Many geophysical systems show emergent phenomena and extreme events at different scales, with signatures of chaos at large scales and an apparently random behavior at small scales. Despite the intrinsic morphological and/or physical difference between geophysical extremes, they all originate as temporary deviations from the typical trajectories of the large scale geophysical flows, resulting in dynamical patterns and structures. This motivated to bring together statistics (extreme value theory) and dynamics (dynamical system theory) to provide a new definition of extremes as rare recurrences in the phase space of physical systems. This means to explore the instantaneous properties of the geometrical object hosting the frequency and probability of all physical states attainable by the system, namely the so-called attractor, to inform us on the predictability, persistence and synchronization of physical states.

Here we present a recently proposed formalism to explore the active number of degrees of freedom and the predictability horizon of multiscale complex systems showing non-hyperbolic chaos, randomness, state-dependent persistence and predictability. We briefly discuss the newly introduced framework in comparison with classical approaches, based on generalized fractal dimensions, Lyapunov exponents, and Renyi entropies. Finally, we demonstrate the suitability of this novel formalism to trace the instantaneous scale-dependent and state-dependent features of climate and geophysical extremes, pointing out how the predictability horizon, the persistence and synchronization of geophysical systems’ states is a matter of scales.

How to cite: Alberti, T.: Extreme events in multiscale systems: theory and applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1388, https://doi.org/10.5194/egusphere-egu23-1388, 2023.

The number of damaging events caused by natural disasters is increasing because of climate change. Projects of public interest such as ClimXtreme (Climate Change and Extreme Events [1, 2]), aim to improve our knowledge of extreme events, the influence of environmental changes and their societal impacts.

ClimXtreme focuses on an integral evaluation through a three-pronged approach, namely: the physical processes behind the extremes, the statistical assessment of them, and their impact. The success of such a project depends on a coordinate effort from many interdisciplinary groups down to the management of computational and data storage resources. The ever-growing amount of available data at the researcher’s disposal is a two-sided blade that craves for greater resources to host, access, and evaluate them efficiently through High Performance Computing (HPC) infrastructures. Additionally, these last years the community is demanding an easier reproducibility of evaluation workflows and data FAIRness [3]. Frameworks like Freva (Free Evaluation System Framework [4, 5]) offer an efficient solution to handle customizable evaluation systems of large research projects, institutes or universities in the Earth system community [6-8] over the HPC environment and in a centralized manner. Mainly written on python, Freva offers:

• A centralized access. Freva can be accessed via command line interface, via web, and via python module (e.g. for jupyter notebooks) offering similar features.
• A standardized data search. Freva allows for a quick and intuitive incorporation and search of several datasets stored centrally.
• Flexible analysis. Freva provides a common interface for user defined data evaluation routines to plug them in to the system irrespective of the programming language. These plugins are able to search from and integrate own results back to Freva. This environment enables an ecosystem of plugins that fosters the interchange of results and ideas between researchers, and facilitates the portability to any other research project that uses a Freva instance.
• Transparent and reproducible results. Every analysis run through Freva (including parameter configuration and plugin version information) is stored in a central database and can be consulted, shared, modified and re-run by anyone within the project. Freva optimizes the usage of computational and storage resources and paves the way of traceability in line with FAIR data principles.

Hosted at the DKRZ, ClimXtreme’s Freva instance (XCES [7]) offers quick access to more than 9 million datafiles of models (e.g. CMIP, CORDEX), observations (stations, gridded) and evaluation outputs. The ClimXtreme community has been actively contributing with plugins to XCES, its biggest asset, with close to 20 plugins of different disciplines at the disposal of everyone within the project, and more than 20,000 analysis run through the system. At present, any researcher can focus on a past, present or future period and a geographical region and run a series of evaluations ranging from coocurrence probabilities of extreme events, their impact on crops to wind tracking algorithms among many others. Freva facilitates comprehensive and exhaustive analysis of extreme events in an easy way.

References:

[1] https://www.fona.de/de/massnahmen/foerdermassnahmen/climxtreme.php

[2] https://www.climxtreme.net/index.php/en/

[3] https://www.go-fair.org/fair-principles/

[4] http://doi.org/10.5334/jors.253

[5] https://github.com/FREVA-CLINT/freva-deployment

[6] freva.met.fu-berlin.de

[7] https://www.xces.dkrz.de/

[8] www-regiklim.dkrz.de

How to cite: Lucio-Eceiza, E. E., Kadow, C., Bergemann, M., Fast, A., Thiemann, H., and Ludwig, T.: Freva for ClimXtreme: an aid to get the bigger picture in analysis of extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14838, https://doi.org/10.5194/egusphere-egu23-14838, 2023.

Evaluating the trends of extreme indices (EI) is crucial to detect and attribute extreme events (EE) and establish adaptation and mitigation strategies to the current and future climate conditions. However, the observational climate data used for the calculation of these indices often contains many missing values and leads to incomplete and inaccurate EI. This problem is even greater as we go back in time due to the scarcity of the older measurements.

To tackle this problem, interpolation techniques such as the kriging method are often used to fill in the gaps. However, it has been shown that such techniques are inadequate to reconstruct specific climatic patterns [1]. Deep-learning based technologies give the possibility to surpass standard statistical methods by learning complex patterns and features in climate data.

In this work, we are using an inpainting technique based on a U-Net neural network made of partial convolutional layers and a loss function designed to produce semantically meaningful predictions [1]. Models are trained using vast amounts of climate model data and can be used to reconstruct large and irregular regions of missing data with few computational resources.

The efficiency of the method is well demonstrated through its application to the HadEX3 dataset [2]. This dataset contains gridded land surface EI, among which the TX90p index that measures the monthly (or annual) frequency of warm days (defined as a percentage of days where daily maximum temperature is above the 90th percentile). As for other EI, there is a lack of TX90p values in many regions of the world, even in recent years. It is particularly true when looking at an intermediate product of HadEX3 where the station-based indices have been combined without interpolation. This is illustrated by the left map of the figure where the gray pixels correspond to missing values. By training our model using data from the CMIP6 archive, we have been able to reconstruct the missing TX90p values for all the time steps of HadEX3 (see right map in the figure) and detect EE that were not included in the original dataset. The reconstructed dataset is being prepared for the community in the framework of the H2020 CLINT project [3] for further detection and attribution studies.

[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)
[2] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)
[3] https://climateintelligence.eu/

How to cite: Plésiat, É., Dunn, R., Donat, M., Morice, C., Ludwig, T., Thiemann, H., and Kadow, C.: Using Artificial Intelligence to Reconstruct Missing Climate Data In Extreme Events Datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13507, https://doi.org/10.5194/egusphere-egu23-13507, 2023.

Climate models aim at representing as closely as possible the statistical properties of the climate components, including the extreme events. This is a fundamental requirement to correctly project changes in their dynamics due to anthropogenic forcing. In order to evaluate how closely models match observations, we need algorithms capable of selecting, processing and evaluating relevant dynamical features of the climate components. This has to be reiterated efficiently for large datasets such as those issued from the Coupled Model Intercomparison Project 6 (CMIP6). In this work, we use Latent Dirichlet Allocation (LDA), a statistical learning method initially designed for natural language processing, to extract synoptic patterns from sea-level pressure data and evaluate how close the dynamics of CMIP6 climate models are to the state-of-the-art reanalyses datasets such as ERA5 or NCEPv2, in general as well as in the case of extremes.

LDA allows for learning a basis of decomposition of maps into objects called "motifs". Applying it to sea-level pressure data, reanalysis or simulation, robustly yields motifs that are known relevant synoptic objects, i.e. cyclones or anticyclones. Furthermore, LDA provides their weight in each of the maps of the dataset, their most probable geographical position and their possible changes due to internal variability or external forcings. LDA decomposition is efficient and sparse, most of the information of a given sea-level pressure map is contained in few motifs, making it possible to decompose any map in a limited number of easy-to-interpret synoptic objects. This allows for a variety of new angles for statistical analysis.

We look at the dominant motifs and their distributions either on entire datasets or conditionally to particular extreme events, such as cold or heat waves, and compare results between reanalysis data and historical simulations. This enables us to assess which models can or cannot reproduce statistical properties of the observations, and whether or not there are properties that no model yet demonstrates. We find that models can capture the statistical synoptic composition of sea-level pressure data in general, but that some drawbacks still exist in the modelling of extreme events. LDA can also be applied separately to each dataset, and the two resulting synoptic bases can be compared. We find the sets of motifs from reanalysis and historical simulations are very similar, even if different spatial resolutions are used.

How to cite: Malhomme, N., Podvin, B., Faranda, D., and Mathelin, L.: Latent Dirichlet Allocation: a new machine learning tool to evaluate CMIP6 climate models atmospheric circulation and extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-382, https://doi.org/10.5194/egusphere-egu23-382, 2023.

Extreme windstorms are of considerable interest due to their potential to cause significant socio-economic damages over very large areas of land. As a result, understanding how climate change may affect the characteristics of the most severe storms is an important question for adaptation planing. However, projections of how the hazard associated with windstorms will change in the future are highly uncertain.

Here, we use an efficient statistical approach that characterises individual windstorms in terms of their intensity and exposure to estimate the present-day risk from such storms. We then use a methodology used widely in detection and attribution of climate change to assess how such characteristics may change into the future. Using windstorms simulated by a diverse set of high-resolution regional climate model projections for Europe, the EURO-CORDEX ensemble, we provide projections of risk over a range of future climate scenarios. Finally, we explore how the variety of driving and regional models influence the associated uncertainties, and how considering the performance and independence of the models can improve the robustness of the projections.

How to cite: Leach, N. J., Messori, G., Crawford, A., Wada, R., Woodhouse, S., and Burke, C.: Severe windstorm projections for Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8224, https://doi.org/10.5194/egusphere-egu23-8224, 2023.

Mid-latitude storms are essential features of atmospheric variability in the cold season. The subsequent damages are caused by high wind speeds and heavy precipitation. Among such events, explosive cyclones can lead to extreme impacts when they make landfall. Climate change is affecting the underlying characteristics of such types of extremes. Being able to understand the way it modifies their dynamics is of great importance. In this work, we assess the influence of anthropogenic climate change on observed explosive cyclones in an Extreme Event Attribution framework using a large ensemble dataset. We evaluate three storms that hit different parts of Europe: Xynthia in February 2010, Alex in October 2020, and Eunice in January 2022. 

We use three ensembles of 35 members of the Community Earth System Model (CESM). We compare two periods of 6-hourly data: present-day climate [1991-2001] and future climate [RCP8.5 scenario, 2091–2101]. We find analogues of the trajectories of the three storms before their highest intensity in both periods. We do that by tracking all cyclones in the dataset and selecting the cyclone tracks that have the minimum Euclidean distance in km from the trajectories of Xynthia, Alex, and Eunice. We explore the characteristics of the analogues of the trajectories in both periods such as frequency of explosive cyclogenesis and intensity to evaluate whether the dynamics of the storms have been affected by climate change. We further compare the analogues in terms of precipitation and low-level wind in the regions of impact.

How to cite: Ginesta, M., Flaounas, E., Yiou, P., and Faranda, D.: Effect of anthropogenic climate change on explosive cyclogenesis cases in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9105, https://doi.org/10.5194/egusphere-egu23-9105, 2023.

Sampling rare events such as extreme heatwaves whose return period is larger than the length of available observations requires developing and benchmarking new  simulation methods. There is growing interest in applying deep learning alongside already existing statistical approaches to better generate and predict rare events. Our goal is to benchmark Stochastic Weather Generator (SWG) [1] based on analogs of circulation, soil moisture and temperature as a tool for sampling tails of distribution as well as forecasting heatwaves in France and Scandinavia using data from General Circulation Model (GCM). Analog method has been successfully implemented in rare event algorithms for low dimensional climate models [2].

SWG is implemented using a Markov chain with hidden states (.e.g. geopotential height at 500 hPa) with Euclidean metric. When applying such methods to climate data two challenges emerge: a large number of degrees of freedom and the difficulty of including slow drivers such as soil moisture alongside circulation patterns. Consequently, we are going to discuss ways of adjusting the distance metric of the analog Markov chain and dimensionality reduction techniques such as EOFs and variational auto encoder. By choosing the correct combination of weighted variables in the Euclidean metric and using analogs of only 100 years and generating long synthetic sequences we are able to correctly estimate return times of order 7000 years, which is validated based on a 7200 year long control run. The teleconnection patterns generated thus also look reliable compared to the control run.

Next we compare SWG forecasts of heatwaves with a direct supervised approach based on a Convolutional Neural Network (CNN). Both CNN and SWG are trained and validated on exactly the same GCM runs which allows us to conclude that CNN performs better in both regions. One could consider SWG as a baseline approach for CNN for this task.

[1] Yiou, P. and Jézéquel, A., https://doi.org/10.5194/gmd-13-763-2020, 2020

[2] D. Lucente at al. https://10.1088/1742-5468/ac7aa7, 2022

[3] DP Kingma, M Welling - https://doi.org/10.48550/arXiv.1312.6114, 2013

[4] G. Miloshevich, at al - https://doi.org/10.48550/arXiv.2208.00971, 2022

How to cite: Miloshevich, G., Lucente, D., Bouchet, F., and Yiou, P.: Stochastic weather generator and deep learning approach for predicting and sampling extreme European heatwaves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6131, https://doi.org/10.5194/egusphere-egu23-6131, 2023.

The Summer Olympic Games in 2024 will take place during the apex of the temperature seasonal cycle in the Paris Area. The midlatitudes of the Northern hemisphere have witnessed a few intense heatwaves since the 2003 epitome event. Those heatwaves have had environmental and health impacts, which often came as surprises. In this paper, we search for the most extreme heatwaves in Ile-de-France that are physically plausible, under climate change scenarios, for the decades around 2024. We apply a rare event algorithm on CMIP6 data to evaluate the range of such extremes. We find that the 2003 record can be exceeded by more than 4°C in Ile-de-France before 2050, with a combination of prevailing anticyclonic conditions and cut-off lows. This study intends to build awareness on those unprecedented events, against which our societies are ill-prepared. Those results could be extended to other areas of the world.

How to cite: Yiou, P., Cadiou, C., Faranda, D., Jézéquel, A., Malhomme, N., Miloshevich, G., Noyelle, R., Pons, F., Robin, Y., and Vrac, M.: Simulating worst case  heatwaves during the Paris 2024 Olympics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11943, https://doi.org/10.5194/egusphere-egu23-11943, 2023.

Atmospheric blocking can be described as a large-scale stationary or quasi-stationary circulation anomaly that blocks the mean westerlies. Blocking often triggers extreme temperature events like heat waves or cold spells. However, dynamical processes leading to the formation, maintenance, and decay mechanisms of blocking are still not well understood.

Moist processes have recently been proven to play a significant role in the formation and maintenance of blocking. However, it is unclear if moist processes generate special properties in the blocking life cycle that cannot be represented by dry dynamics or if they are just there to inject extra energy into the atmospheric disturbances. The following is the question we address in the present study: Is a dry dynamical model with climatology close to the observations capable of representing blocking characteristics correctly? The methodology relies on numerical experiments made with the new IPSL dynamical core called DYNAMICO, which enables high spatial resolutions. DYNAMICO is used here to analyze a long-term simulation in which the model forcing is designed to obtain a realistic climatology for a given season (perpetual winter in the present case). Blocking statistics like frequency of occurrence and duration are provided using two blocking detection algorithms and compared to the re-analysis dataset (ERA5). A focus is made on blocking onsets in the Euro-Atlantic sector. To highlight the differences in the processes leading to blocking onsets, backward Lagrangian trajectories seeded in the blocking regions are systematically computed and analyzed. Additional long-term simulations of the same dry model with the increased horizontal resolution are also analyzed following the same approach.

How to cite: Deshmukh, V., Rivière, G., and Fromang, S.: How are atmospheric blockings represented in a dry general circulation model with wave energy just as powerful as in the observations?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5671, https://doi.org/10.5194/egusphere-egu23-5671, 2023.

The climate and weather over Europe and Asia are strongly influenced by the large-scale atmospheric circulation over the North Atlantic area. During the winter of 2009/10, the usually separate Atlantic and African jets merged into one zonal jet, resulting in unusually cold and wet conditions in Eurasian regions. During this winter the jet was unusually persistent, with characteristics more typical of the Pacific jet stream, which is a mixed thermally-eddy driven jet, suggesting the jet underwent a rare dynamical regime change.  Such a merging was only observed to occur for a whole winter during winters of 1968-69 and 1969-70. In this study, we apply GKTL rare event algorithm to produce an ensemble of PlaSim model runs of similar winter flow conditions, to study such merged jet (mixed thermally-eddy driven jet) transition and its dynamics. We try to understand how the initial conditions during the beginning of the winter could affect the jet to be in a persistent merged state. It is seen that there is a larger probability to continue in a merged jet state if there is a merged jet state at the beginning of winter. Similarly, there is a larger probability to continue in an eddy-driven jet state if there is an eddy-driven jet state at the beginning of winter. On comparing the ensemble of merged jet winter trajectories with the ensemble of eddy-driven jet winter trajectories there is a significant weakening of eddy heat fluxes over the west and central North Atlantic region. Also, the typically poleward-directed eddy momentum fluxes are significantly weaker during the winter merged jet state with small increases in the subtropics over the eastern North Atlantic due to the equatorward shift of the eddies.

How to cite: Suresan, S. and Harnik, N.: Computing and analyzing persistent merged jet state in climate model using rare event algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16917, https://doi.org/10.5194/egusphere-egu23-16917, 2023.

This study investigates winter cyclones that cause extreme 10 m winds in the central North Atlantic region (30^o to 60^o latitude, -50^o to -10^o longitude) in the ERA5 dataset. We employ a bottom-up approach consisting of selection of the extreme 10 m wind events and analysis of the cyclones that caused the extremes.

The 10 m wind extremes were ranked using the Klawa and Ulbrich (2003) destructiveness index, which takes into account wind exceedances over the local 98^th percentiles. The top 1% of destructive events were chosen for further analysis. Cyclones were associated with the extreme winds by finding the closest sea-level pressure lows at the times of maximum wind speeds.

By analyzing various meteorological fields associated with the temporal evolution of the selected cyclones, we find an important role of interactions with other pre-existing cyclones that create suitable conditions for the development of the subsequent extreme windstorms.  

How to cite: Stanković, A., Caballero, R., and Messori, G.: Large-scale perspective on the extreme near-surface winds in the central North Atlantic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4216, https://doi.org/10.5194/egusphere-egu23-4216, 2023.

North American cold spells tend to co-occur with extreme wind and precipitation events over Europe, but the physical mechanisms behind such “pan-Atlantic” compound extremes have not been fully clarified yet. Rather than proposing a single mechanism, we discuss how cold spells over a single North American region can be connected with wind extremes over different European regions through separate, physically consistent dynamical pathways. The first one involves the propagation of a Rossby wave train from the Pacific Ocean, and is associated with windstorms over north-western Europe in the 5-10 days after the cold spell peak. The second one is associated with a high-latitude anticyclone over the North Atlantic and an equatorward-shifted jet, leading to windstorms over south-western Europe already in the days preceding the cold spell peak.

The same dynamical pathways can be independently retrieved from a cluster analysis based on the temporal evolution of the North Atlantic circulation in the days preceding North American cold spells. Such an analysis highlights significantly different stratospheric circulation patterns between the two pathways, with cold spells of the second pathway tied to a weaker than usual stratospheric polar vortex, and an enhanced occurrence of sudden stratospheric warmings.

How to cite: Riboldi, J., Dorrington, J., Leeding, R., Segalini, A., and Messori, G.: Dynamical pathways for pan-Atlantic compound cold and windy extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7908, https://doi.org/10.5194/egusphere-egu23-7908, 2023.

We examine the characteristics of North Atlantic extratropical cyclones in ERA5 data during cold air outbreaks over continental North America. Previous research has established a statistical link between occurrences of North American cold air outbreaks and an increased frequency of extreme wet and windy conditions over Europe. The theoretical understanding of cyclogenesis suggests that greater numbers of extratropical cyclones will be generated in the North Atlantic, resulting from an enhanced temperature difference between the North American continent and the Gulf Stream during cold air outbreaks. Our analysis finds that counts of extratropical cyclones in the North  Atlantic storm track are no greater, or even less than climatology during periods with cold air outbreaks. We instead find anomalous jet stream activity associated with the cold air outbreaks. The jet stream acts to focus extratropical cyclones to a specific region of the North  Atlantic, depending on the regional extent of the cold air outbreak, resulting in significantly higher extratropical cyclone counts for that specific region. The regions found to be experiencing higher counts of extratropical cyclones align with previously established geographical dependencies between co-occurrences of North American cold air outbreaks and wet and windy extremes over Europe. We also find that cold air outbreaks associated with an anomalously strengthened jet result in a general increase in the strength of the extratropical cyclones reaching Europe, whilst a more equatorward-displaced jet, with lower maximum speed, results in more persistent extratropical cyclones over southern Europe. 

How to cite: Leeding, R., Messori, G., and Riboldi, J.: On the Response of North Atlantic Extratropical Cyclones to North America Cold Air Outbreaks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7954, https://doi.org/10.5194/egusphere-egu23-7954, 2023.

Nowadays heat waves are a growing issue, causing detrimental effects on society, people’s health and environment in several parts of the world. Slow drivers such as spring soil moisture and sea surface temperature are known to impact the probability of occurrence of heatwaves in many areas of the globe. However, their influence remains still little understood and studied. Even fewer has been said on the cross effect and relative impact of both factors. 

Our work aims at analysing and comparing the effects of spring soil moisture deficit in Europe and sea surface temperature decadal variability in the North Atlantic (AMV) on the occurrence of typical and more extreme European heat waves. To do that, we use the outputs from three climate models, namely IPSL-CM6A-LR, EC-Earth3 and CNRM-CM6-1, in which North Atlantic sea surface temperatures are nudged to the observed AMV anomalies.

At a methodological level, previous studies mainly focused on typical heat waves. Our work goes beyond that and proposes a new methodology to study events with larger return times. By introducing return time maps we can study rare heatwaves with return time from 10 to 50 years. We find that the temperature and duration of typical and extreme heatwaves are influenced by the AMV and soil moisture. In general, the changes induced by typical AMV or soil moisture anomalies are of comparable amplitude. In many areas of Europe, the influence of AMV and soil moisture over duration or temperature of extreme heatwaves increases when the return time is longer and is statistically significant even for return times of 50 years. In general, the three models give consistent results. 

With positive AMV phase or low soil moisture, the temperature and duration of extreme heatwaves are changed according to regional patterns. As might be expected, positive AMV phase or low soil moisture often induce hotter and longer typical and extreme heatwaves. However, counter-intuitively, they also induce cooler and shorter heatwaves over part of Northern-Eastern Europe. For more extreme events, the impact of the AMV and soil moisture increases, according to rather similar regional patterns. However, the regions with decreased temperature or duration impact extend in size.

In this work, we have improved the study of extreme heat waves and better understood their slow drivers. Studying those drivers is important to enhance heat wave predictability. To move further in this direction, we need to improve the statistics of the events. In this context, developing and using new tools such as rare event simulations might be the right path to follow.

How to cite: Mascolo, V., Le Priol, C., d'Andrea, F., and Bouchet, F.: Influence of the Atlantic Multidecadal Variability and of Soil Moisture on Extreme Heatwaves in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7124, https://doi.org/10.5194/egusphere-egu23-7124, 2023.

Loss and damage (L&D) has been on the international agenda for over 20 years, and recently gained significant headway at UNFCCC COP27. L&D has been a controversial aspect of the international climate negotiations. This is largely due to L&D being connected to responsibility and compensation for the impacts of climate change on vulnerable communities. Researchers and practitioners are beginning to ask how they can help with L&D while many remain unsure about what this may mean.

Loss and Damage (L&D) is associated with the adverse effects of climate change, including the effects that are related to extreme weather events, such as intense typhons, but also occur in slow events, such as at sea level rise. The paper sets out to synthesise three specific challenges to L&D: lack of a coherent definition of L&D, gaps in measuring disproportionate effects of loss and damage on people, including the non economic consequences of L&D events, who it affects, how and why, and on what scale, and finally, absence of coherent understanding of climate governance instruments to influence L&D in ways that do not undermine existing adaptation and development efforts.

How to cite: Boyd, E.: Recasting the disproportionate impacts of climate extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9492, https://doi.org/10.5194/egusphere-egu23-9492, 2023.

Persistence is an important characteristic of many complex systems in nature and of the Earth system in particular. Relating this statistical concept to physical properties of ecosystems is rather elusive, but reflects how long the system remains at a certain state before changing to a different one and is measured via the memory and dependence of values on past states [1]. Characterizing persistence in the terrestrial biosphere is very relevant to understand intrinsic properties of the system such as legacy effects of extreme climate events [2]. Such memory effects are often highly non-linear and therefore challenging to detect in observational records and poorly represented in Earth system models. This study estimates long and short-term non-linear persistence in eddy-covariance flux measurements and remote sensing products in European forests and the corresponding hydro-meteorological data. Characterizing persistence in the data allows us to make inferences on the interaction between Drought-Heat events, forest dynamics, and ecosystem resilience [3]. The comparison of in-situ and Earth Observation (EO) data allows us to infer how meaningful EO data are for monitoring complex dynamics in ecosystems.

For short-term, spatio-temporal persistence, we use echo state networks using the technique suggested in [4] as an explainable AI (XAI) technique. In this context, the persistence of the system can be estimated by the model's response when the input fades abruptly. For the characterization of long-term persistence, we introduce a novel kernel extension of the well-established Detrended Fluctuation Analysis (DFA) [5], a method widely used in atmospheric sciences [1]. The DFA method is a scaling analysis that provides a simple quantitative parameter (the scaling exponent) to represent the correlation properties of a signal and a characteristic time of the event of interest. Unlike DFA, the proposed kernel DFA method can handle non-linear time-scales interactions. 

Estimating the non-linear persistence of forests and climate data allows us to relate characteristic times, crossover points between different scaling exponents, and short-term memory parameters with the duration and intensity of the events, as well as an indicator of change in the vegetation response to hydro-climatic conditions.

[1] Salcedo-Sanz, S., et al. “Persistence in complex systems”. Physics Reports 957, 1-73, (2022).

[2] Bastos, Ana, et al. “Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity." Science advances 6.24 (2020)

[3] Scheffer, M., Carpenter, S. R., Dakos, V. & van Nes, E. H. Generic indicators of ecological resilience: inferring the chance of a critical transition. Annu. Rev. Ecol. Evol. Syst. 46, 145–167 (2015).

[4] Barredo Arrieta, A., Gil-Lopez, S., Laña, I. et al. On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification. Neural Comput & Applic 34, 10257–10277 (2022).

[5] Peng, C‐K., et al. "Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series." Chaos: an interdisciplinary journal of nonlinear science 5.1 (1995): 82-87.

How to cite: Williams, T., Mahecha, M. D., and Camps-Valls, G.: Estimating non-linear persistence for impact assessment in European forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5515, https://doi.org/10.5194/egusphere-egu23-5515, 2023.

The impacts of extreme weather on the agricultural sector are a global concern in a changing climate. In recent years, single and compound weather extremes have increased in frequency, intensity and duration and are expected to worsen in the upcoming decades. Therefore, it is necessary to have a better understanding of extreme weather-related crop yield shock to ensure food security in a growing worldwide population. In this study, we employed a logistic regression model to quantify the risk of major crop yield shocks associated with heat stress, extreme precipitation and frosts. We used reported sub-national level data from Germany and a percentile-based threshold to define yield shock. Climate extreme drivers were based on statistical thresholds over daily maximum temperature, minimum temperature and precipitation. In addition to this,  we investigated how the seasonal meteorological pre-conditions of temperature and precipitation can modulate extreme weather-related yield shock.

How to cite: Stainoh, F., Moemken, J., and Pinto, J.: A probabilistic assessment of extreme weather event impacts on crop yield in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12130, https://doi.org/10.5194/egusphere-egu23-12130, 2023.