NP1.2
Orals: Fri, 28 Apr | Room G2
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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 Apr 2023, EGU23-8224, https://doi.org/10.5194/egusphere-egu23-8224, 2023.
European windstorms experience considerable interannual variability, which makes the quantification of extreme return periods challenging. Estimating 200-year return levels is also complicated by having only ~60 years of comprehensive observational data. Such estimations of return periods are often performed using ‘catastrophe models’, which use complex calibration and tuning processes. We have developed a reliable statistical model to estimate extreme windstorm gust speed return levels from only a multi-year sample of windstorm footprints without the need for the complexities associated with catastrophe models.
We have also been able to include variations of the NAO in our estimates, allowing for the generation of NAO-dependent return levels. Positive phases of the NAO result in larger return levels across the northwest of Europe. Additionally, the NAO is shown to be especially important for modulating low return period gusts, with the most extreme gusts occurring due to further stochastic processes. Using plausible future states of the NAO we also show that return levels have the potential to increase significantly in the next 100 years to rise well above historical uncertainty levels.
How to cite: Priestley, M., Stephenson, D., and Scaife, A.: Return levels of extreme European windstorms, their dependency on the NAO, and potential future risks, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8138, https://doi.org/10.5194/egusphere-egu23-8138, 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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 (30o to 60o latitude, -50o to -10o 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 98th 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 Apr 2023, EGU23-12130, https://doi.org/10.5194/egusphere-egu23-12130, 2023.
Posters on site: Thu, 27 Apr, 16:15–18:00 | Hall X5
Despite the progress in seasonal drought forecasting, it remains challenging to identify suitable drought indices for accurately predicting the impacts of a future drought event. In this study, we identified relationships across Europe between the forecasting skill of various drought indices and the estimated drought impacts. We calculated the indices over various accumulation periods, and assessed the forecasting skill of indices computed based on various seasonal prediction systems. An evaluation was performed by computing the same indices from the ERA5 reanalysis data and comparing them across various verification metrics. We further conducted a literature review of the studies assessing the performance of the indices in terms of estimating drought impacts across Europe. We finally performed a trade-off analysis and mapped the drought indices based on their drought forecasting and drought impact estimating skills.
Overall, this analysis is a step forward to detect the most suitable drought indices for predicting drought impacts across Europe. Here, not only we present a new approach for evaluating the relationship between drought indices and impacts, we also resolve the dilemma of choosing the indices to be incorporated in the impact functions. Such scientific advancements are setting significant contributions to the emerging field of operational impact-based forecasting and operational drought early warning services.
How to cite: Shyrokaya, A., Di Baldassarre, G., Cloke, H., Messori, G., Pappenberger, F., and Pechlivanidis, I.: Drought impact-based forecasting: Trade-offs between indicators and impacts, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1555, https://doi.org/10.5194/egusphere-egu23-1555, 2023.
The vertical structure of Arctic warming is of great importance and attracts increasing attention. This study defines two types of Arctic warming events (viz., deep versus shallow) according to their temperature profiles averaged over the Barents-Kara Seas (BKS), and thereupon compares their characteristics and examines their difference in generation through thermodynamic diagnoses. The deep Arctic warming event—characterized by significant bottom-heavy warming extending from the surface into the middle-to-upper troposphere—emanates from the east of Greenland and then moves downstream towards the BKS primarily through zonal temperature advection. The peak day of deep warming event lags that of the precipitation and resultant diabatic heating over Southeast Greenland by about four days, suggesting that the middle-to-high tropospheric BKS warming is likely triggered by the enhanced upstream convection at the North Atlantic high latitudes. In contrast, the shallow warming event—manifested by warming confined within the lower troposphere—is preceded by the meridional advection of warm air from inland Eurasia. These anomalous southerlies over Eurasian lands during shallow warming events are related to the eastward extension of deepened Icelandic Low. Whereas during deep warming events, the in-situ reinforcement of Icelandic Low favors abundant moisture transport interplaying with the Southeast Greenland terrain, leading to intense precipitation and latent heat release there. Both deep and shallow warming events are accompanied by Eurasian cooling, but the corresponding cooling of deep warming event is profoundly stronger. Further, intraseasonal deep Arctic warming events could explain nearly half of the winter-mean change in warm Arctic-cold Eurasia anomaly.
How to cite: Li, J., Chen, X., Guo, Y., and Wen, Z.: Contrasting Deep and Shallow Arctic Warming Events on the Intraseasonal Time Scale in Boreal Winter, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2146, https://doi.org/10.5194/egusphere-egu23-2146, 2023.
The rainfall in July 2021 that hit West Germany, Netherlands and Belgium was of unprecedented intensity. To assess the probability of such events ocuring in a near and far future (until 2100), the regional climate model MAR has been used to make simulations at a resolution of 7,5 km. To this end, the regional climate model MAR is linked to a set of Earth System models (ESMs) with 4 IPCC SSP scenarios over a domain that includes Belgium and Luxemburg. The analysis focused on the valley of the Vesdre which in Belgium was the most impacted by flooding in terms of damage to human infrastructures.
For some specific climatic conditions, MAR simulates events of similar intensity to those of the 2021-floods over the next 5 decades. To assess the statistic significance of the results, a Peaks Over Threshold analysis (POT) has been applied to MAR outputs for precipitation events of 1,2,3,4 and 5-days. Quantiles associated with high return periods have been calculated for the historical period of simulation (1980-2010) and for the 2011-2040, 2041-2070 an 2071-2100 periods. This shows that the frequency of such events in the periods 2011-2040 and 2041-2070 is likely to increase if climatic conditions are wet enough. For global warming levels above 3 to 4 °C, conditions appear too dry for such events to occur.
How to cite: Brajkovic, J., Van De Vyver, H., Doutreloup, S., Ghilain, N., and Fettweis, X.: Simulation of future extreme rainfall events over Belgium with a focus on the Vesdre valley using the regional climate model MAR. , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6507, https://doi.org/10.5194/egusphere-egu23-6507, 2023.
Extreme event attribution quantifies the influence of climate change on a particular extreme event (EE). Understanding the extent to which climate change is responsible for particular EE is of paramount importance because of the vulnerability of society and ecosystems to these events, especially when it comes to heatwaves that have become more frequent and intense in many parts of the world in recent decades. This led the scientific community to focus its efforts on attribution analysis and the implementation of new techniques for its study. Attribution studies of temperature EE using machine learning (ML) methods are scarce in the specialized literature. Most attribution studies perform statistical comparison between the probability of occurrence of an event today with its probability in the pre-industrial past, making it possible to determine how much more likely that event is due to climate change and how much severe it could be. However, some limitations of these classical methodologies are the difficulty in understanding the links between the physical processes responsible for the occurrence of extreme events and anthropogenic forcing and the impossibility of detecting new trends associated with this forcing. The CLImate INTelligent (CLINT) project aims, among its objectives, to design ML algorithms to improve classical attribution methodologies in some of the aforementioned limitations for three european hot-spots located in Spain, Italy and Netherlands. In this framework, this work presents a preliminary attribution analysis for summer heatwaves focused in Iberian Peninsula and based on deep learning tools such as anomaly detection with autoencoders. The autoencoder is an unsupervised method that comprises two neural networks, one to encode information and the other to decode it. The autoencoder is fed with pre-industrial realizations integrated in the framework of the Coupled Model Intercomparison Project in its sixth version (CMIP6) in such a way that it allows detecting variabilities and trends that are present in the historical run and not in the pre-industrial one. In addition, the influence of climate change for a particular temperature EE could be associated with the AE anomaly for this EE.
How to cite: Zaninelli, P. G., Barriopedro-Cepero, D., Drouard, M., Garrido-Pérez, J. M., Pérez-Aracil, J., Fister, D., García-Herrera, R., Salcedo-Sanz, S., and Alvarez-Castro, M. C.: Deep learning techniques applied to an attribution study for heatwaves in the Iberian Peninsula, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6732, https://doi.org/10.5194/egusphere-egu23-6732, 2023.
Extreme events can cause severe disruption to society on many levels, and the ability to forecast these events represents a significant step towards the ability to reduce their impacts. Anomalously persistent atmospheric configurations are typically regarded to be strongly linked with temperature extremes in Europe, however, traditional methods of analysing atmospheric persistence lack a mathematically well-grounded definition. Furthermore, we are not aware of a metric which allows for quantification of instantaneous atmospheric persistence for forecasts for either an individual ensemble member or a deterministic forecast. We aim to help refine the definition of atmospheric persistence by presenting a mathematically well-grounded definition of persistence, which can potentially also be applied in a forecasting environment. We examine the link between the extremal index, an indicator for atmospheric persistence based on dynamical systems theory, and warm temperature extremes in several regions of Europe. We then consider the applicability of this technique to forecast data, in particular ECMWF extended range reforecast data, discussing its potential value as an additional forecast evaluation metric.
How to cite: Holmberg, E., Messori, G., Caballero, R., Tietsche, S., and Faranda, D.: Diagnosing atmospheric persistence for heatwaves and in extended range forecasts, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7697, https://doi.org/10.5194/egusphere-egu23-7697, 2023.
Water temperature is an important environmental variable that has impacts on the physical, chemical, and biological processes in streamflows. Extreme river water temperatures affect the spawning, development and survival of several fish species, and are considered as important indicators of the health of a river and essential variables in all habitat models. Unfortunately, river water temperature data is characterised by its limited availability: measurement sites are often scarce, and records are regularly very short when available. It is hence crucial to develop regional thermal data estimation models for ungauged and partially gauged locations. Very few studies in the literature focused on the estimation of extreme water temperatures at sites where thermal data are limited or inexistent. A Temperature-Duration-Curve (TDC) model is proposed in this work to provide real-time estimates of river water temperature at ungauged locations during extreme events. The TDCs are estimated at the ungauged locations using a Generalised Additive Model and are then used to provide continuous estimates of river water temperature at these sites based on a spatial interpolation model. The model is developed based on a data base of 126 river thermal stations from Canada. The performance of the method is compared to a simpler approach and results indicate that the developed TDC model is robust and useful in practice.
How to cite: Ouarda, T., Charron, C., and St-Hilaire, A.: A Temperature-Duration-Curve model for the real-time estimation of extreme river water temperatures at ungauged sites, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7087, https://doi.org/10.5194/egusphere-egu23-7087, 2023.
The Atlantic equatorial mode (AEM) is an interannual oceanic internal mode of variability which impacts the tropical circulation during its active phases in the boreal summer. A positive phase of the AEM is characterized by above-normal sea surface temperature anomalies in the eastern equatorial Atlantic which lead to positive rainfall anomalies over the Guinea Coast, a region located in the southern part of West Africa. The AEM appears as the leading oceanic driver of the Guinea Coast rainfall (GCR) during the monsoon season, and the AEM-GCR relation during the last century is stationary. Moreover, extreme rainfall events over the Guinea Coast are also enhanced by the AEM-positive phases. Therefore, there is a need to study how the relationship between the AEM and extreme rainfall indices would change under future global warming. The present work assesses this relationship between the AEM and the Guinea Coast extreme rainfall indices in the historical simulations performed by 24 General Circulation Models (GCMs) participating in the sixth phase of the Coupled Models Intercomparison Project (CMIP6). Results indicate that the extreme rainfall responses to the AEM under present-day climate conditions are qualitatively well reproduced by the GCMs in the 1995-2014 period, although there are substantial biases in their magnitudes. For the future changes, we consider the CMIP6 Shared Socio-economic pathway 5-8.5 (SSP5-8.5) simulations and three different periods: the near-term (2021-2040), the mid-term (2041-2060) and the long-term (2080-2099). Relative to the present-day period, our results indicate an overall gradual increase with time in the mean and variability of the different extreme indices for the Guinea Coast. However, the future influence of the AEM on the extreme rainfall indices decreases with time, which is in line with the projected decrease in the future AEM variability.
How to cite: Worou, K., Fichefet, T., and Goosse, H.: Weakened impact of the Atlantic equatorial mode of variability on the future Guinea Coast extreme rainfall indices, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12095, https://doi.org/10.5194/egusphere-egu23-12095, 2023.
Typhoon In-Fa in 2021 produced an indirect heavy precipitation event (HPE) in central China well over a thousand kilometers away from its center, as well as a direct HPE in eastern China near its eyewall, inner and outer spiral rainbands. Both indirect and direct HPEs of Typhoon In-Fa caused severe impacts on the society. However, the synoptic-scale environments and the impacts of return period estimations of these HPE events remain poorly understood. Here, we first evaluated the spatio-temporal evolution of the two HPEs indirectly and directly induced by Typhoon In-Fa, then examined the synoptic patterns during Typhoon In-Fa for both HPEs in central and eastern China, and finally analyzed how the Typhoon In-Fa-induced HPEs affected local return period estimations of precipitation extremes. Our results show that the remote HPE over central China ~2,200 km ahead of Typhoon In-Fa was a typical predecessor rain event (PRE). A low-level southeasterly jet conveyed abundant moisture from the vicinity of Typhoon In-Fa to central China. Abundant moisture experienced strong convergence and was forced ascent, which caused frontogenesis on the windward slope due to the impacts of orographic forcing, thereby the occurrence of PRE in central China. The PRE occurred beneath the equatorward entrance of the upper-level westerly jet. Meanwhile, Typhoon In-Fa and the PRE favored divergently and adiabatically driving outflow in the upper level, and thus intensified the upper-level westerly jet. In eastern China, the HPE occurred in areas situated less than 200 km from Typhoon In-Fa’s center and left of Typhoon In-Fa’s propagation. The persistent HPE was primarily due to the long duration and slow movement of Typhoon In-Fa. On the one hand, favorable thermodynamic and dynamic conditions, and abundant atmospheric moisture favored the maintenance of Typhoon In-Fa intensity. On the other hand, a saddle-shaped pressure field in the north of eastern China and peripheral weak steering flow impeded Typhoon In-Fa’s movement northward. From the perspective of hydrological impacts, indirect and direct HPEs induced by Typhoon In-Fa led to decreases in return period estimates of HPEs (especially in central China), indicating that such extreme HPEs might increase the failure risk of engineering operations. These results suggest that anomalous HPEs remotely triggered by TCs require improved early warnings, and that more attention should be paid to such HPEs when estimating the design values of hydraulic infrastructure.
How to cite: Wang, L., Gu, X., Slaster, L. J., Lai, Y., Zhang, X., Kong, D., Liu, J., and Li, J.: Indirect and direct impacts of Typhoon In-Fa (2021) on heavy precipitation in inland and coastal areas of China: Synoptic-scale environments and return period analysis, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14675, https://doi.org/10.5194/egusphere-egu23-14675, 2023.
Yangtze River Valley (YRV) locates in Southeast China, is home to about a third of the population in China. Summer extreme precipitation in Yangtze River can lead to extensive social problems and loss of lives. Understanding the characteristics of extreme precipitation and identifying the possible driving factors can increase our ability to plan for, manage and respond to related extreme events over the YRV. This study applies ERA5 data during the period of 1950~2021 to examine the possible influence of ENSO and the sea surface temperature (SST) variability over the Indian Ocean domain on the interannual variability of the extreme precipitation over the YRV. The related physical processes that link the summer Yangtze River extreme precipitation, ENSO and Indian Ocean Dipole (IOD) are investigated.
Using composites analysis and Pearson correlation method, we found that both ENSO and IOD have delayed effects on summer extreme precipitation over the YRV, warm ENSO events and positive IOD phases are in favor of increased extreme precipitation in the subsequent summers, and vice versa. The anomalous anticyclone over the western Pacific Ocean (WNPAC) is the key factor in altering the inter-annual variability of extreme precipitation over the YRV. By comparing the extreme precipitation composites with different ENSO-IOD coupling events, we found that the signals of enhanced extreme precipitation are significant when El Niño occurs with a positive phase of IOD in the previous winter. The results based on the large circulation patterns also support that IOD plays an essential role in modulating the WNPAC. Our research highlights the need for a fundamental exploration into air-sea interactions over the tropical Pacific associate to ENSO-IOD coupling modes, our understanding in learning the impacts of these modes of variability on precipitation extremes over the YRV will contribute to improve the predictability of extreme events over this region.
Keywords:
Yangtze River Valley, extreme precipitation, ENSO, IOD, western North Pacific anomalous anticyclone
How to cite: Lin, Y., Gualdi, S., and Scoccimarro, E.: Delayed Effects of ENSO and Indian Ocean Dipole on the ensuing-summer extreme precipitation over Yangtze River Valley, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15813, https://doi.org/10.5194/egusphere-egu23-15813, 2023.
We present an application of quantile generalised additive models (QGAMs) to study the rela-
tionship between spatially compounding climate extremes - namely extremes that occur (near-)
simultaneously in geographically remote regions. We take as example wintertime cold spells
in North America and co-occurring wet or windy surface weather extremes in Western Europe,
which we collectively term Pan-Atlantic compound extremes. QGAMS are largely novel in cli-
mate science applications and present three key advantages over conventional statistical models
of weather extremes:
1. they do not require a direct identification and parametrisation of the extremes themselves,
since they model all quantiles of the distributions of interest;
2. they do not require any a priori knowledge of the functional relationship between the predic-
tors and the dependent variable;
3. they make use of all information available, and not only of a small number of extreme values.
Here, we use QGAMs to both characterise the co-occurrence statistics and investigate possible
dynamical drivers of the Pan-Atlantic compound extremes. We find that recent cold spells in
North America are a useful predictor of upcoming near-surface extremes in Western Europe,
and that QGAMs can predict those extremes more accurately than conventional peak-over-
threshold models.
How to cite: Olivetti, L., Messori, G., and Jin, S.: A Quantile Generalised Additive Approach for Compound Climate Extremes: Pan-Atlantic Extremes as a Case Study, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2242, https://doi.org/10.5194/egusphere-egu23-2242, 2023.
Droughts are a major disaster in Africa, threatening livelihoods through their influence on crop yields but also by impacting and weakening ecosystems. Modeling the vegetation state can help anticipate and reduce the impact of droughts by predicting the vegetation response over time. Forecasting the state of vegetation is challenging: it depends on complex interactions between the plants and different environmental drivers, which can result in both instantaneous and time-lagged responses, as well as spatial effects. Furthermore, modeling these interactions at the fine resolution of landscape scale can only rely on remote sensing observations, as in-situ measurements are not global and weather models have a coarse grid. With the increasing availability of remote sensing data, deep learning methods are a promising avenue for these spatiotemporal tasks. Here, we introduce both a dataset and a baseline deep neural network, modeling the vegetation response to climate at landscape scale in Africa.
EarthNet2021 [1] introduced leveraging self-supervised learning for satellite imagery forecasting based on coarse-scale weather in Europe. Here, we introduce EarthNet2023 with a more narrow focus on drought impacts in Africa. It contains over 45,000 Spatio-temporal minicubes (each 1.28x1.28km) at representative locations over the whole African continent. Alongside Sentinel-2 reflectance, ERA5 weather, and topography, it also contains Sentinel-1 backscatter, soil properties, and a long-term Normalized Difference Vegetation Index (NDVI) climatology based on Landsat. The latter allows evaluating models on vegetation anomalies, thereby including modeling of drought impacts. EarthNet2023 is intended as an open benchmark challenge, allowing multiple research groups to develop their approaches to drought impact modeling in Africa.
As a baseline for EarthNet2023, we train a Convolutional Long Short-Term Memory (ConvLSTM) deep learning model. Previous work has shown it is suitable for spatiotemporal satellite imagery forecasting [2, 3, 4]. The ConvLSTM baseline captures the seasonal evolution of NDVI over a wide range of vegetation types. General spatial patterns are well-captured as well as a first indication of skill during weather extremes is seen, although the accuracy of the predictions is inconsistent, and the confidence in the model is therefore too low. This suggests, with further development, deep learning approaches are promising for modeling vegetation evolution in Africa, potentially even up to the degree to support anticipatory action with drought impact modeling.
[1] Requena-Mesa, C., Benson, V., Reichstein, M., Runge, J., & Denzler, J. (2021). EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task. In CVPR 2021 (pp. 1132-1142).
[2] Diaconu, C. A., Saha, S., Günnemann, S., & Zhu, X. X. (2022). Understanding the Role of Weather Data for Earth Surface Forecasting Using a ConvLSTM-Based Model. In CVPR 2022 (pp. 1362-1371).
[3] Kladny, K. R. W., Milanta, M., Mraz, O., Hufkens, K., & Stocker, B. D. (2022). Deep learning for satellite image forecasting of vegetation greenness. bioRxiv.
[4] Robin, C., Requena-Mesa, C., Benson, V., Alonso, L., Poehls, J., Carvalhais, N., & Reichstein, M. (2022). Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs. In Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022.
How to cite: Robin, C., Requena-Mesa, C., Benson, V., Alonso, L., Poehls, J., Carvalhais, N., and Reichstein, M.: Modeling vegetation response to climate in Africa at fine resolution: EarthNet2023, a deep learning dataset and challenge., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9123, https://doi.org/10.5194/egusphere-egu23-9123, 2023.
During the summer of 2021, the North American Pacific Northwest was affected by an extreme heatwave that broke previous temperature records by several degrees and lasted almost two months after the initial peak. The event caused severe impacts on human life and ecosystems, and was associated with the superposition of concurrent extreme drivers, whose effects were amplified by climate change. We evaluate whether this record-breaking heatwave could be anticipated prior to 2021, and how climate change affects North American Pacific Northwest worst case heatwave scenarios. We use a stochastic weather generator with empirical importance sampling. The generator simulates temperature sequences with realistic statistics using circulation analogues, chosen with an importance sampling based on the daily maximum temperature over the region that recorded the most extreme impacts. We show how some of the large-scale drivers of the event can be obtained form the circulation analogues, even if such information is not directly given to the stochastic weather generator.
How to cite: Pons, F., Yiou, P., and Jezequel, A.: Simulating the West Pacific Heatwave of 2021 with Analog Importance Samping, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3300, https://doi.org/10.5194/egusphere-egu23-3300, 2023.
Understanding the physical mechanisms leading to extremes of quantities of interest in dynamical systems remains a challenge. Under mild hypothesis, the application of the theory of large deviations to dynamical systems predicts the convergence of trajectories leading to extremes towards a typical, i.e. most probable, one called the instanton. In this paper, we use a 2000 years long simulation of the IPSL-CM6A-LR model under a stationary pre-industrial climate to test this prediction. We investigate the convergence properties of trajectories leading to extreme temperatures at four locations in Europe for several variables. We show the convergence of trajectories for most physical variables, with some geographical and temporal discrepancies. Our results are coherent with the most probable path prediction and suggest that the instanton dynamics leading to extremes is a relevant feature of climate models.
How to cite: Noyelle, R., Yiou, P., and Faranda, D.: Investigating the typicality of the dynamics leading to extreme temperatures in the IPSL-CM6A-LR model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2644, https://doi.org/10.5194/egusphere-egu23-2644, 2023.
The spring-to-summer seasons in recent years were characterized by co-occurring hot, dry, and wet extremes around the globe, leading to questions about the contribution of human-induced global warming to the changing likelihoods of such extreme years. Here we investigate recent trends in the fraction of global (and regional) land-area that is affected by hot days, wet days and dry months. Observed trends are put into context of Earth System Model (ESM) ensemble simulations accounting for present day and pre-industrial climate conditions in a detection and attribution setting. The analysis is applied to the global land area as well as to the regions defined in the sixth IPCC assessment report. Results show that on a global scale as well as on a regional level, observed trends of co-occurring hot, dry and wet events cannot be explained by internal climate variability, but are only captured by model simulations that account for anthropogenic changes in the composition of the atmosphere. Thus, the results show that recent global trends in spatially co-occuring hot and dry extremes are very likely linked to anthropogenic climate change.
How to cite: Biess, B., Gudmundsson, L., and Seneviratne, S. I.: Assessing recent trends in globally co-occurring hot, dry and wet events under climate change, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14879, https://doi.org/10.5194/egusphere-egu23-14879, 2023.
In recent years the understanding of atmospheric blocking has changed from solely a dry phenomena to one that includes moist processes. The primary source of that moisture, the ocean, has, until recently, been neglected as a driver of this basin scale structure. Here, the connection between atmospheric blocking over the North Atlantic and the diabatic influence of the Gulf Stream was investigated using potential vorticity diagnostics. In line with previous research, the reliance atmospheric blocking has on latent heat fluxes over the Gulf Stream and its extension, for induction and maintenance, was shown to be significant. It was shown that not only is it more likely for a North Atlantic block to occur after significant surface latent heat fluxes over the Gulf Stream and its extension, but the resulting block is likely to be anchored on the western flank of the Atlantic, making it more stationary and hence, more impactful. Additionally, blocks that have a longer duration were highly associated with surface latent heat fluxes over the western boundary current, while shorter blocks were not, indicating a positive feedback from the oceanic mesoscale phenomena onto this basin scale structure. Finally, the frequency of the block was seen to correspond to the amount of surplus heat content in the western boundary currents prior to the blocking event which, in the North Atlantic, had leading order dependence on the heat transport via the Gulf Stream.
How to cite: Mathews, J.: Oceanic Maintenance of Atmospheric Blocking, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9945, https://doi.org/10.5194/egusphere-egu23-9945, 2023.
Low-frequency variability (LFV) encompasses atmospheric and climate processes on time scales from a few weeks to decades. This includes atmospheric blockings, heat waves, cold spells, and at longer time scales long-term oscillations like the MJO, the NAO, ENSO….. Better understanding of LFV, could contribute to improved long term forecasts. Identifying and evaluating LFVs in GCMs is computationally expensive, so in this study an idealised low order coupled model is used. They are climate models ‘stripped to the bone’, which links theoretical understanding to the complexity of more realistic models, made by key ingredients and approximations; which hence helps us to study a particular phenomenon by tweaking the parameters affecting them with less computational cost.
The Quasi Geostrophic land atmosphere coupled model is a python implementation of mid-latitude atmospheric model with two layer quasi geostrophic channel atmosphere on beta -plane coupled to a simple land portion. The system exhibits blocking conditions at different time scales depending on the incoming solar radiation and also experiences transitions from blocking to zonal flow after applying different sets of parameters to the model. The predictability and persistence of these regimes is investigated by calculating the local lyapunov exponents at the specified transition points and around them. The findings are discussed in the perspective of the current literature on the predictability of blocking.
How to cite: K Xavier, A., Demaeyer, J., and Vannitsem, S.: Predictability of blocking and zonal flow regimes in a reduced-order land atmosphere coupled model , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8244, https://doi.org/10.5194/egusphere-egu23-8244, 2023.
Posters virtual: Thu, 27 Apr, 16:15–18:00 | vHall ESSI/GI/NP
Regional intensification of precipitation extremes and the emergence of humid heat stress conducive to periling vulnerable populations suggest the need for further nation-specific risk assessments. Here, we conduct the first analysis of present and projected population exposure to extreme wet-bulb temperature (Tw) values in Turkey and concurrently use the generalized extreme value (GEV) theory to model extreme precipitation based on multiple intensity, duration, and frequency metrics. Using simulations dynamically downscaled to 0.11-degree resolution via the COSMO-CLM model, we provide a nationwide picture of the trends in these metrics and derive the number of people exposed to Tw extremes based on the population estimates in the Shared Socioeconomic Pathways (SSPs) under the high-emission RCP 8.5 scenario. As part of the GEV analysis, our main goal is to show how precipitation extremes in Turkey evolve and transform due to the changing climate not only in stationary but also in non-stationary climate settings. Our results convey a detailed understanding of the potentially dangerous conditions across climatologically different regions of Turkey and are relevant for decision-makers.
How to cite: Donmez, B., Donmez, K., Yuruk Sonuc, C., and Unal, Y.: Present and projected humid heat exposure and precipitation extremes in Turkey, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12059, https://doi.org/10.5194/egusphere-egu23-12059, 2023.
Several Medicanes, which have been previously analyzed in the literature, have been studied using ERA-5 reanalyses to identify the environment in which they develop and possibly distinguish tropical-like cyclones from warm seclusions. Initially, the cyclone phase space was analyzed to identify changes in the environmental characteristics. Subsequently, the temporal evolution of several parameters was considered, including sea surface fluxes, CAPE, coupling index, potential intensity, baroclinicity.
Although the results are not consistent for all cyclones, some general characteristics can be identified: cyclones develop in areas of moderate-to-high baroclinicity associated with intense jet streams, while in the mature stage the environment becomes less baroclinic. A general reduction in the horizontal extent of the cyclone can be observed as the cyclones begin to show a shallow warm core. In this phase a progressive reduction of the CAPE can be observed in proximity of the cyclone center. Finally, the wind speed appears strongly underestimated compared to the observations, raising some concerns about the applicability of ERA-5 for the detection of wind features.
How to cite: Gutiérrez-Fernández, J., Miglietta, M. M., González-Alemán, J. J., and Gaertner, M. Á.: Characteristics of Medicanes using ERA-5 reanalysis, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-483, https://doi.org/10.5194/egusphere-egu23-483, 2023.
