Regional Climate Model Projections Research Articles

Multiple regional climate model projections to assess building thermal performance in Brazil: Understanding the uncertainty

Understanding the trends and uncertainties in Building Energy Simulation (BES) performance indicators under future climate conditions is crucial for mitigating issues such as overheating and power outages. To address this, we generated a set of weather files for all 27 state capitals in Brazil, considering six climate model projections (three General Circulation Models as driving models and two nested Regional Climate Models) and two distinct emission scenarios from the CORDEX project. We analyzed the variability in climatic variables and subsequently performed BES on a representative Brazilian social housing unit to evaluate its impact on the performance indicators outcomes. Consistent with previous studies, a substantial increase in cooling-related demands was observed in the more pessimistic scenario (RCP8.5) and mild increases in the more optimistic scenario (RCP2.6), with a trend toward stabilization after 2050. Regarding uncertainties, we found higher Relative Standard Deviation (RSD) values for the cooling degree hours indicator. The capitals in the Central-West, Southeast, and South regions exhibited greater uncertainty regarding temperature indicators, whereas the irradiation parameters displayed higher uncertainties in the Northeast region. For the BES outcomes, RSD values as high as 19.9% were found for cooling load values. It was also demonstrated that locations, periods, and scenarios exhibit different extreme climate model projections. Ideally, employing an ensemble of weather files developed from other models would help assess associated uncertainties in the building performance indicators.

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.

Projected changes in Köppen‒Trewartha climate zones under 1.5–4 °C global warming targets over mid-high latitudes of Northern Asia using an ensemble of RegCM4 simulations

Mid-high latitude Northern Asia is one of the most vulnerable and sensitive areas to global warming, but relatively less studied previously. We used an ensemble of a regional climate model (RegCM4) projections to assess future changes in surface air temperature, precipitation and Köppen‒Trewartha (K‒T) climate types in Northern Asia under the 1.5–4 °C global warming targets. RegCM4 is driven by five CMIP5 global models over an East Asia domain at a grid spacing of 25 km. Validation of the present day (1986–2005) simulations shows that the ensembles of RegCM4 (ensR) and driving GCMs (ensG) reproduce the major characters of the observed temperature, precipitation and K‒T climate zones reasonably well. Greater and more realistic spatial detail is found in RegCM4 compared to the driving GCMs. A general warming and overall increases in precipitation are projected over the region, with these changes being more pronounced at higher warming levels. The projected warming by ensR shows different spatial patterns, and is in general lower, compared to ensG in most months of the year, while the percentage increases of precipitation are maximum during the cold months. The future changes in K‒T climate zones are characterized by a substantial expansion of Dc (temperature oceanic) and retreat of Ec (sub-arctic continental) over the region, reaching ∼20% under the 4 °C warming level. The most notable change in climate types in ensR is found over Japan (∼60%), followed by Southern Siberia, Mongolia, and the Korean Peninsula (∼40%). The largest change in the K‒T climate types is found when increasing from 2 to 3 °C. The results will help to better assess the impacts of climate change and in implementation of appropriate adaptation measures over the region.

Changes in universal thermal climate index from regional climate model projections over European beaches

Millions of people travel to European beaches making beach tourism the largest tourism sector. Understanding how person’s thermal comfort changes with projected climate change, which is paramount in deciding where and when to visit, can help the tourism sector mitigate risk and identify opportunities. Sustainable adaptation strategies for coastal tourism can be developed for different regional changes. This work calculates the Universal Thermal Comfort Index (UTCI), a comprehensive assessment of the human physiological response to the thermal environment, from an ensemble of regional climate models participating in EURO-CORDEX. Under Representative Concentration Pathways (RCP) 8.5, the ensemble’s projections of the UTCI in the near- and far-future, compared to a historical reference period, show a robust and statistically significant increase across the entire EURO-CORDEX domain. Four popular beach regions in Germany, Italy, France, and Spain show the beach season can be extended - starting earlier and ending later each year. Across the Mediterranean, July and August exhibit, on average, 4 more days of ‘very strong’ heat stress and 1 day of ‘extreme’ heat stress at the end of the century. In accordance with FAIR data science principles, the UTCI calculation is available in the xclim open-source python library.

Divergent future drought projections in UK river flows and groundwater levels

Abstract. Hydrological drought is a serious issue globally, which is likely to be amplified by 21st century climate change. In the UK, the impacts of changes in river flow and groundwater drought severity in a future of climate change and higher water demand are potentially severe. Recent publication of a new nationally consistent set of river flow and groundwater level projections (the eFLaG dataset), based on state-of-the-art UKCP18 climate projections, offers a unique opportunity to quantitatively assess future UK hydrological drought susceptibility. The dataset includes a transient, multi-model ensemble of hydrological projections driven by a single regional climate model (RCM), with a 12-member perturbed-parameter ensemble, for 200 catchments and 54 boreholes spanning a period from 1961 to 2080. Assessment of a baseline period (1989–2018) shows that the RCM-driven projections adequately reproduce observed river flow and groundwater level regimes, improving our confidence in using these models for assessment of future drought. Across all hydrological models and most catchments, future low river flows are projected to decline consistently out to 2080. Drought durations, intensities and severities are all projected to increase in most (over 90 %, pooling across different drought characteristics) UK catchments. However, the trajectory of low groundwater levels and groundwater drought characteristics diverges from that of river flows. Whilst groundwater levels at most (> 85 %) boreholes are projected to decline (consistent with river flows), these declines are relatively modest (< 10 % reduction) in transient low groundwater levels by 2080, and, in fact, six show moderate increases. Groundwater drought characteristics in the far future (2050–2079) are often similar to those of the baseline (1989–2018), with only 33 % of boreholes showing an increase (towards worsening drought) of more than 10 % for drought severity (48 % of boreholes for drought intensity). Interestingly, for some boreholes, droughts are projected to be more prolonged and severe in the near future (2020–2049) before returning to shorter durations and lower severity in the far future. A number of explanatory factors for this divergence between river flow and groundwater are discussed. The sensitivity to seasonal changes in precipitation and potential evapotranspiration is proposed as a principal driver of divergence because low river flows are more influenced by shorter-term rainfall deficits in the summer half-year, whilst groundwater drought appears to be offset somewhat by the wetter winter signal in the RCM projections. Our results have important implications for water management, demonstrating a widespread increase in river flow drought severity and diminishing low flows that could have profound societal and environmental impacts unless mitigated. Furthermore, the divergence in projections of drought in river flows and groundwater levels brings into question the balance between surface and subsurface water resources. The projected contrast in fortunes of surface and subsurface water resources identified for the UK may be replicated in other parts of the world where climate projections suggest a shift towards drier summers and wetter winters.

Changes in solar resource intermittency and reliability under Australia’s future warmer climate

The dependency of photovoltaic (PV) power generation on meteorological parameters can impact power production due to weather-induced variability. During the day, fluctuations in radiation introduce intermittency in power generated, raising reliability and grid stability issues at higher penetration levels. Long-term future resource assessment provides an effective tool for estimating resource reliability and future intermittency essential for pre-feasibility site assessments around the world. Australia has high solar power capacity, with several solar farms in operational and developmental stage. Using Australia as a case study, this research aims to understand Australia's solar resource distribution and variability using regional climate model projections under a high emission scenario. Results indicate an abundance of solar resource power density in Australia, especially in the North (450-500Wm−2). The solar resource will be more reliable in Eastern Australia in the future with ∼ 5% increase in resource density. Results suggest reduction in intermittency (∼20-minute lull periods) in the East with increase in clear-sky days/year in the future (∼20 days/year). Resource assessment of Sun Cable and New England solar farm located in Australia, revealed the future scope of increase in clear-sky days at the sites. This long-term future solar variability analysis can help identify regions in Australia where PV systems will be least susceptible to losses due to intermittency. Furthermore, this study will help in critical decision-making processes like planning storage systems, site selection, opportunities to create hybrid solar farms with the co-existence of solar and wind technology, etc., to mitigate the risks associated with future intermittent PV power generation.

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.

Uncertainty analysis of future summer monsoon duration and area over East Asia using a multi-GCM/multi-RCM ensemble

This study examines the spatiotemporal characteristics of the summer monsoon rainy season over East Asia using six regional climate models (RCMs) participating in the Coordinated Regional Domain Experiment (CORDEX) East Asia Phase II project. The framework combining multiple global climate models (GCMs) with multiple RCMs produces a larger spread in summer monsoon characteristics than driving GCMs only, enabling a better quantification of uncertainty factors. On average, the RCM simulations reproduce the observed summer monsoon duration and area better than the corresponding boundary GCMs, implying the added values of downscaling. Both the area and duration of the East Asian summer monsoon are projected to increase by the late 21st century, more strongly in high emission scenarios than in low emission scenarios, particularly in China. Different responses between scenarios, which indicate warming mitigation benefits, only become significant in the late 21st century due to large intersimulation uncertainties. Analysis of variance results show that uncertainty in future monsoon area and duration is larger between boundary GCMs than between RCMs over East Asia and its coastal subregions. A strong intersimulation relationship between RCMs and GCMs supports that boundary GCMs substantially diversify downscaled RCM projections through different climate sensitivities. Furthermore, the distinct subregional responses in future monsoon area and duration emphasize the importance of fine-resolution projections with appropriate uncertainty measures for better preparing region-specific adaptation plans.

Assessing the land-use harmonization (LUH) 2 dataset in Central Asia for regional climate model projection

Although the land-use harmonization (LUH) datasets have been widely applied in regional climate model (RCM) projections for investigating the role of the land-use forcing in future climate changes, few studies have thoroughly assessed them on local scale, which may bring large uncertainties in the resultant climate information for designing adaption and mitigation measures of climate change. The authors use a local land-use dataset (referred to as Li-LU) as the benchmark to assess the latest version of the LUH datasets, LUH2, in Central Asia (CA) which has undergone extensive land-use changes (LUCs) and might undergo extensive LUCs in the future. The results show that LUH2 has large biases in depicting the historical land-use states in CA for 1995–2015. For instance, the area of grassland (cropland) in LUH2 is about 1.4–1.5 (0.4–0.5) times of that of Li-LU. Moreover, the future LUCs predicted by LUH2 for 2045 (relative to 2005) are much smaller than those of Li-LU and these two datasets generally have opposite signals in changes. In addition, the predicted LUCs of LUH2 do not follow the causal mechanisms [the causal connections between the key drivers (e.g. population, economy, and environment) and land use] behind the LUCs in the past. If the future scenario of LUH2 is used for RCM projection in CA with the historical land-use information from Li-LU, the simulation results could be misleading for understanding the impacts of LUCs on future climate changes there. This study suggests that the LUH datasets should be carefully assessed before using them for regional studies and provides practical notes for selecting the appropriate land-use dataset for RCM projections in other areas around the world.

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Sustainability of cover cropping practice with changing climate in Illinois

Climate change could adversely impact the best management practices (BMPs) designed to build a sustainable agro-ecological environment. Cover cropping is a conservation practice capable of reducing nitrate-nitrogen (NO3–N) loadings by consuming water and nitrate from the soil. The objective of this study was to investigate how climate change would impact the proven water quality benefits of cereal rye as a winter cover crop (CC) over the climate divisions of Illinois using the DSSAT model. Moreover, this study explores the sustainability of the CC with the changing climate conditions by using five regional climate models (RCMs) projections of two warming scenarios-rcp45 (a medium emission scenario - radiative forcing of 4.5 W/m2) and rcp85 (a high emission scenario - radiative forcing of 8.5 W/m2)). The CC impact simulated in the warming scenarios for the near-term (2021–2040) and the far-term future (2041–2060) were compared with the baseline scenario (2001–2020). Our results conclude that the climate change may negatively impact [average of CC and no CC (NCC)] maize yield (−6.6%) while positively affecting soybean yield (17.6%) and CC biomass (73.0%) by the mid-century. Increased mineralization caused by rising temperature could increase the nitrate loss via tile flow (NLoss) and nitrate leached (NLeached) up to 26.3% and 7.6% on average by the mid-century in Illinois. Increasing CC biomass could reduce the NLoss more considerably in all the scenarios compared to the baselines. Nevertheless, the NLoss level in the CC treatment can increase from the near-term to far-term future and could get closer to the baseline levels in the NCC treatment. These results suggest that CC alone may not address nitrate loss goals via subsurface drainage (caused by increasing N mineralization) in future. Therefore, more robust and cost-effective BMPs are needed to aid the CC benefits in preventing nutrient loss from the agricultural fields.

Evaluation of Convective Environments in the NARCliM Regional Climate Modeling System for Australia

Severe thunderstorms lead to multiple hazards including torrential precipitation, flash flood, hail, lightning, and wind gusts. The meso- to micro-scale nature of thunderstorms impose great challenges from understanding individual storm dynamics, storm climatology as well as projecting their future activities. High-resolution regional climate models can resolve the convective environments better than global models. Australia, especially the east and southeast parts of the continent, is a global hot spot for severe thunderstorms. This study evaluates the simulated convective environments from the New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) project based on the parameters of CAPE, CIN, 0–6-km vertical wind shear and storm severity. The ensemble regional downscaling is compared against the fifth-generation European Centre for Medium-range Weather Forecast Reanalysis (ERA5). The results show that although there are apparent biases (generally positive for CAPE and negative for CIN, and slightly overestimated vertical wind shear) in the downscaled storm parameters, the climatology of measures of storm severity over land, including their spatial patterns and seasonality, agree well with ERA5. These results have strong implication on the application of the climate projection to assess future convective environments in the region.

Assessment of Meteorological and Agricultural Drought Indices under Climate Change Scenarios in the South Saskatchewan River Basin, Canada

Climate change has amplified the severity of droughts with potentially adverse impacts on agriculture in western Canada. This study assessed meteorological and agricultural drought in the Southern Saskatchewan River Basin (SSRB) using an array of drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Self-Calibrated Palmer Drought Severity Index (scPDSI), the Soil Moisture Deficit Index (SMDI), and the Evapotranspiration Deficit Index (ETDI). These indices were evaluated using multiple regional climate model (RCM) projections assuming 1.5, 2.0, and 3.0 °C thresholds of global warming. A modified Soil and Water Assessment Tool (SWAT-M) was used to simulate the soil water content (SWC), actual evapotranspiration (AET), and potential evapotranspiration. The results of a sensitivity analysis using the SUFI-2 method in SWAT-CUP showed that the model performed well with BIAS lower than 10% and NSE and R higher than 0.7, and the range of SWC output closely matched the observed SWC. According to the RCM projections, the annual precipitation increases for all three global temperature thresholds while the annual mean temperature increases at a greater rate than the rise in global mean temperature. The projected PDSI and the SPEI suggest that drought duration and severity will exceed historical values while SPI will remain largely unchanged. Furthermore, severe drought conditions (SMDI < 2.0) are more frequent under the 3.0 °C global temperature scenario. The mean ETDI was historically 0.58 while the projected value is 0.2, 0.1, and −0.2 for the first to third scenarios, respectively. Simulated values, spatial maps, and heat maps of SMDI and ETDI illustrated that Canesm2.CRCM5 projects the driest conditions among all the RCMs. Agricultural drought indices, which incorporate SWC data, show more significant effects than meteorological drought indices. The increasing dryness will potentially impact agricultural crop production, particularly under the third scenario (3 °C) in the SSRB.

Bias‐Adjustment Methods for Future Subdaily Precipitation Extremes Consistent Across Durations

AbstractModel output from climate projections often requires bias‐adjustment to compensate for systematic model errors. A bias‐adjustment method for extreme precipitation intensity is proposed that preserves the scaling equation for different accumulation levels from hourly to daily, using intensity‐duration‐frequency (IDF) modeling. A validation is performed within a pseudo‐reality setting, based on hourly precipitation from 28 regional climate model projections of the EURO‐CORDEX ensemble over Belgium. The scaling‐based adjustment methods improve upon previous methods, an optimal method is identified, and, analytical quantile mapping methods must be avoided due to three identified problems. The ensemble mean of the adjusted extreme precipitation intensity obeys the above‐mentioned scale‐invariance property, which is consistent with observed extreme intensities. We thus show that IDF modeling provides added value in the context of bias‐adjustment, and, that the particular IDF model proposed balances well between accuracy and the preservation of desired properties such as scale invariance and consistency among rainfall durations.

A Combined Stochastic–Analytical Method for the Assessment of Climate Change Impact on Spring Discharge

This study describes a novel methodology for the prediction of spring hydrographs based on regional climate model (RCM) projections, with the goal of evaluating climate-change impact on karstic-spring discharge. A combined stochastic–analytical modeling methodology to predict spring discharge was developed and demonstrated on the Bukovica spring catchment at the Durmitor National Park, Montenegro. As a first step, climate model projections of the EURO-CORDEX ensemble were selected; and then bias correction was applied based on historical climate data. The regression function between rainfall and peak discharge was established by using historical data. Baseflow recession was described by using a double-component exponential model, where hydrograph decomposition and parameter fitting were performed on the Master Recession Curve. Rainfall time series from two selected RCM scenarios were applied to predict future spring-discharge time series. Bias correction of simulated hydrographs was performed, and bias-corrected combined stochastic–analytical models were applied to predict spring hydrographs based on RCM-simulated rainfall data. Both simulated climate scenarios predict increasing peak discharges and decreasing baseflow discharges throughout the 21st century. The model results suggest that climate change is likely to exaggerate the extremities both in terms of climate parameters and spring discharge by the end of the century both for moderate (RCP 45) and pessimistic (RCP 85) CO2 emission scenarios. To investigate the temporal distribution of extremities throughout the simulated time periods, the annual numbers of flood and drought days were calculated. Annual predicted flood days show an increasing trend during the first simulation period (2021–2050) and a slightly decreasing trend during the second simulation period (2071–2100), according to the RCP45 climate scenario. The same parameter shows a stagnant trend for the RCP 85 climate scenario. Annual predicted drought days show a decreasing trend both for the RCP 45 and RCP 85 climate scenarios. However, the annual number of drought days shows a large variation over time. There is a periodicity of extremely dry years with a frequency between 5 and 7 years. The number of drought days seems to increase over time during these extreme years. The study confirmed that the applied methodology can successfully be applied for spring-discharge prediction and that it offers a new prospect for its wider application in studying karst aquifers and their behavior under different climate-change scenarios.

Framework for multirisk climate scenarios across system receptors with application to the Metropolitan City of Venice.

Climate change influences the frequency of extreme events that affect both human and natural systems. It requires systemic climate change adaptation to address the complexity of risks across multiple domains and tackle the uncertainties of future scenarios. This paper introduces a multirisk analysis of climate hazard, exposure, vulnerability, and risk severity, specifically designed to hotspot geographic locations and prioritize system receptors that are affected by climate-related extremes. The analysis is demonstrated for the Metropolitan City of Venice. Representative scenarios (RCP4.5 and RCP8.5) of climate threats (i.e., storm surges, pluvial flood, heat waves, and drought) are selected and represented by projections of Regional Climate Models for a 30-year period (2021-2050). A sample of results is as follows. First, an increase in the risk is largely due to drought, pluvial flood, and storm surge, depending on the areas of interest, with the overall situation worsening under the RCP8.5 scenario. Second, particular locations have colocated vulnerable receptors at higher risk, concentrated in the urban centers (e.g., housing, railways, roads) and along the coast (e.g., beaches, wetlands, primary sector). Third, risk communication of potential environmental and socio-economic losses via the multirisk maps is useful to stakeholders and public administration. Fourth, the multirisk maps recommend priorities for future investigation and risk management, such as collection of sensor data, elaboration of mitigations, and adaptation plans at hotspot locations.

Spatio-temporal variation of precipitation projection based on bias-adjusted CORDEX-SA regional climate model simulations for arid and semi-arid region

Global warming significantly affects the hydrological cycle that influences hydrological systems worldwide. Knowledge of precipitation variation under a changing climate can provide effective ways to deal with its impacts on water resource management. Hence, in this study, spatiotemporal variability of precipitation under future climatic scenarios was studied using suitable regional climate models (RCMs) extracted from the South Asia Coordinated Regional Climate Downscaling Experiments (CORDEX-SA) based on the control period (1976-2005). Additionally, this research attempted to evaluate the changes in precipitation patterns in Iran by integrating output from a set of RCMs extracted from CORDEX-SA with selected bias-correction (BC) methods, namely quantile ranking-based bias correction (QRBC), linear scaling (LS), empirical quantile mapping (EQM), Gamma quantile mapping (GQM), and Gamma-Pareto quantile mapping (GPQM). Regarding the findings, the performance of RCMs varied based on the climates of regions. Moreover, QRBC played an essential role in improving the output of RCMs to simulate precipitation values. Under RCP4.5 and RCP8.5, the projection of seasonal precipitation indicated that a slight wetting pattern might occur across the northern area in all seasons, especially in the southern regions. Whereas the southern area exhibited a strong wetting tendency in autumn, and the maximum percentage changes (PC) of precipitation were projected to increase by 35% for the mid (2040-2070) and far future (2070-2100) periods, based on RCP4.5 and RCP8.5, respectively. This study will be helpful in understanding the uncertainties in CORDEX-SA RCM projections of climate changes in Iran. Using the outcomes of this study, strategies can be adopted to better manage water resources for various regions of Iran.

Climate Change Impacts on Water Resources in the Danube River Basin: A Hydrological Modelling Study Using EURO-CORDEX Climate Scenarios

Climate change affects the hydrological cycle of river basins and strongly impacts water resource availability. The mechanistic hydrological model PROMET was driven with an ensemble of EURO-CORDEX regional climate model projections under the emission scenarios RCP2.6 and RCP8.5 to analyze changes in temperature, precipitation, soil water content, plant water stress, snow water equivalent (SWE) and runoff dynamics in the Danube River Basin (DRB) in the near (2031–2060) and far future (2071–2100) compared to the historical reference (1971–2000). Climate change impacts remain moderate for RCP2.6 and become severe for RCP8.5, exhibiting strong year-round warming trends in the far future with wetter winters in the Upper Danube and drier summers in the Lower Danube, leading to decreasing summer soil water contents, increasing plant water stress and decreasing SWE. Discharge seasonality of the Danube River shifts toward increasing winter runoff and decreasing summer runoff, while the risk of high flows increases along the entire Danube mainstream and the risk of low flows increases along the Lower Danube River. Our results reveal increasing climate change-induced discrepancies between water surplus and demand in space and time, likely leading to intensified upstream–downstream and inter-sectoral water competition in the DRB under climate change.

Introducing NARCliM1.5: Evaluation and projection of climate extremes for southeast Australia

NARCliM1.5 is the second generation of New South Wales and Australian Regional Climate Modelling (NARCliM) project, which is designed to produce an ensemble of regional climate projections for CORDEX Australasia and southeast Australia. The selected global climate models (GCMs) are used to drive Weather Research and Forecasting (WRF) model for dynamical downscaling. In this study, we evaluate how well the two generations of NARCliM (N1.0 and N1.5) represent the observed record for 12 selected climate extremes and examine their projected changes for southeast Australia. N1.5 ensemble substantially improves upon N1.0 in capturing the spatial patterns of precipitation extremes, however, there is little difference between the two ensembles for temperature extremes. Both N1.0 and N1.5 underestimate dry extreme (consecutive dry day - CDD), hot extremes (warmest daily maximum temperature – TXx, and number of days when maximum temperature is greater than 35 °C - TXge35) and daily temperature range (DTR) but overestimate wet extremes (annual sum of daily precipitation above 99th percentile - R99p, consecutive wet days - CWD, days when precipitation is at least 10 mm - R10mm, maximum 1-day precipitation - Rx1Day and total wet day precipitation -PRCPTOT) and cold extreme (coldest daily minimum temperature - TNn). N1.0 and N1.5 project different spatial patterns of future changes in precipitation extremes but similar changes in temperature extremes. Differences in climate extremes between N1.0 and N1.5 could be attributed to the driving GCMs. The combined future projections of both N1.0 and N1.5 provide a more complete sampling of the future change space. Future projections indicate that precipitation and temperature extremes will become more intense, raising implications for future planning and risk management.

Projected climate change impact on a coastal sea-As significant as all current pressures combined.

Climate change influences the ocean's physical and biogeochemical conditions, causing additional pressures on marine environments and ecosystems, now and in the future. Such changes occur in environments that already today suffer under pressures from, for example, eutrophication, pollution, shipping, and more. We demonstrate how to implement climate change into regional marine spatial planning by introducing data of future temperature, salinity, and sea ice cover from regional ocean climate model projections to an existing cumulative impact model. This makes it possible to assess climate change impact in relation to pre‐existing cumulative impact from current human activities. Results indicate that end‐of‐century projected climate change alone is a threat of the same magnitude as the combination of all current pressures to the marine environment. These findings give marine planners and policymakers forewarning on how future climate change may impact marine ecosystems, across space, emission scenarios, and in relation to other pressures.