It is well established that internal variability arising spontaneously from the chaotic nature of the climate system can amplify or obscure anthropogenically-forced signals, especially at near-term and at regional scale in the extratropics. In this talk, we focus on Northern Europe (NEU) winter climate changes over the 2020-2040 period and propose a set of internal variability storylines (IVS) to tackle related uncertainties. IVS are built from the combined evolution of the North Atlantic Oscillation (NAO) and the Atlantic Meridional Overturning Circulation (AMOC) diagnosed as drivers of variability for temperature over NEU.

We first show, based on a large ensemble of historical-scenario simulations from CNRM-CM6-1, that, depending on the near-term [AMOC-NAO] doublet evolution, anthropogenically-forced changes can be either considerably amplified with much warmer-wetter mean conditions, almost doubled, or considerably masked with marginal warming and unchanged mean precipitation with respect to present day. We then provide evidence for the robustness of our results by using large-ensembles from several models which ultimately allows assessing the full range of uncertainties for near-term climate change.

We finally use the 2010 severe winter case as an illustrative example of the added-value in expressing climate change knowledge in a conditional form through IVS to plan at best climate-related risks and local adaptation strategies at near term. Reframing the uncertain climate outcomes into the physical science space through IVS grapples the complexity of regional situations; it is also informative to more efficiently communicate towards the general public as well as for climate literacy in general.

How to cite: Cassou, C., Line, A., and Msadek, R.: Assessment of climate change at near-term (2020-2040) over Northern Europe through internal variability storylines, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11961, https://doi.org/10.5194/egusphere-egu24-11961, 2024.

Storylines are intended to provide concrete realizations of the climate response to global warming, to help anticipate the possible impacts of climate change on society and nature. Recent studies on climate change storylines have used a multivariate linear regression (MLR) framework to determine those climate realizations, for specific variables, regions and seasons (called target variables); this is achieved by leveraging known climatic interactions across a large number of model projections, which are represented by the covariability of the target variable with pre-determined climate indices (called predictor indices). Yet, a systematic methodology for selecting the best set of predictor indices for a specific target variable is lacking, with the set of predictors usually being chosen according to our current understanding of the most important climatic interactions. Furthermore, the storylines that emerge from it are tailored to explain changes in one specific variable, region and season (the target variable), and thus are unable to be generally applicable to a range of target variables.

Even if the MLR framework succeeds in generating an array of representative climate outcomes for specific cases, we hypothesize that alternative methodologies can be used to generate likely climate outcomes from model simulations while alleviating some of the limitations of the MLR framework. Here, we propose to use clustering analysis to provide possible climate realizations from model projections. Clustering ensures a comprehensive and efficient decomposition of the spread in climate projections found across model simulations, without the need of predefining predictors (both an advantage and inconvenience), but also can be applied to more than one target variable at a time. 

We present findings from various empirical clustering methods, using the three main categories of algorithm (e.g. distribution-, density-, and centroid-based) to produce our so-called empirical storylines. We focus on the Arctic region during the boreal summer season, comparing storylines obtained from each clustering method with findings from a set of “classic” storylines obtained using the MLR framework. We discuss the implications of our results for improving our understanding of the spread in climate projections, and conclude on the existence of a most likely cluster (storyline) by relating our climate change clusters with clusters for the present-day climate. 

How to cite: Levine, X. and Mooney, P.: Empirical storylines of climate change using clustering analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6659, https://doi.org/10.5194/egusphere-egu24-6659, 2024.

Recent heat extremes reached records far out of the observational temperature range. These extremes challenged the risk view of climate scientists on what could be physically possible within the current climate conditions. However, it is precisely such unprecedented events that pose a large risk to underprepared societies. To better anticipate and prepare for such potential extreme events, the climate risk community started producing storylines which are designed to draw potential and plausible worst-case scenarios without aiming to quantify their probability of occurrence.

The recent development of the ensemble boosting method allows investigating physically plausible extreme heatwaves by re-initializing a climate model with random round-off perturbed atmospheric initial conditions shortly before the onset of a great heat anomaly. This allows for creating storylines whilst ensuring physical consistency. However, so far these storylines were only used to estimate the pure physical climate extreme without the additional quantification of impacts on society.

In this study, we therefore aim to produce several storylines for potential worst-case heat-mortality scenarios. For that, we aim to combine ensemble boosted climate model output with methods from environmental epidemiology to quantify heat-mortality. Concretely, we model the empirical relationship between daily mean temperature and daily mortality counts by using quasi-Poisson regression time series analyses with distributed lag nonlinear models, which is a well-established approach in climate change epidemiology. We then combine these empirical temperature-mortality relationships with the bias-corrected extreme storylines that we developed by ensemble boosting a fully-coupled free-running climate model (CESM2).

The findings of this study have significant implications for societies, particularly in the context of public health policy development, to effectively respond to unprecedented but anticipatable heat extremes.

How to cite: Lüthi, S., Fischer, E., and Vicedo-Cabrera, A.: Storylines for heat-mortality extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7744, https://doi.org/10.5194/egusphere-egu24-7744, 2024.

Climate change is causing an increase in the frequency, intensity and persistence of heatwaves and droughts, as seen, for example, in Central Europe in recent years. These changes are expected to be even more severe in the future. Two factors contribute to these changes in extreme events: dynamic changes – changes in the likelihood of weather patterns  – and thermodynamic changes. While the former are uncertain in future climate projections, the latter are characterized by a high signal-to-noise ratio, as there is a robust and ubiquitous rise in land-surface temperatures.

To better understand and analyze both contributions, we employ the so-called "event-based storyline approach", which involves nudging our global CMIP6 coupled climate model (AWI-CM1) towards the observed large-scale free-troposphere winds using various climate background conditions and initial states. This enables us to simulate the same weather conditions, including jet streams and blockings, in different climates: preindustrial, present, and in 2 °C, 3 °C, and 4 ºC warmer worlds. This methodology provides an efficient way of making the consequences of climate change more understandable to experts and non-experts, as extreme events that are fresh in people's memory are simulated in different climates with moderate computational resources.

Our simulations successfully reproduce recent hot and dry extreme events, like the 2019 or 2022 European heatwaves and the record-breaking 2022 drought. Our experiments reveal an intensification of these extremes from preindustrial to present climates (attribution), mainly in southern Europe, with no major changes in Central and Northern Europe. However, we project that this exacerbation will expand northward in future warmer climates, leading to even more severe drought in Central Europe and the Mediterranean by the end of the century. Taking advantage of our methodology we explore the physical mechanisms helping to exacerbate these events in future warmer climates.

How to cite: Sánchez Benítez, A., Ionita, M., Athanase, M., Jung, T., Ma, Q., and Goessling, H.: Storyline simulations suggest a northward expansion of European droughts in warmer climates., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12519, https://doi.org/10.5194/egusphere-egu24-12519, 2024.

Whether cold-air outbreak over mid-latitude in a warmer climate would become more or less extreme is a subject of debate, particularly due to uncertainty links between Arctic amplification and these cold extremes, which complicated by the atmosphere internal variability.  Here we employ an event-based storyline approach, which fixed the atmospheric circulation to the observed  through spectral nudging, to quantify thermodynamic effect on extreme cold events during the winter of 2020/2021 in East Asia under different warming scenarios. Notably, we detect the strongest warming, up to +10K, over Eastern Siberia in the +4K-warmer climate, which is related to warmer cold air mass originating from unfrozen sea ice over Siberia region. In contrast, in the southern China, due to the observed and expected increasing aerosol concentration, peaking by the mid-21^st century and altering the radiative balances, a mild cooling is present from pre-industrial to present-day climates. The cooling in this region is likely to persist in +2K-warmer scenario but was not observed when up to the +4K warmer climate. Correspondingly, no prominent temperature variation is observed in the middle East Asia, with the warming extent largely mirroring the overall climate background.

How to cite: Zhuo, W., Sánchez-Benítez, A., Goessling, H., Athanase, M., and Yung, T.: Storylines of East Asian cold extremes in 2020/2021 under different warming climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3174, https://doi.org/10.5194/egusphere-egu24-3174, 2024.

Over the last few decades, the Arctic region has warmed up at a greater rate than elsewhere at the globe, partly resulting from the on-going loss of sea ice and snow over land. It is projected that the amplified warming of the surface will continue in the future, most likely altering the magnitude and frequency of temperature extremes, such as heat waves and cold spells. In addition, the intensity and frequency of extreme precipitation and droughts are projected to change, which may pose serious threats for the human infrastructure and livelihoods. To assess (future) climate extremes, Earth System Models (ESMs) with (regionally) refined resolution could be helpful, particularly in mountainous regions.

In this study, we use the variable-resolution Community Earth System Model version 2.2 (VR-CESM) to evaluate and assess present-day and future climate extremes, such as heat waves and heavy precipitation, over the Arctic. Applying a globally uniform 1-degree grid and a VR grid with regional grid refinements to 28 km over the Arctic and Antarctica, we run present-day (2005–2014) and future (2090–2099) simulations with interactive atmosphere and land surface models, and prescribed sea ice and surface temperatures. The simulations follow two storylines of Arctic climate change that represent a combination of strong/weak polar Arctic amplification and strong/weak SST warming in the Barents-Kara seas. We evaluate the ability of the VR grid to simulate climatic extremes by comparison with gridded outputs of the globally uniform 1-degree grid and the ERA5 reanalysis and assess future climate extremes by focussing on temperature and precipitation extremes. The initial outcomes generally show that for some temperature/precipitation extremes indices the VR grid performs better than the globally uniform 1-degree grid, while for other indices the globally uniform 1-degree grid performs better. Future projections suggest that warm temperature extremes will generally increase both in magnitude and frequency, whereas cold temperature extremes will decrease in magnitude, especially over regions dominated by large sea ice loss. Further, precipitation is projected to increase in intensity and volume. The outcomes of this study may contribute to an improved understanding on future climate extremes and its implications.

How to cite: Wijngaard, R. R., van de Berg, W. J., Herrington, A. R., and Levine, X.: Impacts of regional grid refinement on climate extremes over the Arctic in storyline-based earth system model simulations., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10006, https://doi.org/10.5194/egusphere-egu24-10006, 2024.

A derecho is a widespread, long-lived, straight-line windstorm that is associated with a fast-moving group of severe thunderstorms known as a mesoscale convective system.

During 18 August 2022, a highly intense and organized convective storm, classified as a derecho, developed over the western Mediterranean Sea affecting Corsica, northern Italy and Austria, with wind gusts up to 62 m/s and giant hail (~11 cm). There were 12 fatalities and 106 people injured. This event received much attention in the media for its extraordinary impact and the rareness over the Mediterranean Sea. The derecho developed over an extreme marine heatwave that persisted during the whole summer. Therefore, the hypothesis of a relationship between the extreme atmospheric event and the extreme marine heatwave rapidly arose, and thus, a possible link with anthropogenic climate change.

This convective event can be considered as extreme from the affected locations point of view (in terms of winds) but also is between one of the most powerful derechos ever recorded in the USA and Europe. Also, the event developed over an extreme marine heatwave that was mainly affecting the western Mediterranean Sea during summer 2022.

Here, by performing model simulations with both the NCAR Model for Prediction Across Scales and the Météo-France nonhydrostatic operational AROME model and using an storyline approach, we find a relationship between the marine heatwave, the actual anthropogenic climate change conditions, and the development of this extremely rare and severe convective event. We also find a future worrying increase in intensity, size and duration of such an event with future climate change conditions.

How to cite: González-Alemán, J. J., Insua-Costa, D., Bazile, E., González-Herrero, S., Miglietta, M. M., Groenemeijer, P., and Donat, M. G.: On the key role of anthropogenic warming in triggering extreme convective events: the case of the destructive Mediterranean derecho in 2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12974, https://doi.org/10.5194/egusphere-egu24-12974, 2024.

Interannual to multi-decadal variability in large-scale dynamics such as atmospheric and oceanic circulation results in significant noise and temporary trends in regional climate. Attempting to understand longer term trends as a result of anthropogenic climate change requires disentangling internal variability and climate change signals. One of these climate signals is the Clausius-Clapeyron (CC) scaling in precipitation resulting from temperature increases. In this work, we characterise and constrain variability in sub-seasonal winter rainfall in the UK resulting from synoptic scale-conditions. The UK experiences periods of sustained precipitation in some winters which result in widespread flooding due to extreme accumulation, such as the winter of 2013/2014. Using categorised sea-level pressure fields and gridded precipitation between 1900-2020, we simulate ‘expected’ precipitation resulting from North Atlantic synoptic conditions. We find a rising trend since the 1980s in observed monthly accumulation which is not reflected in the simulated precipitation timeseries, indicating that recent wet winters in the UK have been wetter than expected given the synoptic conditions. The rising trend in the residual (observed - simulated) mean monthly precipitation is in line with expected CC scaling rate of ~6-7% per degree warming according to changes in UK annual mean temperature. However, the residual in extreme monthly precipitation has scaled at approximately twice that rate. To better understand differences in changes for average and extreme precipitation accumulation, we explore the influence of dynamical feedbacks which may increase precipitation at higher intensities. We find that residual precipitation is influenced by the persistence of synoptic conditions and exhibits remote teleconnections to sea surface temperature and atmospheric conditions in the tropics and sub-tropics. This work highlights the importance of considering variability in large-scale dynamics when identifying climate change signals and sheds light on influences on sub-seasonal to seasonal winter precipitation in the UK.ences on sub-seasonal to seasonal winter precipitation in the UK.

How to cite: Carruthers, J., Guerreiro, S., Fowler, H., and Bannister, D.: Sub-seasonal UK winter precipitation intensifies in-line with expected temperature scaling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2365, https://doi.org/10.5194/egusphere-egu24-2365, 2024.

The typicality of extreme weather and climate events denotes their property to exhibit similarities in spatial patterns, temporal evolution, and underlying physical processes, with this resemblance intensifying as events become more extreme. Recent findings highlight that highly intense heatwaves, defined as prolonged local temperature anomalies, are consistently associated with specific large-scale circulation patterns. This suggests that there is a typical way to realise very extreme local temperature anomalies. Here, I will explore typical ways for the emergence of extremely intense hemispheric anomalies, characterized by notably large zonal variations in air temperature or geopotential height. This investigation aims to shed light on preferred atmospheric configurations leading to the simultaneous occurrence of heatwaves on a hemispheric scale.

How to cite: Galfi, V. M.: Investigating typical patterns for co-occurring heatwaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15314, https://doi.org/10.5194/egusphere-egu24-15314, 2024.

There has been growing evidence suggesting a rising frequency and/or intensity of droughts in tropical regions in a warming climate. Singapore, a water-scarce city heavily reliant on water imports, faces heightened vulnerability to extreme drought episodes. Preparing for unprecedented droughts is thus pivotal for this tropical island city to safeguard a sustainable and resilient water supply. However, the accuracy of quantifying the probability and severity of unprecedented droughts, such as those with a 1000-year return period, is hindered by observations (e.g., in situ measurements, satellite data, etc.) with limited data length, typically spanning only about 50 years. Physics-based regional climate models offer a distinct advantage in simulating extreme droughts beyond historically available data. Yet, naïve Monte Carlo simulations for rare events becomes computationally infeasible at high spatiotemporal resolutions, a scale most relevant in urban drought risk mitigation. In this study, building upon the Giardina-Kurchan-Lecomte-Tailleur algorithm, we develop a computationally efficient framework to simulate Singapore’s unprecedented drought events. Our framework couples the Weather Research and Forecasting (WRF) model with a sequential importance sampling procedure, incorporating the ‘Darwinian pressure’ to favor trajectories conducive to extreme drought conditions. With just slightly over 100 trajectories, we can efficiently simulate very rare drought events (e.g., 1-in-10000-years and rarer) while maintaining their physical plausibility. The WRF model also enables detailed spatiotemporal dynamics of unprecedented droughts, allowing direct estimation of potential compounding extremes, such as concurrent droughts and heatwaves. Moreover, we quantify changes in the likelihood of plausible yet unprecedented droughts under various future climate change scenarios, such as Shared Socioeconomic Pathway 5-8.5 (SSP585), in comparison to the present climate. Our results reveal a robust increase in the chance of unprecedented droughts, emphasizing the importance of developing resilient water strategies for Singapore to prepare for such events in the near future.

How to cite: Peng, X., Long, B., and He, X.: Not as Rare as Expected: Assessing Singapore’s Unprecedented Droughts in a Changing Climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6945, https://doi.org/10.5194/egusphere-egu24-6945, 2024.

The 2021 heatwave in the Pacific Northwest of the United States and Canada was unusual in many regards. In particular, not only was the event deemed impossible prior to the human interference in the climate system, standard out-of-sample non-stationary generalized extreme value (GEV) analyses revealed it to be statistically impossible in 2021 as many observed temperatures were above the upper bound of the upper bound of fitted GEV distributions. Obviously, as the event actually occurred, these statistical models are not fit for the purpose of estimating the influence of climate change on the event’s probability.

By expanding the number of physical covariates beyond just greenhouse gas concentrations and by incorporating spatial statistical techniques in a Bayesian hierarchal framework, we are able to construct a statistical model where observed temperatures during this heatwave were not “impossible” and thus estimate the change in their probabilities leading to Granger-type causal inference attribution statements.

We further extend this statistical framework to all quality daily GHCN station measurements and find that while many physically plausible outlier temperatures are impossible in the simple non-stationary GEV framework, they can be explained using our more complicated non-stationary Bayesian spatial statistical model embedded in a deep learning machinery.

How to cite: Wehner, M., Risser, M., Zhang, L., and Boos, W.: Statistically impossible temperatures., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2574, https://doi.org/10.5194/egusphere-egu24-2574, 2024.

Europe faced many extreme events in the year 2023; storms, heatwaves, intense precipitation, widespread flooding, to mention a few. Long-standing records were broken, and re-broken again. The events invariably received a lot of attention by the media and triggered many questions from journalists, eager to report about them. These questions are typically about the frequency or ‘extremeness’ of the event, whether or how already occurred climate change has impacted this frequency, and what the future perspectives are: Would a similar event in future or past climate have (had) a larger or smaller impact? 

It is a challenge for scientists to answer such (attribution) questions rapidly, i.e., before or on the day of the event, or in the immediate aftermath. Weather attribution teams like WWA (World Weather Attribution) now apply standardised procedures based on combining observations and climate modelling, to produce such analyses within weeks.

Here we discuss an approach that may augment the set of already existing tools and frameworks for rapid attribution analysis. The approach is based on regional downscaling in combination with pseudo global warming (PGW). Each day a small downscaled ensemble is created using as initial and boundary conditions the ECMWF analysis and forecasts. In addition to this ‘present-day’ ensemble, also a ‘past’ and ‘future’ ensemble are created using PGW. Due to the synchronicity of the time-evolution of the past, current and future ensembles, the signal-to-noise ratio is high, allowing an immediate estimate of how (thermodynamic) changes could have contributed to the event, and how a similar event in a future climate could look. Inherent limitation of PGW is that it cannot, or only in a limited way, address the frequency-change aspect. 

We illustrate the PGW-ensemble with a number of events that occurred during 2023 such as storm Hans (August), the December snowfall, and the unprecedented yearly rainfall amount in the Netherlands.

How to cite: de Vries, H., Lenderink, G., van Meijgaard, E., van Ulft, B., and de Rooy, W.: A daily ensemble of Past and Future Weather for rapid attribution and future perspectives, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17759, https://doi.org/10.5194/egusphere-egu24-17759, 2024.

We use a 50-member large ensemble of the CMIP6 version of the MPI-ESM1.2-LR model to examine the future of hot-dry compound events at 1.5 and 2°C of global warming. By targeting the largest maize production areas (breadbasket regions) and their corresponding growing seasons, we tailor our analysis to food production, indicating potential future threats to global food security. Our results suggest a notable shift in the extremes associated with maize harvest failure in the breadbaskets between 1.5 and 2°C of global warming, highlighting the value of mitigating climate change and the future need to adapt to climate challenges in the agricultural sector.

Our analysis shows a significant increase in the likelihood of these extremes during maize growing seasons across almost all examined regions and variables. In particular, the occurrence probability of heat events and hot-dry compounds at least doubles in most regions when the world warms from 1.5 to 2°C. Locally, cumulated heat excess increases everywhere, while the spatial extent of heat consistently expands across all regions in contrast to the relatively stable pattern we find for precipitation as we transition from one level of global warming to another. We additionally explore spatial compounding, where multiple breadbasket regions experience simultaneous extremes in the same growing season, exacerbating global food security challenges. Scenarios that were virtually impossible in the past, such as hot-dry events affecting at least three regions simultaneously, take on non-zero probabilities in a world that is 1.5 or 2°C warmer. The probabilities of simultaneous heat and hot-dry compounds in a 2°C warmer world significantly exceed those in a 1.5°C warmer world, to the extent that there is little to no overlap between the corresponding ensemble spreads.

How to cite: Dietz, V., Baehr, J., and Borchert, L.: The Future of Hot-Dry Events in the World’s Breadbasket Regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8388, https://doi.org/10.5194/egusphere-egu24-8388, 2024.

As climate changes globally and locally, the risk of temperature anomalies, heat waves and droughts have significantly increased. Studies have demonstrated that droughts exert adverse biophysical effects on crop production, posing an unprecedented threat to harvests and resulting in substantial economic losses in Europe. Assessing these biophysical drought impacts on agriculture is crucial for developing effective strategies for drought preparedness, mitigation, and adaptation. This paper contributes to this effort by presenting a framework to estimate economic costs associated with droughts that specifically captures the biophysical impact of climate change on crop output.

Existing analyses for drought damages in agriculture are developed for a specific drought event and primarily focus on the reduction in farmer’s income or crop yields in drought events. In these assessments, the biophysical impacts of droughts are not isolated and evaluated from their effects on other economic variables such as output prices, resulting in inaccurate damages. Additionally, lack of single universal definition of drought adds complexity to estimating the costs of droughts. This paper is aimed to contribute by focusing on agricultural droughts, which occurs when variability in soil moisture affects plant growth and development. We simulate this biophysical effect of drought on crop yields by applying a statistical crop yield model to data on soil moisture, temperature and perception. This approach helps isolate the direct impact of drought on agriculture from other changes in aggregate economic production (e.g. business conditions, commodity prices) and farmer management decisions (e.g. intermediate input use). The simulated biophysical yield effects are then quantified into monetary terms to estimate economic damages of droughts. We further look into the relationship of the economic damages and the intensity of droughts to determine drought thresholds that lead to increased economic losses.

The results provide bottom-up estimates of the economic damages of drought induced water deficiency in agriculture across Germany for the years 2016-2020. The spatio-temporal patterns of drought impacts can be useful for drought policy planning at local and national level. The economic costs estimation framework could be valuable in estimating farmer compensations and loss and damage of droughts. The results of the study can provide reliable estimates of the costs of climate-change-related extreme weather events, which may help inform macroeconomic and integrated impact assessment models of economic losses (and gains).

How to cite: Nagpal, M., Heilemann, J., Klauer, B., Gawel, E., and Klassert, C.: Hydro-economic assessment of biophysical drought impacts on agriculture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17486, https://doi.org/10.5194/egusphere-egu24-17486, 2024.

Extreme heat events are becoming more severe. Attribution studies have demonstrated the effect of anthropogenic climate change on recent devastating events, including the heat waves in Canada in 2021, Northern India in 2022 and the Western Mediterranean in 2023. Such impactful events are very rare with return periods of 100 years and more even in present climate. Their rareness is in stark contrast to the typically considered return periods ranging from less than a year to maybe 20 years. This choice might often be inevitable because of practical limitations, mainly the length of observational and climate model records. But generalising from such analyses to extreme events in general tacitly assumes that very rare events respond to climate change in a similar way as the analysed moderate extreme events. Several studies investigating land-atmosphere feedbacks and atmospheric circulation changes indicate, however, that this assumtion may not be justified.

Here we use three single model initial condition large ensembles (SMILES) to assess differences between projected changes in moderate heat extremes (represented by 2-year return values of the hottest day in a year) and very rare extreme events (represented by corresponding 200-year return values). We analyse changes from 1990-2014 to 2075-2099 according to the SSP5-8.5 scenario.

We find large regions where projected changes in very extreme events are markedly different - both stronger or weaker - to those in moderate extreme events. Model uncertainty about these differences is very high though: all considered SMILES suggest that such regions exist, but they do not agree on the locations.  The underlying mechanisms, however, are robust across models: in regions of increasing soil moisture temperature coupling strength, changes in very rare events can be almost twice as high as those in moderate extremes. Vice versa, in regions of decreasing coupling strength, changes may be much weaker. These changes can to a large extent be traced back to changes in precipitation patterns, highlighting the role of atmospheric circulation changes.  

The corresponding patterns emerge already over shorter time horizons and are thus relevant for mid-century projections, low emission scenarios and event attribution studies. Robust inference about these differences is impossible based on individual model simulations, but requires the sample size of SMILES.  Not accounting for these changes could lead to a dramatic misrepresentation of future climate risks from heat events. Our findings therefore confirm the importance of studies specifically targeting very extreme events.

How to cite: Maraun, D., Schiemann, R., Osso, A., and Jury, M.: Changes in land-atmosphere coupling may amplify increases in very rare temperature extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15334, https://doi.org/10.5194/egusphere-egu24-15334, 2024.

Not only will climate change lead to more intense extreme precipitation, it will also lead to more frequent record-breaking daily rainfall. Given the tendency of society to design critical infrastructure and emergency plans based on (statistics derived from) historical observations, an increasing occurrence of record-breaking events – events that are more intense than ever recorded – poses a high risk for loss and damage. 

A major challenge in the projection of very extreme events is their inherent rarity. This problem is even more prominent for record events: by definition these events are not present in sample data because they have not yet occurred. An additional difficulty, which is particularly challenging for precipitation, is the high internal variability in and local character of very rare extremes. This implies that, by chance, an observed data sample of finite size might contain few extremes, whereas the true probability and intensity of extremes given by the (unknown) underlying distribution is much higher. In practice, this can lead to “surprise extremes”. 

With the help of extreme value theory, we approach this problem from two angles, using multi-model CMIP6 data and two different ground-station based observational datasets. Firstly we assess, for all observed land grid cells, where the last observed precipitation record is “extraordinarily long ago” given the theoretical record breaking rate prescribed by historical and future climate according to the CMIP6 models. Secondly, we assess where the last observed record value is “extraordinarily low in intensity” given the historical and future modelled distribution of extreme precipitation. Combining these two approaches, we highlight regions on earth where the probability of record precipitation events in the near future is high.

We find that grid points where the last observed precipitation record is extraordinarily long ago are ubiquitous and scattered globally. When combining this with the observed record intensity, the number of grid points that stand out for their high near-term record probability decreases drastically. We find a somewhat higher density of high-probability grid points in Australia and southern South America, but the pattern is not very clear. Nonetheless, every world region contains a number of grid points where the current observed record is both extraordinarily long ago and low in intensity, and where the near-term probability of a new precipitation record is thus high.

How to cite: de Vries, I., Fischer, E., Sippel, S., and Knutti, R.: Where and when will the next precipitation record be broken? , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17826, https://doi.org/10.5194/egusphere-egu24-17826, 2024.

A “once-in-a-millennium” super rainstorm battered Zhengzhou, central China, from 07/17/2021 to 07/22/2021 (named “7.20” Zhengzhou super rainstorm). It killed 398 people and caused billions of dollars in damage. ​A pressing question, however, is whether rainstorms of this intensity can be effectively documented by geological archives to understand better their historical variabilities beyond the scope of meteorological data. Here, four land snail shells (Cathaica fasciola) were collected from Zhengzhou in 2021, and weekly to daily resolved snail shell δ¹⁸O records from June to September of 2021 were obtained by gas-source mass spectrometry (GSMS) and secondary ion mass spectrometry (SIMS). The daily resolved records show a dramatic negative shift between 06/18/2021 and 09/18/2021, which has been attributed to is related to the “7.20” Zhengzhou super rainstorm. Moreover, the measured amplitude of the shell δ¹⁸O shift caused by the “7.20” Zhengzhou super rainstorm is consistent with the theoretical value estimated from the flux balance model and local instrumental data within the error range. Our results suggest that the ultra-high resolution δ¹⁸O of land snail shells have the potential to reconstruct local synoptic scale super rainstorm events quantitatively. And the proposed “best practice” of current work indicated that fossil snail shells in sedimentary strata can be valuable material for investigating the historical variability of local super rainstorms under different climate background conditions.

How to cite: Wang, G., Dong, J., and Yan, H.: Quantitative reconstruction of a single super rainstorm using daily resolved δ18O of land snail shell, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4973, https://doi.org/10.5194/egusphere-egu24-4973, 2024.