ITS2.7/AS5.2

Soy harvest failure events can severely impact farmers, insurance companies and raise global prices. Reliable seasonal forecasts of mis-harvests would allow stakeholders to prepare and take appropriate early action. However, especially for farmers, the reliability and lead-time of current prediction systems provide insufficient information to justify for within-season adaptation measures. Recent innovations increased our ability to generate reliable statistical seasonal forecasts. Here, we combine these innovations to predict the 1-3 poor soy harvest years in eastern US. We first use a clustering algorithm to spatially aggregate crop producing regions within the eastern US that are particularly sensitive to hot-dry weather conditions. Next, we use observational climate variables (sea surface temperature (SST) and soil moisture) to extract precursor timeseries at multiple lags. This allows the machine learning model to learn the low-frequency evolution, which carries important information for predictability. A selection based on causal inference allows for physically interpretable precursors. We show that the robust selected predictors are associated with the evolution of the horseshoe Pacific SST pattern, in line with previous research. We use the state of the horseshoe Pacific to identify years with enhanced predictability. We achieve very high forecast skill of poor harvests events, even 3 months prior to sowing, using a strict one-step-ahead train-test splitting. Over the last 25 years, 90% of the predicted events in February were correct. When operational, this forecast would enable farmers (and insurance/trading companies) to make informed decisions on adaption measures, e.g., selecting more drought-resistant cultivars, invest in insurance, change planting management.

How to cite: Vijverberg, S., Coumou, D., and Hamed, R.: Skilful US Soy-yield forecasts at pre-sowing lead-times, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1065, https://doi.org/10.5194/egusphere-egu22-1065, 2022.

Accurate estimates and predictions of the global carbon fluxes are critical for our understanding of the global carbon cycle and climate change. Reducing the uncertainty of the terrestrial carbon sink and closing the budget imbalance between sources and sinks would improve our ability to accurately project future climate change. Net Ecosystem Exchange (NEE), the net flux of biogenic carbon from the land surface to the atmosphere, is only directly measured at a sparse set of globally distributed eddy-covariance measurement sites. To estimate the terrestrial carbon flux at the regional and global scale, a global gridded estimate of NEE must be accurately upscaled from a model trained at the ecosystem level. In this study, the Fluxcom system* is used to train a site-level model on remotely-sensed and meteorological variables derived from site measurements, MODIS and ECMWF ERA5 atmospheric reanalysis data. The non-representative distribution of these site-level data along with missing disturbance histories impart known biases to current upscaling efforts. Observations of atmospheric carbon may provide important additional information, improving the accuracy of the upscaled flux estimate. 

This study adds an atmospheric observational operator to the model training process that connects the ecosystem-level flux model to top-down observations of atmospheric carbon by adding an additional term to the objective function. The target data are regionally integrated fluxes from an ensemble of atmospheric inversions corrected for fossil-fuel emissions and lateral fluxes.  Calculating the regionally integrated flux estimate at each training step is computationally infeasible. Our hypothesis is that the regional flux can be modeled with a limited set of points and that this sparse model preserves sufficient information about the phenomena to act as a constraint for the underlying ecosystem-level model, improving regional and global upscaled products.  Experimental results show improvements in the machine learning based regional estimates of NEE while preserving features such as the seasonal variability in the estimated flux.

*Jung, Martin, Christopher Schwalm, Mirco Migliavacca, Sophia Walther, Gustau Camps-Valls, Sujan Koirala, Peter Anthoni, et al. 2020. “Scaling Carbon Fluxes from Eddy Covariance Sites to Globe: Synthesis and Evaluation of the FLUXCOM Approach.” Biogeosciences 17 (5): 1343–65. 

How to cite: Upton, S., Bastos, A., Gans, F., Kraft, B., Peters, W., Nelson, J., Walther, S., Jung, M., and Reichstein, M.: Machine learning based estimation of regional Net Ecosystem Exchange (NEE) constrained by atmospheric inversions and ecosystem observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11111, https://doi.org/10.5194/egusphere-egu22-11111, 2022.

Climate change is a global crisis to the world which influences the human race and society's development. Threatens of climate change have become increasingly recognized to the public and government in both environments, society, and economy across the globe; because the consequence of climate change is not only shown up as the increasing of global temperature, also expressed in an intensive natural hazard, such as floods, droughts, wildfires, and hurricanes. For the sustainability development in the globe, it is crucial to provide a response to mitigating climate change through the government’s policy and decision-making; however, the public's engagement in the actions towards the critical environmental crisis still needs to be largely promoted. Analyzing the relationship between the public awareness of climate change and natural disasters is an essential aspect in climate change mitigation and policymaking. In this study, based on the abundance of the text message in social media, especially Twitter, the public understanding and discussions upon climate change from the surrounding environment was recognized and analyzed through the human as the sensor which receiving information of climate change. Twitter content analysis and filed data impact analysis were conducted; text mining algorithms are implemented in the Twitter big-data information to find the similarity based on a cosine similarity score (CSS) between the climate change corpus and the natural events corpora. Then, the factors of nature disaster influence were predicted utilizing a multiple linear regression model and climate change tweets dataset. This research shows that the public is more pretend to link the natural events with climate change when they tweeting when serious natural disasters happened. The developed regression model indicated that natural events caused by the consequence of climate change influenced the people’s social media activity through messages on Twitter with having the awareness of climate change. From this study, the results indicated that the public experience of natural events including intensive disasters can lead them to link the climate change with the natural events easily; when compared with the people who rarely experience natural events.

Acknowledgment

This research was supported by the project (NRF-2021R1A2C2007838) through the National Research Foundation of Korea (NRF) and the Korea Ministry of Environment (MOE) as Graduate school specialized in Climate Change.

How to cite: Heo, S., Ifaei, P., Moosazadeh, M., and Yoo, C.: Public perception assessment on climate change and natural disaster influence using social media big-data: A case study of USA, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3482, https://doi.org/10.5194/egusphere-egu22-3482, 2022.

Introduction: Relative humidity (RH) is a meteorological variable of great importance as it affects other climatic variables and plays a role in plant and animal life as well as in human comfort and well-being. However, the commonly used weather station observations are inefficient to represent the great spatiotemporal RH variability, leading to exposure misclassification and difficulties to assess local RH health effects. There is also a lack of high-resolution RH spatial datasets and no readily available methods for modeling humidity across space and time. To tackle these issues, we aimed to improve the spatiotemporal coverage of RH data in Germany, using remote sensing and machine learning (ML) modeling.

Methods: In this study, we estimated German-wide daily mean RH at 1km² resolution over the period 2000-2020. We used several predictors from multiple sources, including DWD RH observations, Ta predictions as well as satellite-derived DEM, NDVI and the True Color band composition (bands 1, 4 and 3: red, green and blue). Our main predictor for estimating the daily mean RH was the daily mean Ta. We had already mapped daily mean Ta in 1km² across Germany through a regression-based hybrid approach of two linear mixed models using land surface temperature. Additionally, a very important predictor was the date, capturing the day-to-day variation of the response-explanatory variables relationship. All these variables were included in a Random Forest (RF) model, applied for each year separately. We assessed the model’s accuracy via 10-fold cross-validation (CV). First internally, using station observations that were not used for the model training, and then externally in the Augsburg metropolitan area using the REKLIM monitoring network over the period 2015-2019.

Results: Regarding the internal validation, the 21-year overall mean CV-R² was 0.76 and the CV-RMSE was 6.084%. For the model’s external performance, at the same day, we found CV-R²=0.75 and CV-RMSE=7.051% and for the 7-day average, CV-R²=0.81 and CV-RMSE=5.420%. Germany is characterized by high relative humidity values, having a 20-year average RH of 78.4%. Even if the annual country-wide averages were quite stable, ranging from 81.2% for 2001 to 75.3% for 2020, the spatial variability exceeded 15% annually on average. Generally, winter was the most humid period and especially December was the most humid month. Extended urban cores (e.g., from Stuttgart to Frankfurt) or individual cities as Munich were less humid than the surrounding rural areas. There are also specific spatial patterns for RH distribution, including mountains, rivers and coastlines. For instance, the Alps and the North Sea coast are areas with elevated RH.

Conclusion: Our results indicate that the applied hybrid RF model is suitable for estimating nationwide RH at high spatiotemporal resolution, achieving a strong performance with low errors. Our method contributes to an improved spatial estimation of RH and the output product will help us understand better the spatiotemporal patterns of RH in Germany. We also plan to apply other ML techniques and compare the findings. Finally, our dataset will be used for epidemiological analyses, but could also be used for other research questions.

How to cite: Nikolaou, N., Bouwer, L., Valizadeh, M., Dallavalle, M., Wolf, K., Stafoggia, M., Peters, A., and Schneider, A.: High-resolution hybrid spatiotemporal modeling of daily relative humidity across Germany for epidemiological research: a Random Forest approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6543, https://doi.org/10.5194/egusphere-egu22-6543, 2022.

This presentation reports on support done under the aegis of Helmholtz AI for a wide range of machine learning based solutions for research questions related to Earth and Environmental sciences. We will give insight into typical problem statements from Earth observation and Earth system modeling that are good candidates for experimentation with ML methods and report on our accumulated experience tackling such challenges with individual support projects. We address these projects in an agile, iterative manner and during the definition phase, we direct special attention towards assembling practically meaningful demonstrators within a couple of months. A recent focus of our work lies on tackling software engineering concerns for building ML-ESM hybrids.

Our implementation workflow covers stages from data exploration to model tuning. A project may often start with evaluating available data and deciding on basic feasibility, apparent limitations such as biases or a lack of labels, and splitting into training and test data. Setting up a data processing workflow to subselect and compile training data is often the next step, followed by setting up a model architecture. We have made good experience with automatic tooling to tune hyperparameters and test and optimize network architectures. In typical implementation projects, these stages may repeat many times to improve results and cover aspects such as errors due to confusing samples, incorporating domain model knowledge, testing alternative architectures and ML approaches, and dealing with memory limitations and performance optimization.

Over the past two years, we have supported Helmholtz-based researchers from many subdisciplines on making the best use of ML methods along with these steps. Example projects include wind speed regression on GNSS-R data, emulation of atmospheric chemistry modeling, Earth System model parameterizations with ML, marine litter detection, and rogue waves prediction. The poster presentation will highlight selected best practices across these projects. We are happy to share our experience as it may prove useful to applications in wider Earth System modeling. If you are interested in discussing your challenge with us, please feel free to chat with us.

How to cite: Weigel, T., Albrecht, F., Arnold, C., Caus, D., Grover, H., and Vlasenko, A.: Unlocking the potential of ML for Earth and Environment researchers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7011, https://doi.org/10.5194/egusphere-egu22-7011, 2022.

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate. In this context, the Linear Inverse Modeling (LIM) approach (closely related to Dynamic Mode Decomposition DMD), by helping capture a few essential interactions between dynamical components of the full system, has proven valuable in being able to give insights into the dynamical behavior of the full system. While nonlinear extensions of the LIM approach have been attempted none have gained widespread acceptance. We demonstrate that Reservoir Computing (RC), a form of machine learning suited for learning in the context of chaotic dynamics, by exploiting the phenomenon of generalized synchronization, provides an alternative nonlinear approach that comprehensively outperforms the LIM approach.  Additionally, the potential of the RC approach to capture the structure of the climatological attractor and to continue the evolution of the system on the attractor in a realistic fashion long after the ensemble average has stopped tracking the reference trajectory is highlighted. Finally, other dynamical systems based methods and probabilistic deep learning methods are considered and a broader perspective on the use of data-driven methods in understanding climate predictability is offered

How to cite: Nadiga, B.: Data Driven Approaches for Climate Predictability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9461, https://doi.org/10.5194/egusphere-egu22-9461, 2022.

Data-driven algorithms, in particular neural networks, can emulate the effects of unresolved processes in coarse-resolution Earth system models (ESMs) if trained on high-resolution simulation or observational data. However, they can (1) make large generalization errors when evaluated in conditions they were not trained on; and (2) trigger instabilities when coupled back to ESMs.

First, we propose to physically rescale the inputs and outputs of neural networks to help them generalize to unseen climates. Applied to the offline parameterization of subgrid-scale thermodynamics (convection and radiation) in three distinct climate models, we show that rescaled or "climate-invariant" neural networks make accurate predictions in test climates that are 8K warmer than their training climates. Second, we propose to eliminate spurious causal relations between inputs and outputs by using a recently developed causal discovery framework (PCMCI). For each output, we run PCMCI on the inputs time series to identify the reduced set of inputs that have the strongest causal relationship with the output. Preliminary results show that we can reach similar levels of accuracy by training one neural network per output with the reduced set of inputs; stability implications when coupled back to the ESM are explored.

Overall, our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes may improve their ability to generalize across climate regimes, while quantifying causal associations to select the optimal set of inputs may improve their consistency and stability.

How to cite: Beucler, T., Iglesias-Suarez, F., Eyring, V., Pritchard, M., Runge, J., and Gentine, P.: Climate-Invariant, Causally Consistent Neural Networks as Robust Emulators of Subgrid Processes across Climates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-722, https://doi.org/10.5194/egusphere-egu22-722, 2022.

The Madden–Julian Oscillation (MJO) is the dominant source of sub-seasonal variability in the tropics. It consists of an Eastward moving region of enhanced convection coupled to changes in zonal winds. It is not possible to predict the precise evolution of the MJO, so subseasonal forecasts are generally probabilistic. Ideally the spread of the forecast probability distribution would vary from day to day depending on the instantaneous predictability of the MJO. Operational subseasonal forecasting models do not have this property. We present a deep convolutional neural network that produces skilful state-dependent probabilistic MJO forecasts. This statistical model accounts for intrinsic chaotic uncertainty by predicting the standard deviation about the mean, and model uncertainty using a Monte-Carlo dropout approach. Interpretation of the mean forecasts from the neural network highlights known MJO mechanisms, providing confidence in the model, while interpretation of the predicted uncertainty indicates new physical mechanisms governing MJO predictability.

How to cite: Christensen, H. and Delaunay, A.: Interpretable Deep Learning for Probabilistic MJO Prediction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12720, https://doi.org/10.5194/egusphere-egu22-12720, 2022.

Many deep learning technologies have been applied to the Earth sciences, including weather forecast, climate prediction, parameterization, resolution improvements, etc. Nonetheless, the difficulty in interpreting deep learning results still prevents their applications to studies on climate dynamics. Here, we applied a convolutional neural network to understand El Niño–Southern Oscillation (ENSO) dynamics from long-term climate model simulations. The deep learning algorithm successfully predicted ENSO events with a high correlation skill of 0.82 for a 9-month lead. For interpreting deep learning results beyond the prediction skill, we first developed a “contribution map,” which estimates how much each grid point and variable contribute to a final output variable. Furthermore, we introduced a “sensitivity,” which estimates how much the output variable is sensitively changed to the small perturbation of the input variables by showing the differences in the output variables. The contribution map clearly shows the most important precursors for El Niño and La Niña developments. In addition, the sensitivity clearly reveals nonlinear relations between the precursors and the ENSO index, which helps us understand the respective role of each precursor. Our results suggest that the contribution map and sensitivity would be beneficial for understanding other climate phenomena.

How to cite: Shin, N.-Y., Ham, Y.-G., Kim, J.-H., Cho, M., and Kug, J.-S.: How to utilize deep learning to understand climate dynamics? : An ENSO example., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10888, https://doi.org/10.5194/egusphere-egu22-10888, 2022.

Understanding the mechanisms causing river flooding and their trends is important to interpret past flood changes and make better predictions of future flood conditions. However,  there is still a lack of quantitative assessment of trends in flooding mechanisms based on observations. Recent years have witnessed the increasing prevalence of machine learning in hydrological modeling and its predictive power has been demonstrated in numerous studies. Machine learning makes hydrological predictions by recognizing generalizable relationships between inputs and outputs, which, if properly interpreted, may provide us further scientific insights into hydrological processes. In this study, we propose a new method using interpretive machine learning to identify flooding mechanisms based on the predictive relationship between precipitation and temperature and flow peaks. Applying this method to more than a thousand catchments in Europe reveals three primary input-output patterns within flow predictions, which can be associated with three catchment-wide flooding mechanisms: extreme precipitation, soil moisture excess, and snowmelt. The results indicate that approximately one-third of the studied catchments are controlled by a combination of the above mechanisms, while others are mostly dominated by one single mechanism. Although no significant shifts from one dominant mechanism to another are observed for the catchments over the past seven decades overall, some catchments with single mechanisms have become dominated by mixed mechanisms and vice versa. In particular, snowmelt-induced floods have decreased significantly in general, whereas rainfall has become more dominant in causing floods, and their effects on flooding seasonality and magnitude are crucial. ​Overall, this study provides a new perspective for understanding climatic extremes and demonstrates the prospect of artificial intelligence(AI)-assisted scientific discovery in the future.

How to cite: Jiang, S., Zheng, Y., and Zscheischler, J.: Exploring flooding mechanisms and their trends in Europe through explainable AI, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2248, https://doi.org/10.5194/egusphere-egu22-2248, 2022.

Understanding the cause and effect relationships that govern natural phenomena is central to the scientific inquiry. While being the gold standard for inferring causal relationships, there are many scenarios in which controlled experiments are not possible. This is for example the case for most aspects of Earth's complex climate system. Causal relationships then have to be learned from statistical dependencies in observational data, a task that is commonly referred to as (observational) causal discovery.

When applied to time series data for learning causal relationships in dynamical systems, methods for causal discovery face additional statistical challenges. This is so because, as licensed by an assumption of stationarity, samples are taken in a sliding window fashion and hence autocorrelated rather than iid. Moreover, strong autocorrelations also often occlude other relevant causal links. The recent PCMCI algorithm (Runge et al., 2019) and its variants PCMCI+ (Runge, 2020) and LPCMCI (Gerhardus and Runge, 2020) address and to some extent alleviate theses issues.

In this contribution we present the Ensemble-PCMCI method, an adaption of PCMCI (and its variants PCMCI+ and LPCMCI) to cases in which the data comprises several time series, i.e., measurements of several instances of the same underlying dynamical system. Samples can then be taken from these different time series instead of a in a sliding window fashion, thus avoiding the issue of autocorrelation and also allowing to relax the stationarity assumption. In particular, this opens the possibility to analyze temporal changes in the underlying causal mechanisms. A potential domain of application are ensemble forecasts.

Related references:
Jakob Runge et al. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances 5 eaau4996.

Jakob Runge (2020). Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). Proceedings of Machine Learning Research 124 1388–1397. PMLR.

Andreas Gerhardus and Jakob Runge (2020). High-recall causal discovery for autocorrelated time series with latent confounders. In Advances in Neural Information Processing Systems 33 12615–12625. Curran Associates, Inc.

How to cite: Gerhardus, A. and Runge, J.: Causal Discovery in Ensembles of Climate Time Series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6958, https://doi.org/10.5194/egusphere-egu22-6958, 2022.

Despite the importance of the Atlantic Meridional Overturning Circulation (AMOC) to the climate on decadal and multidecadal timescales, Earth System Models (ESM) exhibit large differences in their estimation of the amplitude and spectrum of its variability. In addition, observational data is sparse and before the onset of the current century, many reconstructions of the AMOC rely on linear relationships to the more readily observed surface properties of the Atlantic rather than the less explored deeper ocean. Yet, it is conceptually well established that the density distribution is dynamically closely related to the AMOC, and in this contribution, we investigate this connection in model simulations to identify which density information is necessary to reconstruct the AMOC. We chose to establish these links in a data-driven approach. 

We use simulations from a historically forced large ensemble as well as abruptly forced long term simulations with varying strength of forcing and therefore comprising vastly different states of the AMOC. In a first step, we train uncertainty-aware neural networks to infer the state of the AMOC from the density information at different layers in the North Atlantic. In a second step, we compare the performance of the trained neural networks across depth and with their linear counterparts in simulations that were not part of the training process. Finally, we investigate how the networks arrived at their specific prediction using Layer-Wise-Relevance Propagation (LRP), a recently developed technique that propagates relevance backwards through the network to the input density field, effectively filtering out important from unimportant information and identifying regions of high relevance for the reconstruction of the AMOC.

Our preliminary results show that in general, the information provided by only one density layer between the surface and 1100 m is sufficient to reconstruct the AMOC with high precision, and neural networks are capable of generalizing to unseen simulations. From the set of these neural networks trained on different layers, we choose the surface layer as well as one subsurface layer close to 1000 m for further investigation of their decision-making process using LRP. Our preliminary investigation reveals that the LRP in the subsurface layer identifies regions of potentially high physical relevance for the AMOC. By contrast, the regions identified in the surface layer show little physical relevance for the AMOC.

How to cite: Mayer, B., Barnes, E., Marotzke, J., and Baehr, J.: Reconstructing the Atlantic Meridional Overturning Circulation in Earth System Model simulations from density information using explainable machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8411, https://doi.org/10.5194/egusphere-egu22-8411, 2022.

Based on the ERA5  data of hourly  meteorological parameters [1], we investigate temporal effects of  12 meteorological parameters on  the extreme values occurring in  wind speed.  We approach the problem by using the Granger causal inference, namely by the heterogeneous graphical Granger model (HGGM) [2]. In contrary to the classical Granger model proposed for causal inference among Gaussian processes, the HGGM detects causal relations among time series with distributions from the exponential family, which includes a wider class of common distributions. In previous synthetic experiments, HGGM combined with the genetic algorithm search based on the minimum message length principle has been shown superior in precision over the baseline causal methods [2].  We investigate various experimental settings of all 12 parameters with respect to the wind extremes in various time intervals. Moreover, we compare the influence of various data preprocessing methods and evaluate the interpretability of the discovered causal connections based on meteorological knowledge.

[1] https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview

[2] Behzadi, S, Hlaváčková-Schindler, K., Plant, C. (2019) Granger causality for heterogeneous processes, In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, pp. 463-475.

[3] Hlaváčková-Schindler, K., Plant, C. (2020) Heterogeneous graphical Granger causality by minimum message length, Entropy, 22(1400). pp. 1-21 ISSN 1099-4300 MDPI (2020).

How to cite: Hlavackova-Schindler (Schindlerova), K., Fuchs, A., Plant, C., Schicker, I., and DeWit, R.: The influence of meteorological parameters on wind speed extreme events:  A causal inference approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5756, https://doi.org/10.5194/egusphere-egu22-5756, 2022.

Understanding complex dynamical systems is a major challenge in many scientific disciplines. There are two aspects which are of particular interest when analyzing complex dynamical systems: 1) the temporal patterns along which they evolve and 2) the governing causal mechanisms.

Temporal patterns in a time-series can be extracted and analyzed through a variety of time-series representations, that is a collection of filters. Discrete Wavelet and Fourier Transforms are prominent examples and have been widely applied to investigate the temporal structure of dynamical systems.

Causal Inference is a framework formalizing questions of cause and effect. In this work we propose an elementary and systematic approach to combine time-series representations with Causal Inference. Hereby we introduce a notion of cause and effect with respect to a pair of arbitrary time-series filters. Using a Singular Value Decomposition we derive an alternative representation of how one process drives another over a specified time-period. We call the building blocks of this representation Causal Orthogonal Functions. Combining the notion of Causal Orthogonal Functions with a Wavelet or Fourier decomposition of a time-series yields time-scale specific Causal Orthogonal Functions. As a result we obtain a time-scale specific representation of the causal influence one process has on another over some fixed time-period. This allows to conduct causal effect analysis in discrete-time stochastic dynamical systems at multiple time-scales. We illustrate our approach by examining linear VAR processes.

How to cite: Reiter, N.-D., Runge, J., and Gerhardus, A.: Causal Orthogonal Functions: A Causal Inference approach to temporal feature extraction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9112, https://doi.org/10.5194/egusphere-egu22-9112, 2022.

The winter stratospheric polar vortex exhibits considerable variability in both magnitude and zonal wave structure, which arises in part from stratosphere-troposphere coupling associated with tropospheric precursors and can result in extreme polar vortex events. These extremes can subsequently influence weather in the troposphere and thus are important sources of surface prediction. However, the predictability limit of these extreme events is around 1-2 weeks in the state-of-the-art prediction system. In order to explore and improve the predictability limit of the extreme vortex events, in this study, we train an artificial neural network (ANN) to model stratospheric polar vortex anomalies and to identify strong and weak stratospheric vortex events. To pinpoint the origins of the stratospheric anomalies, we then employ two neural network visualization methods, SHapley Additive exPlanations (SHAP) and Layerwise Relevance Propagation (LRP), to uncover feature importance in the input variables (e.g., geopotential height and background zonal wind). The extreme vortex events can be identified by the ANN with an averaged accuracy of 60-80%. For the correctly identified extreme events, the composite of the feature importance of the input variables shows spatial patterns consistent with the precursors found for extreme stratospheric events in previous studies. This consistency provides confidence that the ANN is able to identify reliable indicators for extreme stratospheric vortex events and that it could help to identify the role of the previously found precursors, such as the sea level pressure anomalies associated with the Siberian high. In addition to the composite of all the events, the feature importance for each of the individual events further reveals the physical structures in the input variables (such as the locations of the geopotential height anomalies) that are specific to that event. Our results show the potential of explainable neural networks techniques in understanding and predicting the stratospheric variability and extreme events, and in searching for potential precursors for these events on subseasonal time scales. 

How to cite: Wu, Z., Beucler, T., de Fondeville, R., Székely, E., Obozinski, G., Ball, W., and Domeisen, D.: Identifying precursors for extreme stratospheric polar vortex events  using an explainable neural network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-591, https://doi.org/10.5194/egusphere-egu22-591, 2022.

Tackling climate change needs to understand the complex phenomena occurring on the Planet. Discovering  teleconnection patterns is an essential part of the endeavor. Events like El Niño Southern Oscillation (ENSO) impact essential climate variables at large distances, and influence the underlying Earth system dynamics. However, their automatic identification from the wealth of observational data is still unresolved. Nonlinearities, nonstationarities and the (ab)use of correlation analyses hamper the discovery of true causal patterns.  Classical approaches proceed by first, extracting principal modes of variability and second, by performing lag-correlations or Granger causal analysis to identify possible teleconnections. While the principal modes are an effective representation of the data, they could be causally not meaningful. 
To address this, we here introduce a deep learning methodology that extracts nonlinear latent representations from spatio-temporal Earth data that are Granger causal with the index altogether. The proposed algorithm consists of a variational autoencoder trained with an additional causal penalization that enforces the latent representation to be (partially) Granger-causally related to the considered signal. The causal loss term is obtained by training two additional autoregressive models to forecast some of the latent signals, one of them including the target signal as predictor. The causal penalization is finally computed by comparing the log variances of the two autoregressive models, similarly to the standard Granger causality approach. 

The major drawback of deep autoencoders with respect to the classical linear principal component approaches is the lack of a straightforward interpretability of the representations learned. 
To address this point we perform synthetic interventions in the latent space and analyse the differences in the recovered NDVI signal.
We illustrate the feasibility of the approach described to study the impact of ENSO on vegetation, which allows for a more rigorous study of impacts on ecosystems globally. The output maps show NDVI patterns which are consistent with the known phenomena induced by El Niño event. 

How to cite: Varando, G., Fernández-Torres, M.-Á., and Camps-Valls, G.: Learning ENSO-related Principal Modes of Vegetation via a Granger-Causal Variational Autoencoder, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11388, https://doi.org/10.5194/egusphere-egu22-11388, 2022.

A key challenge in climate science is to quantify the forced response in impact-relevant variables such as precipitation against the background of internal variability, both in models and observations. Dynamical adjustment techniques aim to remove unforced variability from a target variable by identifying patterns associated with circulation, thus effectively acting as a filter for dynamically induced variability. The forced contributions are interpreted as the variation that is unexplained by circulation. However, dynamical adjustment of precipitation at local scales remains challenging because of large natural variability and the complex, nonlinear relationship between precipitation and circulation particularly in heterogeneous terrain. 

In this talk, I will present the Latent Linear Adjustment Autoencoder (LLAAE), a novel statistical model that builds on variational autoencoders. The Latent Linear Adjustment Autoencoder enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner. To predict circulation-induced precipitation, the LLAAE combines a linear component, which models the relationship between circulation and the latent space of an autoencoder, with the autoencoder's nonlinear decoder. The combination is achieved by imposing an additional penalty in the cost function that encourages linearity between the circulation field and the autoencoder's latent space, hence leveraging robustness advantages of linear models as well as the flexibility of deep neural networks. 

We show that our model predicts realistic daily winter precipitation fields at high resolution based on a 50-member ensemble of the Canadian Regional Climate Model at 12 km resolution over Europe, capturing, for instance, key orographic features and geographical gradients. Using the Latent Linear Adjustment Autoencoder to remove the dynamic component of precipitation variability, forced thermodynamic components are expected to remain in the residual, which enables the uncovering of forced precipitation patterns of change from just a few ensemble members. We extend this to quantify the forced pattern of change conditional on specific circulation regimes. 

Future applications could include, for instance, weather generators emulating climate model simulations of regional precipitation, detection and attribution at subcontinental scales, or statistical downscaling and transfer learning between models and observations to exploit the typically much larger sample size in models compared to observations.

How to cite: Heinze-Deml, C., Sippel, S., Pendergrass, A. G., Lehner, F., and Meinshausen, N.: Latent Linear Adjustment Autoencoder: a novel method for estimating dynamic precipitation at high resolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-696, https://doi.org/10.5194/egusphere-egu22-696, 2022.

Statistical downscaling is a very popular technique to increase the spatial resolution of existing global and regional climate model simulations and to provide reliable climate data at local scale. The availability of tailored information is particularly crucial for conducting local climate assessments, climate change studies and for running impact models, especially in complex terrain. A crucial requirement is the ability to reliably downscale the mean, variability and extremes of climate data, while preserving their spatial and temporal correlations.

Several machine learning-based approaches have been proposed so far to perform such task by extracting non-linear relationships between local-scale variables and large-scale atmospheric predictors and they could outperform more traditional statistical methods. In recent years, deep learning has gained particular interest in geoscientific studies and climate science as a promising tool to improve climate downscaling thanks to its greater ability to extract high-level features from large datasets using complex hierarchical architectures. However, the proper network architecture is highly dependent on the target variable, time and spatial resolution, as well as application purposes and target domain.

This contribution presents a Deep Convolutional Encoder-Decoder Network (DCEDN) architecture which was implemented and evaluated for the first time over Trentino-South Tyrol in the Eastern Italian Alps to derive 1-km climate fields of daily temperature and precipitation from ERA-5 reanalysis. We will show that in-depth optimization of hyper-parameters, loss function choice and sensitivity analyses are essential preliminary steps to derive an effective architecture and enhance the interpretability of results and of their variability. The validation of downscaled fields of both temperature and precipitation confirmed the improved representation of local features for both mean and extreme values, even though lower performances were obtained for precipitation in reproducing small-scale spatial features. In all cases, DCEDN was found to outperform classical schemes based on linear regression and the bias adjustment procedures used as benchmarks. We will discuss in detail the advantages and recommendations for the integration of DCEDN as an efficient post-processing block in climate data simulations supporting local-scale studies. The model constraints in feature extraction, especially for precipitation, over the limited extent of the study domain will also be explained along with potential future developments of such type of networks for improved climate science applications.

How to cite: Crespi, A., Frisinghelli, D., Klisho, T., Petitta, M., Jacob, A., and Pittore, M.: A Convolutional Neural Network approach for downscaling climate model data in Trentino-South Tyrol (Eastern Italian Alps), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10120, https://doi.org/10.5194/egusphere-egu22-10120, 2022.

Existing databases for extreme weather events such as floods, heavy rainfall events, or droughts are heavily reliant on authorities and weather services manually entering details about the occurrence of an event. This reliance has led to a massive geographical imbalance in the likelihood of extreme weather events being recorded, with a vast number of events especially in the developing world remaining unrecorded. With continuing climate change, a lack of systematic extreme weather accounting in developing countries can lead to a substantial misallocation of funds for adaptation measures. To address this imbalance, in this pilot study we combine socio-economic data with climate and geographic data and use several machine-learning algorithms as well as traditional (spatial) econometric tools to predict the occurrence of extreme weather events and their impacts in the absence of information from manual records. Our preliminary results indicate that machine-learning approaches for the detection of the impacts of extreme weather could be a crucial tool in establishing a coherent global disaster record system. Such systems could also play a role in discussions around future Loss and Damages.

How to cite: Schwarz, M. and Pretis, F.: Filling in the Gaps: Consistently detecting previously unidentified extreme weather event impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12165, https://doi.org/10.5194/egusphere-egu22-12165, 2022.

In this study we evaluate the use of generative adversarial networks (GANs) to model satellite-based estimates of precipitation conditioned on reanalysis temperature, humidity, wind, and surface latent heat flux.  We are interested in the climatology of precipitation and modeling it in terms of atmospheric state variables, in contrast to a weather forecast or precipitation nowcast perspective.  We consider a hierarchy of models in terms of complexity, including simple baselines, generalized linear models, gradient boosted decision trees, pointwise GANs and deep convolutional GANs. To gain further insight into the models we apply methods for analyzing machine learning models, including model explainability, ablation studies, and a diverse set of metrics for pointwise and distributional differences, including information theory based metrics.  We find that generative models significantly outperform baseline models on metrics based on the distribution of predictions, particularly in capturing the extremes of the distributions.  Overall, a deep convolutional model achieves the highest accuracy.  We also find that the relative importance of atmospheric variables and of their interactions vary considerably among the different models considered. 

How to cite: Nash, C., Nadiga, B., and Sun, X.: Generative Adversarial Modeling of Tropical Precipitation and the Intertropical Convergence Zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8068, https://doi.org/10.5194/egusphere-egu22-8068, 2022.

How to cite: Vosper, E., Mitchell, D., Watson, P., Aitchison, L., and Santos-Rodriguez, R.: Using Generative Adversarial Networks (GANs) to downscale tropical cyclone precipitation., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8454, https://doi.org/10.5194/egusphere-egu22-8454, 2022.

The high computational cost of super-resolution (< 250 m) climate simulations is a major barrier for generating climate change information at such high spatial and temporal resolutions required by many sectors for planning local and asset-specific climate change adaptation strategies. This study couples machine learning and physical modelling paradigms to develop a computationally efficient simulator-emulator framework for generating super-resolution climate information. To this end, a regional climate model (RCM) is applied over the city of Montreal, for the summers of 2015 to 2020, at 2.5 km (i.e., low resolution – LR) and 250 m (i.e., high resolution – HR), which is used to train and validate the proposed super-resolution deep learning (DL) model. In the field of video super-resolution, convolutional neural networks combined with motion compensation have been used to merge information from multiple LR frames to generate high-quality HR images. In this study, a recurrent DL approach based on passing the generated HR estimate through time helps the DL model to recreate fine details and produce temporally consistent fields, resembling the data assimilation process commonly used in numerical weather prediction. Time-invariant HR surface fields and storm motion (approximated by RCM-simulated wind) are also considered in the DL model, which helps further improve output realism. Results suggest that the DL model is able to generate HR precipitation estimates with significantly lower errors than other methods used, especially for intense short-duration precipitation events, which often occur during the warm season and are required to evaluate climate resiliency of urban storm drainage systems. The generic and flexible nature of the developed framework makes it even more promising as it can be applied to other climate variables, periods and regions.

How to cite: Teufel, B., Carmo, F., Sushama, L., Sun, L., Khaliq, N., Belair, S., Shamseldin, A. Y., Nagesh Kumar, D., and Vaze, J.: Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8649, https://doi.org/10.5194/egusphere-egu22-8649, 2022.

The interdisciplinary research project "BayTreeNet" investigates the reactions of forest ecosystems to current climate dynamics. In the mid-latitudes, local climatic phenomena often show a strong dependence on the large-scale climate dynamics, the weather types (WT), which significantly determine the climate of a region through frequency and intensity. In the topographically diverse region of Bavaria, different WT show various weather conditions at different locations.

The meaning of every WT is explained for the different forest regions in Bavaria and the results of the climate dynamics sub-project provide the physical basis for the "BayTreeNet" project. Subsequently, climate-growth relationships are established in the dendroecology sub-project to investigate the response of forests to individual WT at different forest sites. Complementary steps allow interpretation of results for the past (20th century) and projection into the future (21st century). One hypothesis to be investigated is that forest sites in Bavaria are affected by a significant influence of climate change in the 21st century and the associated change in WT.

The automated classification of large-scale weather patterns is presented by Self-Organizing-Maps (SOM) developed by Kohonen, which enables visualization and reduction of high-dimensional data. The poster presents the evaluation and selection of an appropriate SOM-setting and its first results. Besides, it is planned to show first analyses of the environmental conditions of the different WT and how these are represented in global climate models (GCMs) in the past and future.

How to cite: Wehrmann, S. and Mölg, T.: Classifying weather types in Europe by Self-Organizing-Maps (SOM) with regard to GCM-based future projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3105, https://doi.org/10.5194/egusphere-egu22-3105, 2022.

Clouds remain a core uncertainty in quantifying Earth’s climate sensitivity due to their complex dynamical and microphysical  interactions with multiple components of the Earth system. Therefore it is pivotal to observationally constrain possible cloud changes in a changing climate in order to evaluate our current generation of Earth system models by a set of physically realistic sensitivities. We developed a novel observational regime framework from over 15 years of MODIS satellite observations, from which we have derived a set of regimes of cloud controlling factors. These regimes were established using the relationship strength, as measured by using the weights of a trained, simple machine learning model. We apply these as observational constraints on the ​​r1i1p1f1 and r1i1p1f3 historical runs from various CMIP6 models to test if CMIP6 climate models can accurately represent key cloud controlling factors.. Within our regime framework, we can compare the observed environmental drivers and sensitivities of each regime against the parameterization-driven, modeled outcomes. We find that, for almost every regime, CMIP6 models do not properly represent the global distribution of occurrence, raising into question how much we can trust our range of climate sensitivities when specific cloud controlling factors are so badly represented by these models. This is especially pertinent in southern ocean and marine stratocumulus regimes, as the changes in these clouds’ optical depths and cloud amount have increased the ECS from CMIP5 to CMIP6. Our results suggest that these uncertainties in CMIP6 cloud parameterizations propagate into derived cloud feedbacks and ultimately climate sensitivity, which is evident from a regimed based analysis of cloud controlling factors.

How to cite: Douglas, A. and Stier, P.: Defining regime specific cloud sensitivities using the learnings from machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8848, https://doi.org/10.5194/egusphere-egu22-8848, 2022.

Mapping the spatio-temporal distribution of near-surface urban air temperature is crucial to our understanding of climate-sensitive epidemiology, indoor-outdoor thermal comfort, urban biodiversity, and interactive impacts of climate change and urbanity. Urban-scale decision-making in face of future climatic uncertainties requires detailed information on near-surface air temperature at high spatio-temporal resolutions. However, reaching such fine resolutions cannot be currently realised by traditional observation networks, or even by regional or global climate models (Hamdi et al. 2020). Given the complexity of the processes affecting air temperature at the urban scale to the regional scale, here we apply Machine Learning (ML) algorithms, in particular, XGBoost gradient boosting method to build predictive models of near surface air temperature (Ta at 2-meter height). These predictive models establish data-driven relations between crowd-sourced measured Ta (data produced by citizens’ sensors) and a set of spatial and spatio-temporal predictors, primarily derived from Earth Observation satellite data including Modis Aqua/Landsat 8 Land Surface Temperature (LST), Modis Terra vegetative indices, and Sentinel-2 water vapour product. We use our models to predict sub-daily (at Modis Aqua satellite passing times) variation in urban scale Ta in city of Warsaw, Poland at spatial resolution of 1 km for the months July-September and the years 2016 to 2021. A 10-fold cross-validation of the developed models shows a root mean square error between 0.97 and 1.02 °C and a coefficient of determination between 0.96 and 0.98, which are satisfactory according to the literature (Taheri-Shahraiyni and Sodoudi 2017). The resulting maps allow us to identify regions of Warsaw that are vulnerable to heat stress. The strength of the method used here is that it can be easily replicated in other EU cities to achieve high resolution maps due to the accessibility and open-sourced nature of the training and predictor data. Contingent on data availability, the predictive framework developed also can be used for monitoring and downscaling of other urban governing climatic parameters such as relative humidity in the context of future climate uncertainties.

Hamdi, R., H. Kusaka, Q.-V. Doan, P. Cai, H. He, G. Luo, W. Kuang, S. Caluwaerts, F. Duchêne, B. J. E. S. Van Schaeybroek and Environment (2020). "The state-of-the-art of urban climate change modeling and observations." 1-16.

Taheri-Shahraiyni, H. and S. J. T. S. Sodoudi (2017). "High-resolution air temperature mapping in urban areas: A review on different modelling techniques."  21(6 Part A): 2267-2286.

How to cite: Hassani, A., Castell, N., and Schneider, P.: Application of Machine Learning for spatio-temporal mapping of the air temperature in Warsaw, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3009, https://doi.org/10.5194/egusphere-egu22-3009, 2022.

Recent studies have shown that the increasing sizes of offshore wind farms can cause a reduced energy production through mesoscale interactions with the atmosphere. Therefore, accurate nowcasting of the energy yields of large offshore wind farms depend on accurate predictions of the large synoptic weather systems as well as accurate predictions of the smaller mesoscale weather systems. In general, global or regional forecasting models are very well suited to predict synoptic-scale weather systems. However, satellite or radar data can support the nowcasting of shorter, smaller-scale systems. 

In this work, a first step towards nowcasting of the mesoscale wind using satellite images has been taken, namely the coupling of the mesoscale wind component to cloud properties that are available from satellite images using a deep learning framework. To achieve this, a high-resolution regional atmospheric model (COSMO-CLM) was used to generate one year of high resolution cloud en hub-height wind data. From this wind data the mesoscale component was filtered out and used as target images for the deep learning model. The input images of the model were several cloud-related fields from the atmospheric model. The model itself was a Deep Convolutional Neural Network (a U-Net) which was trained to minimize the mean squared error. 

This analysis indicates that cloud information can be used to extract information about the mesoscale weather systems and could be used for nowcasting by using the trained U-Net as a basis for a temporal deep learning model. However, future validation with real-world data is still needed to determine the added value of such an approach.

How to cite: Jamaer, S., Neirynck, J., and van Lipzig, N.: Can cloud properties provide information on surface wind variations using deep learning?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4853, https://doi.org/10.5194/egusphere-egu22-4853, 2022.

The Earth's radiation budget may be altered by changes in atmospheric composition or land use. This is called radiative forcing. Among the human-generated influences in radiative forcing, aerosol-cloud interactions are the least understood. A way to quantify a key uncertainty in this regard, the adjustment of cloud liquid water path (LWP), is by the ratio (sensitivity) of LWP to changes in cloud droplet number concentration (Nd). A key problem in quantifying this sensitivity from large-scale observations is that these two quantities are not retrieved by operational satellite products and are subject to large uncertainties. 

In this work, we use machine learning techniques to show that inferring LWP and Nd directly from satellite observation data may yield a better understanding of this relationship without using retrievals, which may lead to large and systematic uncertainties. In particular, we use supervised learning on the basis of available high-resolution ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model) simulations from the HD(CP)² project (High Definition Clouds and Precipitation for advancing Climate Prediction) and forward-simulated radiances obtained from the radiative transfer modeling (RTTOV, Radiative Transfer for TOVS) which uses MODIS (Moderate Resolution Imaging Spectroradiometer) data as a reference. Usually, only two channels from the reflectance of MODIS can be used to estimate the LWP and Nd. However, having access to 36 bands allows us to exploit data and find other patterns to get these parameters directly from the observation space rather than from the retrievals. A machine learning model is used to create an emulator which approximates the Radiative Transfer Model, and another machine learning model to directly predict the sensitivity of LWP - Nd from the satellite observation data.

How to cite: Gonzalez, J., Sourdeval, O., Camps-Valls, G., and Quaas, J.: Machine learning to quantify cloud responses to aerosols from satellite data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6093, https://doi.org/10.5194/egusphere-egu22-6093, 2022.

Clouds and their radiative feedback mechanisms are of vital importance for the atmospheric cycle of the Earth regarding global weather today as well as climate changes in the future. Climate models and simulations are sensitive to the vertical distribution of clouds, emphasizing the need for broadly accessible fine resolution data. Although passive satellite sensors provide continuous cloud monitoring on a global scale, they miss the ability to infer physical properties below the cloud top. Active instruments like radar are particularly suitable for this task but lack an adequate spatio-temporal resolution. Here, recent advances in Deep-Learning models open up the possibility to transfer spatial information from a 2D towards a 3D perspective on a large-scale.

By an example period in 2017, this study aims to explore the feasibility and potential of neural networks to reconstruct the vertical distribution of volumetric radar data along a cloud’s column. For this purpose, the network has been tested on the Full Disk domain of a geostationary satellite with high spatio-temporal resolution data. Using raw satellite channels, spectral indices, and topographic data, we infer the 3D radar reflectivity from these physical predictors. First results demonstrate the network’s capability to reconstruct the cloud vertical distribution. Finally, the ultimate goal of interpolating the cloud column for the whole domain is supported by a considerably high accuracy in predicting the radar reflectivity. The resulting product can open up the opportunity to enhance climate models by an increased spatio-temporal resolution of 3D cloud structures.

How to cite: Brüning, S., Tost, H., and Niebler, S.: Inferring the Cloud Vertical Distribution from Geostationary Satellite Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6998, https://doi.org/10.5194/egusphere-egu22-6998, 2022.

Cloud base height (CBH) is an important geometric parameter of a cloud and shapes its radiative properties. The CBH is also further of practical interest in the aviation community regarding pilot visibility and aircraft icing hazards. While the cloud-top height has been successfully derived from passive imaging radiometers on satellites during recent years, the derivation of the CBH remains a more difficult challenge with these same retrievals.

In our study we combine surface observations and passive satellite remote-sensing retrievals to create a database of CBH labels and cloud properties to ultimately train a machine learning model predicting CBH. The labels come from the global marine meteorological observations dataset (UK Met Office, 2006) which consists of near-global synoptic observations made on sea. This data set provides information about CBH, cloud type, cloud cover and other meteorological surface quantities with CBH being the main interest here. The features based upon which the machine learning model is trained consist in different cloud-top and cloud optical properties (Level 2 products MOD06/MYD06 from the MODIS sensor) extracted on a 127km x 127km grid around the synoptic observation point. To study the large diversity in cloud scenes, an auto-encoder architecture is chosen. The regression task is then carried out in the modelled latent space which is output by the encoder part of the model. To account for the spatial relationships in our input data the model architecture is based on Convolutional Neural Networks. We define a study domain in the Atlantic ocean, around the equator. The combination of information from below and over the cloud could allow us to build a robust model to predict CBH and then extend predictions to regions where surface measurements are not available.

How to cite: Lenhardt, J., Quaas, J., and Sejdinovic, D.: Combining cloud properties and synoptic observations to predict cloud base height using Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7355, https://doi.org/10.5194/egusphere-egu22-7355, 2022.

The IASI remote sensor measures Earth’s thermal infrared radiation over 8461 channels between 645 and 2760 cm^-1. Atmospheric temperatures at different altitudes can be retrieved from the radiances measured in the CO₂ absorption bands (645-800 cm^-1 and 2250-2400 cm^-1) by selecting the channels that are the most sensitive to the temperature profile. The three IASI instruments on board of the Metop suite of satellites launched in 2006, 2012 and 2018, will provide a long time series for temperature, adequate for studying the long term evolution of atmospheric temperature. However, over the past 14 years, EUMETSAT, who processes radiances and computes atmospheric temperatures, has carried out several updates on the processing algorithms for both radiances and temperatures, leading to non-homogeneous time series and thus large difficulties in the computation of trends for temperature and atmospheric composition.

In 2018, EUMETSAT has reprocessed the radiances with the most recent version of the algorithm and there is now a homogeneous radiance dataset available. In this study, we retrieve a new temperature record from the homogeneous IASI radiances using an artificial neural network (ANN). We train the ANN with IASI radiances as input and the European Centre for Medium-Range Weather Forecasts reanalysis ERA5 temperatures as output. We validate the results using ERA5 and in situ radiosonde temperatures from the ARSA database. Between 750 and 7 hPa, where IASI has most of its sensitivity, a very good agreement is observed between the 3 datasets. This work suggests that ANN can be a simple yet powerful tool to retrieve IASI temperatures at different altitudes in the upper troposphere and in the stratosphere, allowing us to construct a homogeneous and consistent temperature data record.

We use this new dataset to study extreme events such as sudden stratospheric warmings, and to compute trends over the IASI coverage period [2008-2020]. We find that in the past thirteen years, there is a general warming trend of the troposphere, that is more important at the poles and at mid latitudes (0.5 K/decade at mid latitudes, 1 K/decade at the North Pole). The stratosphere is globally cooling on average, except at the South Pole as a result of the ozone layer recovery and a sudden stratospheric warming in 2019. The cooling is most pronounced in the equatorial upper stratosphere (-1 K/decade).

How to cite: Bouillon, M., Safieddine, S., Whitburn, S., Clarisse, L., Aires, F., Pellet, V., Lezeaux, O., Scott, N. A., Doutriaux-Boucher, M., and Clerbaux, C.: Time evolution of temperature profiles retrieved from 13 years of IASI data using an artificial neural network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11451, https://doi.org/10.5194/egusphere-egu22-11451, 2022.