High-resolution Regional Climate Model Research Articles

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

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

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

Uncertainty in simulated streamflow using runoff driven by the outputs of a high-resolution regional climate model

Uncertainty in simulated streamflow using runoff driven by the outputs of a high-resolution regional climate model

Possible changes and occurrences of future wave power in an enclosed sea using a high-resolution regional climate model

The present study evaluates future wave power resources and detects a possible impact of climate change on wave power in an enclosed sea (Black Sea) for the 21st century. The wave data are obtained from a third-generation spectral wave model using a validated RCM (Regional Climate Model). The wave power projection and projected occurrences are analyzed for near and middle future (NF, MF) periods based on two RCP (Representative Concentration Pathway) scenarios (4.5 and 8.5). Possible changes in wave power for the NF 2021–2060 and MF 2061−2100 periods are evaluated by comparing the historical (1970–2005) period. According to two RCP scenarios, the wave power potential for the NF is expected to increase markedly over the entire basin, while, for the MF, the changes are projected by decreases, especially in the northeastern and western parts of the basin. Furthermore, projected occurrences are examined using Energy Level Occurrences known as available, moderate, and rich level occurrences. The 15 reference points over the basin are chosen to detect the most suitable location(s) for future wave power developments. All spatial and local analyses indicate that the western basin, especially the southwest, will continue having high wave power potential and occurrences in future periods.

Climatic impacts induced by winter wheat irrigation over North China simulated by the nonhydrostatic RegCM4.7

Quantification of the impact of winter wheat irrigation on the climate and the occurrence of extreme climatic events over North China is crucial for regional adaptation planning. Previous related studies mainly focused on the impact on surface processes; however, few focused on the effects of extreme events using high-resolution nonhydrostatic regional climate models. Here, the 9-km-resolution nonhydrostatic RegCM4.7 was coupled with a crop irrigation scheme and an updated winter wheat irrigation dataset to better simulate irrigation effects. Two experiments were conducted with and without winter wheat irrigation to isolate the effects of irrigation. Results showed that irrigation simulation reduces the model biases in temperature, precipitation, latent heat flux, soil moisture, sensitive heat flux, and top-layer soil moisture. Moreover, it also reduces the bias and increases the correlation with observations obtained in irrigated areas, especially in summer, indicating better representation of irrigation schemes. Winter wheat irrigation tends to cause substantial cooling of the local surface maximum, minimum, and mean air temperatures (by −0.79, −0.34, and −1.68 °C, respectively) over irrigated areas of North China, with the largest changes observed in relation to maximum temperature. Additionally, precipitation is found to increase during spring and summer, which is strongly related to water vapor transport in the lower levels of the atmosphere. Further analyses indicated that the number of annual mean hot days decrease (−13.9 d), whereas the number of both comfort days (+10.2 d) and rainy days (days with total precipitation greater than 1 mm: +6.6 d) increase over irrigated areas, demonstrating beneficial feedback to human perception and agriculture. Fortunately, although the heat wave risk increases (number of annual mean heat wave days: +5.8 d), the impact is limited to small areas within irrigated region. Additionally, no notable change was found in terms of heavy rainfall events and precipitation intensity, which might be an undereastimation caused by the less water use in model simulation. Although winter wheat irrigation does not have notable impact on the climate of the surrounding region, it is an important factor for the local-scale climate.

Larger wind turbines as a solution to reduce environmental impacts

The EU aims for carbon neutrality by 2050, focusing on offshore wind energy. Investments in North Sea wind farms, with optimal wind resources, play a crucial role. We employed a high-resolution regional climate model, which incorporates a wind farm parametrization, to investigate and address potential mitigating impacts of large wind farms on power generation and air-sea fluxes. Specifically, we examined the effects of replacing 5 MW turbines with larger 15 MW turbines while maintaining total capacity. Our study found that substituting 15 MW turbines increases the capacity factor by 2–3%, enhancing efficiency. However, these turbines exhibit a slightly smaller impact on 10 m wind speed (1.2–1.5%) and near-surface kinetic energy (0.1–0.2%), leading to reduced effects on sea surface heat fluxes compared to 5 MW turbines. This was confirmed by a stronger reduction in net heat flux of about 0.6–1.3% in simulations with 5 MW compared to 15 MW wind turbines. Air-sea fluxes influence ocean dynamics and marine ecosystems; therefore, minimizing these impacts is crucial. Overall, deploying 15 MW turbines in offshore wind farms may offer advantages for ocean dynamics and marine ecosystems, supporting the EU's carbon–neutral objectives.

Comparison of RegCM4.7.1 Simulation with the Station Observation Data of Georgia, 1985–2008

The global climate change, driven by natural processes and increasing human activities, is especially significant for Georgia. The region is experiencing increases in temperature, desertification, redistribution of precipitation, and a rise in the frequency and severity of extreme weather events. Georgia’s complex topography and its proximity to the Black and Caspian seas make it essential to employ high-resolution regional climate models to evaluate future climate change risks. In this study, we examine the results of a high-resolution simulation of mean and extreme precipitation and temperature using the Abdus Salam International Centre for Theoretical Physics Regional Climate Model version 4.7.1 for the period 1985–2008, providing an initial evaluation of the model’s performance for the territory of Georgia. The model domain (1524 km; 2388 km) encompasses the entirety of Georgia’s territory and surrounding regions. The simulation, conducted at a 12 km horizontal grid spacing using ERA5 data as boundary conditions, indicates that the least discrepancy between observed and modeled average annual temperatures and precipitation, falling within a −1 to 1 °C and −200 to 200 mm range, respectively, was observed at most stations of eastern Georgia. The largest disparities between the model and observed average annual precipitation totals were noted along the Black Sea coast, in the Kolkheti Lowland, and in some high mountain stations in western Georgia. The most significant differences in average annual temperatures between the model and observations were observed in Ambrolauri, Mt. Sabueti, and Dedoplistskaro. For Georgia territory, such a long run with such a high resolution using ERA5 as boundary conditions was conducted for the first time. Overall, the modeling results are quite satisfactory, providing a solid basis for the successful utilization of the regional climate model RegCM4.7.1 with the selected parameterization for modeling monthly mean and extreme temperatures and precipitation in Georgia.

Improving our understanding of future tropical cyclone intensities in the Caribbean using a high-resolution regional climate model

The Caribbean region is prone to the strong winds and low air pressures of tropical cyclones and their corresponding storm surge that driving coastal flooding. To protect coastal communities from the impacts of tropical cyclones, it is important to understand how this impact of tropical cyclones might change towards the future. This study applies the storyline approach to show what tropical cyclones Maria (2017) and Dorian (2019) could look like in a 2 °C and 3.4 °C warmer future climate. These two possible future climates are simulated with a high-resolution regional climate model using the pseudo global warming approach. Using the climate response from these simulations we apply a Delta-quantile mapping technique to derive future changes in wind speed and mean sea level pressure. We apply this Delta technique to tropical cyclones Maria and Dorian’s observed wind and pressure fields to force a hydrodynamic model for simulating storm surge levels under historical and future climate conditions. Results show that the maximum storm surge heights of Maria and Dorian could increase by up to 0.31 m and 0.56 m, respectively. These results clearly show that future changes in storm surge heights are not negligible compared to end-of-the-century sea level rise projections, something that is sometimes overlooked in large-scale assessments of future coastal flood risk.

Severe reduction in spawning days and larval abundance of walleye pollock under future warming in the western East/Japan Sea

The collapse of walleye pollock catch in the Korean fishing region during the late 1980s remains unresolved despite enormous efforts to recover its stock. To investigate the future fate of walleye pollock in the western East/Japan Sea (EJS) in the late 21st century, we implemented a dynamical downscaling approach by developing high-resolution regional ocean climate models (1/20°) with the selected Coupled Model Intercomparison Project 6 (CMIP6) global climate models. We analyzed the changes in the spawning days and larval distribution of walleye pollock in the western EJS. Under the SSP5–8.5 global warming scenario, the suitable spawning period ratio of walleye pollock was drastically reduced by 76%. The severe reduction in spawning days in the western EJS was mainly attributed to ocean warming caused by a decrease in atmospheric surface cooling. Moreover, our particle tracking experiment showed substantial loss of eggsand larvae south of 38° N. Our study projected a drastic collapse of walleye pollock in the western EJS and proposed a variety of collapse patterns based on local circulation under future warming conditions. These findings can help the local fishing industry adapt and assist neighboring governments in planning future fisheries management strategies.

Cloud- and ice-albedo feedbacks drive greater Greenland Ice Sheet sensitivity to warming in CMIP6 than in CMIP5

Abstract. The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors, such as melt–albedo feedbacks and cloud-related feedbacks, will partly impact future melt and sea-level rise contribution, yet little is known about the role of these regional factors in producing differences in GrIS surface melt projections between CMIP6 and CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modèle Atmosphérique Régional (MAR) to physically downscale six CMIP5 Representative Concentration Pathway (RCP) 8.5 and five CMIP6 Shared Socioeconomic Pathway (SSP) 5-8.5 extreme high-emission-scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is the largest during autumn with a reduction of 27.7 ± 9.5 Gt per season for a regional warming of +6.7 ∘C and 24.6 Gt per season more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – despite enhanced cloud optical depth during autumn – to be the main driver of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors: (1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures and (2) previously unnotified cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.

Validation of CORDEX Regional climate models to simulate climate trends and variability across various agro-ecological zones of North Shewa, Ethiopia

Climate variability significantly affects the overall agricultural production and productivity over space and time. To quantify the effect of climate variability on natural systems on Earth in the future, the use of regional climate models (RCMs) as a tool is mandatory. Thus, the use of well-validated RCM provides efficient climate data; and predicts future climate variability over agroecological zones (AEZs). Eleven RCMs from CORDEX-Ethiopia were evaluated to simulate maximum temperature (Tmax), minimum temperature (Tmin) and rainfall variability over space and time, to validate their performance and to select the best-fit models. The temporal variability Tmax and rainfall was smaller with a CV of 10 %, indicating uniform distribution over stations. However, CV for Tmin over stations shows satisfactory distributions. Most of the evaluated RCMs have a better capability to capture the spatial variability of rainfall,Tmax, and Tmin in some stations. However, some of the RCMs can handle the spatial variability of Tmax while others are not. None of the RCMs can handle the spatial variability of Tmin in Debre Birhan, Majete, and Kewat AEZs. In general, most RCMs can capture the spatial and temporal variability of rainfall and temperature. It might be better to use the ensemble average of the output from these RCMs for precise estimation of data than specific RCMs. Therefore, this work could suggest the use of high-resolution RCMs for the projection of temperature and rainfall over different AEZs.

Performance evaluation of a high-resolution regional climate model in West Africa: sensitivity to land surface schemes

Performance evaluation of a high-resolution regional climate model in West Africa: sensitivity to land surface schemes

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.

Projected changes in heatwaves and its impact on human discomfort over India due to global warming under the CORDEX-CORE framework

Due to climate change, rapid warming and its further intensification over different parts of the globe have been recently reported. This has a direct impact on human health, agriculture, water availability, power generation, various ecosystems, and socioeconomic conditions of the exposed population. The current study thus investigates the frequency and duration of heatwaves, human discomfort, and exposure of the human population to these extremes using the high-resolution regional climate model experiments under two Representative Concentration Pathways (RCP2.6, RCP8.5) over India. We find that more than 90% of India will be exposed to uncomfortable warm nights by the end of the 21st century with the highest rise over western India, Madhya Pradesh (MP), Uttar Pradesh (UP), Punjab, and the Haryana region. States like Odisha, Chhattisgarh, eastern parts of MP and UP, and some parts of J&K will be the worst hit by the intense and frequent heatwaves and human discomfort followed by the densely populated Indo-Gangetic plains under RCP8.5. Strict enforcement of the stringent policies on stabilization of population growth, improvement of local adaptive capacities, and economic status of the vulnerable population along with enforcing effective measures to curb greenhouse gas emissions are important to reduce human exposure to future heat stress. We demonstrate that a proper mitigation-based development (RCP2.6) instead of a business-as-usual scenario (RCP8.5) may help to reduce 50–200 heatwave days, 3–10 heatwave spells, and 10–35% warm nights over the Indian region. Consequently, this can avoid the exposure of 135–143 million population to severe discomfort due to extreme heat conditions by the end of the 21st century.

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Simulation of Climate Change Impacts on Crop Yield in the Saskatchewan Grain Belt Using an Improved SWAT Model

Climate change has a potentially significant influence on agricultural production in southern Saskatchewan. Crop yields are susceptible to weather patterns and seasonal fluctuations in this sub-humid region owing to the predominance of rain-fed farming practices. A modified Soil and Water Assessment Tool (SWAT-M) and the output from 10 high-resolution (0.22°) regional climate models (RCMs) were used to develop simulations of spring wheat and rain-fed canola in 296 rural municipalities (RM) for a historical baseline period (1975–2004) and three 30-year future periods: near (2010–2039), middle (2040–2069), and far (2070–2099). We combined SWAT-M with the S-curve method to adjust yield to the original drought stress in the source code and evaluated eight indices of extreme precipitation and temperature. Results of calibration and validation suggest that the simulated crop yields generally agree with observed data. Crop yield showed lesser performance compared with streamflow and soil water content (SWC) along with percent bias, ranging from −9.6% to −14.8%, while streamflow calibration ranges from −5.3% to −7.7%. The multi-model ensemble median showed increasing radiative forcing in the temperature and precipitation indices, such that the RCM-projected weather indices were found to be warmer and wetter than those estimated using regional historical data. The results of simulating canola and spring wheat indicate an increase in crop yield of 17% and 9.7% in the near future, 28.2% and 15.6% in the middle future, and 44.7% and 32% in the far future, respectively. Although, there has been an increase in the median wheat and canola yields, a significant reduction in the annual production is observed. This decline in yield amounts to around 1000 kg/ha and is anticipated to occur in the near and middle future. This trend is quite pronounced in the extreme south and southwest regions. Overall, this innovative research framework, along with the region-specific model outcomes in the form of crop yield projections, will aid in the formulation of future agricultural policies aimed at promoting effective climate adaptation strategies.

Estimation of Crops Future Irrigation Water Needs in a Mediterranean Plain

Agriculture is a vulnerable sector to climate change due to its sensitivity to weather conditions. Changes in climatic parameters such as temperature and precipitation significantly affect productivity as well as the consumption of natural resources like water to meet irrigation water needs. There has been a large amount of research on regional climate change. However, this study placed specific crops at first place and considered their irrigation water needs that will arise due to evapotranspiration increase. The aim of this study was to estimate the future irrigation water needs of wheat, cotton, and alfalfa in the east part of Thessaly Plain in central Greece, where Lake Karla, a recently restored lake, is located. The Weather Research and Forecasting (WRF) model was applied as a high-resolution regional climate model to simulate temperature and precipitation for two 5-year periods, namely 2046–2050 (future period) and 2006–2010 (reference period). Simulations refer to the RCP8.5 emission scenario (worst-case). A methodology proposed by the Food and Agriculture Organization (FAO) of the United Nations was followed to estimate the reference crop evapotranspiration, the crop evapotranspiration based on each crop factor, which was determined for each crop, the effective rainfall, and finally, the irrigation water needs for each crop, for the two 5-year periods. Based on WRF simulations, temperature was projected to be 1.1 °C higher in the future period compared to the reference period, while precipitation and effective precipitation were projected to decrease by 32% and 45%, respectively. Based on the WRF projections, by 2025, the irrigation water needs of wheat and alfalfa are expected to increase by more than 16% and more than 11%, respectively, while irrigation water needs of cotton are expected to increase by 7%. An extension of wheat’s irrigation period for one month (i.e., December) was also identified. Good practices that could be applied in the frame of precision agriculture principles in order to save irrigation water were suggested. The results of this study could be exploited by water resources and land use managers when planning short and long-term strategies to adapt to climate change impacts.

Projected changes in rainfall amount and distribution in the Democratic Republic of Congo – Evidence from an ensemble of high-resolution climate simulations

Extreme precipitation has been threatening many sectors of human society and is likely to intensify with global warming. In the present study, we have analysed the impact of future climate change on daily mean rainfall and heavy rainfall on seasonal and annual scales in the Democratic Republic of Congo (DRC) by using ten long-term runs of high-resolution regional climate models. We initially assessed the performance of each run and the multi-model ensemble mean in simulating the daily mean rainfall (PRE), the maximum one-day rainfall (Rx1day), the simple rainfall intensity index (SDII), the count of days when rainfall is greater than or equal to 20 mm (RR20mm), the total rainfall when the daily rainfall exceeds the 95th percentile of the wet-day rainfall (R95p) and the maximum number of consecutive days with more than or equal to 1 mm (CWD). Next, we examined the future changes of these indices, focusing on the multi-model ensemble mean and spread under the high emission scenario RCP8.5. The time frames considered are the mid and end of the twenty-first century (2035–2065 and 2070–2100, respectively). The results indicate that the performance of REMO2015-MPI-ESM was close to the multi-model ensemble mean in representing the mean rainfall and most heavy rainfall indices, while CanRCM4-CamESM2 was identified as the worst performing model. The key finding of this study is that the multi-model ensemble mean project no significant change regarding the daily mean rainfall throughout the year and across all seasons by the middle of the 21st century, except of the western region of the country where a decrease is projected. Simultaneously, Rx1day, SDII, RR20mm, and R95p are projected to decrease almost everywhere during all seasons. Moreover, a consistent decrease in the number of wet days is projected. Focusing on the end of the century, the multi-model ensemble mean project an overall decrease in daily mean rainfall, especially in the western region of the DRC, with a more pronounced effect during MAM. The increase in Rx1day, SDII, RR20mm, and R95p, along with a decrease in CWD, is amplified in this period. These findings are useful for predicting the potential threats of precipitation-related diseases and natural hazards, as well as for designing climate-resilient infrastructure and socioeconomic activities in the DRC.

Climate change potentially induces ecological change in the Ethiopian Rift Valley Lakes Basin

Streamflow regime management is critical for preserving species in riverine and riparian habitats. Quantifying flow-ecology relationships can help to characterize the effect of flow regime change on species; however, dealing with this in ungauged catchments is challenging. By regionalizing simulation of streamflow to ungauged catchments, the Indicators of Hydrological Alterations (IHA) are calculated and used as a proxy for quantifying ecological impacts. High-resolution Regional Climate Models are used to derive the future streamflow in both gauged and ungauged catchments of the Ethiopian Rift Valley Lakes Basin (RVLB). Consequently, 32 IHAs representing the magnitude, duration, frequency, rate, and timing of streamflow events during their historical and future periods are calculated. The number of cases for future IHAs deviating from their historical range is calculated, which is called the non-attainment rate of future IHAs. A threshold value for the non-attainment rate is set to classify the impact as no-impact, low, medium, or high. The non-attainment rate for the future period indicates that, high ecological impact resulted in October, which is the local high flow month. Spatially, a shift towards a later date of the Julian date indicating the maximum flow resulted in a high ecological impact. This approach can be adapted to other regions with reliable regionalization results and suitable future climate models, which would be useful for the quantification and management of ecological impacts in ungauged regions.

Climate change information tailored to the agricultural sector in Central Europe, exemplified on the region of Lower Franconia

There is a growing societal, economic, and political demand to translate available data on regional climate change into sector-specific, practice-oriented, and user-friendly information. The study presents a demand-driven approach to specify the impacts of regional climate change on agriculture, viticulture, and fruit and vegetable growing in Lower Franconia, southern Germany, a region with heterogeneous topography, diversified land use patterns, and intense activities in the sectors specified above. The approach is based on an ensemble of high-resolution regional climate model projections, a bias correction tool, and a large spectrum of meteorological (extreme) indicators that are crucial to the agricultural sector in Central Europe, as inferred from a stakeholder survey.For several decades, Lower Franconia represents a hotspot region of climate change with enhanced heat waves, prolonged droughts, and intermittent local flooding by heavy rainfall events. Results of the high-resolution regional climate model projections indicate an increase of hot days and tropical nights by a factor of 5 and 12, respectively, if greenhouse gas emissions continue to grow until 2100 according to the RCP8.5 emission scenario. At the same time, droughts will occur more frequently and last longer while rainfall intensity enhances. A longer growing period starting more than 40 days earlier (compared to the reference period 1970 to 1999) implies a higher risk of late frost damage for crops, fruits, grapes, and even some tree species. In contrast, the thermal prerequisites for viticulture will be satisfied across the entire region, even at higher-elevation sites. These facets of regional climate change are made accessible to users and the public via an interactive field-resolving web portal. Altogether, they gravely challenge the historically developed land use systems in Lower Franconia and require timely adaptation and mitigation strategies.