions. For cities with different development modes, the XGBoost regression model with SHAP algorithm was employed to calculate the contribution rate of various factors to carbon emissions in different types of cities. Additionally, the analysis considered the temporal changes of these factors.

The main conclusions are as follows:

(i)Comparing urban built-up areas extracted from the nighttime light dataset with China's national land use and cover change dataset, the results reveal a minimum correlation of 0.72-0.82 and an average overall accuracy of 78%.

(ii)The urbanization process of 68 cities exhibits a predominant pattern of normal fluctuations, with a coexistence of expansion and contraction. The results indicate that over the past 20 years, expanding cities have been concentrated mainly in coastal regions such as the Yangtze River Delta and the Pearl River Delta, while contracting cities are primarily found in inland areas characterized by traditional industrial cities. It is observed that the development processes of most cities involve an initial phase of intensive expansion (or contraction), followed by a gradual trend towards stability in the later stages.

(iii)The factors influencing carbon emissions in expanding and contracting cities share commonalities and differences. Population and energy efficiency both have significant impacts on carbon emissions in different types of cities. For expanding cities, the impact of green area on carbon emissions is more pronounced. Conversely, in contracting cities, the influence of foreign trade is more significant.

How to cite: Qian, J. and Cai, D.: The impact of the expansion and contraction of China’s cities on CO2 emissions,2002-2021,evidence from integrated nighttime light data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-821, https://doi.org/10.5194/egusphere-egu24-821, 2024.

Machine learning (ML) and Artificial Intelligence (AI) models have been increasingly adopted for geospatial tasks. However, geospatial data (such as points and raster cells) are often influenced by underlying spatial effects, and current model designs often lack adequate consideration of these effects. Determining the efficient model structure for representing geospatial data and capturing the underlying complex spatial and contextual effects still needs to be explored. To address this gap, we propose a Transformer-like encoder-decoder architecture to first represent geospatial data with respect to their corresponding geospatial context, and then decode the representation for task-specific inferences. The encoder consists of embedding layers that transform the input location and attributes of geospatial data into meaningful embedding vectors. The decoder comprises task-specific neural network layers that map the encoder outputs to the final output. Spatial contextual effects are measured using explainable artificial intelligence (XAI) methods. We evaluate and compare the performance of our model with other model structures on both synthetic and real-world datasets for spatial regression and interpolation tasks. This work proposes a generalizable approach to better modeling and measuring complex spatial contextual effects, potentially contribute to efficient and reliable urban analytic applications that require geo-context information.

How to cite: Deng, R., Li, Z., and Wang, M.: A Transformer-Based Model for Effective Representation of Geospatial Data and Context, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1003, https://doi.org/10.5194/egusphere-egu24-1003, 2024.

In recent years, Sri Lanka has experienced a high prevalence of chronic kidney disease (CKDu) in certain regions, especially in the North Central Province (NCP). The etiology of this disease is not yet clearly understood, although several hypotheses involving environmental and occupational factors have been proposed. To better understand the patterns of CKDu incidence and its potential relationship to environmental factors, a spatial and temporal analysis was conducted using geographic information system (GIS) tools. In this study, we identified the geographical hotspots of CKDu incidence over a period of eleven years (from 2010 to 2020) in the NCP, of Sri Lanka. The analysis was done for the districts of Anuradhapura and Polonnaruwa in NCP. Furthermore, we analysed the temporal trends of CKDu incidence by comparing the disease burden between different years. Finally, we examined the association between river basins and CKDu incidence by overlaying the spatial layers of the disease incidence and river basins. Our results showed that there were significant spatial and temporal variations in CKDu incidence in the region over the study period. The disease is characterized by a fluctuating trend. Also, the number of hotspots has decreased over time, and the number of CKDu-affected patients has also decreased. Similarly found that CKDu hotspots were concentrated around the mainly 4 river basins in the region, indicating a possible link between water resources and the disease. By identifying CKDu hotspots and understanding the disease's movement over time, public health officials can target their efforts more effectively, reducing the disease's impact on affected communities. This study provides important insights into the spatial and temporal patterns of CKDu and suggests the need for further research to investigate the potential environmental risk factors contributing to this disease.

Key Words: Chronic Kidney Disease of Unknown Etiology (CKDu), Hotspots Analysis, Spatial and Temporal Variation, Geographical Information System (GIS)

How to cite: Arambegedara, C. S., Lijun, Y., Cai, D., Zhu, J., Ranasinghe, A. V., and Silva, A. P. D.: Spatial and Temporal Analysis for Identifying the Movement of Chronic Kidney Disease (CKDu) Hotspots; in Reference to River Basins in North Central Province, Sri Lanka, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1223, https://doi.org/10.5194/egusphere-egu24-1223, 2024.

Cities play a crucial role in climate neutrality because although they occupy only 4% of the EU land area, they host 75% of its population. In addition, they consume over 65% of global energy and account for more than 70% of global CO2 emissions. As climate change mitigation depends on urban action, the EU has decided to support cities in accelerating their green and digital transformation. The EU Mission on Climate-Neutral and Smart Cities aims to make the participating cities climate neutral and smart by 2030, in areas such as energy, waste management, transport, and buildings, to improve the quality of life. A WEBGIS Smart City Geospatial Framework has been developed for the Limassol Municipality in Cyprus. The establishment of a Smart City Geospatial Framework is imperative for several reasons. Firstly, it enables data-driven decision-making, allowing city officials to make informed choices about urban planning and resource allocation. Secondly, it enhances the efficiency of public services, such as transportation and emergency response, by leveraging real-time spatial data. Moreover, the framework promotes sustainability by providing insights into environmental factors, contributing to eco-friendly urban development. Lastly, the integration of geospatial technologies fosters citizen engagement, transparency, and overall improvement in the quality of life for urban residents. Under this WEBGIS smart city framework, the authors explore the importance of supporting the Limassol Municipality under the EU Mission for climate-neutral and smart cities by 2030 initiative, using the proposed WEBGIS smart city framework.  Results are presented using the GIS dashboard.

How to cite: Papantoniou, A., Danezis, C., and Hadjimitsis, D.: Exploring the importance of using a novel Smart City Geospatial Integrated Framework for supporting Cities participating in EU Mission for climate-neutral and smart cities by 2030: the case study of Limassol in Cyprus., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1502, https://doi.org/10.5194/egusphere-egu24-1502, 2024.

Information on urban groundwater in Vienna is important not only to secure a sustainable use and supply but also to protect groundwater quality. Here, we provide a compilation of available information and data to cover all relevant aspects of hydrogeology within the city in order to improve planning and policy making with regard to water extraction, geothermal use and groundwater protection.

We propose a grouping of the Quaternary and Neogene sediments as well as of the underlying sedimentary rocks of the Flysch zone and the Calcareous Alps, into hydrogeological units with distinct properties. Each unit is described regarding lithology, aquifer type, groundwater occurrence and yield. Additionally, the area percentage of sealed ground surface and the conditions of groundwater recharge are defined. Finally, the types of groundwater use, withdrawal rates, hydrochemical signatures and heavy metal contents are characterized.

Limestones and dolomites of the Calcareous Alps represent high yield karst aquifers with calcium-magnesium-bicarbonate-type hydrochemistry, used as spa water drawn from 800 m deep, artesian wells.

Within the Flysch zone, clay- and marlstones act as aquitards while sandstones constitute fractured or double-porosity aquifers which are partially confined, of low yield and used locally for drinking water, industrial water and irrigation. At the surface, the zone occurs in the Vienna Woods, where groundwater recharge through rain water can be high within sandstone areas.

Where Neogene silts and clays contain sand and gravel layers, these represent porous aquifers of low to medium yield, used mainly for irrigation, industrial water and geothermal purposes. Groundwater recharge from the surface is impeded by a thick loess cover. In the eastern part of the city, groundwater in a conglomerate layer of 300 m thickness and 3000 m below ground, reaches temperatures of up to 100°C and is considered Vienna’s future geo-energy reservoir.

Pleistocene terraces are made of gravel and, with decreasing age, show decreasing amounts of sand and silt intercalations, while the groundwater shows increasing yield, increasing mineralisation and major ion contents shifting from Ca and Mg dominance towards more Na and K. The terraces’ occurrence coincides with intense urban land use, sealing of the ground surface, low recharge and potential infiltration of leaking sewage water.

Within the Danube plain, 60 % of the land is used for agriculture and recreation where rain water can infiltrate easily into Holocene gravel. Recharge also happens partially through river bank filtrate of the Danube, partially through artificial recharge. Among all groundwater units in Vienna, this continuous aquifer shows the highest yield and the most intense use for irrigation and groundwater heat pumps. During peak periods of water demand, groundwater is also used as drinking water.

Vienna’s water consumption amounts to 200 litres per person per day approximatively. In periods of normal demand, drinking water is provided exclusively by Alpine karst springs captured up to 120 km southwest of the city.

How to cite: Pfleiderer, S.: The hydrogeological units of Vienna - land use, groundwater use and groundwater chemistry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1818, https://doi.org/10.5194/egusphere-egu24-1818, 2024.

Urban blue (water) and green (vegetation) spaces are natural refuges of cool spots for citizens to escape from the extreme heat outdoors and have been widely used in traditional and modern urban designs called ‘water towns’ (i.e., buildings are sited along rivers and trees), particularly in Southern China with rich water resources. This study represents the first comprehensive investigation into the cooling effect of urban river networks at different climatic scales in Shanghai, a Chinese megacity characterized by a significant presence of water towns. At the neighborhood scale, we conducted fine-resolution street-level monitoring of microclimatic data along various rivers during the 2022 heatwave periods in central Shanghai and applied an advanced spatial regression algorithm to quantify the synergistic effect of river and vegetation. At the city scale, we quantified the cooling buffer zones and cooling intensities of urban river networks by integrating fine-resolution urban river network maps with multi-source remotely sensed datasets. We found that the width of rivers, coverage ratio, density, and morphology of river networks are the key factors affecting the cooling potential. The confluence or proximity of river tributaries can also bring an enhanced cooling effect than standalone ones. In a diurnal cycle, rivers can lead to an averaged cooling intensity of 0.4–0.8 °C in air temperature with a maximum value of 3.5 °C in the afternoon, as well as a cooling distance ranging from 100 m to 700 m at various riverside neighborhoods. On the other hand, city-scale results show that river networks can provide a considerable cooling buffer zones covering 36.9% of Shanghai and a maximum cooling intensity of 5.5 °C in surface temperature. Our study implies that urban river networks cannot be neglected in urban climatic studies and should be incorporated into a new conceptualization of water-included urban local climate zone classifications in the world urban database.

How to cite: Song, J., Shi, D., and Zhong, Q.: How do urban river networks regulate city climate? A case study in Shanghai, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2965, https://doi.org/10.5194/egusphere-egu24-2965, 2024.

We present the coupling of the Surface Urban Energy and Water Scheme (SUEWS) into the Weather Research and Forecasting (WRF) model, which includes pre-processing to capture spatial variability in surface characteristics. Fluxes and mixed layer height observations from southern UK were utilised to assess the WRF-SUEWS system over two-week periods across different seasons. Mean absolute errors are lower in residential Swindon compared to central London for turbulent sensible and latent heat fluxes (QH, QE), with increased accuracy on clear days at both locations. The model's performance exhibits clear seasonality, showing enhanced precision for QH and QE during autumn and winter due to more frequent clear days than in spring and summer. Using the coupled system, we explored how anthropogenic heat flux emissions affect boundary layer dynamics by contrasting areas with varying human activities within Greater London; higher emissions not only raise mixed layer heights but also create a warmer, drier near-surface atmosphere. Future updates will align the coupled system with the latest SUEWS version, focusing on detailed surface-layer diagnostics that can support various urban climate applications such as building energy modelling and human thermal comfort assessments.

How to cite: Sun, T., Omidvar, H., Li, Z., Zhang, N., Huang, W., Kotthaus, S., Ward, H., Luo, Z., and Grimmond, S.: WRF-SUEWS Coupled System: Development and Prospect, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3246, https://doi.org/10.5194/egusphere-egu24-3246, 2024.

Urban overheating is becoming an increasingly pressing concern under the dual challenges of global warming and urban heat island effect. One effective way to mitigate urban overheating problems is to create urban cool spots via urban blue-green spaces (BGS).  To investigate the synergistic cooling effect of urban BGS, we proposed a new urban BGS coupling system by integrating a new urban water module with the state-of-the-art urban vegetation module in the framework of an urban canopy model (UCM). This coupled BGS system can represent complicated radiative exchanges between building, tree, and water, and simulate dynamic variations of shadow length, temperature, humidity, as well as energy and water fluxes within the urban street canyon. The new urban BGS model has been evaluated in typical neighborhoods with building and trees siting along rivers (also named ‘water towns’) in two Chinese megacities, i.e., Shanghai and Hong Kong. Based on this model, we investigated the synergistic cooling effect of BGS in different ‘water town’ design scenarios with different combinations of BGS characteristics (e.g., tree crown radius and height, river width, the distance between tree and river) and street canyon characteristics (e.g., geometries and orientations). Our study emphasizes the importance of optimizing 'water town' design to offer more effective cool spots for urban citizens facing escalating heat stress.

How to cite: Shi, D. and Song, J.: Investigating the synergistic cooling effect of urban blue and green spaces via an advanced urban canopy model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4483, https://doi.org/10.5194/egusphere-egu24-4483, 2024.

The urban subsurface is increasingly disturbed by human activity and/or covered by anthropogenic deposits. This is particularly true for city centres, with thick and heterogeneous subsurface archives related to historical urban development, as well as for modern residential and industrial areas, that are often built on extensive sheets of filling sand. The anthropogenic deposits may be very diverse in nature, ranging from natural aggregates (crushed rock, gravel, sand or clay) to various types of novel anthropogenic materials (e.g. steelworks slags, concrete and rubble), as well as mixtures of these.

Although anthropogenic deposits could be represented on subsurface maps and in 3D models, these deposits are often omitted. Their lateral extent and thickness are not well constrained and relevant information on the lithological properties of the deposits is generally lacking. At the same time, the demand for complete and detailed subsurface information in the built environment is increasing and relates to anything from building stability and ground heat extraction to preserving cultural heritage and mitigating the effects of climate change.

This presentation therefore focusses on the lithological characterisation and stratigraphical subdivision of anthropogenic deposits in order to improve their representation in 3D geological subsurface models. We will evaluate current lithological standards and stratigraphic approaches and present the principles of the approach that we are developing in the Netherlands. We will discuss the practical consequences and give examples of bringing our approach into practice. Ultimately, a well-thought lithological description and classification system of anthropogenic deposits is a prerequisite to produce reliable subsurface and coupled surface-subsurface models. In that way, we can address the many challenges related to the ever-increasing use of  urban space and thus improve the wellbeing of our citizens.

How to cite: Schokker, J. and Dijkstra, J.: Improved representation of anthropogenic deposits in 3D urban geological subsurface models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6045, https://doi.org/10.5194/egusphere-egu24-6045, 2024.

The infrastructures of Régie Autonome des Transports Parisiens (RATP) system are significant for the transportation of the Île-de-France region, providing essential social and economic services. In order to assess and mitigate the negative impact of climate change, this study aims to characterise the flood and heat wave risks of RATP infrastructures under climate change on multiple scales. Extreme flood events and heat wave events may result in the functional disruptions to the RATP infrastructures by interrupting circulation for more or less long periods. Therefore, a better understanding of the multi-scales risk (combining hazard, exposure and vulnerability indicators) of RATP infrastructures could enhance their resilience to climate change. With this respect, a multi-scale analysis of flood and heat wave risks of RATP infrastructures is presented by integrating the Universal Multifractal (UM) framework and analytic hierarchy process (AHP). The UM framework is a stochastic method that allows analysis of the natural hazards (extreme precipitation and temperature) and risks under three future climate scenarios (RCP2.6, RCP4.5, RCP8.5) across a range of scales. The AHP method is applied for quantifying the various risks by weighting hazard, exposure and vulnerability indicators based on experts’ knowledge. The results show that a certain number of RATP stations and lines are prone to flood and heat waves under climate change, especially in the RCP8.5 scenario. By undertaking the multiple scales of flood and heat wave risks of RATP infrastructures, this study seeks to contribute valuable insights that will inform strategic planning and resilience-building initiatives for RATP infrastructures under climate change (adaptation measures). It provides a theoretical basis for multiple risk assessments in other metropolitan areas worldwide.

How to cite: Qiu, Y., Versini, P.-A., Mifsud-Couchaux, N., and Tchiguirinskaia, I.: Multiscale characterisation of varied risks for transportation infrastructures under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7877, https://doi.org/10.5194/egusphere-egu24-7877, 2024.

Groundwater flooding within the urban infrastructure can play a major role in determining the resilience of urban environments. Urban groundwater models can be used to simulate the complex interactions between surface water and groundwater within the urban system and can be developed to jointly account for groundwater-surface water processes and subsurface characterization. They can be used to simulate potential groundwater flooding and help understand the role of groundwater in urban resilience to climate change. However, urban groundwater is a component of the wider urban water system that has traditionally been overlooked, and the complex interactions between surface water and groundwater may obscured by urban infrastructure and its influence on groundwater flow. Furthermore, the subsurface characterisation is an integral part of any groundwater model, however it’s influence on model performance is not yet fully understood. Therefore, the inherent complexities of the urban environment, combined with the scarcity of appropriate groundwater and subsurface data, can lead to increased model uncertainty. It is argued that robust urban groundwater modelling depends on a strong conceptual understanding of the groundwater system, and constraining the uncertainty in the subsurface characterisation.

This project aims to assess model sensitivity to the geological interpretation in simulating groundwater dynamics that represent regions of groundwater flooding. It accounts for uncertainty in the subsurface information to develop an ensemble of different geological interpretations and evaluate the influence of the subsurface characterisation on groundwater flow model performance, within the Ouseburn watershed in the greater Newcastle upon Tyne area.

How to cite: Ntigkakis, C., Birkinshaw, S., Stirling, R., and Thomas, B.: Urban hydrogeologic uncertainty characterisation to evaluate risk of groundwater flooding, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8391, https://doi.org/10.5194/egusphere-egu24-8391, 2024.

Desealing appears as an option to disartificialise soils. It embraces several territorial issues like water management, adaptation to climate change, the well-being of inhabitants and biodiversity. In practice, many desealing operations are carried out. The areas to be desealed are most often linked to opportunities such as development projects or target actions (like school playgrounds). There are in fact few potential maps to support desealing strategies. Existing methods systematically take certain criteria into account (e.g. water infiltration). Environmental criteria are however more or less considered.

The DésiVille project (2021-2024) aims to provide decision-making tools to support desealing strategies. In particular, it is preparing a methodological guide to map the potential for desealing, in order to propose a harmonized and concerted framework. The methodology considers 4 thematics: i) the characteristics of the sealed surfaces, ii) the potential of infiltration of soils, iii) the environmental risks and the protection of resources, and iv) the benefits of desealing.

The thematics linked to the potential of infiltration of soils and to the environmental risks consider information on the subsurface. In particular the presence of clay and the groundwater depth feed the potential of infiltration. The environmental risks and protection of resources integrate the presence of soluble rocks, the risk of soil pollution, the risk of flooding due to groundwater rise, the geotechnical risk, area of protection of the water resource. A multicriteria spatial analysis crosses the information per thematic on one side, and among thematics on the other side. The study case of Nantes Métropole (France) illustrates the influence of the potential of infiltration and of the environmental risks and protection of resource on the global potential of desealing maps.

The subsurface needs to be considered to build desealing strategies. More generally, it is essential to consider it in urban planning and development. Although out of sight, it must not be out of mind.

How to cite: Le Guern, C., Prézeau, F., Chrétien, P., and Clozel, B.: Subsurface in territorial soil desealing strategies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8476, https://doi.org/10.5194/egusphere-egu24-8476, 2024.

Naples is the third largest Italian city by size and population. Over 75% of its area is urbanized and the development of the city, often disorderly over the centuries, have occurred despite that the city is exposed to numerous geological hazards, namely: the volcanic and seismic hazard associated with a possible reactivation of Vesuvius and Phlegraean Fields volcanic centres; the seismic hazard connected with the  Apennine seismic activity; the landslide hazard due to the geologically immature landscape and the sinkhole hazard associated with the anthropic use of the subsoil. The studies undertaken and commissioned in the past by the Municipal Administration of Naples, starting from those aimed at facing the so-called Naples’ Subsoil Emergency in the early 2000s, have allowed the acquisition of a large amount of geological information relating to the subsoil, which requires a new and more modern data management structure. For this purpose, the Ufficio Servizio Difesa Idrogeologica del Territorio of the Municipality of Naples has started a project aimed at valorising and updating the enormous amount of data in its possession, through the creation of an digital platform aimed at representing the subsoil of the municipal territory. In this work the preliminary results of the project are presented. The objective of the project is to build a dataset of the geological subsoil information, structured by means of a system of coherent and organic relationships, which will concern not only the geological features (stratigraphic logs, geotechnical parameters etc.) but also the anthropic features (man-made cavities, underground services, tunnels etc.) and that will be included, in the future, within a broader digital  platform concerning the housing and underground public facilities. ISPRA, via the Department for the Geological Survey of Italy, has carried out numerous studies in the Neapolitan area in recent years, also in collaboration with the Municipality of Naples. In this context, ISPRA will provide scientific support and data in its possession for the construction of an updated geological model of the subsoil and the revision of the city’s geological map. With the accomplishment of the project, the digital platform of the subsoil of the city of Naples will become the reference geo-informatics tool of the municipal GIS; it will also have a strong participatory value open to all stakeholders, with the possibility of activating exchanges between citizens and institutions aimed at a continuously updating the acquired knowledge.

How to cite: Guarino, P. M., Barba, A., Marra, F., Pascarella, F., and Roma, M.: The subsoil of the city of Naples: accomplishment of a digital platform for its representation, management and protection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8492, https://doi.org/10.5194/egusphere-egu24-8492, 2024.

Understanding and accurate identification of long-term urban greening dynamics in China are critical for the sustainable urban management (Sustainable Development Goals, SDG11) and living environment of humans. But it was often challenging because a lack of continuous high-frequent data at high spatial resolution and over large time scales. Here, we proposed a framework for identifying detailed evolution processes and regime shifts in relation to urban greening based on characterization of urban greenness in continuous fields over space and time. We utilized annual, fractional estimates of urban green vegetation (GV) endmember time series from per-pixel Landsat composites, using a standardized spectral mixture Vegetation-Impervious surface-Soil (VIS) model in China over the past three decades. A Google Earth Engine platform-based non-linear model (logistic curves) was developed to derive the magnitude, timing and duration of urban greening at a per-pixel basis during these time series records. These parameters were combined to characterize heterogeneous pattern of urban greening throughout the entire China in 1990-2019. We found that the unmixed fractions of urban GV exhibited a generally consistent agreement with estimated fractions from high-spatial-resolution Google earth images (RMSE =11.30%), demonstrating its high suitability and reliability. Using detailed geographic process model with logistic trajectory fitting curves, our findings indicate that the ratio of the area with significant greening trends during 1990-2019 account for nearly 3.0% to the overall urbanized area in China. These greening changes are predominantly distributed in eastern coastal region and northeast Plain. In particular, the Jing-jin-ji, Ha-Chang and Middle-Southern Liaoning are the top three urban agglomerations contributing the greening for this period. Notably, Urumqi, the capital city in north-western China, has the highest ratio of the area with significant increasing GV relative to the urbanized space of the entire city, due to great achievements of urban green construction (i.e., the newly established parks or street plants), and relatively low greenness before 1990. Based on the derived change parameters, our results also reveal the economic impacts on the timing of urban greening are prevalent. For instance, the timing of turning points for urban greening in three major highly-urbanized and developed urban agglomerations, that is, the Jing-jin-ji, Yangtze River Delta, Pearl River Delta showed 2-3 years earlier than other regions. Compared to the state-of-the-art approaches, this framework has the potential to detect high-frequent urban greening process as continuous spatial and time fields with multi-dimensional thematic, thus could help support sustainable urban management practices.

How to cite: Zhang, P., Wu, H., Chen, H., and Sun, Q.: Remotely sensed monitoring of urban greening in China from 1990-2019 to support SDG11, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8678, https://doi.org/10.5194/egusphere-egu24-8678, 2024.

Building heights play a crucial role in various urban research fields, including 3D modeling, urban environmental analysis, sustainable development, and urban planning and management. Numerous methods have been developed to derive building heights from different data sources, including street view imagery, which offers detailed, ground-level perspectives of buildings. However, occlusions from street elements such as trees and vehicles present significant challenges, especially in densely built or complex urban areas. To address this challenge, we propose the use of advanced deep learning models for occlusion reduction, enhancing building height estimation from street view images. As trees typically cause the most occlusion, we employ an open-set detector and a large segmentation deep neural network to create tree masks in the images. Subsequently, we use a stable diffusion model for image inpainting, restoring parts of buildings occluded by trees. These inpainted images are then processed through building instance segmentation, yielding clearer building boundaries for height estimation. Moreover, we integrate a single-view metrology-based height estimation method with a building footprint auxiliary approach, leveraging their respective strengths and mitigating the impact of varying distances between street view cameras and buildings. Our methodology is validated using a dataset comprising 954 buildings and 3814 images. Experimental results demonstrate that our approach increases the percentage of height estimates within a two-meter error margin by approximately 7%, confirming its effectiveness. This work offers a cost-effective solution for large-scale building height mapping and updating, and it opens new avenues for urban research requiring accurate building height data.

How to cite: Yan, Y., Huang, B., Wang, W., Xie, L., Guo, R., and Zhao, Y.: Enhancing Building Height Estimation through Occlusion Reduction with Advanced Deep Learning Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9413, https://doi.org/10.5194/egusphere-egu24-9413, 2024.

A full access to high-quality geological data is fundamental to address all different aspects of land management, such as adapting to existing geohazard and ensuring the availability of georesources (e.g. critical raw materials and geothermal energy). This is particularly relevant in urban areas, where a multidisciplinary and integrated approach to diverse geological issues is imperative.

GeoSciences IR is a geological research infrastructure currently being implemented through NextGenerationEurope funds, with the aim of meeting the needs of Regional Geological Surveys (RGS), the local technical offices having a specific mandate on geological topics at regional and local level, including the urban environment.

Through the GeoSciences IR platform, it will be possible to access data, services, tools, and training modules developed in accordance with the FAIR principles and the INSPIRE Directive, which require fully open accessibility, interoperability, and reusability.

The priority topics of GeoSciences IR have been selected according to the RGS'needs and encompass various geological themes, including 2D and 3D geological mapping, marine geology, geoheritage conservation, geohazard mapping and monitoring, sustainable mining, and land consumption.

Among datasets under preparation, some will be of more specific interest for the urban environment, including i) stratigraphies from boreholes; ii) characterization of local geohazard related to landslides, sinkholes, active and capable faulting; iii) structural works for the mitigation of hydrogeological risk; iv) ground motion mapping and monitoring for low-velocity slope movements and subsidence; v) soil sealing and land consumption monitoring.

Users will also benefit from the full interoperability among services and will be able to access innovative tools based on specific algorithms available for cloud data processing.

Furthermore, a specific section of GeoSciences IR will be dedicated to e-learning modules built to increase the transfer of knowledge from scientists to end-users of GeoSciences IR. These modules have mainly focused on the methodological approach for data collection and on the use of available datasets and tools.

GeoSciences IR is under implementation by a large consortium composed by 13 Italian universities and 3 research institutes, coordinated by ISPRA, Geological Survey of Italy. The infrastructure will open to the public in 2025 and will be maintained for at least 10 years.

In this long-term perspective, a dialogue with external stakeholders (from institutions and the private sector) has already started with the aim of building a reference infrastructure for geological data in Italy, taking into account also their feedback and, in some cases, including additional contributions in terms of data, services and tools. Meanwhile, a constant interaction has been established with other existing research infrastructures available at European level (e.g. EPOS ERIC, EGDI) to ensure their complementarity and identify eventual gaps and overlaps.

How to cite: Guerrieri, L., Rizzo, M., and Passaquieti, R.: GeoSciences IR: a geological research infrastructure for land management in urban areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9711, https://doi.org/10.5194/egusphere-egu24-9711, 2024.

Abstract

Temporal structure functions are usually defined as the q-th order statistical moment of the absolute fluctuation in a time series over a temporal lag at a given resolution. However, applying this in analyzing a temperature time series results in the possibility of simulating only a similar fluctuation over a temporal lag at a resolution and not the temperature directly. Since the aim is to simulate a temperature time series this simulated fluctuation series can be added to an assumed mean temperature to obtain a temperature time series. However, proceeding this way seems to necessitate some ad-hoc moving average technique that seems difficult to be physically reasoned. Secondly but more importantly both diurnal and seasonal periodicity have to be forcibly introduced once again in a non-rigorous manner. A drastic yet reasonably useful alternative would be to modify the definition of the structure-function instead. For order of statistical moment q  the modified structure function is now defined here as

S_q(Δt)=⟨ΙT_λ - T_λ/2,2Ι^q⟩

Where the scale ratio λ∝1/ΙΔtΙ; 2^m/2^m=1≤λ≤Λ=2^m/2⁰ and ΙΔtΙ is the time lag, whereas 2^m is the largest possible scale out of the scales analyzed that can be represented as a power of 2. While T_λ is the temperature at scale ratio λ or scale l, T_λ/2,2 is the upscaled (by a scale ratio of 2) temperature at scale ratio λ/2 or scale 2l, and the subscript ‘2’ indicates that each element of  T_λ/2 (upscaled temperature) is repeated twice consecutively. It should be noted that T_λ/2,2 is not the same as T_λ because the former is an upscaled series, twice repeated (consecutively) of the latter. The largest scale ratio considered in the analysis is Λ. By defining the structure-function in this way temperature at a larger scale after being repeated a sufficient number of times can be directly added to the fluctuation at a smaller scale to result in the temperature at a smaller scale. The universal multifractal parameters obtained from the modified structure-function analysis are not necessarily equal to those obtained from the usual structure-function analysis (i.e. the two different structure functions follow two different scaling laws). An iterative curve fitting technique is used to estimate the values of Universal Multifractal (UM) parameters C₁, H, and a  while the value of α  is estimated using a normalized form of the modified structure function along with the un-normalized one. A simulation procedure that utilizes the aforementioned modified structure function definition is proposed here to generate temperature scenarios. Finally, reference evapotranspiration is estimated based on the simulated temperature using a simple empirical power law function. The actual evapotranspiration is estimated using the reference evapotranspiration and water content via a different, simpler empirical function. The tentative methodology proposed here when used along with simulated reference rainfall scenarios could help design zero-emission green roof solutions.

Keywords

Multifractals, Non-linear geophysical systems, Cascade dynamics, Scaling, Hydrology, Meteorology.

How to cite: Ramanathan, A., Versini, P.-A., Schertzer, D., Tchiguirinskaia, I., Perrin, R., and Sindt, L.: Simulating Temperature and Evapotranspiration using a Universal Multifractal approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11021, https://doi.org/10.5194/egusphere-egu24-11021, 2024.

Residential and commercial buildings jointly account for 39% of energy consumption and 28% of greenhouse gas emissions in the U.S. In densely populated urban areas, the share of energy use and emissions attributable to buildings can be even higher. The future evolution of building energy use and associated carbon emissions is uncertain, with potentially substantial variations in climate conditions, socioeconomic development, and power sector trajectories; accounting for these in future projections is often compounded by limited data availability and resolution of conventional modeling approaches. To address these challenges, in this study, we employed a bottom-up, high-resolution modeling approach and evaluated city-scale building energy consumption and CO₂ emissions across 277 urban areas in the U.S. under various mid-21^st century scenarios. Our findings reveal substantial spatial and temporal variations in future changes in building energy use and CO₂ emissions among U.S. cities under a variety of climate, socioeconomic, and power sector evolution scenarios. On average, a 1°C warming at the city scale projects a 13.8% increase in building energy use intensity for cooling, accompanied by an approximately 11% decrease in energy use intensity for heating, albeit with notable spatial disparities. Collectively, driven by global warming and socioeconomic development, mid-century city-level building energy use is projected to rise on average by 17.5–39.8% under all scenarios except for SSP3-7.0 when compared with the last decade. In contrast, city-level building CO₂ emissions are projected to decrease in most urban areas (averaging from 10.6% to 66.0% under different scenarios), with spatial variations primarily influenced by climate change and power sector decarbonization.

How to cite: Wang, C., Reyna, J., Horsey, H., and Jackson, R.: Urban energy futures: Unraveling the dynamics of city-scale building energy use and CO2 emissions under mid-century scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13309, https://doi.org/10.5194/egusphere-egu24-13309, 2024.

Urban drainage models have been used in many cities for analysis, prediction, and control related to urban flooding. Many sources of uncertainties exist in these models comprising model parameters, meteorological forcings, and surface conditions. Thus, it is necessary to calibrate models before using them in reality. A common choice in calibration is to fit the model outputs with observations through many cases. This strategy is known as the offline mode in calibration and works on the stationary assumption of model parameters. If parameters vary in time, this method usually yields the climatological range of the parameters, which are not necessarily optimal in specific cases. In this study, instead of the offline model we follow the online mode in estimating model parameters by using an ensemble Kalman filter (EnKF). Furthermore, we estimate not only model parameters but also model states simultaneously utilizing the EnKF. Note that originally, EnKF is a data assimilation technique that is based on sampling in estimating any system states given observations, and later is used for the purpose of parameter estimation. The combination of EnKF and an urban drainage model is expected to lead to a real-time monitoring system for urban flooding similar to reanalysis systems in numerical weather prediction.

How to cite: Duc, L., Jo, J., and Sawada, Y.: Realtime monitoring of urban flooding by ensemble Kalman filters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14334, https://doi.org/10.5194/egusphere-egu24-14334, 2024.

Evaluation of the outdoor thermal comfort and comprehension of the impact of urban morphology are essential for assessing heat-related risks and implementing urban planning strategies that enhance the resilience of urban populations to extreme heat events. However, the challenge lies in achieving city-wide thermal comfort mapping at high spatial and temporal resolutions, which requires consideration of the complex urban morphology (urban geometry and land cover) at a microscale, as well as the background meteorological factors at larger scale. Here, we introduce an effective framework for city-scale thermal comfort mapping at high spatial-temporal resolution that integrates WRF-UCM and SOLWEIG model, aiming to achieve fine-grained thermal comfort mapping at the city scale and to explore the impact of urban morphology on these thermal conditions.

In the proposed framework, we employ the WRF-UCM model (The Weather Research and Forecasting model coupled with the urban canopy model) to establish the background meteorological condition at local-scale (500m resolution). Additionally, we utilize the SOLWEIG (Solar and Longwave Environmental Irradiance Geometry) model for the simulation of mean radiant temperature at a finer micro-scale (10m resolution), a critical determinant of thermal comfort. These simulations are performed using detailed 3D urban morphological data and land cover information. Subsequently, the Universal Thermal Climate Index (UTCI) is calculated on hourly basis, integrating the aforementioned factors.

A case study conducted for a Chinese city with a population of 15 million demonstrates a significant correction between the rise in the UTCI during daytime and an increase in impervious surface area, evidenced by a maximum correlation coefficient of 0.80. Furthermore, our findings emphasize the significance of tree canopy coverage in mitigating heat, demonstrating that an implementation of 40% tree cover could diminish daytime UTCI by approximately 1.5 to 2.0 ºC. This methodological framework is not only instrumental in assessing heat-related risks and human thermal discomfort within intricate urban environments but also offers pivotal insights for the adoption of climate-resilient urban planning strategies.

How to cite: Ding, X., Zhao, Y., Strebel, D., Fan, Y., Ge, J., and Carmeliet, J.: A cross-scale methodological framework for the quantification of the impact of urban features on intra-city microclimate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14606, https://doi.org/10.5194/egusphere-egu24-14606, 2024.

Urban areas are major contributors to climate change, accounting for 71 to 76% of CO₂ emissions from global final energy use [1]. Nevertheless, cities are growing in both size and number. By 2030, it is projected that 730 million people will live in megacities (cities with at least 10 million inhabitants) compared to 500 million people in 2016 [2]. The number of megacities will also increase from 29 to 43 [3]. On the other side, solar radiation is an important component in the energy balance of urban areas. Urban form impacts the production of building-integrated photovoltaics, solar heat gains and heating/cooling demand of buildings. Relevant urban form characteristics include urban layout, population density, and individual building characteristics, such as height, wall orientation, roof slope, and construction material. Optimization of the urban form design can contribute to better energy performance of buildings. However, optimization is a large multivariable problem that is computationally intensive. A good understanding of the urban form impact can guide the optimization. In this work, the influence of shadow from surrounding buildings on solar radiation incident on buildings is studied provided a three-dimensional (3D) model of an area.

Open Access 3D models for many cities are made available by local authorities. Standardized data formats for 3D modelling are well-established. The scientific community has been working towards understanding urban forms, their impact on energy demand, and the potential for realizing sustainable urban forms. So far, the available work relied on different tools to analyze the impact of urban form on space heating/cooling demand for a specific city making reproducibility difficult. 

This work shows the advantage of using the standardized CityJSON format to establish an open-source Python-based framework to calculate hourly solar irradiance on building facades, considering the shadow of surrounding buildings, generate a thermal model of building envelopes, and calculate heat losses, gains, and the heating load of a building. The proposed methodology involves three phases. First is data collection and pre-processing. Second is the calculation of direct solar radiation on building facades and roofs. For that, hourly sun positions have been determined.  Maximum shadow length is calculated for each sun position. The geometry of buildings is analyzed, shared walls are excluded, and exemplary window vertices are allocated on the free walls such that the window-to-wall ratio ranges between 15% and 25%. Orientations of walls and slopes of tilted roofs were identified. Hyper-points are deployed on each surface in a 0.5m grid. With that, shadow height at each hyper-point and direct solar radiation were calculated. Third is the estimation of the heating or cooling load.

An exemplary neighborhood in Munich is presented as a real case study. Preliminarily results confirm that urban form is influencing the energy performance of buildings. Less shadowing on a building implies higher solar exposure but not necessarily reduced heating demand despite identical thermal properties of buildings’ envelope.

References:

[1] United Nations. (2017). Urban Environment. https://unfccc.int/resource/climateaction2020/media/1308/Urban_Environment_17.pdf

[2] United Nations. (2016). The World’s cities in 2016: data booklet. http://digitallibrary.un.org/record/1634928

[3] European Commission. (2020). Urbanisation worldwide. https://knowledge4policy.ec.europa.eu/foresight/topic/continuing-urbanisation/urbanisation-worldwide_en

How to cite: Alfouly, M. and Hamacher, T.: Evaluating Urban Form Influence on Solar Exposure and Corresponding Building Energy Demands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15532, https://doi.org/10.5194/egusphere-egu24-15532, 2024.

The UK Government Office for Science has recently commissioned a Foresight Project on the ‘Future of the Subsurface’. The project draws on experts across different government departments and industry - including representative from the geological and environmental community, planning specialists, infrastructure and service providers, city authorities and energy specialists - to understand the future demands that will be placed on the subsurface to deliver our sustainable development goals; What are the high-value future societal subsurface uses? What climatic and environmental pressures are expected? What policy interventions will be required to protect and enhance the value of the subsurface in the longer-term? We present outcomes from the Foresight project's subsurface issues paper, alongside recommendations from the National and Regional level expert elicitation. Drawing on our research in urban geosciences and subsurface assessment we highlight how geological surveys can, and are, responding to the issues and recommendations highlighted by the Foresight project.  Some common themes emerge for which the geological survey has a role, for example, ensuring coordinated and interdisciplinary approaches to planning; Assessing opportunities to update or streamline subsurface governance and regulation; Improving the coverage, quality, availability and interoperability of data.

In addition to these overarching principles, the variability of regional geology in the UK and its impact on subsurface issues is a prominent outcome of the Foresight project and necessitates place-based approaches, tailored to distinct geologies and geographies, to define a hierarchy of subsurface need.  The UK has a particularly varied geology spanning the whole Phanerozoic this means that there are very different geological problems in different cities. Taking this placed-based approach we show how the evolution of 3D geology mapping and geospatial tools at the British Geological Survey (BGS), has shifted towards multi-assessment to appraise the diverse integrated and competing subsurface uses. We highlight the practical applications of 3D models in improving data availability and accessibility e.g. by updating geological maps, enhancing data products, and facilitating user accessibility through tools like model viewers. The paper concludes by emphasizing the importance of geological information to help facilitate dialogue and stakeholder consultation, and support evidence-based policymaking.

How to cite: Kearsey, T., Bricker, S., Terrington, R., Kessler, H., Burke, H., and Thorpe, S.: How do geological surveys respond to evolving uses and interaction in the urban subsurface?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15561, https://doi.org/10.5194/egusphere-egu24-15561, 2024.

This work has benefited from a multidisciplinary scientific and technical contributions geared by the HM&Co Lab of the Ecole des Ponts ParisTech (hmco.enpc.fr) towards the sustainable, desirable, and resilient city. The deepening of the Universal Multifractal (UM) concepts and the encouragement of their operational applications have been linked to several initiatives launched in recent years to better integrate the heterogeneity/intermittency into public policy practices. Considering the complex, dynamic interactions between geophysical and anthropogenic fields within a conurbation such as the Ile de France region, a transition towards the shared value economy has been considered to best stimulate sober and collaborative development, and there exist at least 3 ways to approach today’s discussions about future transformations. Their intercomparison is the core of this presentation.

Following the United Nations 2030 Agenda, the first most conventional approach is based on notions of sustainable development, supported by appropriate adaptation and mitigation of climate change.

Combining the notions of extreme variability and complexity would require linking together geophysical and urban scales within extreme variability, and therefore considering geosciences, and not just geophysics! Such a synergistic and integrative approach would help move beyond traditional silo thinking, addressing the complexity of data- and/or theory-driven urban geosciences.

Finaly, combining the notions of scaling and nonlinear variability would ultimately require linking cascades, multiplicative chaos, and multifractals. This would initiate a break with linear stochastic models towards stronger heterogeneity / intermittency, which would in turn lead to a plausible clustering of field and activity fluctuations. The appearance of multifractal phase transitions then becomes possible, considerably amplifying the impact of any action, and would make future transformations fully efficient, effectively imitating the way in which Nature acts. This will be finally illustrated using several examples of so-called Nature Based Solutions (NBS).

How to cite: Tchiguirinskaia, I., Qiu, Y., and Schertzer, D.: Different Approaches to the Impacts of Climate Change, with a Common Goal: a Healthy Planet, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16662, https://doi.org/10.5194/egusphere-egu24-16662, 2024.

This work focuses on two case studies carried out in Spain, whe