The accurate detection of changes in regional climate extremes is sometimes challenging due to observational uncertainties, such as non-climatic series discontinuities or station scarcity in regions like Africa or high altitudes. Reliable attribution of regional climate extremes usually relies on model skills in simulating the extremes. Global models actually provide some useful evidence for the role of human influence, while regional climate models could boost confidence in attribution to regional forcings such as land use/cover or urbanization. The attribution uncertainties could be caused by different attribution methodologies used, e.g., optimal fingerprinting or Bayesian statistics, and different model strategies employed, e.g., multi-models or single-model large ensembles. Besides, how to address internal climate variability remains a key source of the attribution uncertainties. Emerging advanced techniques like artificial intelligence (AI), have the great potential to substantially reduce these uncertainties.
This session provides a venue to present the latest progress in reliable detection, modelling, and attribution of regional climate extremes, especially in quantifying or reducing their uncertainties for better risk management. We welcome abstracts focused on, but not limited to:
- address the quality issue of daily observation data relevant at the regional scale
- assess the fitness of global or regional modelling by designing tailored diagnostics for climate extremes and their drivers in a regional context
- improve climate models to realistically represent regional climate extremes, in particular to convection-permitting scale at a fine resolution or to mega-heatwaves by adding relevant land-atmosphere feedbacks such as through dynamic downscaling
- reveal and evaluate the strengths and weaknesses of attribution methodologies used for different regional climate extremes
- develop new detection and attribution techniques for regional climate extremes, including large ensemble and AI algorithms
- find key physical or causal processes to constrain the attribution uncertainties
Finally, abstracts associated with projection uncertainties of regional climate extremes are also appreciated.
Supercells are the rarest type of thunderstorms, well known because of their multi-hazard characteristics in the Great Plains region of the US. These hazards include tornadoes, very large hail, lightning, damaging wind gusts, and/or excessive precipitation resulting in flash floods. However, their spatial footprints are not limited to there, with the whole of the midlatitudes, including Europe, having their share. Little is known about the spatial and temporal distribution of supercells outside the US. In Europe for instance, there are only a few short-term observational studies addressing the topic.
Supercells are characterized by the presence of a mesocyclone, which is a rotating updraft. Using an updraft helicity metric, which aims to extract such mesocyclonic features within a convection-permitting climate model (CPM), we present a supercell climatology for Europe and an assessment of their future changes based on RCP8.5. The climatology is based on a 20-y long hindcast, and we also assess three further 10-y long simulations: 1) control, 2) mid-century future, and 3) end-of-century future.
Our results show that supercells are more frequent in southern Europe, compared to the north, and predominantly occur in summer. Left-movers, which are conventionally overlooked, but observations suggest can produce as much hazards in Europe (e.g. very large hail), consist of 15% of all supercells. With warming, the frequency increases in the south and to a lesser extent in the north, whilst there are decreases in Central Europe. Finally, we claim that changes in favourable environmental conditions of severe thunderstorms might not directly translate into the changes in severe thunderstorms themselves, highlighting the need for CPMs for assessing hazardous weather extremes at small spatial scales.
How to cite: Kahraman, A., Short, C., Fowler, H., and Kendon, E.: Supercells in Europe modelled by a Convection-Permitting Climate Model: Climatological features and future change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9550, https://doi.org/10.5194/egusphere-egu25-9550, 2025.
Regional Climate Models (RCMs) are valuable tools capable of providing finer-scale climate information, which is particularly relevant in regions like southern South America (SA), where the complex topography and the land-coast contrast strongly influence climate. Despite this, RCMs present systematic errors that need to be corrected for their proper use in impact studies, especially those relying on climate impact indices exceeding specific thresholds, such as heat-stress conditions. In these cases, bias adjustment (BA) methods are commonly used. These methods link climate model historical simulations and observations through the calibration of transfer functions that are subsequently applied to adjust systematic errors in the simulated distribution. In this study, different BA methods were evaluated for southeastern South America (SESA) with a special focus on the estimation of multivariate heat-stress indices, namely the wet bulb temperature and a simplified version of the wet bulb globe temperature. Both indices are based on temperature and humidity variables. The BA methods were calibrated using the historical CORDEX-CORE RCM simulations for the SA domain and the MSWX high-resolution observation dataset. The assessment accounted for: a) two adjustment strategies for estimating the bias-corrected indices (direct and indirect); b) comparison of univariate and multivariate BA methods; c) evaluation of trend-preserving and non-trend-preserving methods. In all cases, BA methods were trained and validated with a cross-validation scheme in the austral summer season during the historical period.
Results show that under the indirect approach (i.e. adjusting individual variables involved in the indices calculation), all univariate methods presented similar performance, with no remarkable differences between trend- and non-trend-preserving methods. Notwithstanding, in this set up, the multivariate method considerably improved the representation of the thermal indices. This improvement was evident for the RegCM4.7 simulations, where the calculation of the indices using the individually adjusted variables amplified the errors. The lowest biases were found under the direct approach (i.e. adjusting indices directly), although performance among methods varied depending on the heat stress index analyzed.
Overall, this study provides insight into the suitability of the BA methods for estimating multivariate thermal indices and paves the way for future assessments of heat stress conditions over SA.
Acknowledgement: A.C. acknowledges support from project PROTECT (PID2023-149997OA-I00) funded by MICIU/AEI/10.13039/501100011033 and ERDF A way of making Europe.
How to cite: Balmaceda-Huarte, R., Casanueva, A., and Bettolli, M. L.: Evaluation of multiple bias-adjustment methods for estimating heat stress conditions in southern South America, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-724, https://doi.org/10.5194/egusphere-egu25-724, 2025.
Compared to increasingly clear responses of temperature and precipitation to anthropogenic climate change, forced changes in relative humidity (RH) remain largely elusive. This is mainly because climate models failed to capture sharp decline in RH shown in both observations and reanalysis data. Despite growing attention to plausible drivers for the observation—model discrepancy, none of the theoretical reconstructions or dedicatedly-designed simulations could reproduce the drying as strong as observed.
We here propose another possibility, that is, observations/reanalysis (incl. HadISDH and ERA5) are wrong due to overlooked data inhomogeneity arising from region-wide changes in observing instruments, as showcased in China in the 2000s. Such simultaneous changes in instruments failed the detection of ‘break-points’ by automated methods via a neighbor-comparison scheme. By getting access to detailed meta data information provided by the National Meteorological Information Center of China, we now could adjust for the inhomogeneity through combining automated methods and manual checks. The newly homogenized data corrects previously estimated strong and significant drying trends into weak and insignificant ones across humid China, in consistent with most of CMIP and single model initial condition large ensemble (SMILE) simulations.
The homogenized RH dataset is then used for attribution and projection of forced changes in RH and associated compound events (extreme wet-bulb temperature and VPD) over Eastern China, within a Bayesian statistical framework. We find historical forcings of greenhouses gases and aerosols on RH nearly counteracted each other, leading to a weak and non-detectable regional trend. The constrained projection shows that raw CMIP6 projections underestimated the magnitude of forced drying of RH. The Bayesian constraint narrows the uncertainty range of projected RH changes by around ~30%, and effectively eliminates the possibly of wetting response at late 21st century. Given the stronger-than-expected drying of RH, raw projections slightly overestimate future increases in extreme heat stress but substantially underestimate future rises in extreme VPD accordingly, with the inter-model spread in projections narrowed by 30~40% conditioned on the homogenized historical observational series.
How to cite: Chen, Y.: Projection of forced changes in regional relative humidity and associated compound extremes constrained by homogenized observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2410, https://doi.org/10.5194/egusphere-egu25-2410, 2025.
Changes in short-term precipitation events have significant local impacts of climate change, yet are poorly captured by coarse-resolution global climate models. We analyse the projected changes in warm season precipitation events in Finland from a convection-permitting regional climate model HARMONIE-Climate, operating at 3-kilometer resolution. Realistic modeled precipitation characteristics are verified against multiple observational datasets for 1986–2018, and projected changes in precipitation events are analyzed until 2041–2060 and 2081–2100.
Our results show that all simulations agree on an increase in mean wet hour precipitation intensity and a decrease in the number of wet hours. The proportion of wet hours with respect to the all hours (calculated from the whole area of Finland) decrease from 11–13 % to 9–11 % by mid-century, and further reducing to around 9 % across all simulations and scenarios by late century. We also find that as climate change proceeds, the frequency of precipitation events over 2 mm h⁻¹ increases and the changes become greater for the categories of higher intensities, while lower intensity events become less frequent.
Of particular interest are the projected changes in intensity categories used in alert classification by the Finnish Meteorological Institute, wherein heavy rain is identified at a threshold of 7 mm h⁻¹, and the national alert level for potentially dangerous rainfall is set at 20 mm h⁻¹. According to the simulations, the frequency of such events in Finland will increase greatly as the climate change proceeds and their contribution to overall wet hours increase. In a strong climate change scenario (RCP8.5), extremely heavy precipitation exceeding 20 mm h⁻¹ will become twice to three times as common (three to six times) in 2041–2060 (2081–2100) compared to 1986–2005, while simultaneously the total number of wet hours is projected to decrease by 12–16 % (18–25 %).
How to cite: Utriainen, L., Virman, M., Laakso, A., Ritvanen, J., Jylhä, K., and Merikanto, J.: Less frequent but more intense summertime precipitation in Finland: results from a convection-permitting climate model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6128, https://doi.org/10.5194/egusphere-egu25-6128, 2025.
According to the Sixth Assessment Report of the IPCC, the global surface temperature has been higher in each of the last 4 decades compared to any previous decade since 1850. With the ongoing intensification of global warming, the intensity and frequency of extreme drought events have significantly increased. Based on the CN05.1 observational dataset and the results of five regional simulations from RegCM4.0 under future warming scenarios from the CORDEX (Regional Climate Downscaling Experiment), extreme dry periods are characterized by the number of consecutive dry days (CDD index). The Generalized Extreme Value (GEV) distribution is used to model the probability distribution of the CDD index in China to assess the spatiotemporal variations of extreme dry periods. By analyzing the spatiotemporal trends of the CDD index in China over the past 60 years and utilizing simulation techniques to assess the probability distribution characteristics of the CDD index, the study incorporates the Taylor diagram to evaluate the simulation performance of RegCM4.0. Finally, projections of the temporal and spatial distribution of the CDD index in China under a stable 2°C warming scenario, as well as changes in the ensemble mean of the five regional simulations relative to historical climate conditions, are presented.
The results indicate that the spatial distribution of the CDD index in China gradually decreases from northwest China and the Qinghai-Tibet Plateau to the eastern coastal areas. Over the past 60 years, the CDD index has decreased in northwest China and the Qinghai-Tibet Plateau, while it has increased in the eastern coastal regions, Northeast China, and North China, showing significant trends in these areas. The eastern coastal areas exhibit the lowest CDD index and minimal inter-annual variability, while northwest China and the Qinghai-Tibet Plateau show the highest CDD index values and the most significant inter-annual variability. According to the Taylor diagram, under the RCP4.5 emission scenario, the CDD index from simulations using the RegCM4.0 regional climate model, corrected by the Quantile Delta Mapping (QDM) method, shows better performance than uncorrected simulations. The corrected model results are strongly correlated with the CN05.1 observational dataset, with minimal error. Under a 2°C steady warming scenario, Generalized Extreme Value (GEV) analysis of the CDD index and return periods suggests that extreme dry periods will increase in northwest China, the Qinghai-Tibet Plateau, and Northeast China, with a growing disparity in extreme dry periods between the northern and southern regions as the return period increases. Multi-model ensemble projections for future periods under a 2°C stable warming scenario indicate a decreasing trend in the annual mean CDD index in northern China and an increasing trend in southern China.
How to cite: Feng, T.: Projection of Spatiotemporal Changes in the Probability Distribution of Extreme Dry Periods in China under a 2°C Stable Warming Scenario, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1922, https://doi.org/10.5194/egusphere-egu25-1922, 2025.
Droughts are among the most impactful and complex climatic phenomena, widely recognised as pervasive natural hazards posing serious challenges to ecosystems and societies. Assessing droughts is challenging due to difficulties in accurately determining their spatial and temporal dimensions. This study introduces a novel daily drought index, the Generalized Drought Index (GDI), calculated using the Iberian Gridded Dataset (1971–2015) with precipitation and precipitation minus potential evapotranspiration data. The daily resolution enables the identification of flash droughts, which proves highly valuable for future research efforts.
A comparative analysis was conducted against the daily Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI), and a Z-Score standardisation. Seven accumulation periods (7, 15, 30, 90, 180, 360, 720 days) were evaluated, with focus on direct comparison amongst indices in their ability to conform to the standard normal distribution. Results showed that GDI, SPI, and SPEI follow the normal standard distribution, while Z-Score depends on the data's original distribution. Using the Distribution Added Value (DAV) technique, GDI demonstrated gains over other indices, with DAV up to 35% compared to SPI and SPEI. The spatial extent of the 2004–2005 drought was also analysed, with GDI, SPI, and SPEI providing similar results, while Z-Score exhibited limitations at shorter accumulation periods.
GDI was also applied to the Coordinated Regional Climate Downscaling Experiment for Europe (EURO-CORDEX), using data from 13 regional climate models (1971–2100) for RCP2.6, RCP4.5, and RCP8.5. An ensemble-based index approach was considered using all models and the same accumulation periods. Findings show Iberia's high vulnerability to climate change, with increases in drought severity, frequency, and duration, especially by mid-century. Under RCP2.6, changes were observed early in the century. RCP4.5 projects similar impacts in mid and end century, with up to 30 additional events at shorter timescales and 50 extra moderate drought days for longer periods. RCP8.5 projects dramatic increases, with over 50 severe events at shorter scales and durations exceeding 100 days for longer periods by the century's end. These results highlight the urgent need for mitigation policies and targeted adaptation strategies to address water challenges and minimize drought impacts in Iberia.
This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UID/50019/2025 and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020).
How to cite: Careto, J., Cardoso, R., Russo, A., Lima, D., and Soares, P.: Generalised Drought Index: a novel Multi-Scale Daily Drought Index, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10118, https://doi.org/10.5194/egusphere-egu25-10118, 2025.
Extreme Climate Indices for precipitation are imperative climate indicators, especially in small islands such as the Azores, which are highly susceptible to climate variability and change. This study uses results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to evaluate historical and projected trends in precipitation related extreme climate indices within the Azores and spatial patterns extensive to the Northeast Atlantic region. Using a robust ensemble of climate models, this work analyzes the annual total precipitation, the annual number of wet days (≥20 mm/day) (R20) and the annual number of consecutive dry days (< 1mm) (CDD), examining their historical accuracy and future projections under different Shared Socioeconomic Pathways (SSPs).
Historically, CMIP6 simulations demonstrated a reasonable alignment with ERA5 reanalysis data for total precipitation, with a insignificant bias of −6.3 ± 123.7 mm for the Azores during the 1961–1990 baseline period. The models captured spatial variability across the Northeast Atlantic, although some localized discrepancies persisted. While annual precipitation projections show no significant changes, CDD and R20 mm increases are small but very likely for the SSP 5 8.5 scenario.
Projections under SSP scenarios reveal nuanced changes in precipitation patterns. The total annual precipitation exhibits small positive trends under SSP1-2.6 and SSP2-4.5 scenarios, with rates of 3.79 mm/decade and 2.11 mm/decade, respectively, while SSP5-8.5 projects a slight negative trend (−1.23 mm/decade). These findings suggest a potential stabilization or marginal increase in precipitation levels under low-to-moderate emission pathways but highlight the risk of drier conditions under high-emission scenarios.
The R20, a critical measure for agriculture, risk management and water resource planning, is projected to follow a similar pattern. Under SSP1-2.6 and SSP2-4.5, the annual count of wet days increases slightly, reflecting potential intensification in extreme precipitation events. Conversely, SSP5-8.5 shows a reduction in wet days, aligning with broader CDD trends projected for high-emission scenarios. These projections emphasize the heightened variability in precipitation characteristics under higher emission climate pathways.
Spatial analysis highlights significant heterogeneity across the Northeast Atlantic, with adjacent continental regions experiencing more pronounced changes compared to the oceanic regions. Furthermore, the role of topography in shaping localized precipitation patterns remains a critical area for future research, particularly given the limitations of coarse model resolutions in capturing island-scale processes.
The findings underscore the importance of integrating climate projections into regional planning and adaptation strategies. While the Azores may face relatively modest changes in precipitation under certain scenarios, the potential for increased variability and intensity of extreme events cannot be overlooked. As wet days and consecutive dry days trends evolve, their implications for water management, agriculture, risk management and infrastructure resilience warrant proactive measures. Continued refinement of climate models and increased resolution for small island systems are essential to enhance the accuracy and applicability of future projections.
This study contributes to a growing knowledge on the impacts of climate change on small island states and emphasizes the critical need for adaptation policies that consider the unique vulnerabilities of these regions.
How to cite: Meirelles, M., Carvalho, F., Henriques, D., Vasconcelos, H., Navarro, P., and Porteiro, J.: Assessing Extreme Climate Indices for Precipitation in the Azores: Insights from CMIP6 Climate Projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13800, https://doi.org/10.5194/egusphere-egu25-13800, 2025.
Signs of global warming are evident in extreme daily maximum temperature events Tx , especially those that break historical records. In the Iberian Peninsula, [Castillo-Mateo, 2023] demonstrated that the frequency of such records shows a trend surpassing what is expected under stationary conditions and varies spatially. A novel approach is introduced here to analyse and interpret the spatial variability and spatio-temporal patterns of this phenomenon.
Daily TX data spanning 1960–2023 from 36 Spanish stations were obtained from the European Climate Assessment & Dataset. Geopotential variables at 12 p.m. for pressure levels of 300, 500, and 700 hPa, on a 1o x 1o grid covering [45oN, 10oW, 35oS, 5oE], were sourced from ERA5 reanalysis data as the predictor database. The analysis focused on summer (JJA) days.
An algorithm was used to derive an optimal model for each station using logistic regression, along with several global models. The target variable was defined as a binary indicator of daily threshold exceedance for Tx . For each station s, the threshold was determined as the 95th percentile of maximum temperatures during the reference period 1981–2010, specifically for the summer months (June, July, and August). Mathematically, the threshold for station s is expressed as us = Q 0.95 (Tx s,t,l t ∈ [1981, 2010] , l ∈ [1, 92]) where Tx s,t,l denotes the maximum temperature at station s for year t and day l, with l corresponding to the summer days. The binary indicator is defined as I s,t,l = 1 if Tx s,t,l > us , and 0 otherwise.
The series of geopotential covariates at the grid points corresponding to the four farthest corners, as well as the closest grid point to each station, were used as predictors. These variables were further expanded by including a lag and their second-order polynomial terms. The algorithm involved multiple steps; 1) Stepwise regression was employed at each station to identify optimal predictors; 2)The most significant and frequently selected predictor variables from these models were then used to construct a global model. 3)Three interaction models were developed by introducing interactions between the selected predictors and geodesic, climatic, and spatial factors, followed by stepwise regression. Data from the first 51 years were used for training, while the last 13 years were reserved for testing. To address class imbalance, the AUC was used as a measure of model performance.
The simplest global model demonstrated strong overall performance with an AUC of 0.88 and k = 15 parameters, though it exhibited lower scores for stations located near the coast. Notable improvements in coastal station AUC values were achieved in the three interaction models. The model including climatic interactions achieved an AUC of 0.89 with k=34 parameters. The model with climatic interactions was selected as the most top performer.
In conclusion, we analyzed extreme maximum temperature events in the Iberian Peninsula using station-specific and global models with geopotential predictors. Interaction models improved performance, particularly for coastal stations, with the climatic interaction model achieving the best balance of accuracy and simplicity.
How to cite: Barrio, E., Gracia-Tabuenca, Z., Asín, J., Abaurrea, J., Castillo, J., and Cebrián, A.: Modelling a Binary Threshold Indicator for Maximum Temperatures with a Selection Algorithm for Spatio-Temporal Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20561, https://doi.org/10.5194/egusphere-egu25-20561, 2025.
The erratic and non-linear nature of extreme precipitation due to climate change presents significant challenges for water resource management. This study investigates disproportionate patterns of extreme precipitation under future climate scenarios (SSP 245 and SSP 585) by integrating precipitation extreme indices and advanced ensemble modeling techniques. The ensemble approach combines projections from multiple General Circulation Models (GCMs) to improve prediction reliability. Taylor Skill Score (TSS) rankings and seasonal evaluations were used to identify the most skillful models, such as BCC-CSM, CNRM-CERFACS, MPI, and MRI-ESM2.0. Weighted ensemble combinations progressively incorporated top-ranked models. Advanced regression techniques, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), optimized model merging. Ensemble performance was validated using metrics like RMSE and correlation, demonstrating improved accuracy compared to individual models.
Future precipitation patterns were analyzed under SSP 245 and SSP 585, revealing amplified extremes. Extreme precipitation indices were divided into two categories: Quantitative Upper-Tail Threshold Analysis (R90p, R95p, R99p) and Duration-Integrated Metrics (CWD, SDII, PRCPTOT). Results indicated an increase in days exceeding the 90th and 95th percentiles but a decline in days exceeding the 99th percentile, suggesting a threshold effect. Future projections show decreased CWD and increased PRCPTOT, reflecting fewer wet days but higher annual precipitation.
Correlation analysis revealed non-linear relationships. Quantitative Upper-Tail Threshold metrics showed increasing correlation with CWD under SSP 245 (336.84–500%) before declining under SSP 585 (17.78–96.88%). Their correlation with SDII increased from observed to SSP 245 (27.27–144.83%) but stabilized under SSP 585 (-1.43% to 1.41%). These findings highlight an evolving interplay between moderate and extreme precipitation events under intensifying climate conditions.
The results offer critical insights for water resource management, including optimized agricultural practices, adaptive urban infrastructure for flood management, and region-specific policies to enhance resilience against changing precipitation dynamics.
How to cite: Debnath, M. and Alamdari, N.: Ensemble Modeling and Threshold Analysis for Assessing Non-Linearity of Extreme Precipitation Under Future Climate Scenarios , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18963, https://doi.org/10.5194/egusphere-egu25-18963, 2025.
Soil moisture plays a crucial role in surface hydrological processes and land–atmosphere interactions. It can influence vegetation growth directly, serving as a significant indicator for monitoring agricultural drought. However, spatially continuous datasets of root zone soil moisture rely on model simulations, introducing numerous uncertainties associated with model parameters and input data. Currently, multiple soil moisture products derived from model simulations exist, but their representation at spatial scales remains unclear. Moreover, their abilities to express soil–atmosphere and soil–vegetation interactions within land–atmosphere coupling are not understood, leading to divergent inclinations toward drought. This study investigates the performance of five soil moisture products, European Centre for Medium-Range Weather Forecasts Reanalysis v5-Land (ERA5-Land), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and SoMo.ml, under drought conditions. The bias, correlation, and difference of standard deviation (STDD) were calculated between these products and the observations from International Soil Moisture Network stations. The causal probability of soil, meteorological, and agricultural drought was calculated using the causal-effect Peter and Clark (PC) Momentary Conditional Independence (MCI) method to evaluate the data propensity of these products. ERA5-Land and SoMo.ml gave a similar performance with the highest accuracy, which was attributed to the use of the same meteorological forcing data. The biases of soil moisture from these two products at surface, middle and deep depths against station observations are below 0.1 m3/m3., and the STDD is within 0.05 m3/m3. The accuracy of GLDAS is comparatively lower, characterized by lower correlations (below 0.2 for deeper layers) and high bias (above 0.15 and 0.2 for middle and deep layers, respectively). This discrepancy could be attributed to substantial biases in the precipitation forcing data. ERA5-Land shows higher spatial resolution and greater spatial heterogeneity, whereas MERRA-2 underperformed in this area. MERRA-2 had the strongest connection to agricultural drought, with a propensity probability of 0.477. Conversely, SoMo.ml demonstrates the strongest connection to meteorological drought, with a propensity probability of 0.234. Due to the errors in simulated and observational data during the MERRA data assimilation, substantial biases in the soil moisture data, and low accuracy in meteorological forcing of GLDAS, there was no clear causal relationship between soil moisture drought and meteorological drought between these two products. These findings provide recommendations for the use of soil moisture products in drought research.
How to cite: Xue, S. and Wu, G.: Causal Inference of Root Zone Soil Moisture Performance in Drought, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-82, https://doi.org/10.5194/egusphere-egu25-82, 2025.
The current work appraises the temporal and spatial discrepancies of temperature for the historical data (1951-2020) for the Lesser Himalayan Region. The temperature data has been obtained from the Indian Metrological Department (IMD), Pune. The 70-year data has been analysed using the temperature indices recommended by experts on Climate Change Detection and Indices (ETCCDI) and Expert Team on Sector-Specific Climate Indices (ET-SCI) for the divergence in the temperature using extreme climate indices. These indices will assimilate the climate prognosis and foreseen weather-space-relevant atmospheric disruptions leading to outright know-how of alterations in the temperature pattern in the developing Lesser Himalayan Region over a century. The study focuses on understanding the progression of dynamic temperature changes using certain key indices such as Summer days, Tropical Nights, Warm nights/days, and cold nights/days to detect patterns and trends in temperature behaviour across regions. The findings highlight the region’s susceptibility to rising temperature trends, a growing frequency of extreme heat events and their connection with extreme events and climate anomalies. The results will attempt to find out the effect of dynamic temperature changes occurring spatially and temporally in the region professing the impact of alterations in temperature patterns and will also figure out the atmospheric dynamics robustly occurring under what circumstances and time of the year and at what specific spots. Ultimately, the research contributes to a comprehensive understanding of temperature variability and its implications for the Lesser Himalayan Valley, offering a strong base for focused mitigation and adaptation strategies in the era of abrupt climate change and rapid urbanization.
How to cite: Gupta, S., Mishra, P. K., and Khare, D.: Navigating through Climatic Oscillations and Atmospheric Drift using Temperature Indices on Historical Data for a Lesser Himalayan Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1034, https://doi.org/10.5194/egusphere-egu25-1034, 2025.
Heat waves are considered one of the most hazardous climate extremes in relation to climate change. To better understand their driving mechanisms in CORDEX regional climate models (RCMs) over Middle Europe, we employ a recently introduced approach to study heat waves as three-dimensional phenomena (Lhotka & Kyselý 2024). We classify them based on their vertical cross-sections of temperature anomalies into near-ground, lower-tropospheric, mid-tropospheric, and vertically extensive types. We show that even driven by the reanalysis, most RCMs tend to simulate substantially more lower-tropospheric heat waves than those located near the surface, which is in contrast to reference data from ERA5. This bias is associated with overly frequent southerly flow that is excessively warm especially at the lower-tropospheric level. We also identify large differences among the RCMs in simulations of near-ground and vertically extensive heat wave types, which are possibly related to deficiencies in links between easterly flow and those heat wave types.
Lhotka, O., Kyselý, J. Three-dimensional analysis reveals diverse heat wave types in Europe. Commun Earth Environ 5, 323 (2024). https://doi.org/10.1038/s43247-024-01497-2
How to cite: Lhotka, O., Plavcová, E., and Kyselý, J.: Three-dimensional insight into heat waves in EURO-CORDEX regional climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3609, https://doi.org/10.5194/egusphere-egu25-3609, 2025.
We evaluate climatological characteristics (temperature anomalies, mean precipitation, and the Climatic Water balance index defined as the difference between potential evapotranspiration and precipitation) and links to atmospheric circulation for three-dimensional (3D) heat wave types in several European regions. Heat waves are classified according to their 3D structure of positive temperature anomalies in ERA5 over 1979–2022 (the satellite period) into near-surface, lower-tropospheric, higher-tropospheric, and omnipresent types (Lhotka & Kyselý 2024, https://www.nature.com/articles/s43247-024-01497-2). The Jenkinson–Collison classification of daily mean sea level pressure patterns is used to identify circulation types with increased frequency during the individual heat wave types compared to the June–September climatology. We show large differences in surface temperature anomalies and dryness among the heat wave types, as well as different links to circulation patterns. The differences are most pronounced between near-surface and higher-tropospheric heat waves and point to processes important for their onset and development. The analysis contributes to better understanding the interrelationships between heat waves, atmospheric circulation, and other driving mechanisms.
How to cite: Poppova, Z., Lhotka, O., Stryhal, J., and Kysely, J.: Climatological characteristics and atmospheric circulation associated with 3D heat wave types in European regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9748, https://doi.org/10.5194/egusphere-egu25-9748, 2025.
We evaluate links to atmospheric circulation for three-dimensional heat wave types in Middle Europe over 1979–2022. Heat waves are classified according to their vertical structures of temperature anomalies in ERA5 into near-surface (HWG), lower-tropospheric (HWL), higher-tropospheric (HWH), and omnipresent (HWO) types. Jenkinson–Collison classification of daily mean sea level pressure patterns is used to identify circulation types (CTs) with increased frequency for the individual heat wave types. In all heat wave types, CTs with southerly flow are more common compared to the June–September climatology but differences are found for other groups of CTs. In HWG, the CT occurring most frequently is indeterminate flow, corresponding to a little pronounced pressure field with no clear role of anticyclonic vorticity or flow direction. The expected pattern of increased anticyclonic and decreased cyclonic flow is clearly manifest only for HWH, while it is reversed during HWG. The role of warm advection increases for the other two heat wave types, HWL and HWO. Anticyclonic circulation supporting gradual warming is important mainly before the onset of most heat wave types, except for HWH. The reported differences reflect diverse processes leading to the various heat wave types, with the dominant roles of anticyclonic vorticity for HWH and land–atmosphere coupling under little pronounced circulation patterns, following previous drying, for HWG.
How to cite: Kysely, J., Poppova, Z., Stryhal, J., and Lhotka, O.: Links to atmospheric circulation vary for individual heat wave types in Middle Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9815, https://doi.org/10.5194/egusphere-egu25-9815, 2025.
Global warming is intensifying the frequency and severity of extreme heat events, significantly impacting human thermal comfort (HTC), particularly in vulnerable regions such as the United Arab Emirates (UAE). Heat waves rank among the most dangerous natural hazards, directly affecting public health and well-being. Vulnerable populations, including children, the elderly, and individuals with pre-existing health conditions, particularly face heightened risks. The UAE, classified as a hyper-arid desert region with most of its major cities located along the coast, experiences a hot and humid climate. This makes it imperative to develop robust estimates of future HTC to implement effective measures against potential adverse health outcomes.
This study examines past trends and future projections of human thermal comfort (HTC) in selected cities across the UAE. Two 10-year simulations were conducted: a historical run (2005–2014) driven by ERA5 reanalysis data and a Pseudo Global Warming (PGW) simulation using perturbation derived from the CMIP6 CCSM4 model under the Representative Concentration Pathway (RCP) 8.5 (business-as-usual) emission scenario. Key heat indices, Universal Thermal Climate Index (UTCI), Physiologically Equivalent Temperature (PET), wet-bulb temperature, and apparent temperature, were calculated and compared between the historical and future scenarios to evaluate changes in HTC.
The results reveal a substantial increase in all heat indices under future climate conditions, with UTCI showing the highest rise of over 5°C, while wet-bulb temperature exhibited the smallest increase. Heat index values were most pronounced from June to August, with Abu Dhabi recording the highest values among studied cities, followed by Dubai, Al Fujairah, and Al Ain. This research provides critical insights for developing intervention strategies to address future HTC challenges in the UAE. Future work aims to refine projections using ensemble modeling and alternative emission scenarios to reduce uncertainties.
How to cite: Khan, B., Paparella, F., Pauluis, O., and Halder, S.: Future projections of human thermal comfort in the United Arab Emirates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20028, https://doi.org/10.5194/egusphere-egu25-20028, 2025.
Climate change has led to significant shifts in precipitation patterns, with spatial inhomogeneity emerging as a key feature, which is directly related to extreme flooding or drought events. A quantitative method estimating how extreme events affect global precipitation inhomogeneity is crucial for monitoring, understanding, and predicting the role of precipitation variability in driving regional or global climate extremes under ongoing climate change.
In this presentation, we introduce a novel but simple framework that is able to (1) quantify the spatial inhomogeneity of global precipitation and its variability, (2) estimate contributions of different precipitation intensities and (3) assess contributions of regional disparities. Based on this framework, we show that the inhomogeneity of global annual precipitation has increased consistently across multiple datasets in the satellite era (1979–2021), attributed to the increasing area of both extremely high precipitation (over 2000mm per year) and low precipitation (under 250mm per year). Based on the Global Precipitation Climatology Project (GPCP) dataset, the increase in inhomogeneity of global precipitation is primarily contributed by the intra-regional inhomogeneity component of Northern Hemispheric tropical ocean (+60.2%) and Southern Hemispheric tropical ocean (+40.3%), and is partly offset by the inter-regional inhomogeneity component of Northern Hemispheric mid-latitude ocean (-4.5%). Further applied to high-resolution datasets, our framework is particularly effective in revealing the impacts of isolated extreme events, which are often obscured by surrounding normal precipitation or dismissed as noise in global average calculations.
How to cite: Liu, S., Leung, J. C.-H., Xu, J., Tu, S., and Zhang, B.: A Global-to-Regional Framework for Assessing Precipitation Inhomogeneity and Its Connection to Extreme Events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3496, https://doi.org/10.5194/egusphere-egu25-3496, 2025.
Understanding sub-daily scale temperature variability can be used for evaluating the impacts of climate change on human health, agriculture and daily life. In particular, higher temperature fluctuations (within a day, or from one day to another) increase physiological and psychological stress, demanding more frequent adaptation in areas such as clothing choices and energy management. The Pannonian Basin, encompassing Hungary, provides an ideal setting for this analysis due to its distinctive climate, well-defined four seasons, and sensitivity to global warming, which amplify the effects of temperature variability.
This study focuses on diurnal temperature range (DTR), inter-day temperature variability, and intra-day temperature changes using various thresholds. These metrics are analyzed on monthly and seasonal scales. Historical (1971-2024) DTR and inter-day variability were derived from the homogenized, high-resolution HuClim database using daily mean, maximum and minimum temperatures. For intra-day changes, we utilize hourly temperature data from ERA5-Land, a reanalysis product. Future projections are derived from an ensemble of EURO-CORDEX regional climate model simulations, encompassing multiple scenarios: RCP2.6 (limiting global warming to 2 °C), RCP4.5 (moderate mitigation), and RCP8.5 (business-as-usual).
Preliminary results indicate an increase in DTR and inter-day temperature variability across all months, especially in spring and late summer. Intra-day temperature changes show substantial increases during spring and minor changes during winter. Positive intra-day temperature changes peak in spring, while negative changes are more frequent in summer. These findings highlight the growing temperature volatility in the region, emphasizing the need for adaptive strategies in agriculture and human health to moderate the impacts of climate change.
Acknowledgements. This work has been implemented by the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014) project within the framework of Hungary's National Recovery and Resilience Plan supported by the Recovery and Resilience Facility of the European Union. In addition, this study has been supported by the European Climate Fund (G-2409-68866).
How to cite: Szabó, P., Kis, A., Divinszki, F., and Pongrácz, R.: The analysis of sub-daily scale temperature changes for Hungary , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20252, https://doi.org/10.5194/egusphere-egu25-20252, 2025.
