The circulation response to climate change shapes regional climate and extremes. Over the last decade an increasing number of atmospheric circulation signals have been documented, with some attributed to human activities. The circulation signals represent an exciting opportunity for improving our understanding of dynamical mechanisms, testing our theories and reducing uncertainties. The signals have also presented puzzles that represent an opportunity for better understanding the circulation response to climate change, its contribution to climate extremes, interactions with moisture, and connection to thermodynamic discrepancies. The next decade is likely to be a golden age for atmospheric dynamics with many advances possible.
How to cite: Shaw, T., Arblaster, J., Birner, T., Butler, A., Domeisen, D., Garfinkel, C., Garny, H., Grise, K., and Karpechko, A.: Emerging Climate Change Signals in Atmospheric Circulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3365, https://doi.org/10.5194/egusphere-egu25-3365, 2025.
Canada’s land mass, which extends well into the Arctic, has experienced some of the most rapid warming on Earth in recent decades. Here we apply a Bayesian observational constraint method to consistently assess the externally forced past, present and future warming in Canada, despite the presence of possible internal variability. Using observational constraints based on annual observational records of six Canadian sub-regions together with historical CMIP6 climate change simulations, we estimate that external forcing, which is almost entirely due to human activity, has warmed Canada by 2.1 [1.3, 2.8] °C between the 1850-1900 pre-industrial period and the recent 2015-2024 decade. Applying these same observational constraints to CMIP6 simulations of future climate conditions indicates that Canada will warm to 4.8 [3.5, 6.3] °C above pre-industrial levels by the end-of-century under an intermediate emissions scenario SSP 2-4.5, and to 6.2 [4.7, 7.9] °C under a high-emissions scenario SSP 3-7.0, with the largest warming projected for Northern Canada, followed by Quebec. The observational constraints, which were chosen and extensively evaluated using an imperfect model testing approach, reduce projected warming estimates for Canada relative to their unconstrainted counterparts, also materially reduce projection uncertainty.
How to cite: Zhang, X., Li, T., and Zwiers, F.: Constrained estimates of externally forced past and future warming for Canada, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12923, https://doi.org/10.5194/egusphere-egu25-12923, 2025.
Observational evidence shows that Sahel summer precipitation has experienced a considerable increase since the 1980s, coinciding with significant diverging trends of increased sulfate emissions in Asia and decreased emissions in Europe (dipole pattern of aerosols between Asia and Europe). The decrease in European sulfate aerosols has substantial effects on the Sahel summer precipitation increase, but the corresponding effect of increased Asian sulfate is unknown. Multi-model simulations in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) show, compared to decreased European aerosols, that increased Asian aerosols similarly enhance the Sahel summer precipitation but with different large-scale atmospheric circulation changes. Further analysis of the Sixth Coupled Model Intercomparison Project (CMIP6) simulations under historical attribution and various emission scenarios reinforces the results about the climate impacts of anthropogenic aerosols and suggests that in future scenarios with strong international cooperation and rapid climate mitigations (SSP2-45), the Sahel drought will be intensified likely due to the decline in Asian aerosol emissions. Our results suggest that Asian anthropogenic aerosols are likely a non-negligible driver of the recent recovery in Sahel precipitation amounts.
How to cite: Guo, J., Xie, X., Myhre, G., Shindell, D., Kirkevåg, A., Iversen, T., Samset, B. H., Shi, Z., Li, X., Sun, H., Liu, X., and Liu, Y.: Increased Asian sulfate aerosol emissions remarkably enhance Sahel summer precipitation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2244, https://doi.org/10.5194/egusphere-egu25-2244, 2025.
The separation of human-induced climate change and internal unforced variability to precipitation changes is well established, yet the full extent of anthropogenic forcing in regulating terrestrial precipitation remains unclear. Here we present a novel approach that combines dynamical adjustment with fully coupled nudged circulation climate model simulations using the Community Earth System Model Version 2 (CESM2) to attribute anthropogenic contributions to forced thermodynamic and dynamic processes driving seasonal mean precipitation trends over the past seven decades, while isolating the influence of internal variability. We show that although anthropogenic forced thermodynamics increases terrestrial precipitation over the majority of land areas in boreal summer (~52.3% ) and winter (~78.3%), it dampens precipitation over hot and humid areas, likely because of terrestrial evaporation failing to balance the rising saturation deficit of a rapidly warming atmosphere. Opposing the traditional expectation of small human-induced changes in circulation patterns, we find that atmospheric circulation patterns shift in response to anthropogenic forcing and modulate mean precipitation with similar magnitudes as forced thermodynamics, in particular suppressing precipitation in humid regions. Our results not only reveal that the climate system responds to anthropogenic emissions with declining precipitation in some wet areas, but also demonstrate that these tendencies are influenced by both thermodynamic changes and shifting large-scale dynamic systems.
How to cite: Gu, L., Sippel, S., Schumacher, D., Noyelle, R., Singh, J., Fischer, E., and Knutti, R.: Disentangling anthropogenic forced dynamic and thermodynamic precipitation changes from internal variability over the past seven decades, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7864, https://doi.org/10.5194/egusphere-egu25-7864, 2025.
Precipitation has become an increasingly critical issue, particularly in the wake of events such as severe pluvial flooding and the subsequent emergence of fluvial flooding concerns during recent anomalously wet winters in many places in Europe. Observational data indicate a steady increase in precipitation over recent decades in many regions. While this trend is believed to be linked to global warming, it does not exhibit a simple linear relationship with rising temperatures. Instead, it likely results from a combination of factors, including indirect effects such as alterations in atmospheric circulation, which are influenced by both climate change and natural variability. This study seeks to quantify the relative contributions of these drivers to observed changes in precipitation.
The analysis begins with a dataset comprising of key drivers of regional precipitation variability - in this case the focus is Denmark. The reanalysis data used includes pressure, temperature and sea surface temperature (SST), along with indices for the North Atlantic Oscillation (NAO), global warming and latitudinal position of the polar front, all on monthly timescales. SST patterns are clustered using dynamical time warping, the clusters to be included in the analysis are based on their correlation to regional precipitation. Multilinear regressions are applied pointwise, with the target variable being monthly average of total precipitation. This produces a spatial extent of the relative importance of the different drivers. The importance ranking is also verified using permutation importance and mediation and suppression. The temporal evolution of the different drivers is also examined by taking field means over select areas and looking at how the drivers covary with precipitation.
Attribution of the observed precipitation changes is based on the weights derived from multilinear regressions, supported by validation tests and the temporal evolution of the identified drivers. The results provide insights into the dynamics underpinning precipitation changes, highlighting the interplay of thermodynamical and dynamical influences.
This study contributes to a deeper understanding of how precipitation patterns may evolve under global warming scenarios and offers a clearer perspective on the circulation changes driving these trends. Although the current focus is on a specific region, the methodology is generalizable to other areas, provided appropriate domain knowledge is incorporated. Future applications could expand the analysis to Northern Europe or similar climatological contexts with suitable datasets.
How to cite: Vang, A., Düsterhus, A., Jomo Danielsen Sørup, H., and Hesselbjerg Christensen, J.: Attributing Local Precipitation Variability to Climate and Circulation Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9000, https://doi.org/10.5194/egusphere-egu25-9000, 2025.
Sea ice and snow are crucial components of the cryosphere and the climate system. Both sea ice and spring snow in the Northern Hemisphere (NH) have been decreasing at an alarming rate in a changing climate. Changes in sea ice and snow in the NH have been linked with a variety of climate and weather extremes including cold spells, heatwaves, droughts and wildfires. Understanding of these linkages will benefit the predictions of climate and weather extremes. However, existing work on this has been largely fragmented and are subject to large uncertainties in physical pathways and methodologies. This has prevented further substantial progress in attributing climate and weather extremes to sea ice and snow change, and will potentially miss a critical window for climate change mitigation. In this review, we synthesize the current progress in attributing climate and weather extremes to sea ice and snow change by evaluating the observed linkages, their physical pathways, uncertainties in these pathways and a way forward for future research efforts. By adopting the same framework for both sea ice and snow, we highlight their combined influence and the cryospheric feedback to the climate system. We suggest that future research will benefit from improving observational networks, addressing the causality and complexity of the linkages using multiple lines of evidence, adopting large-ensemble approaches and artificial intelligence, achieving synergy between different methodologies/disciplines, and widening the context and international collaboration.
How to cite: Ye, K., Cohen, J., Chen, H. W., Zhang, S., Luo, D., and Hamouda, M. E.: Attributing climate and weather extremes to Northern Hemisphere sea ice and terrestrial snow: Progress, challenges and ways forward, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6947, https://doi.org/10.5194/egusphere-egu25-6947, 2025.
Extratropical storms shape midlatitude weather and are influenced by both the slowly evolving climate and rapid changes in synoptic conditions. While the impact of each factor has been extensively studied, their relative importance remains uncertain, creating challenges in resolving the signal-to-noise ratio necessary for attributing extratropical weather events to current anthropogenic climate change. Here, we quantify the climate's relative importance in both climatic storm activity and individual storm development using 84 years of ERA-5 data, tracks of 100,00 cyclones and 50,000 anticyclones, and Convolutional Neural Networks (CNNs). We find that the constructed CNN model predicts over 90% of the variability in climatic storm activity, indicating that, from a climatic perspective, internal variability is negligible. In contrast, a similar model predicts less than one-third of the variability in individual storm features, such as intensity, growth time, and trajectory, demonstrating that their variability is dominated by internal variability. Using this estimate of internal variability and the mean impact of present-day climate change, we calculate a signal-to-noise ratio for attribution of storm intensity of approximately 0.2%, highlighting the significant challenge in attributing extreme individual storms to anthropogenic climate change. However, a signal-to-noise ratio ten times higher is obtained for warm heat anomalies associated with storms, emphasizing the potential for attributing storm-related impacts that are directly linked to climate change.
How to cite: Hadas, O. and Kaspi, Y.: Quantifying the Internal Variability of Midlatitude Storms Using Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15424, https://doi.org/10.5194/egusphere-egu25-15424, 2025.
The frequency and intensity of some extreme weather events, such as heatwaves,
are increasing due to climate change, with significant implications for socio-economic
sectors globally. This study focuses on the question of how we quantify the rate at
which the probability of these severe events is changing, enhancing our ability to
support effective adaptation strategies and deepen our understanding of climate
change’s diverse impacts on different regions and populations. Our initial case
study uses the ECMWF’s Reforecast dataset to analyse trends and the evolving
risk of extreme high summer temperatures in the Pacific Northwest. We specifi-
cally explore the relationship between the probability of exceeding climatological
temperature thresholds and local, regional, and global temperature changes. We
identify a consistent relationship, statistically significant in the case of local and
regional trends, allowing large-scale temperature trend information to be used to
provide an estimate of a risk-doubling time to complement other approaches. Our
results indicate that regional and local rising temperatures significantly elevate
the likelihood of extreme weather events, highlighting the growing risk associated
with climate change. Our evaluation confirms the ECMWF Reforecast data as a
reliable model to use as a potential source for such analyses, although limited by
the relatively short (20-year) length of the dataset.
How to cite: Ayim, O. V., Allen, M., and Leach, N.: From Weather to Climate: Using Medium-Range Forecasts to Quantify Long-Term Trends in Extreme Events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18112, https://doi.org/10.5194/egusphere-egu25-18112, 2025.
Global mean temperatures are currently warming at a rate unprecedented in the observational record and most likely in millennia. Europe has experienced one of the highest warming rates globally in the last three decades. The high warming rate results from global greenhouse gas emissions and is regionally amplified by reduced aerosol emissions. Internal variability can both further amplify and damp the rate locally, for extended periods of time.
The exceptionally high forced warming rate is a crucial factor explaining the high frequency of record-breaking and record-shattering heat over land and oceans in recent decades. The ratio of occurrence of daily surface temperature records since 1950 (in ERA5 and the BEST gridded observational data set) relative to the theoretically expected occurrence in a stationary climate, is now about 3–3.5 for hot records globally.
To prepare for future record events, it is crucial to identify regions where the probability of breaking or shattering the local standing record is highest. Here, we use statistical tools to quantify the record probability conditional on the standing record level and identify hotspots of high record probability in the coming years. Our method is evaluated using several single-model initial condition large ensembles.
We demonstrate that the conditional probability of setting a new record is particularly high in years and regions where the standing record level is low relative to the forced response. This typically happens after periods of little to no warming, when a forced warming trend has been slowed down or muted by unforced internal variability. Ironically, it is thus often regions where recent trends were low that deserve particular attention with regard to preparing for record-shattering extremes.
We demonstrate that the biggest source of uncertainty relates to the separation of historical warming trends into the forced response and internal variability. We evaluate different methods to estimate the forced response and show that while the exact conditional probability is uncertain, the hot spot regions where the conditional record probability is high can be robustly identified.
How to cite: Fischer, E., Huser, R., de Vries, I., and Sippel, S.: Where is the probability of the next record-shattering extreme highest?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15063, https://doi.org/10.5194/egusphere-egu25-15063, 2025.
The global tropospheric temperature hit an all-time maximum in 2023. While new global average temperature records are routinely set as a result of greenhouse warming, the year-to-year rise in temperature from 2022 to 2023 is infrequently reproduced in climate models (e.g., Raghuraman et al. 2024). It is possible that such a rise in temperature could result from a unique manifestation of internal climate variability (e.g., Cattiaux et al. 2023). Earth recently experienced a transition from a prolonged La Niña event to an El Niño event. This phasing of ENSO variability is associated with increased odds of a spike in global temperature in climate models (e.g., Raghuraman et al. 2024). Other analyses indicate that multi-year trends in low cloud cover, aerosols, and the peak of the 11-year solar cycle are also important factors (e.g., Goessling et al. 2024). Now into 2025, we find that the year-to-year rise in temperature (over 2023 to 2024) is again large and the annual mean global tropospheric temperature anomaly (in 2024) exceeds the record set in the previous year. We examine the rapid and persistent rise in global tropospheric temperature using microwave-based measurements of tropospheric temperature from satellites. We contextualize the exceptional 2023-2024 warmth using model simulations of the a) pre-industrial period and b) the satellite era in order to estimate the effects of internal variability and greenhouse warming. We also explore the sensitivity of our results to model climate sensitivity and the representation of interannual variability with data from several large initial condition ensembles.
Cattiaux, J., Ribes, A., & Cariou, E. (2024). How extreme were daily global temperatures in 2023 and early 2024? Geophysical Research Letters, 51(19). https://doi.org/10.1029/2024gl110531
Goessling, H. F., Rackow, T., & Jung, T. (2024). Recent global temperature surge intensified by record-low planetary albedo. Science. https://doi.org/10.1126/science.adq7280
Raghuraman, S. P., Soden, B., Clement, A., Vecchi, G., Menemenlis, S., & Yang, W. (2024). The 2023 global warming spike was driven by the El Niño–Southern Oscillation. Atmospheric Chemistry and Physics, 24(19), 11275–11283. https://doi.org/10.5194/acp-24-11275-2024
Research at Lawrence Livermore National Laboratory was performed under the auspices of U.S. DOE Contract DE-AC52-07NA27344. This research was performed as part of the PCMDI Project, which is funded by the RGMA program area of the Office of Science at DOE.
How to cite: Po-Chedley, S.: The Exceptional Rise Global Tropospheric Temperature over 2022-2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14244, https://doi.org/10.5194/egusphere-egu25-14244, 2025.
Between July 2023 and June 2024, each month set a new temperature record, all exceeding the 1.5 °C threshold established by the Paris Agreement. The global mean surface temperature anomaly peaked in September 2023 at 1.8 °C above pre-industrial levels, exceeding the previous record by an unparalleled 0.5 °C.
Using a probabilistic attribution framework, we assess the likelihood of this global heat. Our analysis shows that both the absolute temperature anomaly and the year-to-year temperature difference were extremely unlikely from an observational perspective, and are generally not reproduced by CMIP6 models. Yet, the occurrence probability of the absolute temperature anomaly rises sharply within just a few years into the future. In contrast, the temperature jump from September 2022 to 2023 remains highly unlikely throughout the next decades, even under higher warming levels. A process-based analysis highlights water vapour feedback and resulting longwave forcing as key drivers of the heat build-up in September 2023, with no indication of nonlinearities in the climate models. Our findings suggest that the September 2023 temperature jump was an extremely rare event which would strongly challenge our understanding of the climate system if it were to reoccur.
How to cite: Seeber, S., Schumacher, D. L., Gudmundsson, L., and Seneviratne, S. I.: New normal? Attributing the unprecedented global heat in September 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9789, https://doi.org/10.5194/egusphere-egu25-9789, 2025.
Heatwaves represent some of the most impactful extreme weather events, with profound implications for ecosystems, human health, and economies globally. Accurately attributing changes in their occurrence probabilities, intensity, and duration is crucial for effective climate change adaptation strategies. The intensity, frequency, and duration of heatwaves have increased globally, yet their attribution is not straightforward (Oldenborgh et al., 2022) because diverse factors influence regional heatwave trends. A common practice for calculating heatwave return periods relies on extreme value statistics, where the Generalized Extreme Value distribution (GEV) shifts linearly with a covariate on global mean temperature (GMT) for the location parameter (the “standard method”). This approach is widely used in rapid event attribution studies. However, local temperature trends in recent decades have been influenced by anthropogenic aerosol emissions (AER), depending on the region. AER have a predominantly cooling effect on heatwaves via reflecting incoming solar radiation. AER trends can therefore counteract or amplify the warming effect of greenhouse gases (GHG), depending on emission trends. These trends may affect regional climate dynamics and thermodynamic processes and, consequently, the return periods of extreme temperature events. In this study, we use state-of-the-art large ensemble and single forcing large ensemble climate model simulations from the Community Earth System Model 2 (CESM2). To examine the impact of AER on extreme event trends, we added an additional covariate on local aerosol optical depth (AOD) for the location parameter. We then compared this approach with the standard method. Our results indicate a substantial bias in the standard method during periods of strong regional AER trends. This bias is most pronounced in major industrial regions, where regional AER trends show the strongest deviation from the GMT covariate. In contrast, in some regions, AER trends have little or no impact. Adding AOD as an additional covariate reduces these biases and improves the goodness of the GEV fit. In regions such as North America, Central and Eastern Europe, and China, the GEV fit improves significantly for nearly all individual ensemble members with the addition of the covariate on AOD. For example, in Central Europe and the Midwest US, the standard method overestimates extreme temperatures by more than 1°C in the 1970s and 1980s, whereas this bias disappears when AOD is added as a covariate. This study underscores the importance of incorporating regional aerosol trends into attribution studies to improve the estimation of return periods, and thus attribution statements.
How to cite: Kraulich, F., Pfleiderer, P., and Sippel, S.: Impact of aerosol forcing on heat extreme event attribution results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3107, https://doi.org/10.5194/egusphere-egu25-3107, 2025.
In austral mid-winter 2024, East Antarctica experienced an unprecedented heatwave with an estimated return period of approximately 1-in-130 years. Temperatures in parts of East Antarctica soared nearly 20°C above the climate norm, making it the most extreme heatwave ever recorded in the region. This record-breaking event was primarily driven by the weakening of the polar vortex, which trapped heat over the region and prolonged the warming. Here, we present initial findings from the first multi-method attribution study of this East Antarctic heatwave: pulling together the circulation analog method, statistical attribution analysis (leveraging multiple global model simulations and large-ensemble atmospheric model simulations), and a storyline approach using a set of pseudo-global warming regional climate simulations dedicated for this event.
Attribution based on the circulation analog method reveals that changes in the dynamical flow explain a fraction of more than 60% of increases in the probability of heatwaves. Furthermore, statistical attribution method indicates that such an event would have been potentially impossible without human-induced warming. The storyline approach demonstrates that background warming due to greenhouse gas emissions has significantly intensified the heatwave's magnitude and extended its duration. Under future warming scenarios, similar heatwave events are projected to become more frequent and intense. Our study underscores the growing vulnerability of the Antarctic climate system to future heatwave events, serving as a stark warning of the potential for polar extremes to intensify under global warming.
How to cite: Tang, H., Li, S., and Jones, J.: Unprecedented Winter Heatwave over East Antarctica in 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11237, https://doi.org/10.5194/egusphere-egu25-11237, 2025.
In August 2023, South America experienced one of the most extreme heatwave events ever recorded, marking the warmest start to August in 117 years with temperature anomalies 10–20°C above the seasonal average. The heatwave impacted parts of Chile, northern Argentina, and southwestern Brazil, with temperatures in the Chilean Andes surpassing 38°C. Remote sensing images revealed widespread snowmelt across the Andes, and observations also suggested significant impacts on hydrological patterns, including spikes in winter runoff, affecting downstream water availability. Snow and glaciers in the Andes are essential reservoirs that sustain water supplies for millions of people. Winter plays a pivotal role in snowpack formation, which is crucial for the long-term stability of these resources. Extreme heatwave events during winter disrupt these processes, accelerating glacier retreat and snowmelt. This not only threatens water availability for agriculture, hydropower, and human consumption but also alters critical hydrological and ecological systems.
This study performs the first dedicated attribution on the 2023 unprecedented winter heatwave in the Andes, with a novel multi-method approach, combining 1) circulation analogue method, 2) statistical attribution method, and 3) physical-based storyline approach through a set of convective-permitting scale (4-km resolution) regional model simulations (CPRCM) over the Andes, to unpack the roles played by the blocking anticyclone, developing El Niño in the Pacific Ocean that year, and anthropogenic greenhouse gas emissions. The study leverages existing multiple global model simulations from Coupled Model Intercomparison Project Phase 6, a set of model simulations from the Large Ensemble Community Project, and the CPRCM simulations run in-house. Here we present initial findings from this attribution study on the attributed contributions from the different drivers to the spatial extent, intensity, and duration of the heatwave, as well as results on the changing frequency of occurrences between the current and pre-industrial climates using the large ensembles.
As global temperatures continue to rise, extreme winter heatwaves like this are projected to become more frequent, with profound consequences for snowpack dynamics and glacier stability in the Andes. This extraordinary winter heatwave serves as a stark reminder of the accelerating effects of climate change and the urgent need for adaptive strategies to protect critical Andean water systems.
How to cite: Li, S., Bhattacharjee, S., Potter, E., Jones, J., Davies, B., and Ely, J.: Unprecedented 2023 Winter Heatwave in the Andes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17239, https://doi.org/10.5194/egusphere-egu25-17239, 2025.
During March to May (MAM) 2023, Southwestern China experienced a prolonged hot and dry spring. This study investigates the role of anthropogenic climate change (ACC) in this extreme event and its impact on crop yields, using a 525-member ensemble from the HadGEM3 atmospheric model and the WOFOST agricultural model, respectively. Observational results indicate that the area-averaged surface air temperature anomaly (TAS) was 1.4°C higher than usual, and the percentage of anomalous precipitation (PAP) was nearly 33% below normal, making both metrics the most extreme since 1960. The hot event was predominantly driven by ACC, contributing about 60% to the TAS strength and increasing its likelihood 7 fold. In contrast, the severe drought was mainly influenced by internal climate variability, though ACC still increased its likelihood 9 fold. When these two extreme conditions are considered together as a concurrent hot-drought event, ACC increased its probability by about 8 fold. An attribution analysis of crop yields in the area was also conducted, revealing that ACC significantly shifts the probability distribution westward and reduces yields for both the winter wheat and rapeseed. Specifically, using the simulated 2023 crop yield results driven by ERA5, the likelihood of achieving the yield for winter wheat would have decreased by a factors of about 2. However, the results for winter rapeseed lack robustness due to model deficiency. Consequently, accurately modeling and projecting crop yields under changing climate conditions remains a challenge. Overall, global warming caused by anthropogenic activities has significantly increased the frequency of extreme hot drought events in Southwestern China, posing a severe threat to agricultural output and necessitating urgent action by policymakers.
How to cite: Liang, W., Tett, S., Liang, H., and Dong, W.: Attribution of the 2023 Extreme Spring Hot Drought Event in Southwest China: Meteorological and Agricultural Perspectives, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4257, https://doi.org/10.5194/egusphere-egu25-4257, 2025.
The Adige River basin has been affected several times in recent years by the concurrence of very hot and dry conditions. In summers 2015, 2017 and more recently in 2021-2022 severe hydrological droughts compounded and cascaded with wildfire and heatwave events. The chained effect of snow deficit in winter, higher-than-normal temperatures in early spring and heatwaves during summer caused multiple drought impacts.
For these reasons, we initially developed a procedure to identify and rank past compound drought and heatwave events (CDHW) which occurred in the basin using precipitation and temperature observations from EOBS covering 1950 to 2023. CDHWs were identified combining two criteria: while heatwaves were defined as periods of at least three consecutive days with daily maximum temperatures (TX) exceeding the 90th percentile of the calendar day. A CDHW event was identified when both conditions occurred simultaneously over at least 60% of the catchment area. Detected CDHWs are then sorted by severity, as combination of their intensity and spatial extent. This ranking allowed us to characterise and determine the temporal extent of the major CDHW event that occurred in the late spring of 2022 (10–28 May). This event was selected for subsequent analyses since it is one of the most recent episodes when compound hot and dry conditions in spring and summer caused severe water use restrictions in the basin area affecting key sectors such as water supply, agriculture, and hydropower, among others. It was identified as the most intense CDHW events of the last 15 years and ranked sixth out of 119 events recorded since 1950.
To better understand the drivers of the May 2022 event, we reconstructed its atmospheric conditions using a flow-analogue attribution approach based on ERA5 Z500. We compared the characteristics of the event in two different periods: 1951–1980 (low anthropogenic forcing) and 1991–2020 (moderate-high anthropogenic forcing). Our analysis shows that heatwaves like the one in May 2022 are now significantly hotter—by 1–4°C—than historical analogues and occur in a much drier context, characterised by pronounced precipitation deficits. These conditions have also exacerbated river flow reductions and water stress in the recent period compared to the past.
We also evaluated the ability of 25 EURO-CORDEX climate model simulations to reproduce the observed changes in TX and SPI-6 through flow-conditioned reconstructions based on the May 2022 event. The assessment reveals that EURO-CORDEX models mostly fail to capture the observed signal and magnitude of changes in flow-analogue attribution experiments. Specifically, when identifying flow-conditioned analogues, nearly half of the models fail to reproduce the observed temperature increases, either in terms of sign or magnitude. Similarly, for drought conditions, models fail to reproduce both the direction and magnitude of observed changes.
This research was supported by the European Space Agency (ESA) under the EO4MULTIHA project (2023–2025), contract number 4000141754/23/I-DT.
How to cite: Lemus-Canovas, M., Crespi, A., Maines, E., Pittore, M., and Terzi, S.: Observed and modelled changes in the drivers that trigger compound drought and heatwave events in the Adige River basin (Eastern Italian Alps) with a focus on May 2022., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5993, https://doi.org/10.5194/egusphere-egu25-5993, 2025.
As anthropogenic emissions of greenhouse gasses continue to increase, in 2024 we have experienced the hottest year on observational record globally, while the last ten years are the warmest ten years. Accelerating global climate change is intensifying the water cycle, leading to more frequent and/or severe droughts and floods as well as rapid transition between these opposite extremes in many parts of the world. Wide areas of the Amazon and Orinoco watersheds have endured severe drought in 2023 (the 2nd hottest year on record) and 2024 while El Niño-Southern Oscillation (ENSO, the dominant mode of internal climate variability at interannual timescales) was in El Niño (positive) phase and neutral conditions for most of this time. Some of the Amazon’s largest tributaries dropped to their lowest levels since records began in 1902, while more than 1700 schools and 760 health centres in the Amazon were inaccessible or out of reach at least at one point in the last two years.
We examine dynamic and thermodynamic mechanisms leading to drought in both dry (JJASON) and wet (DJFMAM) seasons and their multiple combinations in the Amazon and Orinoco watersheds in 2023 and 2024. We employ multi-method attribution analysis to reveal the role of climate change leading to such prolonged and impactful drought conditions in this wider tropical region of the South America. We combine observational and reanalysis products with large ensembles of CMIP5/6 historical simulations and future projections to analyse the role of climate change and ENSO leading to these extreme events on timescales from six months to two years. We also use Met Office HadGEM3-A attribution system to assesses to what extent anthropogenic forcing has modified the probability and magnitude of multiple classes of meteorological droughts in the region experienced over the last two years. We explore both dynamically unconditional and conditional perspectives in our attribution analysis. Preliminary results point to a key role of climate change in likelihood and intensity of such droughts in both dry and wet seasons as well as from annual perspective in 2023 and 2024.
How to cite: Fučkar, N., Allen, G., Allen, M., and Obersteiner, M.: On the nature and attribution of severe droughts in the Amazon and Orinoco River basins in 2023 and 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18150, https://doi.org/10.5194/egusphere-egu25-18150, 2025.
In the aftermath of an impactful heatwave, storm or flood, attribution studies often ask whether events like this are becoming more intense due to climate change. Answers to this question can be deduced both from unconditional extreme value statistics and from approaches like storylines or circulation analogues, which analyze the event conditional on the dynamical weather conditions. But can we compare these two kinds of statements? How should we interpret situations where they disagree? This talk takes a systematic look at different notions of intensity changes. We see examples where conditional and unconditional approaches are fundamentally incomparable and find special cases where they can reasonably be compared and potentially combined into a single attribution statement.
How to cite: Buschow, S.: Unconditional apples and conditional oranges: can we compare different styles of attribution?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15959, https://doi.org/10.5194/egusphere-egu25-15959, 2025.
Since 2004, methods for event attribution have been developed across many groups. Early studies showed that answers to attribution questions are sensitively dependent on the framing of the study used but recently developed methods for storyline attribution have not been compared in detail.
Here, we compare three common methods for storyline attribution, alongside the probabilistic method, based on the midlatitude cyclone Babet. This storm caused flooding in the UK and Ireland in October 2023. The three storyline methods are flow analogues, pseudo-global warming, and forecast-based attribution. We discuss four questions that might be asked of attribution studies by the public: (1) Has climate change impacted the event? (2) How has climate change impacted the frequency of the event? (3) How has climate change impacted the event severity? (4) Were the dynamics of the event influenced by climate change and if yes, how?
We argue that storyline methods are better suited to answer questions about severity changes in events but that probabilistic methods are needed to determine changes in frequency of the event. Limitations and opportunities of the methods need to be clearly communicated to the public when publishing event attribution studies.
Finally, we compare the framing of storyline attribution to that of probabilistic attribution. To the best of our knowledge, this comparison of methods is the first study discussing the differences in the framing and the quantitative results of storyline attribution methods. We hope it represents a basis for future systematic comparisons.
How to cite: Ermis, S., Thompson, V., Leach, N., de Vries, H., Lenderink, G., Zhou, L., Hope, P., Clarke, B., Kew, S., Sparrow, S., Lott, F., and Weisheimer, A.: A comparison of storyline attribution methods for a midlatitude cyclone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4522, https://doi.org/10.5194/egusphere-egu25-4522, 2025.
Understanding the impact of climate change on environmental extremes is of key importance for effective adaptation strategies. In this study, we used an innovative automated system that unveils the thermodynamical climate change signal of the day in near-real-time. To do so, we employed the so-called "event-based storyline approach". This method involves nudging our global CMIP6 coupled climate model (AWI-CM1) towards the observed large-scale free-troposphere winds, including the jet-stream, using various climate background conditions — preindustrial, present and 4ºC warmer climates.
Our analysis focuses on storm Boris, which resulted in record-breaking rainfall across Central and Eastern Europe in September 2024, causing catastrophic flooding. Our findings indicate that human-induced warming led to a 9% increase in rainfall associated with this storm. Furthermore, the area affected by extreme rainfall (exceeding 100 mm) expanded by 18% and would continue expanding in a future warmer climate. However, this future expansion is simulated to be more moderate due to a complex interplay between dynamics and thermodynamics. The case of Storm Boris demonstrates the potential of near-real-time storylines for rapid evidence-based climate change attribution and communication.
How to cite: Sánchez Benítez, A., Athanase, M., Monfort, E., Jung, T., and Goessling, H. F.: How climate change intensified storm Boris’ extreme rainfall, revealed by near-real-time storylines, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12014, https://doi.org/10.5194/egusphere-egu25-12014, 2025.
Human-induced climate change is threatening hard-earned progress in public health. Extreme weather events, rising temperatures and related socioeconomic impacts are responsible for a substantial mortality and morbidity burden. The escalating health impacts are expected to amplify in coming decades as warming progresses and other societal challenges such as urbanisation, ageing and inequalities continue to expand. Quantifying robust estimates of health impacts attributed to anthropogenic climate change is a pressing issue and a high-priority research area nowadays. In recent years, substantive progress has been made in this field by developing new research and providing cutting-edge evidence. The main booster has been the establishment of interdisciplinary initiatives that have allowed the exchange of ideas, data and methods between mainly climate scientists and epidemiologists. The talk will provide an overview on the latest developments in health impact attribution (e.g., storylines of climate-health impacts, accounting for adaptation), and discuss the current and potential synergies between research fields, applications (e.g., climate litigation) and existing knowledge gaps.
How to cite: Vicedo Cabrera, A. M.: Translating the effects of climate change into health impacts: new perspectives in health attribution studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8970, https://doi.org/10.5194/egusphere-egu25-8970, 2025.
While event attribution has made considerable progress in the last two decades, event impact attribution, which calculates the attributable share of impacts from extreme weather events, remains challenging. Impacts result from the interaction between the intensity of hazards, the exposure of affected areas and the vulnerability of individuals, infrastructures and the environment. Across different types of extreme weather events, impacts and world regions, a wide range of datasets and approaches need to be considered to tackle this complex and interdisciplinary field of research.
Here, we aim to develop simple methods that can be deployed rapidly and globally to estimate attributable impacts in the aftermath of extreme weather events. We will present initial work on attributing direct economic impacts from tropical cyclones and on an updated global physical asset database used in this context.
This initiative produces near-real-time results that can be communicated in a timely manner to a broad audience, raising awareness about the impacts of extreme weather and the role of climate change. It ultimately seeks to provide valuable information on losses and damages and levels of adaptation, which can be instrumental for policymaking, climate justice and preparing societies for future extremes.
How to cite: Theokritoff, E., Sparks, N., Otto, F., Rogelj, J., and Toumi, R.: Building a global and rapid event impact attribution framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6830, https://doi.org/10.5194/egusphere-egu25-6830, 2025.
Anthropogenic climate change can affect wide-spread river floods through changes in atmospheric circulation patterns and thermodynamic effects. Changes of atmospheric circulation patterns due to climate change are rather uncertain, while the thermodynamic effects can be simulated more accurately. Here, we employ a storyline approach to attribute the magnitude of recent extreme European floods to the thermodynamic effect of climate change. We use Newtonian nudging to constrain the zonal and meridional winds in simulations of the Community Earth System Model (CESM2) to reanalysis data, to generate factual and counterfactual weather conditions based on historical and pre-industrial greenhouse gases and aerosol concentrations respectively. Downscaled precipitation and temperature, along with observed climatology for leaf area index and fixed land cover from 2009, are used to force the mesoscale Hydrological Model (mHM), thereby simulating counterfactual and historical discharge at 0.125 degree resolution. However, spatial shifts in precipitation extremes observed in nudged circulation simulations present challenges for event attribution, particularly for smaller-scale phenomena. Thus, our focus is on large-scale floods, where we examine changes in flood patterns and explore the intensification of historical events in the context of climate change. By focusing directly on discharge, our approach is closer to actual flood impacts and thus goes beyond traditional flood attribution approaches that usually rely on precipitation extremes only. The presented approach also provides a blueprint for other types of impact attribution using impact models forced with nudged circulation climate model simulations.
How to cite: Miersch, P., Dunkl, I., Sippel, S., and Zscheischler, J.: Attributing floods to anthropogenic climate change using a hydrological model forced with climate simulations under nudged atmospheric circulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9238, https://doi.org/10.5194/egusphere-egu25-9238, 2025.
As climate change intensifies, regions worldwide face increasing impacts from extreme weather events including, compound and successive occurrences. These events pose significant risks to agriculture, where weather variability directly affects crop yields and revenue. Understanding the role of human-induced climate change in exacerbating these effects is essential for informed decision-making. In this study, we quantify the direct yield damages and associated revenue losses of extreme weather events attributable to human-induced climate change in Germany from 2018 to 2022. We achieve this by simulating crop yields using crop-specific statistical yield model under observed (factual) climate data and counterfactual climate simulations, where the human-induced climate change trend is removed from observed climate data. The statistical yield model isolates the impact of multiple extreme weather events on crop yields. It employs the Least Absolute Shrinkage and Selection Operator (LASSO) approach, a penalized regression method that selects the most relevant predictors. This model is applied to eight key crops—winter wheat, winter barley, rapeseed, maize, spring barley, spring oats, sugar beets, and potatoes—that collectively account for 75% of Germany's agricultural area. The model integrates predictors derived using precipitation, temperature, and soil moisture data to represent yield-influencing extreme weather events.
Our preliminary findings reveal that the extreme weather impacts of climate change on crop yields vary significantly across crop types. Winter crops, including rapeseed, winter barley, and winter wheat, demonstrate average yield gains attributable to climate change across 2018–2022, with increases of 5.71%, 3.08%, and 1.56%, respectively. In contrast, the impacts on summer crops is mixed with crops like sugar beets and potatoes show average yield gains of 3.05% and 1.74%, respectively, silage maize and oats experience yield reductions, with silage maize yields declining by 2.52%. Despite yield gains for most field crops, the revenue losses highlight significant economic damage, with annual revenue damage due to extreme events attributable to climate change amounting to 184 million Euros across Germany from 2018 to 2022. The findings provide valuable insights for cost-benefit analyses in mitigation strategies and support climate-resilient agricultural policymaking to address the growing challenges posed by extreme weather events.
How to cite: Nagpal, M., Heilemann, J., Klassert, C., Bevacqua, E., Rakovec, O., Samaniego, L., Klauer, B., and Gawel, E.: Attribution of observed impacts of climate change on crop yields and economic damages from extreme weather events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19155, https://doi.org/10.5194/egusphere-egu25-19155, 2025.
Climate extremes such as heatwaves, river floods, droughts, crop failures, including aspects of wildfires and tropical cyclones, are increasingly attributable to anthropogenic climate change. Yet how this translates into unprecedented levels of extreme event exposure in one’s lifetime remains unclear. Here we show that, neglecting adaptation, many of today’s youth will experience unprecedented exposure to extremes during their lifetimes. For the events above, the share of people facing unprecedented lifetime exposure is projected to at least double from 1960 to 2020 birth cohorts under current mitigation policies aligned with a global warming pathway reaching 2.7 °C above pre-industrial temperatures by 2100. In a 1.5 °C pathway, ∼50% of people born in 2020 will experience unprecedented lifetime exposure to heatwaves. If global warming reaches 3.5 °C by 2100, this rises 30 to ∼90% of this birth cohort. For the same cohort and warming pathway, ∼30% will live with unprecedented exposure to crop failures and ∼10% to river floods. Further, under current policies, two indicators of vulnerability show that the most vulnerable experience significantly more unprecedented exposure to heatwaves than the least vulnerable. Our results call for sustained greenhouse gas emissions reductions to lower the burden of climate change on young generations
How to cite: Thiery, W., Grant, L., Vanderkelen, I., Gudmundsson, L., Fischer, E., and Seneviratne, S. I.: Global scale mapping of unprecedented lifetime exposure to climate extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11898, https://doi.org/10.5194/egusphere-egu25-11898, 2025.
The Mongolian Plateau (MP) has undergone a significant drought trend in recent decades,
presenting a substantial threat to local ecosystems and environments. The debate persists on
whether this observed drought trend stems from external forcings or is a result of internal
variability. Utilizing the large-ensemble simulations of the climate model and dynamical
adjustment method, we have identified that the atmospheric circulation anomalies are the main
drivers of drought trends in MP. A zonal atmospheric wave train, triggered by internally-generated
warming of the North Atlantic sea surface temperature (NAS), is responsible for nearly 57% of the
drought trend observed in MP. While external forcings could potentially induce a moistening trend
in MPvia direct thermodynamic processes, the atmospheric circulation anomalies linked to the
forced NAS warming can not only offset its direct effect but also further amplify the drought trend
in MP, accounting for 43% of the drought trend observed in MP.
How to cite: Li, Y., Gong, H., Chen, W., and Wang, L.: Attribution of drought trends on the Mongolian Plateau over the past decades, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1944, https://doi.org/10.5194/egusphere-egu25-1944, 2025.
Over recent decades, Antarctica has experienced significant changes, contributing notably to global sea level rise. This mass loss is partially offset by precipitation accumulation, which is strongly influenced by extreme precipitation events. However, the extent to which human activities amplify these extremes remains uncertain. In this study, we analyze precipitation and extreme precipitation patterns over Antarctica using formal detection and attribution methods. Leveraging the ERA5 reanalysis dataset and CESM2 Large Ensemble (CESM2-LE) climate model simulations, we investigate the forced response in observed precipitation trends. Our analysis, focused on the Rignot basins in West Antarctica and the Dronning Maud Land region in East Antarctica, reveals that anthropogenic forcing, particularly from greenhouse gas emissions, has been the dominant driver of precipitation and its extremes since the 1980s, in conjunction with natural variability. These findings span the period 1979–2023 and provide critical insights into the role of human influence on Antarctic precipitation trends.
How to cite: Chittella, S. P. S., Deb, P., and Orr, A.: Attribution of Antarctic Precipitation and Extremes to Anthropogenic and Natural Drivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2081, https://doi.org/10.5194/egusphere-egu25-2081, 2025.
The probabilistic attribution has become a valuable tool for analysing the influence of anthropogenic climate change on recent extreme weather events in rapid attribution studies.
For this rapid analysis, the methods of the World Weather Attribution group (WWA) described by Philip et al. (2020) can be used. This method is a straightforward option for evaluation especially in a trend of operationalizing the probabilistic attribution. When estimating the General Extreme Value (GEV) distribution, the global mean surface temperature (GMST) is introduced as covariate such that the GEV shifts or scales with this temperature. This has the advantage that the complete timeseries of every observation and model dataset can be analysed to detect anthropogenic influences. The covariate method provides a trend proportional to the covariate and allows extrapolation of existing datasets to a past or future climate. Depending on the context, this can be seen as an advantage or disadvantage.
To avoid a proportional trend, an alternative method consists in evaluating time slices instead. Two 30-year blocks for a past climate and the current climate or a counterfactual and factual climate are analysed. Optionally, a future scenario from climate models can be included. While only one GEV with one additional covariate is estimated to describe a single model in the previous method, a standard GEV is used for every defined slice. In this case, the single 30-year climate periods are independent from the other time slices. The challenge here is the selection of climate models and scenarios which simulate a similar trend of anthropogenic impacts. Additionally, observation datasets can only be used when the time series is long enough to allow extraction of two independent time slices of 30 years each.
The difference between the two methods and the difference in the results will be analysed and presented. Both methods can be understood as part of the same toolbox and are both equally valid. Here, the main interest is in the ability to understand and explain upcoming differences in extreme weather attribution studies between the two methods.
The research of this project is part of the ClimXtreme Network, funded by the German Federal Ministry of Education and Research (BMBF). Focus of this project are extreme weather events and impacts caused by anthropogenic climate change.
Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: A protocol for probabilistic extreme event attribution analyses, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, 2020.
How to cite: Schröter, J., Tivig, M., Lorenz, P., Sauerbrei, R., and Kreienkamp, F.: Comparison of the GMST covariate and the time slice method for probabilistic extreme weather event attribution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2874, https://doi.org/10.5194/egusphere-egu25-2874, 2025.
Changes in apparent temperature (APT), due to global warming can have a significant impact on human health and society. A recent study based on ERA5 reanalysis data and HadISD observations post 1979 has shown that the probability of extreme heat discomfort events over the Northern Hemisphere oceans has significantly increased, with the largest changes occurring in the tropics and parts of the Arctic. However, current APT studies have mostly focused on changes on land, while changes in marine APT (MAPT), which pose a potential threat to ocean-based economies and living conditions of people dependent on the sea, still remain unclear to a large extent. We examine historical changes in MAPT and the dominant role of different meteorological factors by separating their contribution to changes during 1950-2023.
The heat stress indicator - APT, is a function of surface air temperature, relative humidity (RH) and near-surface wind speed (NSWS) and is computed from 20CRv3-ERA5 reanalysis monthly average data. MAPT and MAT warm at 0.109±0.011 and 0.122±0.015 ℃/10a respectively. MAPT warms faster than MAT but with similar spatial patterns. We estimate contributions from changes in MAT, RH and NSWS to changes in MAPT using a linear sensitivity analysis. Global MAPT changes are mainly dominated by the change of MAT, RH has little influence and NSWS has some regional influence especially in parts of the equatorial Pacific and the Southern Ocean.
To explore whether reanalysis changes could be reproduced by CMIP6 simulations, contribution by changes in meteorological factors to changes in MAPT from REA and CMIP6 were computed. CMIP6 is consistent with reanalysis with both showing the dominant contribution is from changes MAT, but there are some differences in the spatial pattern of the RH contribution, which only has a small influence and NSWS which may impact regional change. Reanalysis-CMIP6 differences of MAT as well as differences of RH and NSWS in most mid-high latitudes are consistent with internal variability, while differences of RH and NSWS in low latitudes are beyond the range of internal variability. The consistency between reanalysis MAPT/MAT & CMIP-6 multi-model ensemble means we could attribute the bulk of the ‘obs’ changes to anthropogenic climate change.
How to cite: Liao, L., Tett, S., and Li, Q.: Changes in Apparent Temperature Over the Ocean during 1950-2023: Long-term Trends and Contributions of Meteorological Factors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4607, https://doi.org/10.5194/egusphere-egu25-4607, 2025.
When an extreme weather event occurs scientists may be asked if similar events have occurred in the past. If the frequency of such events have changed. If such events are becoming more intense or more persistent. And if the impacts of such events are increasing or decreasing. We present a tool that uses atmospheric circulation analogues to provide a framework to answer such questions.
When using our analogues tool there are methodological choices that need to be considered. Extreme weather events are, by their very definition, rare, so how can we assess if the analogues are close enough to the observed event to be useful? Analogues can be calculated using spatial correlation or Euclidean distance, from sea level pressure or 500 hPa geopotential height, over different domains, and for different timescales. Investigating how sensitive results are to these choices allows us to provide a set of rules for using our tool for a range of different types of climate extremes, from heatwaves to extreme rainfall.
Through a series of case studies, we consider the methodological choices required when assessing analogues, and assess which events are most suited to analogues methods.
How to cite: Thompson, V., Pinto, I., Kew, S., and Philip, S.: Development of the Climate Explorer Circulation Analogues Tool , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8242, https://doi.org/10.5194/egusphere-egu25-8242, 2025.
The 2023/2024 storm season for the UK and Ireland was exceptionally wet as a whole, as well as ‘hosting’ a notably large number of named storms, some of which led to devastating flooding, with cascading impacts on human health, food production and the cost of living.
Here we present a attribution study for the Oct-Mar season, quantifying the role of human-induced climate change on the frequency and intensity of strong winds and heavy rainfall on storm days as well as total precipitation for the season as a whole. The storm severity index (SSI) is used to identify stormy days and as an indication of wind intensity. We use probabilistic attribution methods following the world weather attribution protocol, synthesising trends in observations with climate models and communicating uncertainties.
The rainfall associated with storms was found to have become about 20% more intense and that the 2023/24 level has become about a factor of 10 more likely. Climate change was also found to have a strong influence on Oct-Mar precipitation totals, in line with expectations. The influence of climate change on storm winds, was less clear however, with average wind (SSI) on stormy days being found to have decreased slightly. Possible reasons for this will be discussed in the light of relevant literature. We also highlight the importance of vulnerability and exposure information in combination with attribution outcomes to provide recommendations for reduced impacts.
How to cite: Kew, S., McCarthy, M., Wallace, E., Pirret, J., Claydon, O., Lott, F., Ryan, C., Coonan, B., Pope, J., Murtagh, E., Vahlberg, M., Amankona, A., Pinto, I., Barnes, C., Philip, S., Otto, F., and Fraser-Baxter, S.: Autumn and Winter storms over UK and Ireland about 20% wetter due to human-induced Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18518, https://doi.org/10.5194/egusphere-egu25-18518, 2025.
Attribution studies are crucial for understanding the anthropogenic contributions to meteorological extremes and have com-
monly relied on approaches that compare historical observations with global climate model (GCM) simulations that are driven
solely by natural forcing (Bindoff et al. (2013)). However, GCM simulations have limited spatial resolution and are biased by
parameterized climate processes and uninitialized conditions that lead to the lack of representation of historical meteorological
events (Cannon et al. (2015); Eyring et al. (2016); Almazroui (2021); Zhang et al. (2023)). We address these gaps by devel-
oping a novel, high-resolution dataset that provides daily average global temperatures over the past four decades without the
influence of anthropogenic climate forcing. We use quantile delta mapping (QDM), a quantile trend-preserving bias adjust-
ment method, to remove anthropogenic warming from the fifth generation of the European Centre for Medium-Range Weather
Forecasts Reanalysis (ERA5) using historical and natural-forcing-only simulations from the Coupled Model Intercomparison
Project Phase 6 (CMIP6). The resulting dataset consists of historical Global Daily Natural temperature (henceforth, GDNat)
records at 0.25◦ x 0.25◦ spatial resolution from 1979 - 2020, providing a valuable resource for attributing extremes and their
impacts to anthropogenic warming.
How to cite: Fofrich, R., McCusker, K., Malevich, S., and Kopp, R.: GDNat: a global, daily, high-resolution, natural-forcing only temperature data set for attribution research, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20721, https://doi.org/10.5194/egusphere-egu25-20721, 2025.
Compound weather and climate events refer to combinations of multiple weather and climate rivers and/or hazards that lead to potentially large impacts. Not only will single extreme events become more frequent in the future, but there will also be a higher likelihood of high-impact compound events. Extreme events are more likely to occur simultaneously, leading to increased damage and impact on the territory. Extreme Event Attribution (EEA) is an emerging field in climate sciences. One of the goals is to describe whether and how the probability of an event depends on climate change. Extreme event attribution typically focuses on univariate assessments, often leading to an underestimation of the risks and actual damages attributable to climate change.
While compound weather and climate events can result in significant socioeconomic consequences, their attributability to climate change remains largely unexplored. Here, we present a compound event attribution study assessing the change in probability of having co-occurrent hot and dry events in Belgium with and without climate change.
How to cite: Deidda, C., Willems, P., Zscheischler, J., and Thiery, W.: Compound extreme event attribution: hot and dry events in Belgium, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17873, https://doi.org/10.5194/egusphere-egu25-17873, 2025.
In August 2022, an unprecedented compound heatwave and drought event (CHDE) lasting 24 days occurred in the Yangtze River valley (YRV), leading to severe reduction of crop, fresh water and power supply. We constructed a joint cumulative probability distribution of heatwave and drought intensity, and found that the lowest probability-based index (PI) of 0.06 in 2022 was estimated as a 1-in-662-year event over 1961–2022 climate. We then detected fingerprint of greenhouse gas forcing to the observed PI in a generalized extreme value framework, but not the aerosol forcing, suggesting the leading contribution of greenhouse gas forcing on such extreme CHDE. Furthermore, anthropogenic influence had increased the probability of such CHDE by more than 10 times compared to the counterfactual climate. Also, the PI decreased from about 0.30 at the present climate to about 0.14 at the 3°C global warming level, indicating that CHDE will become more extreme over YRV.
How to cite: Chen, D., Qiao, S., and Dong, W.: Contribution of anthropogenic influence to the 2022-like Yangtze River valley compound heatwave and drought event, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10296, https://doi.org/10.5194/egusphere-egu25-10296, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 5
EGU25-1831 | ECS | Posters virtual | VPS5
Quanatifying the contributions of internal varibility in South Asian near-surface wind speedMon, 28 Apr, 14:00–15:45 (CEST) | vP5.13
Near-surface wind speed (NSWS) plays a critical role in water evaporation, air quality, and energy production. Despite its importance, NSWS changes in South Asia, a densely populated region, remain underexplored. This study aims to understand and quantify the uncertainties in projections of NSWS over South Asia, particularly in relation to internal variability. Utilizing a 100-member large ensemble simulation from the Max Planck Institute Earth System Model, we identified the Interdecadal Pacific Oscillation (IPO) as the leading mode of internal variability influencing South Asian NSWS in the near future. Our findings reveal that the IPO could significantly impact future NSWS, with its positive phase being linked to strengthened westerly flows and increased NSWS across South Asia. Notably, the study shows that accounting for the IPO's impact could reduce NSWS projection uncertainty by up to 8% in the near future and 15% in the far future. This underscores the key role of internal variability, particularly the IPO, in shaping regional NSWS projections. By reducing uncertainties in these projections, our findings can inform climate adaptation strategies for South Asia, helping optimize wind resource assessments in the context of changing wind patterns.
How to cite: Yuan, H.-S. and Shen, C.: Quanatifying the contributions of internal varibility in South Asian near-surface wind speed, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1831, https://doi.org/10.5194/egusphere-egu25-1831, 2025.
