Ensemble Of Regional Climate Models Research Articles

Warming promotes expansion of a key demersal fishing resource of the western Mediterranean

Climate change is affecting marine ecosystems altering the distribution and abundance of organisms, with implications for fisheries and food security. This warming-induced reshuffle in species abundance could bring threats and opportunities to the fisheries, but needs to be assessed to promote effective actions and to foster resilience. We analyzed the density and distribution patterns of deep-sea rose shrimp (Parapenaeus longirostris), as well as identified the main environmental have identified the environmental drivers shaping its habitat along the western Mediterranean (Iberian Peninsula) during the period 2001–2020. Using spatial distribution models developed concurrently with an ensemble of four Regional Climate Models (RCMs), we have projected the density of this species during the next century under two climate scenarios (RCP4.5 and RCP8.5). Bathymetry and sea bottom temperature drove the density of the species, leading to a marked northward expansion during the last two decades. Our results projected an increase in its distribution and especially in density throughout the area along the 21st century, mirroring the effect of global warming. Consequently, the most distant period (i.e. 2100s) and the warmest scenario (RCP8.5) presented also the highest densities and low internal variability of the ensemble. We discussed the power of assessing uncertainties using a RCM ensemble, particularly under complex oceanographic features, to bring robust information for an effective scientific advice to fisheries management.

Assessing the ecological complexity and uncertainty of predicting forest ecosystem services under climate change

Climate change affects Central European forest ecosystems in different ways and, consequently, these changes result in different feedbacks on the provision of forest ecosystem services. Regarding the complexity and the variability in climate-forest interactions outcome, forest decision makers necessitate reliable information about changes in the forest ecosystem services for planning and adaptation purposes. However, forest productivity predictions incorporate multiple levels of uncertainty that have to be regarded to ensure building realistic expectations in forest decision-making. Besides the chosen forest simulation model, uncertainties come from the climate change data represented by a set of representative concentration pathways (RCP), within the underlying ensemble of global circulation and regional climate models (GCM-RCM), and further in the treatment of the CO2-fertilization effect. We considered the mentioned uncertainties in a framework on simulating forest growth and water services for two forest sites, a Sessile oak and a Scots pine stand in Rhineland-Palatine, Germany. The framework revealed a high variability in future forest ecosystem services. Particularly, the variability among the selected GCM-RCM models within the same Representative Concentration Pathway (RCP) was higher than the variability among different RCPs (RCP2.6 and RCP8.5 representing the low and high CO2-emission scenarios, respectively). Sessile oak productivity increased under all scenarios, whereas Scots pine growth declined in the lower end of the RCP8.5 scenario. Water services remained mostly stable at both sites. Moreover, we applied a panel data model to estimate what climate indices caused changes in the forest ecosystem services. We found that Scots pine is more sensitive to a multitude of climate indices, such as temperature changes and Sessile oak showed strong response to the CO2-fertilization. We propose applying this framework to evaluate forest management options under climate change.

Climate change impacts on seasonal runoff in the source region of the Yellow River: Insights from CORDEX experiments with uncertainty analysis

The source region of the Yellow River (SRYR) plays a crucial role in water resources to downstream areas. This study investigates the potential impacts of climate change on seasonal runoff in the SRYR using an ensemble of Regional Climate Models (RCMs) from two CORDEX experiments under RCP2.6 and RCP8.5 scenarios coupled with the WaSiM hydrological model. Two versions of the WaSiM hydrological model (WaSiM-PM and WaSiM-Hamon) are employed to validate the historical runoff, and WaSiM-PM’s superior performance make it the preferred choice for projecting climate change impacts on runoff. After bias-correction, RCM outputs show significant improvements, with correlation coefficients above 0.99 for temperature and 0.95 for precipitation. Under RCP2.6, temperature is projected to increase gradually (0.01–0.12 °C/decade), while under RCP8.5, warming is more pronounced (0.49–0.74 °C/decade). Precipitation generally shows an increasing trend, with variations between scenarios and experiments. Runoff projections indicate consistent peak flows in July across all scenarios. By the 2090 s under RCP8.5, substantial decreases in runoff are projected, especially in autumn and winter (approaching 40 %), with annual mean runoff decreasing by 30.3 %-31.5 %. Uncertainty analysis reveals that GCMs are the primary source of uncertainty (37–47 %) in the 2040 s, while emission scenarios dominate (43–58 %) in the 2090 s. Hydrological models show seasonal variability in uncertainty contribution, and downscaling experiments also play a significant role. This study highlights the potential for significant decreases in water resources in the SRYR, particularly under high emission scenarios, emphasizing the need for robust adaptation strategies in water resource management.

Transferability and Explainability of Deep Learning Emulators for Regional Climate Model Projections: Perspectives for Future Applications

Abstract Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering multiple scenarios and driving Global climate models (GCMs) across regions. RCM emulators based on deep learning models have recently been introduced as a cost-effective and promising alternative that requires only short RCM simulations to train the models. Therefore, evaluating their transferability to different periods, scenarios, and GCMs becomes a pivotal and complex task in which the inherent biases of both GCMs and RCMs play a significant role. Here, we focus on this problem by considering the two different emulation approaches introduced in the literature as perfect and imperfect, that we here refer to as perfect prognosis (PP) and model output statistics (MOS), respectively, following the well-established downscaling terminology. In addition to standard evaluation techniques, we expand the analysis with methods from the field of explainable artificial intelligence (XAI), to assess the physical consistency of the empirical links learnt by the models. We find that both approaches are able to emulate certain climatological properties of RCMs for different periods and scenarios (soft transferability), but the consistency of the emulation functions differs between approaches. Whereas PP learns robust and physically meaningful patterns, MOS results are GCM dependent and lack physical consistency in some cases. Both approaches face problems when transferring the emulation function to other GCMs (hard transferability), due to the existence of GCM-dependent biases. This limits their applicability to build RCM ensembles. We conclude by giving prospects for future applications. Significance Statement Regional climate model (RCM) emulators are a cost-effective emerging approach for generating comprehensive ensembles of regional climate projections. Promising results have been recently obtained using deep learning models. However, their potential to capture the regional climate dynamics and to emulate other periods, emission scenarios, or driving global climate models (GCMs) remains an open issue that affects their practical use. This study explores the potential of current emulation approaches incorporating new explainable artificial intelligence (XAI) evaluation techniques to assess the reliability and transferability of the emulators. Our findings show that the different global and regional model biases involved in the different approaches play a key role in transferability. Based on the results obtained, we provide some prospects for potential applications of these models in challenging problems.

Impacts of fire prevention strategies in a changing climate: an assessment for Portugal

Abstract Climate change poses a formidable strain on societies worldwide, demanding viable and timely adaptation measures to ensure future prosperity while avoiding the impact of more frequent and intense extreme events, like wildfires, that affect all continents and biomes, leaving authorities grappling to respond effectively. Here, we focus on mainland Portugal that is inserted in the Mediterranean climate change hotspot and investigate the impact of different adaptation strategies on wildfire risk. Relying on an ensemble of regional climate models from the EURO-CORDEX initiative, we project Fire Weather Index (FWI) and Fire Radiative Power (FRP) for various Representative Concentration Pathways (RCPs).
Our findings reveal that very energetic fires, with energy release exceeding 1000 MW, may increase up to more than three-fold, depending on the RCP. Even under strong mitigation scenarios, the likelihood of having megafires increases by 1.5-fold. This underscores the need for proactive adaptation regardless of mitigation efforts.
We present three different ignition avoidance strategies under different climate change scenarios. For all cases results indicate that a reduction between 20 and 60% is achievable for intense wildfires (above 1000 MW).

Projected changes in heat, extreme precipitation, and their spatially compound events over China’s coastal lands and seas through a high-resolution climate models ensemble

China’s coastal lands and seas are highly susceptible to the changing environment due to their dense population and frequent economic activities. These areas experience more significant impacts from climate change-induced extreme events than elsewhere. The most noticeable effects of climate change are extreme high temperatures and extreme precipitation. We employ an ensemble of RCMs (Regional Climate Models) to investigate and project changes in temperature, precipitation, and Compound Heat-Precipitation Extreme events (CHPEs) over selected China’s coastal lands and seas for both historical (1985–2004) and future periods (2080–2099). The multi-model ensemble projects that daily temperature extremes will increase by 2.9 °C to 5.4 °C across China’s coastal lands and seas, with land areas showing a higher temperature increase than marine areas. Extreme precipitation shows a high geographical heterogeneity with a 2.8–3.9 mm d−1 reduction over the 15–25°N marine areas while a 2.2–5.4 mm d−1 increment over the 25°N-35°N land areas. We use the Clausius–Clapeyron relationship to reveal that the peak of daily extreme precipitation will increase by 2–7 mm d−1 and the temperature at which extreme precipitation peaks will increase by 2 °C to 6 °C by warming. The land area of 25–30°N has the highest peak precipitation increase of 9.87 mm d−1 and a peak temperature increase of 6 °C. As precipitation extremes intensify with daily temperature extremes increase, CHPEs are projected to occur more frequently over both land and marine areas. Compared with the historical period, the frequency of CHPEs will increase by 40.9%-161.2% over marine areas, and by 36.2%-163.6% over land areas in the future. The 15–20°N area has the highest frequency increase of CHPE events, and the 25–30°N area has the largest difference in frequency increase under two different scenarios. It indicated that the 25–30°N area will be more easily affected by climate change.

Future Projections of Precipitation Extremes for Greece Based on an Ensemble of High-Resolution Regional Climate Model Simulations

An assessment of the projected changes in precipitation extremes for the 21st century is presented here for Greece and its individual administrative regions. The analysis relies on an ensemble of high-resolution Regional Climate Model (RCM) simulations following various Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The simulated changes in future annual total precipitation (PRTOT) under the examined scenarios are generally negative but statistically non-robust, except towards the end of the century (2071–2100) over high-altitude mountainous regions in Western Greece, Peloponnese, and Crete under RCP8.5. The pattern of change in the number of very heavy precipitation days (R20) is linked to the respective pattern of the PRTOT change with a statistically robust decrease of up to −5 days per year only over parts of the high-altitude mountainous regions in Western Greece, Peloponnese, and Crete for 2071–2100 under RCP8.5. Contrasting the future tendency for decrease in total precipitation and R20, the changes in the intensity of precipitation extremes show a tendency for intensification. However, these change patterns are non-robust for all periods and scenarios. Statistical significance is indicated for the highest 1-day precipitation amount in a year (Rx1day) for the administrative regions of Thessaly, Central Greece, Ionian Islands, and North Aegean under RCP8.5 in 2071–2100. The changes in the contribution of the wettest day per year to the annual total precipitation (RxTratio) are mainly positive but non-robust for most of Greece and all scenarios in the period 2021–2050, becoming more positive and robust in 2071–2100 for RCP8.5. This work highlights the necessity of taking into consideration high-resolution multi-model RCM estimates in future precipitation extremes with various scenarios, for assessing their potential impact on flood episodes and the strategic planning of structure resilience at national and regional level under the anticipated human-induced future climate change.

Characteristics of extreme meteorological droughts over the Brazilian Pantanal throughout the 21st century

The Brazilian Pantanal is a remarkable ecosystem, encompassing vital aspects such as fish, fruit, and grain production, greenhouse gas emission regulation, temperature and precipitation modulation, and groundwater recharge. However, 2019 and 2020 experienced prolonged and severe drought that profoundly impacted the Pantanal region. Although these impacts are still under review, their effects on wildlife, vegetation, and the local population are undeniable. To enhance our understanding of the magnitude and consequences of the 2019/2020 drought, the present study conducted a comprehensive evaluation of drought occurrences in the Pantanal biome. Utilizing two well-established drought indices, the standard precipitation index (SPI) and the standard precipitation evapotranspiration index (SPEI), we analyzed the available observational data provided by the Climate Research Unit (CRU). Additionally, we endeavored to project droughts throughout the 21st century, thereby facilitating the visualization of potential reoccurrences of hazardous events in the Pantanal area. Two regional climate model (RCM) ensembles, the Eta ensemble (E1) and CORDEX ensemble (E2), along with representative concentration pathways (RCP4.5 and RCP8.5 scenarios) were used to project the severity of droughts in the Brazilian Pantanal until 2098/2099. The findings indicate that the hydrological year of 2019/2020 was the driest on record in the Pantanal over the past 80 years. The E1 ensemble did not project such an extreme event, whereas the E2 ensemble forecasted extreme droughts before the 2040s under the RCP4.5 and RCP8.5 scenarios. Using the SPEI, both ensembles showed similar results, with extreme droughts projected toward the end of the 21st century. Notably, the SPI demonstrated effectiveness in detecting short-term droughts, while the SPEI was more efficient for long-term assessments, projecting extreme drought events for the last two decades. The SPEI, employed with the Eta and CORDEX ensembles, can provide valuable insights into drought projections. Moreover, the potential recurrence of extreme droughts, characterized by significant below-average precipitation spanning vast areas throughout the hydrological year, poses a severe threat to the Pantanal biome in this century.

Inter‐Comparison of Precipitation Simulation and Future Projections Over China From an Ensemble of Multi‐GCM Driven RCM Simulations

AbstractAn analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing.

Links of atmospheric circulation to cold days in simulations of EURO-CORDEX climate models for central Europe

Despite ongoing climate change and warming, extreme cold events still negatively affect human society. Since cold air incursions are related to specific circulation patterns, the main aims of this study are (1) to validate how well current EURO-CORDEX regional climate models (RCMs) reproduce these synoptic links and (2) to assess possible future changes in atmospheric circulation conducive to cold events. Using anomalies of daily minimum temperature, we define cold days (CDs) in central Europe and analyse their characteristics over the historical (1979−2020) and future (2070−2099) periods. We classify wintertime atmospheric circulation by applying a novel technique based on Sammon mapping to the state-of-the-art ERA5 reanalysis output. We discover that circulation types (CT) conducive to CDs are characterised by easterly advection and/or clear-sky anticyclonic conditions. While the RCM ensemble generally reproduces these synoptic links relatively well, we observe biases in the occurrence of CDs in individual simulations. These biases can be attributed to inadequately reproduced frequencies of CTs conducive to CDs (primarily propagating from driving data), as well as to deviations in the conduciveness within these CTs (primarily originating in the RCMs). Interestingly, two competing trends are identified for the end of the twenty-first century: (1) most RCMs project an increased frequency of CTs conducive to CDs, suggesting more frequent CDs, while (2) the same CTs are projected to warm faster compared to their counterparts, suggesting weaker CDs. The interplay between these opposing trends contributes to the overall uncertainty surrounding the recurrence and severity of future winter extremes in central Europe.

Assessment of historical and projected changes in extreme temperatures of Balochistan, Pakistan using extreme value theory.

The fundamental consequences of global warming include an upsurge in the intensity and frequency of temperature extremes. This study provides an insight into historical trends and projected changes in extreme temperatures on annual and seasonal scales across "Balochistan, Pakistan". Historical trends are analyzed through the Mann Kendal test, and extreme temperatures (Tmax and Tmin) are evaluated using generalized extreme value (GEV) distribution for historicalperiod (1991-2020) from the observational data and the two projected periods as near-future (2041-2070) and far-future (2071-2100) using a six-member bias-corrected ensemble of regional climate models (RCMs) projections from the coordinate regional downscaling experiment (CORDEX) based on the worst emission scenario (RCP8.5). The evaluation of historical temperature trends suggests that Tmax generally increase on yearly scale and give mixed signals on seasonal scale (winter, spring, summer, and autumn); however, Tmin trends gave mixed signals at both yearly and seasonal scale. Compared to the historical period, the return levels are generally expected to be higher for Tmax and Tmin during the both projection periods in the order as far-future > near-future > historical on yearly and seasonal basis; however, the changes in Tmin are more evident. Station-averaged anomalies of + 1.9°C and + 3.6°C were estimated in 100-year return levels for yearly Tmax for near-future and far-future, respectively, while the anomalies in Tmin were found to be + 3.5°C and + 4.8°C which suggest the intensified heatwaves but milder colder extreme in future. The findings provide guidance on improved quantification of changing frequencies and severity in temperature extremes and the associated impacts.

Future flood envelope curves for the estimation of design flood magnitudes for highway bridges at river crossings

Creager flood envelope curves, which serve as the upper bound/limit of observed extreme flows, are commonly used by practitioners to estimate design flood magnitudes, which in the case of most river-crossing highway bridges is 75-year flood magnitude in Canada. This study proposes a novel framework for climate change adaption of Creager curves for estimating future design floods. These curves, for the current period, are assessed considering 417 observation stations, located in seven major Canadian river basins (i.e., Fraser, Nelson, Mackenzie, Yukon, Churchill, St Lawrence and St John). The Creager coefficient C, which defines flood envelope curves, varies between 1 and 45 across the studied river basins. To adapt Creager curves for future changes in streamflow, a correction factor, RC, which is the ratio of future to current period C values, is proposed. These factors are obtained for observation sites, using streamflow data from an ensemble of Regional Climate Model (RCM) simulations for current and future periods, through two Regional Frequency Analysis approaches. The first approach, considering only the RCM cells where the stations are located, suggests RC in the 0.3–1.6 range, with southeasterly basins showing values < 1. The second approach, considering all RCM cells for a given region, yields a wider range for RC and adds useful information in that RC values can also be established at ungauged locations. From a practical viewpoint, the proposed framework for estimating future design floods is robust and transferrable to other basins, but can benefit using streamflow projections from other models for better uncertainty quantification.

Convection-permitting climate models offer more certain extreme rainfall projections

Extreme precipitation events lead to dramatic impacts on society and the situation will worsen under climate change. Decision-makers need reliable estimates of future changes as a basis for effective adaptation strategies, but projections at local scale from regional climate models (RCMs) are highly uncertain. Here we exploit the km-scale convection-permitting multi-model (CPM) ensemble, generated within the FPS Convection project, to provide new understanding of the changes in local precipitation extremes and related uncertainties over the greater Alpine region. The CPM ensemble shows a stronger increase in the fractional contribution from extreme events than the driving RCM ensemble during the summer, when convection dominates. We find that the CPM ensemble substantially reduces the model uncertainties and their contribution to the total uncertainties by more than 50%. We conclude that the more realistic representation of local dynamical processes in the CPMs provides more reliable local estimates of change, which are essential for policymakers to plan adaptation measures.

Assessing the impact of climate change on a coastal karst aquifer in a semi-arid area

This study investigates the potential future state of a coastal karst aquifer located in the Salento peninsula of southern Italy, examining the impact of climate change. After a historical and future overview of the climate in the study area, an analysis of meteorological indices (SPIs and SPEIs) and groundwater levels (GWLs) during the historical period revealed a strong correlation between them. Then, straightforward linear regression models were applied to establish relationships between SPIs/SPEIs and GWLs for the analysed wells. To estimate future SPIs and SPEIs, an ensemble of regional climate models was used, and estimates of GWLs until the end of the century were obtained by incorporating these projections into the regression models. Two emission pathways, the RCP4.5 and the RCP8.5, were considered in the analysis. The results based on SPIs suggest no significant changes in future GWLs across all wells. However, when considering also temperature through the use of SPEIs, a general decline in groundwater levels is expected, with the decrease being more pronounced under the RCP8.5.

Higher quantiles of sea levels rise faster in Baltic Sea Climate projections

Absolute sea levels in the Baltic Sea will rise under the influence of climate warming, similar to those in the world ocean. For extreme sea levels, there are indications that they will rise even faster than mean sea levels, but that topic is still controversially discussed and existing studies point into different directions. We analyzed a regional climate model ensemble for the Baltic Sea for future sea level changes. We find that the rate of change differs between high sea levels and the average: In the eastern part of the Baltic Sea, the 99th percentile of sea level was predicted to rise faster than the median. In the south-western part, the relation was opposite. Thus, our simulations predict a change not only in the sea level mean, but also in its distribution. This pattern was almost consistent between the individual ensemble members. We investigated the 99th percentile as a proxy for extreme sea levels, since their partially stochastic nature limits the predictive skill of our 20-member ensemble. Our findings imply that adapting coastal protection to mean sea level change only may be regionally insufficient.

Estimating the Incidence and Economic Cost of Lyme Disease Cases in Canada in the 21st Century with Projected Climate Change.

Lyme disease (LD) is emerging in Canada owing to the range expansion of the tick vector Ixodes scapularis (I. scapularis). Our objective was to estimate future LD incidence in Canada, and economic costs, for the 21st century with projected climate change. Future regions of climatic suitability for I. scapularis were projected from temperature output of the North American Coordinated Regional Climate Downscaling Experiment regional climate model ensemble using greenhouse gas Representative Concentration Pathways (RCPs) 4.5 and 8.5. Once regions became climatically suitable for ticks, an algorithm derived from tick and LD case surveillance data projected subsequent increasing LD incidence. Three scenarios (optimistic, intermediate, and pessimistic) for maximum incidence at endemicity were selected based on LD surveillance, and underreporting estimates, from the United States. Health care and productivity cost estimates of LD cases were obtained from the literature. Projected annual LD cases for Canada ranged from 120,000 to by 2050. Variation in incidence was mostly due to the maximum incidence at endemicity selected, with minor contributions from variations among climate models and RCPs. Projected annual costs were substantial, ranging from to a year by 2050. There was little difference in projected incidence and economic cost between RCPs, and from 2050 to 2100, because projected climate up to 2050 is similar for RCP4.5 and RCP8.5 (mitigation of greenhouse gas emissions captured in RCP4.5 does not impact climate before the 2050s) and by 2050 the most densely populated areas of the study region are projected to be climatically suitable for ticks. Future incidence and economic costs of LD in Canada are likely to be substantial, but uncertainties remain. Because densely populated areas of Canada are projected to become endemic under conservative climate change scenarios, mitigation of greenhouse gas emissions is unlikely to provide substantial health co-benefits for LD. https://doi.org/10.1289/EHP13759.

Predicting malaria risk considering vector control interventions under climate change scenarios

Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010–2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min–Max]: 16,785 [9,902–74,382] vs 21,289 [11,796–70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Evaluating the Present and Future Heat Stress Conditions in the Grand Duchy of Luxembourg

The impact of elevated air temperature and heat stress on human health is a global concern. It not only affects our well-being directly, but also reduces our physical work capacity, leading to negative effects on society and economic productivity. Climate change has already affected the climate in Luxembourg and, based on the results of regional climate models, extreme heat events will become more frequent and intense in the future. To assess historical conditions, the micro-scaleRayManPro 3.1 model was used to simulate the thermal stress levels for different genders and age classes based on hourly input data spanning the last two decades. For the assessment of future conditions, with a special emphasis on heat waves, a multi-model ensemble of regional climate models for different emission scenarios taken from the Coordinated Regional Climate Downscaling Experiment (CORDEX) was used. For both, the past and future conditions in Luxemburg, an increase in the heat stress levels was observed. Small differences for different age groups and genders became obvious. In addition to the increase in the absolute number of heat waves, an intensification of higher temperatures and longer durations were also detected. Although some indications of the adaptation to rising air temperatures can be observed for high-income countries, our results underscore the likelihood of escalating heat-related adverse effects on human health and economic productivity unless more investments are made in research and risk management strategies.

An increase in widespread extreme precipitation events during the northeast monsoon season over south peninsular India

While the spatio-temporal characteristics of Indian summer monsoon precipitation and its extreme spells have been extensively studied, the northeast monsoon, which occurs from October to December (i.e., post-monsoon season) and affects the southern peninsula of India, has not received as much attention. In light of this, the present study explores the spatio-temporal characteristics of precipitation during the northeast monsoon, with a particular emphasis on widespread extreme precipitation events and their associated large-scale synoptic systems, using recent ensemble of high-resolution regional climate models (RCMs) simulations and the Indian monsoon data assimilation and analysis (IMDAA) reanalysis. The study reveals that both models tend to underestimate the intensity and frequency of observed precipitation events, although their skills in reproducing the observed spatial patterns of both mean and extreme precipitation are quite high (r > 0.75). A substantial increase in widespread extreme precipitation events (nearly twofold), along with a 30% rise in precipitation intensity, has been observed in the recent decade compared to the 1980s, and models demonstrate a similar directional change but tend to underestimate the magnitude of observed precipitation. This increase appears to be linked to the rapid warming of the Indian Ocean, which, in turn, increases the water vapor in the atmosphere, ultimately supplying more moisture to the southeastern peninsular India. On the other hand, observed discrepancies in replicating some of the reported widespread impactful extreme precipitation events in the years 2007 and 2015 over the southern India region underscore the need for caution when interpreting model simulations. Low-pressure systems, such as troughs, associated with cyclonic circulations originating from the Bay of Bengal, have been identified as the primary sources of moisture fueling heavy precipitation during these events. Cluster analysis highlights varying synoptic patterns within the general framework, emphasizing the need for a more nuanced approach in simulating and forecasting extreme precipitation events. Overall, this study underscores the importance of enhancing modeling capabilities to better understand and prepare for the growing challenges posed by extreme precipitation events.

Prolongation of Compound Dry–Hot Seasons Over Europe Under Climate Change Scenarios

AbstractCompound effects of drought and heat are regarded as one of the greatest hazards in relation to climate change. We study characteristics of dry–hot seasons in Europe in an ensemble of CORDEX regional climate models (RCMs). Evaluation against the E‐OBS gridded data set for 1976–2005 showed that the RCMs were able to reproduce the spatial pattern of the dry–hot season length but the simulated seasons tended to start later and interannual variability of their length was underestimated. Bias was larger (smaller) in the case of maximum (minimum) length over the 30‐year period compared to the median length. Changes in the dry–hot seasons were then analyzed for three time slices (2006–2035, 2036–2065, and 2066–2095) and low and high greenhouse gas concentration pathways. Distinct prolongation (compared to the 1976–2005 simulated climate) was projected for 2036–2065 in the Mediterranean and Western Europe (10–30 days), regardless of the concentration pathway. The dry–hot seasons length was similar in the 2036–2065 and 2066–2095 time slices under the low concentration pathway but major extensions were found under the high concentration scenario over large parts of Europe (20–50 days). The projected spatial patterns of changes in the dry–hot seasons length depend primarily on the driving global climate model. Although the extensions are predominantly driven by increasing temperature, simulated precipitation changes modulate the resulting pattern by amplifying (reducing) the dry–hot seasons length in Southern (Northern) Europe.