The unprecedented changes in Earth's climate are reshaping atmospheric dynamics on a global scale, with profound implications for various sectors, including aviation. One of the critical facets of this transformation lies in the alterations to atmospheric circulation patterns and the concurrent modifications in turbulence characteristics.

We investigate the intricate interplay between climate change, atmospheric circulation patterns, and turbulence modifications over Europe, with a specific focus on their implications for commercial flight operations. Drawing upon ERA5 reanalysis data at 200 hPa pressure level we explore the evolving climatic conditions shaping the European airspace. By jointly looking at the cube-rooted eddy dissipation rate, vorticity, and horizontal divergence we evidence increasing trends of anomalously turbulent conditions over UK and Northern Europe. Otherwise, decreasingly frequency anomalies are associated with light-turbulent conditions over Central and Mediterranean Europe. Overall, increasing trends also correspond to increased severity in terms of turbulence strength levels, with a clear increase in moderate-to-severe turbulence episodes, mainly associated with both converging and diverging atmospheric ridges.

The study highlights the region's vulnerability to significant changes in atmospheric pattern dynamics, emphasizing the potential increase in turbulence-related episodes and severity, impacting aviation safety, fuel efficiency, and passenger comfort. Our analysis aims to provide valuable insights for aviation stakeholders, policymakers, and researchers, contributing to the development of adaptive strategies and operational guidelines.

How to cite: Alberti, T.: Atmospheric circulation changes and turbulence modifications over Europe: implications for commercial flight operations in a changing climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18608, https://doi.org/10.5194/egusphere-egu24-18608, 2024.

Extreme cold winters have been projected to decrease in the future, although their impacts on society are still significant. The goal of this study is to assess whether climate change affects the atmospheric mechanisms leading to cold winters.

We first explore the dynamics of 15-day winter cold spells in France, as observed since 1950. We find that the most extreme events tend to have the same atmospheric circulation pattern, consisting of an eastward-shifted NAO- dipole. We calculate an atmospheric index that characterizes this dipole. Then, using a stochastic weather generator with importance sampling, we show that this is a sufficient condition to trigger extremely cold temperatures in France, and that it performs better than a classical North Atlantic Oscillation index. This suggests that a dipole of atmospheric circulation is a necessary and sufficient condition leading to extreme cold spells in France.

We use this atmospheric index to select the CMIP6 models that best reproduce the identified dynamics leading to extreme cold spells of 15 days. Using a stochastic weather generator with importance sampling, we run simulations of worst-case winter cold spells from 2015 to 2100, following different emission trajectories for the selected models. 15-day winter cold spells in France will reach less extreme temperatures at the end of the century, especially in the case of a high-emission scenario (SSP5-8.5). However, the simulated ensembles of extreme cold spells do not show the same warming trend as the mean temperature, and very extreme cold spells are still possible in the near future. The atmospheric circulation prevailing during these events is analyzed and compared with the circulation observed during previous events.

How to cite: Cadiou, C. and Yiou, P.: Assessing changes in the intensity and dynamics of extreme cold spells in France from CMIP6, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-622, https://doi.org/10.5194/egusphere-egu24-622, 2024.

The European summer of 2022 has been widely recognized as the warmest since mid-19^th century. Our updated analyses of instrumental and reconstructed temperature series since 1500 indicate that the European summer (June-to-August) of 2022 was the warmest on record, exceeding the previous hottest summer of 2021 by a large margin. In fact, the past three summers of 2021–2023 have been among the hottest ones of the last five centuries.

By applying a heatwave (HW) detection algorithm to reanalysis data, we identify three large European HW events that affected ample regions of the continent in mid-June, mid-July and August/early September 2022. These episodes were triggered by high-pressure systems with noticeable differences in their characteristics. Additional analyses confirm that high-latitude blocks were largely responsible for the August 2022 HW, whereas subtropical ridges dominated during the June and July 2022 HWs. These HW events were also accompanied by dry soils and warm Sea Surface Temperatures (SSTs) over the Mediterranean. Indeed, summer 2022 displayed the largest marine heatwave activity of the 1982–2023 period due to an unusually high frequency of long-lasting and intense events, particularly over western Mediterranean.

Taking the June 2022 HW over Iberia as an example, we address the role of dynamical (atmospheric circulation) and thermodynamical (regional soil moisture and western Mediterranean SSTs) drivers in the severity of the event. Flow analogues of the June 2022 heatwave are used to reconstruct the expected temperatures under different combinations of these thermodynamical drivers and assess their separate and combined influences on the intensity of the event. Results show a measurable intensification of the heatwave event (of ~1 °C) by both dry land and warm sea conditions. Although these two drivers are significantly correlated, southwestern European HWs are aggravated if dry soils concur with warm SSTs over western Mediterranean. The magnitude of the Mediterranean SST influence could depend on the soil moisture state, being larger for dry than wet conditions, as well as on the atmospheric circulation.

R.M.T., A.R., S.P. and A.T.M. thank Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020). A.R. and R.M.T. thank also FCT (https://doi.org/10.54499/2022.09185.PTDC, (http://doi.org/10.54499/JPIOCEANS/0001/2019). A.R. was supported by FCT through https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006.

How to cite: Trigo, R., Barriopedro, D., Garrido-Pérez, J., Simon, A., Plecha, S., Teles-Machado, A., Russo, A., and García-Herrera, R.: The outstanding European and Mediterranean heatwave activity during summer 2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2460, https://doi.org/10.5194/egusphere-egu24-2460, 2024.

Multiple recent record shattering weather events raise questions about the adequacy of climate models to effectively predict and prepare for unprecedented climate impacts on human life, infrastructure, and ecosystems.

While it appears that  the record breaking global mean temperatures of 2023 are still within the models spread, we show that climate models fail to reproduce the observed emergence of several heatwave hotspots on impact relevant temporal and spatial scales. We identify several heatwave hotspots worldwide in which the warming of the tail of the distribution outpaces the mean warming. In these regions we find that the observed trends by far exceed what is projected by the multi-model mean of state-of-the-art model frameworks. In multiple regions, observed trends are outside of the model spread and persist irrespective of resolution or model design: We investigate models from HighResMip project (49 members) and CAM6 and the ECHAM5 SST forced large ensemble (60 members) and find that biases persist throughout the vast number of climate model experiments with different architectures (fully coupled and forced with observed SSTs), resolutions (25 km – 250km), and large numbers of realizations (Kornhuber et al. submitted).

We discuss potential reasons for the models’ shortcomings. Recently we found that models tend to underestimate the surface anomalies of specific deep atmospheric circulation patterns, which were fundamental to some of the most extreme heat waves in the past (Kornhuber et al. Nat. Comms. 2023). Europe is identified as a heatwave hotspot, driven in part by trends in atmosphere dynamics (Rousi, Kornhuber et al. Nat. Comms. 2022), which models tend to understimate (Vautard et al. Nat. Comms 2023). In addition, non-linear interactions between high pressure, soil moisture deficiencies and temperature have been shown to be pivotal for the record shattering Pacific Northwest heatwave, amplifying its magnitude by 40% (Bartusek, Kornhuber, Ting, Nat. Climate Change 2022). Such dependence structures might not be accurately represented in most models. We conclude that while climate models have been useful and on point regarding their global mean temperature response to increased greenhouse gas concentrations, trends in the far-end tails of the temperature distribution are not well captured, missing emerging high-risk hotspots. This highlights the need to better understand and model the drivers of extreme heat and to rapidly mitigate greenhouse gas emissions to avoid further harm from climate surprises.  

How to cite: Kornhuber, K., Bartusek, S., Seager, R., and Ting, M.: Global emergence of regional heatwave hotspots outpaces climate model projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3164, https://doi.org/10.5194/egusphere-egu24-3164, 2024.

Machine Learning (ML) encompasses various techniques and algorithms that have proven highly effective in addressing complex climate science tasks. In particular, using ML to detect and forecast extreme events has gained much attention recently. Considering the vast volume of spatial and temporal data available, the employment of data-driven methodologies becomes indispensable for effectively uncovering potential drivers of these events. This study arises with the ambition of proposing a comprehensive and general framework that provides insights about interactions between heatwaves and potential physical drivers across multiple spatio-temporal scales. A novel feature-selection methodology is presented to advance the detection of heatwaves. It is based on a two-step procedure. In the first stage, a non-supervised task is developed for spatially clustering the different variables. Thus, there is a reduced initial pool of driver candidates. In the second stage, a wrapper methodology is applied to determine which time periods are representative for each of the clusters in the occurrence of heatwaves. This algorithm discerns which clusters are selected as drivers and which are discarded, along with the time period, relative to the heatwave occurrence, during which each cluster should be investigated. Thus, the feature selection is developed based on the spatio-temporal distribution of the different variable clusters.

Experiments have been developed for detecting heatwaves in the Lake Como region, a region of key agricultural activity in Northern Italy. A  wide range of ocean and atmospheric variables taken from ERA5 are used (e.g. sea ice concentration, precipitation). The framework allows the identification of the time lag for each variable from short-term to seasonal time scales (up to 180 days). Results have spotted drivers on the subseasonal to seasonal timescale. Important drivers have been identified for short-term periods (less than one week). Local variables are shown to be of much significance for these periods. The method allows for identifying the strong influence of local variables whilst identifying correlations between the heatwave occurrence and different variables and spatial locations. The importance stages of the variable candidates can be established by running the model while removing the most critical variables.

How to cite: Peláez-Rodríguez, C., Pérez-Aracil, J., McAdam, R., Squintu, A., Scoccimarro, E., and Salcedo-Sanz, S.: A Spatio-Temporal Optimization- Based Feature Selection Framework for detecting drivers of heatwaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10153, https://doi.org/10.5194/egusphere-egu24-10153, 2024.

Extreme climate events have major impacts on human societies and ecosystems. The most detrimental events are often extremely rare, with return time of centuries or even millennia. Studying these events in the context of climate change is crucial to help adaptation efforts globally. Yet the study of these extremely rare events is extremely challenging due to the lack of data. Indeed, such events have likely not been observed in the instrumental period. An alternative is to use a global climate model to simulate these extremely rare events. However, this comes at a huge computational cost : gathering good statistics on centennial events would require to run a few thousand years of simulation. 

Rare event algorithms have recently been introduced in the field of climate science to tackle this difficulty [1]. By concentrating the computational effort on the trajectories most susceptible to lead to the extreme event of interest, they allow for the sampling of extremely rare events at a much lower computational cost than standard simulations. 

In this study, we run a rare event algorithm to sample extreme heatwave seasons in a heatwave hotspot of South Asia, using the intermediate complexity model Plasim. We compare the outcome of the algorithm against an extremely long – 8000 years – control run. This comparison allows us to demonstrate that the algorithm not only estimates return times with high precision (as shown in previous work), but also exhibits high precision in the estimation of composite statistics: composite maps conditioned on centennial heatwave seasons estimated from the algorithm, are in very good agreement with the ones from the 8000-year long control simulation. Our results suggest that extreme heatwave seasons in the studied region are associated with a quasi-stationary atmospheric wave-pattern stretching from the North Atlantic towards South Asia. 

We also show that the algorithm correctly estimates the intensity-duration-frequency statistics of subseasonal heatwaves occuring within centennial heatwave seasons. Thus rare event algorithms could, for instance, be combined with seasonal forecasts to provide information regarding expected number of heatwave days and the distribution of the duration and intensity in an extreme heatwave season, which could be useful for adaptation planning. 

References

[1] F. Ragone, J. Wouters, and F. Bouchet, “Computation of extreme heat waves in climate models using a large deviation algorithm,” Proc Natl Acad Sci USA, vol. 115, pp. 24–29, Jan. 2018. 

How to cite: Le Priol, C., Monteiro, J. M., and Bouchet, F.: Study of extreme heatwave seasons in South Asia using rare event simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10586, https://doi.org/10.5194/egusphere-egu24-10586, 2024.

Heatwaves heavily affect European public health, society and economy. A full understanding of the drivers behind the occurrence and intensity of heatwaves (HW) is one of the priorities of H2020 CLimate INTelligence (CLINT) project. Particular attention is given to the detection and attribution of HWs in future climate projections. However, it is important to assess the capability of climate models to thoroughly describe relationships between the drivers and the occurrence and intensity of HWs. For this reason, a feature selection framework, based on the Coral Reef Optimization (Salcedo-Sanz et al., 2014) has been developed. This has been applied to ERA5 summer data, using as a target the Lake Como HW occurrence and, as candidate predictors, time series of weather variables calculated on clustered areas on European and global scales. The same algorithm has been applied to historical climate simulations included in CMIP6. The comparison of the results of these two steps has first focused on the similarities in maximum temperature and HW trends in the target region of Lake Como. Then, the selected drivers in each historical climate simulation have been evaluated, using ERA5 results as benchmark. Thanks to this, the models that better resemble the statistical properties and teleconnections described by the reanalysis have been identified. This set of models will be considered for the detection and attribution of heatwaves in future climate projections under different emission scenarios. In this upcoming phase the goal will be to analyse changes in the relationship between the drivers and HW occurrence and intensity, giving an insight about possible future evolutions in heatwaves frequency and magnitude.

How to cite: Squintu, A. A., McAdam, R., Peláez Rodríguez, C., Pérez Aracil, J., Alvarez Castro, C., and Scoccimarro, E.: Drivers of heatwaves in CMIP6 models: an evaluation on historical simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11807, https://doi.org/10.5194/egusphere-egu24-11807, 2024.

During the last years, the statistical analysis of compound extremes has gained increasing interest among the scientific community due to the multiple threats presented by such events to society, economy, and the environment. In many situations, the statistics of compound extremes is based on bivariate extreme value theory and measures provided by this framework. Such choice of statistical methodology may however not properly address two relevant aspects: the non-zero duration of such events (which can be rather persistent, e.g. in the case of droughts or heatwaves, which heavily violates the independence assumption of classical extreme value theory) and the fact that not all events of practical revelance can be described as cases falling into the tails of the distribution of some observable of interest.

A general framework addressing the non-extremeness aspect is provided by event coincidence analysis (ECA), which quantifies the empirical frequency of co-occurring events of arbitrary types and allows ist comparison with the values for certain random null models like independent Poisson processes with prescribed event rates. While classical ECA is based on temporal point processes and hence may be criticized for not capturing the statistical characteristics of persistent events very well, I will present a new methodological variant, interval coverage analysis (InCA), as an alternative for specifically addressing co-occurrences of persistent events. In the limit of vanishing event durations, the new interval coverage rates of InCA are identical to the event coincidence rates provided by ECA. By allowing for mutual time shifts between the different types of events under study as well as a temporal tolerance regarding their respective timing, fixed and even distributed time lags can be taken into consideration.

This presentation introduces and compares the basic methodological concepts behind both ECA and InCA (including their extension to studying multivariate and conditional dependency), and demonstrates an example of their respective application in geoscientific contexts. Specifically, the spatial patterns of dependency between the timing of heatwaves and large-scale circulation anomalies of the atmospheric jet stream in the Northern hemisphere is studied. The corresponding analysis reveals specific regions with elevated likelihood of heatwaves along with the emergence of a split (double-banded) jet stream, while the emergence of heatwaves is suppressed at the same time in other regions. The obtained results may thus guide further targeted research regarding the specific mechanisms leading to this regional differentiation in heatwave frequency.

How to cite: Donner, R. V.: Studying statistical dependency among short and persistent events – recent developments and application to mid-latitude circulation anomalies associated with heatwaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14476, https://doi.org/10.5194/egusphere-egu24-14476, 2024.

How to cite: Mathews, J., Czaja, A., Vitart, F., and Roberts, C.: The Impact of Gulf Stream Moisture Flux Suppression on Atmospheric Blocks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3633, https://doi.org/10.5194/egusphere-egu24-3633, 2024.

Variabilities in the jet streams have a significant influence on our weather and climate, and could potentially increase the likelihood of a range of extreme weather events. The winter of 2009/2010 witnessed an unusual equatorward displacement of the Atlantic jet and its subsequent convergence with the African jet, leading to the emergence of a persistent zonally oriented merged jet. At the same time, intense and prolonged negative phase of the North Atlantic Oscillation (NAO) and unusually cold and extreme weather conditions were reported over the Northern Hemisphere. Such a merging was only observed to occur for a whole winter during the winters of 1968-69 and 1969-70. Preliminary results indicate that such persistent winter merged jets could be more frequent in a future global warming scenario and thus it is important to understand this dynamical regime transition of Atlantic jet and its effects on the weather patterns. In this study, we explore the extreme weather distribution over the northern hemisphere during such winter merged jets and its relation to NAO and ENSO. We show that merged jet winter months have a signature weather pattern distribution that is different from the negative NAO phase. We see a decrease in the surface eddy kinetic energy over the midlatitude during such winter leading to an equatorward shift in storm tracks over the Atlantic region and larger stormtrack density over the western Greenland which could potentially lead to the observed distribution of weather patterns. On comparing the surface temperature anomaly composites between the winters of strong negative NAO, EL Nino, and merged jet months we see that the merged jet winters have a significant persistent temperature distribution signature over the tropics and the Arctics. Similar analysis over the north hemisphere for surface wind, precipitation, and snowfall anomalies also shows a preferred persistent distribution over certain regions during the merged jet-state winters.

How to cite: Suresan, S., Harnik, N., and Caballero, R.: Persistent winter merged jets over the Atlantic and extreme weather anomalies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13496, https://doi.org/10.5194/egusphere-egu24-13496, 2024.

Intraseasonal variability of extreme rainfall events (EREs) during the South Asian Summer Monsoon season is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), a convective system of organised heavy rainfall that moves periodically from the Indian Ocean to the Western Pacific over the subcontinent. The BSISO, in turn, is typically characterised using indices obtained from the leading components of an Empirical Orthogonal Analysis (EOF) of outgoing longwave radiation (OLR) and lower and upper troposhere wind data from the region [1] ). A primary motivation for using OLR and wind data is that the EOF-analysis is not well suited for heavy-tailed data such as precipitation. 

Here, we propose to estimate climate indices directly from EREs in the South Asian Summer Monsoon by applying the Hidden Climate Index (HCI) - framework introduced by Renard et al. (2022) [2]. The method is designed to work with binary, event-like data and utilizes a Bayesian hierarchical model incorporating spatial Gaussian process priors, to capture spatial and temporal interdependencies by sampling from a Bernoulli distribution.

Using the HCI framework, we estimate latent variables that underlie the ERE dynamics in the South Asian Monsoon domain, and show that these are related to large-scale modes of climate variability., We demonstrate that the ERE-based HCIs correlated well to the BSISO and in addition, we find relationships between the observed large-scale spatial ERE patterns  to the El Nino Southern Oscillation and the Silk Road Pattern.

[1] Kikuchi, K., Wang, B. & Kajikawa, Y. Bimodal (2012). Representation of the tropical intraseasonal oscillation. Clim. Dyn. 38, 1989–2000.

[2] Renard, B., Thyer, M., McInerney, D., Kavetski, D., Leonard, M., & Westra, S. (2022). A Hidden Climate Indices Modeling Framework for Multivariable Space-Time Data. Water Resources Research, 58

How to cite: Früh, D., Strnad, F., and Goswami, B.: Climate Indices of Extreme Rainfall in the South Asian Monsoon Domain using a Bayesian Hierarchical Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17701, https://doi.org/10.5194/egusphere-egu24-17701, 2024.

Windstorms are extreme midlatitude cyclones and one of the major natural hazards that cause damage and losses in Europe. While the processes involved in their genesis and intensification are generally well understood, there are still considerable uncertainties in the estimation of associated impacts like widespread wind damage and flooding. The compounding characteristics of the events further enhances the complexity of this task. This is even more true for the impact forecasting of windstorms on weather and sub-seasonal time scales. Additionally, there are large uncertainties on how windstorms and their impacts will change in a warmer climate, particularly regarding the role played diabatic processes in a warmer atmosphere. This study presents examples of recent developments regarding windstorms and discusses some new avenues for interdisciplinary research towards bridging the gap between fundamental research and practical applications.

How to cite: Pinto, J. G.: European Windstorms: bridging the gap between fundamental research and practical applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12471, https://doi.org/10.5194/egusphere-egu24-12471, 2024.

Recent years were characterized by spatially co-occurring hot, wet or dry years around the globe. Spatially compounding extreme events can strongly amplify societal impacts as economic supply chains are increasingly interlinked, highlighting the increasing importance of advancing our knowledge of the effects of human-induced climate change on such events. We assess the occurrence of spatially compounding hot, wet and dry years under different future warming levels of 1.5°C , 2°C, 3°C and higher levels of global warming. We focus our analysis on the top-producing agricultural regions that have historically provided the global food systems with large quantities of wheat, maize, soybean or rice. The occurrence of spatially compounding events and area affected in future climates is determined using Earth System Model simulations from the 6th Phase of the Coupled Model Intercomparison Project (CMIP6). The simulations project a strong increase in the global land area that is concurrently affected by hot, wet and dry extremes under continued global warming. On regional scales, the world’s breadbasket regions are particularly affected by strong increases in the simultaneous occurrence of hot, wet or dry extremes under continued global warming. The spatial extent of agricultural land potentially threatened by climate extremes will increase drastically if global mean temperatures shift from +1.5 °C to +2.0 °C, and will be further amplified with every tenth of degree of warming. This highlights that ambitious climate action needs to be taken in order to limit global warming if we want to keep the global agricultural land in a safe climatic space.

How to cite: Biess, B., Gudmundsson, L., Windisch, M., and Seneviratne, S. I.: Future changes in spatially compounding hot, wet or dry events and their implications for the world's breadbasket regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5796, https://doi.org/10.5194/egusphere-egu24-5796, 2024.

Over the last decade, researchers have devoted considerable efforts to exploring the feasibility of attaining convection-permitting scales in regional climate model projections. The primary objective has been to comprehend the effects of global warming on climate extremes. Initially conducted as isolated model experiments, these investigations have evolved into coordinated ensemble experiments operating at convection-permitting scales across diverse continents. The implementation of such coordinated ensembles has provided a platform for evaluating model reliability at high resolutions and conducting signal-to-noise analyses on identified climate change signals.

Nevertheless, the constraints imposed by limited computational resources have confined these experiments to smaller domains compared to the conventional continental scale employed in dynamical downscaling, as seen in initiatives like the CORDEX community and time slice mode.

Despite these inherent limitations, these experiments have successfully showcased the models' capacity to simulate present-day climate conditions. Notably, improvements in various statistical metrics at sub-daily scales have been observed in contrast to parametrized models. Furthermore, the ensemble approach has contributed to reducing uncertainties in assessing both present-day climate and future projections, particularly in terms of frequency, intensity, and extreme precipitation at the hourly time scale.

The explicit representation of convection has additionally enabled the study of convective storm system evolution, allowing for an assessment of large-scale feature changes and related physical mechanism driving observed extreme precipitation variations.

Preliminary attempts have also been carried on for building convection permitting climate emulator to reduce the computational HPC demand required by the dynamical downscaling models.

These collective findings underscore the necessity of advancing to the next phase of coordination, involving the establishment of multiple coordinated platforms spanning different continents. These platforms will serve as collaborative spaces for discussing model enhancements, aiming to refine existing models by incorporating a more precise representation of Earth system components and defining domains that maximize the number of models capable of generating convection-permitting climate projections.

How to cite: Coppola, E.: Understanding global warming impact on climate extremes by mean of Coordinated Ensemble Experiments of Convection-Permitting Climate Projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16130, https://doi.org/10.5194/egusphere-egu24-16130, 2024.

Extreme temperature events can cause severe disruptions to society from negative health consequences to infrastructure damage. Early warning systems are a key element in the mitigation of such impacts, with literature highlighting the potential societal benefit of information from sub-seasonal forecasts. We investigate the relationship between the persistence of an atmospheric state and the practical predictability of surface temperatures focusing on medium to extended range time scales. Persistence is assessed objectively leveraging techniques from dynamical systems theory whilst practical predictability is defined in terms of the forecast error in surface temperature. Atmospheric persistence provides potential value for the practical predictability of temperature in some cases with the results varying depending on season and location. Wintertime temperature forecasts at lead times up to three weeks, and cold spell forecasts up to two weeks in lead time are highlighted as cases where persistence appears to show an association with practical predictability.

How to cite: Holmberg, E., Tietsche, S., and Messori, G.: Persistence and the practical predictability of surface temperatures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5328, https://doi.org/10.5194/egusphere-egu24-5328, 2024.

Heat waves are a growing issue in the current climate, causing damage to human societies and other living beings. As climate warms, heat waves are one of the extreme events that will be exacerbated by the rising average temperatures. Understanding the mechanisms that drive heat waves is hence of vital importance to analyze them and to make predictions to mitigate their impacts. However, the rarity of any extreme event makes it particularly hard to study, especially if one wishes to understand the relation between predictors and probability, for instance using machine learning techniques, which are notoriously data-hungry. A possible solution is to use climate model simulations, but they introduce biases and can be prohibitively expensive to run for appropriate dataset lengths.

In this work, we introduce a simple and powerful statistical model, that is able to skillfully predict rare heat waves in a regime of lack of data, as it is the case for the ERA5 reanalysis dataset.

We focus on two-week heat waves over France, and we notice that composite maps of very extreme events are similar to the ones of much less extreme ones. This holds true for an increasing hierarchy of model complexity, including ERA5. We can thus analyze very extreme events by looking at less rare ones, having the advantage of increasing the available statistics. This effect can be explained assuming that the set of predictors and the heat wave amplitude are jointly gaussian. The prediction task can be thus rephrased into estimating the conditional probability of an extreme heat wave happening conditioned on the state of the set of predictors. This quantity, called committor function, is generally hard to estimate given the high dimensionality of the variables involved. Our assumption performs a dimensionality reduction, where an estimate is obtained through an optimal linear projection. The projection map will suggest the most relevant predictors.

We first demonstrate that this Gaussian approximation performs well on a 8000 years run of a model of intermediate complexity, PlaSim, for both analysis and prediction tasks when 500 hPa geopotential height, temperature and soil moisture are used as input fields. When we compare with a machine learning estimation of the committor, which is possible here thanks to the huge amount of available data, our Gaussian model is less skillful, but not by much. We then apply the Gaussian assumption to the 500hPa geopotential height field of the ERA5 dataset, outperforming the neural networks and extending the predictability horizon by several days.

The prediction method we propose is simple and effective. It opens possibilities to achieve skilfull predictions that outperform all known approaches, for instance machine learning, notably when data is scarce. Our method also serves as a better baseline than the climatology to benchmark more complex approaches. Since our statistical model is also interpretable, this framework has potential to go beyond prediction skill only and, thanks to the optimal projection map, foster the study of fast and slow drivers, and the effect climate change has on them.

How to cite: Mascolo, V., Lovo, A., Herbert, C., and Bouchet, F.: A Gaussian framework for optimal prediction of extreme heat waves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18866, https://doi.org/10.5194/egusphere-egu24-18866, 2024.

Theoretical and numerical studies have shown that transient atmospheric motions leading to weather extremes can be classified through the instantaneous dimension and stability of a state of a dynamical system [Faranda et al., Sci. Rep., 2017]. The asymptotic values of these quantities can be computed theoretically only for specific systems, while their numerical counterpart for climate observables provides information on the rarity, predictability, and persistence of specific states. In this work, we present a first attempt to relate the presence of extreme events with the elements that make up a templex of the system under study, both in the deterministic [Charó et al., Chaos, 2022] and stochastic frameworks [Charó et al., Chaos, 2023]. The templex provides the key characteristics of the topological structure underlying a dynamical system. This work will present results for the classical, deterministic Lorenz [JAS, 1963] attractor and for the Lorenz Random Attractor, dubbed LORA [Ghil & Sciamarella, NPG, 2023].

How to cite: Charó, G. D., Faranda, D., Ghil, M., and Sciamarella, D.: Extreme events in a templex, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11997, https://doi.org/10.5194/egusphere-egu24-11997, 2024.

As we attempt to infer a system's response to external driving from measurements, random errors in the measurement of the drivers can lead us to mistakenly infer a non-linear response. In particular, we are likely to underestimate the system's response during extreme and rare driving conditions due to uncertainty in the drivers. We demonstrate this phenomenon for extreme space weather and its impact on Earth's magnetosphere, where due to random errors in the measurements of solar wind drivers, there is a non-linear bias in the magnetosphere's response. We propose that the underlying statistical effect (related to the more well-known regression to the mean effect) is generalizable to the other fields that study different systems' responses to driving, like extreme climate studies. 

How to cite: Sivadas, N. and Sibeck, D.: Underestimating extremes due to random uncertainty, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13645, https://doi.org/10.5194/egusphere-egu24-13645, 2024.