Ensemble Of Regional Climate Models Research Articles

Future summer warming pattern under climate change is affected by lapse-rate changes

Abstract. Greenhouse-gas-driven global temperature change projections exhibit spatial variations, meaning that certain land areas will experience substantially enhanced or reduced surface warming. It is vital to understand enhanced regional warming anomalies as they locally increase heat-related risks to human health and ecosystems. We argue that tropospheric lapse-rate changes play a key role in shaping the future summer warming pattern around the globe in mid-latitudes and the tropics. We present multiple lines of evidence supporting this finding based on idealized simulations over Europe, as well as regional and global climate model ensembles. All simulations consistently show that the vertical distribution of tropospheric summer warming is different in regions characterized by enhanced or reduced surface warming. Enhanced warming is projected where lapse-rate changes are small, implying that the surface and the upper troposphere experience similar warming. On the other hand, strong lapse-rate changes cause a concentration of warming in the upper troposphere and reduced warming near the surface. The varying magnitude of lapse-rate changes is governed by the temperature dependence of the moist-adiabatic lapse rate and the available tropospheric humidity. We conclude that tropospheric temperature changes should be considered along with surface processes when assessing the causes of surface warming patterns.

A Deep Learning Approach for the Identification of Long-Duration Mixed Precipitation in Montréal (Canada)

ABSTRACT Long-duration mixed-precipitation events (freezing rain and/or ice pellets) are important cold-season hazards and understanding how climate change alters their occurrence is of high societal interest, particularly in urban areas. This study introduces a two-staged approach that employs deep learning to identify long-duration mixed precipitation over the Montréal area (Quebec, Canada) in archived climate model data using large-scale pressure patterns. The dominant dynamic mechanism leading to mixed precipitation in Montréal is pressure-driven channelling of winds along the St. Lawrence River Valley. A convolutional neural network (CNN) identifies the corresponding synoptic pattern by using a large training database derived from an ensemble of the Canadian Regional Climate Model, version 5 (CRCM5). The CRCM5 uses the diagnostic method of Bourgouin (2000) to simulate mixed precipitation and delivers training examples and corresponding class affiliations (labels) for this supervised classification task. The CNN correctly identifies more than 80% of the Bourgouin mixed-precipitation cases. In the next stage, the CNN is combined with temperature and precipitation conditions, which consider important preconditions for mixed precipitation and improve the performance of the approach. The evaluation of a CRCM5 run driven by ERA-Interim reanalysis data gives a Matthews correlation coefficient of 0.50. The deep learning approach can be applied to ensembles of regional climate models on the North American grid of the Coordinated Regional Downscaling Experiment (CORDEX-NA).

Future Changes in Snowpack, Snowmelt, and Runoff Potential Extremes Over North America

AbstractSnowpack and snowmelt‐driven extreme events (e.g., floods) have large societal consequences including infrastructure failures. However, it is not well understood how projected changes in the snow‐related extremes differ across North America. Using dynamically downscaled regional climate model (RCM) simulations, we found that the magnitudes of extreme snow water equivalent, snowmelt, and runoff potential (RP; snowmelt plus precipitation) decrease by 72%, 73%, and 45%, respectively, over the continental United States and southern Canada but increase by up to 8%, 53%, and 41% in Alaska and northern Canada by the late 21st century. In California and the Pacific Northwest, there is a notable increase in extreme RP by 21% contrary to a decrease in snowmelt by 31% by the late century. These regions could be vulnerable to larger rain‐on‐snow floods in a warmer climate. Regions with a large variability among RCM ensembles are identified, which require further investigation to reduce the regional uncertainties.

Impacts of climate change on groundwater droughts by means of standardized indices and regional climate models

This paper investigates the impacts of climate change on groundwater droughts making use of regional projections and standardized indices: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Groundwater Index (SGI). The method adopted, using historical precipitation and temperature data and water levels collected in monitoring wells, first investigates the possible correlations between meteorological and groundwater indices at each well. Then, if there is a correlation, a linear regression analysis is used to model the relationships between SGIs and SPIs, and SGIs and SPEIs. The same relationships are used to infer future SGIs from SPI and SPEI projections obtained by means of an ensemble of Regional Climate Models (RCMs), under different climate scenarios (RCP 4.5 and RCP 8.5). This methodology has been applied to data collected in northern Tuscany (Italy) in an area served by a water company, where historical series of daily climate variables (since 1934) and daily records for 16 wells, covering the period 2005–2020, are available. The impacts on groundwater have been computed in the short- (2006–2035), medium- (2036–2065) and long-term (2066–2095). The analysis indicates that, in the historical period and for most of the monitoring wells, there is a good correlation between SGIs and SPIs or SPEIs. The results point out that making use of the SGI-SPI relationships, slight variations in the availability of groundwater are expected in the future. However, in a global warming scenario, the influence of temperature on evapotranspiration phenomena cannot be overlooked and, for this reason, the SGI-SPEI relationships seem more suitable to forecast groundwater droughts. According to these relationships, negative effects on groundwater levels in almost all wells are estimated for the future. For the RCP 4.5 scenario, the largest decline in groundwater level is expected in the medium-term, while for the RCP 8.5 scenario future SGIs will significantly decrease over the long-term. Due to the type of data required and its simplicity, this methodology can be applied to different areas of interest for a quick estimate of groundwater availability under climate change scenarios.

Impacts of climate change and rising atmospheric CO2 on future projected reference evapotranspiration in Emilia-Romagna (Italy)

The continuous increase of atmospheric CO2 content mainly due to anthropogenic CO2 emissions is causing a rise in temperature on earth, altering the hydrological and meteorological processes and affecting crop physiology. Evapotranspiration is an important component of the hydrological cycle. Thus, understanding the change in evapotranspiration due to global warming is essential for better water resources planning and management and agricultural production. In this study, the effect of climate change with a focus on the combined effect of temperature and elevated CO2 concentrations on reference evapotranspiration (ETo) was evaluated using the Penman–Monteith equation. A EURO-CORDEX regional climate model (RCM) ensemble was used to estimate ETo in five locations in the Emilia-Romagna region (Northern Italy) during the period 2021–2050. Then, its projected changes in response to different CO2 concentrations (i.e., 372 ppm and 550 ppm) under two Representative Concentration Pathways (RCP) scenarios (i.e., RCP4.5 and RCP8.5) were analyzed. Simulation results with both scenarios, without increasing CO2 levels (372 ppm), showed that the annual and summertime ETo for all locations increased by an average of 4 to 5.4% with regard to the reference period 1981–2005, for an increase of air temperature by 1 to 1.5 °C. When the effect of elevated CO2 levels (550 ppm) was also considered in combination with projected changes in temperature, changes in both annual and summer ETo demand for all locations varied from − 1.1 to 2.2% during the 2021–2050 period with regard to the reference period 1981–2005. This shows that higher CO2 levels moderated the increase in ETo that accompanies an increase in air temperature.

Evaluation of the Large EURO‐CORDEX Regional Climate Model Ensemble

AbstractThe use of regional climate model (RCM)‐based projections for providing regional climate information in a research and climate service contexts is currently expanding very fast. This has been possible thanks to a considerable effort in developing comprehensive ensembles of RCM projections, especially for Europe, in the EURO‐CORDEX community (Jacob et al., 2014, 2020). As of end of 2019, EURO‐CORDEX has developed a set of 55 historical and scenario projections (RCP8.5) using 8 driving global climate models (GCMs) and 11 RCMs. This article presents the ensemble including its design. We target the analysis to better characterize the quality of the RCMs by providing an evaluation of these RCM simulations over a number of classical climate variables and extreme and impact‐oriented indices for the period 1981–2010. For the main variables, the model simulations generally agree with observations and reanalyses. However, several systematic biases are found as well, with shared responsibilities among RCMs and GCMs: Simulations are overall too cold, too wet, and too windy compared to available observations or reanalyses. Some simulations show strong systematic biases on temperature, others on precipitation or dynamical variables, but none of the models/simulations can be defined as the best or the worst on all criteria. The article aims at supporting a proper use of these simulations within a climate services context.

Observed and estimated consequences of climate change for the fire weather regime in the moist-temperate climate of the Czech Republic

The occurrence of major wildfires in countries such as Portugal, Italy and Spain (2017) and Sweden (2018) indicated that wildfires pose a risk across Europe. While Central Europe has not been at the center of such events, observed climate data and climate projections suggest a tendency toward more years with wet and warm winters and dry and hot summers as well as fuel accumulation, leading to more hazardous conditions. Although some existing studies analyzed the differences in wildfire occurrence in this territory based on terrain, soil or vegetation characteristics, the effects of climate change have not been properly appraised. To fill this knowledge gap, we used and tested an ensemble of nine fuel aridity metrics, including three dedicated fire danger rating indices, and evaluated their level of agreement with actual fire occurrence, their ability to explain the interannual variability in wildfire frequency, and their temporal trends. The analysis covered the entire territory of the Czech Republic at 500 m spatial resolution. Two periods were included based on observed (1956–2015) and projected (2020–2100) meteorological data using ensembles of five regional climate models (RCMs) and five global circulation models (GCMs) based on Euro-CORDEX and CMIP5 datasets. For the future, we considered Representative Concentration Pathway (RCP) 4.5. Our results showed that since 1956, most of the Czech territory has exhibited an increasing frequency of fire weather days (i.e., days with highly conducive wildfire conditions) and an increasing area affected by weather conducive to wildfire occurrence, with the trends accelerating after 2000. The annual variation in the fuel aridity levels (derived solely from meteorological data) explained more than 2/3 of the reported wildfire variability during 1991–2015 over the Czech Republic. The future projections based on the RCM or GCM ensembles indicated a significant increase in fuel aridity and an increase in the area under fire-conducive conditions. Recommendations derived from such robust results are provided for stakeholders seeking to implement adaptation measures.

Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

Climate change is a major threat to global biodiversity, although projected changes show remarkable geographical and temporal variability. Understanding this variability allows for the identification of regions where the present-day conservation objectives may be at risk or where opportunities for biodiversity conservation emerge. We use a multi-model ensemble of regional climate models to identify areas with significantly high and low climate stability persistent throughout the twenty-first century in Europe. We then confront our predictions with the land coverage of three prominent biodiversity conservation initiatives at two scales. The continental-scale assessment shows that areas with the least stable future climate in Europe are likely to occur at low and high latitudes, with the Iberian Peninsula and the Boreal zones identified as prominent areas of low climatic stability. A follow-up regional scale investigation shows that robust climatic refugia exist even within the highly exposed southern and northern macro-regions. About 23–31% of assessed biodiversity conservation sites in Europe coincide with areas of high future climate stability, we contend that these sites should be prioritised in the formulation of future conservation priorities as the stability of future climate is one of the key factors determining their conservation prospects. Although such focus on climate refugia cannot halt the ongoing biodiversity loss, along with measures such as resilience-based stewardship, it may improve the effectiveness of biodiversity conservation under climate change.

Zika virus outbreak in Brazil under current and future climate

IntroductionZika virus (ZIKV) is primarily transmitted byAedes aegypti and Aedes albopictus mosquitoes between humans and non-human primates. Climate change may enhance virus reproduction in Aedes spp. mosquito populations, resulting in intensified ZIKV outbreaks. The study objective was to explore how an outbreak similar to the 2016 ZIKV outbreak in Brazil might unfold with projected climate change. MethodsA compartmental infectious disease model that included compartments for humans and mosquitoes was developed to fit the 2016 ZIKV outbreak data from Brazil using least squares optimization. To explore the impact of climate change, published polynomial relationships between temperature and temperature-sensitive mosquito population and virus transmission parameters (mosquito mortality, development rate, and ZIKV extrinsic incubation period) were used. Projections for future outbreaks were obtained by simulating transmission with effects of projected average monthly temperatures on temperature-sensitive model parameters at each of three future time periods: 2011–2040, 2041–2070, and 2071–2100. The projected future climate was obtained from an ensemble of regional climate models (RCMs) obtained from the Co-Ordinated Regional Downscaling Experiment (CORDEX) that used Representative Concentration Pathways (RCP) with two radiative forcing values, RCP4.5 and RCP8.5. A sensitivity analysis was performed to explore the impact of temperature-dependent parameters on the model outcomes. ResultsClimate change scenarios impacted the model outcomes, including the peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and the duration of the ZIKV outbreak. Comparing 2070–2100 to 2016, using RCP4.5, the peak incidence was 22,030 compared to 10,473; the time to epidemic peak was 12 compared to 9 weeks, and the outbreak duration was 52 compared to 41 weeks. Comparing 2070–2100 to 2016, using RCP8.5, the peak incidence was 21,786 compared to 10,473; the time to epidemic peak was 11 compared to 9 weeks, and the outbreak duration was 50 compared to 41weeks. The increases are due to optimal climate conditions for mosquitoes, with the mean temperature reaching 28 °C in the warmest months. Under a high emission scenario (RCP8.5), mean temperatures extend above optimal for mosquito survival in the warmest months. ConclusionOutbreaks of ZIKV in locations similar to Brazil are expected to be more intense with a warming climate. As climate change impacts are becoming increasingly apparent on human health, it is important to quantify the effect and use this knowledge to inform decisions on prevention and control strategies.

Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling

AbstractClimate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies.

Assessment of the Canary current upwelling system in a regionally coupled climate model

The Canary current upwelling is one of the major eastern boundary coastal upwelling systems in the world, bearing a high productive ecosystem and commercially important fisheries. The Canary current upwelling system (CCUS) has a large latitudinal extension, usually divided into upwelling zones with different characteristics. Eddies, filaments and other mesoscale processes are known to have an impact in the upwelling productivity, thus for a proper representation of the CCUS and high horizontal resolution are required. Here we assess the CCUS present climate in the atmosphere–ocean regionally coupled model. The regional coupled model presents a global oceanic component with increased horizontal resolution along the northwestern African coast, and its performance over the CCUS is assessed against relevant reanalysis data sets and compared with an ensemble of global climate models (GCMs) and an ensemble of atmosphere-only regional climate models (RCMs) in order to assess the role of the horizontal resolution. The coupled system reproduces the larger scale pattern of the CCUS and its latitudinal and seasonal variability over the coastal band, improving the GCMs outputs. Moreover, it shows a performance comparable to the ensemble of RCMs in representing the coastal wind stress and near-surface air temperature fields, showing the impact of the higher resolution and coupling for CCUS climate modelling. The model is able of properly reproducing mesoscale structures, being able to simulate the upwelling filaments events off Cape Ghir, which are not well represented in most of GCMs. Our results stress the ability of the regionally coupled model to reproduce the larger scale as well as mesoscale processes over the CCUS, opening the possibility to evaluate the climate change signal there with increased confidence.

Projected future daily characteristics of African precipitation based on global (CMIP5, CMIP6) and regional (CORDEX, CORDEX-CORE) climate models

We provide an assessment of future daily characteristics of African precipitation by explicitly comparing the results of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models, specifically highlighting the similarities and inconsistencies between them. Results for seasonal mean precipitation are not always consistent amongst ensembles: in particular, global models tend to project a wetter future compared to regional models, especially over the Eastern Sahel, Central and East Africa. However, results for other precipitation characteristics are more consistent. In general, all ensembles project an increase in maximum precipitation intensity during the wet season over all regions and emission scenarios (except the West Sahel for CORE) and a decrease in precipitation frequency (under the Representative Concentration Pathways RCP8.5) especially over the West Sahel, the Atlas region, southern central Africa, East Africa and southern Africa. Depending on the season, the length of dry spells is projected to increase consistently by all ensembles and for most (if not all) models over southern Africa, the Ethiopian highlands and the Atlas region. Discrepancies exist between global and regional models on the projected change in precipitation characteristics over specific regions and seasons. For instance, over the Eastern Sahel in July–August most global models show an increase in precipitation frequency but regional models project a robust decrease. Global and regional models also project an opposite sign in the change of the length of dry spells. CORE results show a marked drying over the regions affected by the West Africa monsoon throughout the year, accompanied by a decrease in mean precipitation intensity between May and July that is not present in the other ensembles. This enhanced drying may be related to specific physical mechanisms that are better resolved by the higher resolution models and highlights the importance of a process-based evaluation of the mechanisms controlling precipitation over the region.

Future drought changes and associated uncertainty over the homogenous regions of India: A multimodel approach

AbstractDrought frequency and intensity have increased in recent decades and are expected to escalate in future under the changing climate scenario. However, a wide range of uncertainty exists regarding the risk, variability and severity of the drought. This study evaluates the future drought and associated uncertainty over homogeneous regions of India using the suites of CMIP5 global climate models (GCMs) and CORDEX‐SA regional climate models (RCMs). The drought characteristics and its projected future changes are analysed using probability density functions derived from hydroclimatic parameters, including the standardized precipitation index (SPI) and the standardized precipitation‐evapotranspiration index (SPEI). Besides, uncertainties from various sources such as inter‐model variability, indices type, timescale, adapted methods, and time‐slices are explored under the RCP8.5 emission scenario to the end of the 21st century. Our study reveals large biases in the individual model; however, both the multi model ensembles (GCM and RCM) generally demonstrate better performance with respect to observation. In particular, the RCM ensemble showed limitations in capturing the regional precipitation pattern while temperature and potential evapotranspiration (PET) showed considerable enhancement concerning GCMs. SPI (SPEI) generally exhibited enhanced wetness (dryness) derived from increased precipitation (PET), although a few discrepancies were noticed. The regional heterogeneity was also found to exist, although some robust changes were noticed in drought frequency and severity with different return periods. Our finding underscores a wide range of uncertainties in drought projection, with maximum contribution from indices selection followed by model variability whereas other sources have the least contribution. The primary drivers for all these uncertainty sources arise due to variations among models simulated hydroclimatic variables that need to be parameterized more precisely for sustainable drought management.

Statistical modelling and climate variability of compound surge and precipitation events in a managed water system: a case study in the Netherlands

Abstract. The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a managed water system in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present an impact-focused multivariate statistical framework to model the dependence between these flooding drivers and the resulting return periods of inland water levels. This framework is applied to the same managed water system using the aforementioned large ensemble. Composite analysis is used to guide the selection of suitable predictors and to obtain an impact function that optimally describes the relationship between high inland water levels (the impact) and the explanatory predictors. This is complex due to the high degree of human management affecting the dynamics of the water level. Training the impact function with subsets of data uniformly distributed along the range of water levels plays a major role in obtaining an unbiased performance. The dependence structure between the defined predictors is modelled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. Regarding the return levels, this is quantified by a root-mean-square deviation of 0.02 m. The proposed framework is able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. Even though the framework has only been applied and validated in one study area, it shows great potential to be transferred to other areas. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50-year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

How Strongly Are Mean and Extreme Precipitation Coupled?

AbstractChanges in mean and extreme precipitation are among the most important consequences of climate change. Here, we examine the relationship between the mean and three different measures of extreme precipitation over Australia, from a regional climate model (RCM) ensemble. We show that model uncertainty in mean and extreme precipitation are tightly coupled for both the present‐day climate and future changes. On the continental scale, strong correlations (0.79–0.99) are observed across models between the changes in mean and extreme precipitation. We also find that except in eastern coastal regions, precipitation statistics projected by RCM are highly predictable (up to 60%–70% of variance explained) from the mean precipitation of the global climate model (GCM) providing the boundary conditions. In coastal locations where RCMs are more accurate than GCMs for the present climate, they are less predictable, hence provide information—but this impact disappears for climate change, suggesting that improved present‐day accuracy may not carry over to climate change.

Evaluating NA‐CORDEX historical performance and future change of western U.S. precipitation patterns and modes of variability

AbstractThe western U.S. precipitation climatology, simulated by the NA‐CORDEX regional climate model ensembles, with spatial resolutions of 0.44° (14 model members) and 0.22° (15 model members), is examined to evaluate the capability of these ensembles to reproduce (1) the annual and semi‐annual precipitation cycle of several hydrologically‐important western U.S. regions and (2) localized seasonality in the amount and timing of precipitation. Similar to previous organized downscaling projects, NA‐CORDEX ensembles continue to over‐simulate wintertime precipitation over mountainous regions. However, both NA‐CORDEX ensembles showed improvements to the Desert Southwest's summertime precipitation associated with the North American Monsoon. Furthermore, the predominant dry bias found throughout the 0.44° ensemble was improved in many of the higher resolution 0.22° ensemble members; associated with enhanced mesoscale processes and improved thermodynamics driving the model's convective parameterizations. When compared against observation‐based gridded precipitation, the ability to capture the annual and semi‐annual modes of variability was starkly improved in the higher resolution 0.22° ensemble. The higher resolution members reproduced more consistent spatial patterns of variance featuring lower errors in magnitude—especially with respect to the winter‐summer and spring‐fall seasonality. A great deal of spread in model performance was found for the semi‐annual cycles, although the higher‐resolution ensemble exhibited better clustering of performance metrics. Ultimately, NA‐CORDEX model performance is still tied to the fidelity of the large‐scale parent model although we found performance is also closely related to which regional climate model was used while the future trend was more related to the parent GCM. Additionally, the value‐added via finer domain grid spacing is clear. Lastly, future projections of precipitation patterns were analysed to examine trends in the seasonality of precipitation intensity and modes of variability.

From Changing Environment to Changing Extremes: Exploring the Future Streamflow and Associated Uncertainties Through Integrated Modelling System

Climate and land-use changes can alter the dynamics of hydro-climatic extremes by modifying the flow regimes. Here, we have attempted to disentangle the relationship between changing environmental conditions and hydro-climatic extremes considering associated uncertainties for the Subarnarekha, a flood prone-basin of India. A comprehensive, integrated modelling system was developed that incorporates a spatially explicit land-use model, a hydrological model, and an ensemble of regional climate models (RCMs). MIKE SHE/MIKE HYDRO RIVER was used to simulate the hydrological processes. The uncertainties associated with model parameters, model inputs, and model structures are analysed collectively using ‘quantile regression.’ A transferable framework was developed for the analysis of hydro-climatic extremes that deal with numerous aspects like sensitivity, occurrences, severity, and persistence for four-time horizons: baseline (1976–2005) and early (2020s), mid (2050s), end-centuries (2080s). ANOVA is used for partitioning uncertainty due to different sources. The results obtained from numerous analysis of the developed framework suggests that low, high, and medium flows will probably increase in the future (20%-85% increase), indicating a higher risk of floods, especially in the 2050s and 2080s. Partitioning of uncertainty suggests RCMs contribute 40%-62% to the uncertainty in streamflow projections. The developed modelling systems incorporates a flexible framework so update any other water sustainability issue in the future. These findings will help better meet the challenges associated with the possible risk of increasing high flows in the future by ceding references to the decision-makers for framing better prevention measures associated with land-use and climate changes.

Boundary condition and oceanic impacts on the atmospheric water balance in limited area climate model ensembles

Regional climate models (RCMs) are indispensable in climate research, albeit often characterized by biased terrestrial precipitation and water budgets. This study identifies excess oceanic evaporation, in conjunction with the RCMs’ boundary conditions, as drivers contributing to these biases in RCMs with forced sea surface temperatures in a CORDEX RCM ensemble over Europe. The RCMs are relaxed to the prescribed lateral boundary conditions originating from a global model, effectively matching the driving model's overall atmospheric moisture flux divergence. As a consequence, excess oceanic evaporation results in positive precipitation biases over land due to forced internal recycling of moisture to maintain the overall flux divergence prescribed by the boundary conditions. This systematic behaviour is shown through an analysis of long-term atmospheric water budgets and atmospheric moisture exchange between oceanic and continental areas in a multi-model ensemble.

An Application of ANN Ensemble for Estimating of Precipitation Using Regional Climate Models

Climate change scenarios are used for predicting future precipitation. More detailed regional climate change scenarios are being used through dynamic downscale based on global circulation model results. There is a global tendency to utilize simulated precipitation data from downscaled regional climate models (RCMs) suitable for each country. In Korea, there are studies for improving the accuracy of climate change scenario precipitation forecasts compared with observed precipitation. In this study, the precipitation of five regional climate models and actual observed precipitation provided in Korea are applied to ANN (artificial neural network), which suggests ways to improve prediction accuracy for precipitation. The ANN ensemble of RCMs simulates the actual observed precipitation more accurately than the individual RCM. In particular, it is more effective inland than in coastal areas, where precipitation patterns are complex. Pearson correlation coefficient of ANN is high as 0.04 compared with MRA. It is expected that more detailed analysis will be possible if it is applied not only to four cities but also to other regions in Korea. If observed precipitation data are collected in sufficient quantity, the applicability of the ANN model will widen.

Evolution of Dry and Wet Spells Under Climate Change Over North‐Eastern North America

AbstractRisks associated to extreme hydrological conditions, such as floods and droughts, are expected to increase in future climate because of projected changes in precipitation and temperature. Assessing how wet and dry persisting conditions (or spells) will evolve in future climate is a crucial step in the study of extreme hydrological events. Projected changes over the north‐eastern part of North America in the annual number of wet days, wet, and dry spells characteristics (number, duration), and wet spells intensities are analyzed. Two regional climate model ensembles are considered: the multimember ensemble from the Canadian RCM v5 (CRCM5‐LE), and 16 simulations from the NA‐CORDEX multimodel ensemble both using the Representative Concentration Pathway 8.5 (RCP8.5) scenario. Comparison of observed and simulated wet and dry spells characteristics is first performed in present climate (1971–2000). Regional Climate Model (RCM)s tend to generate too many wet days and wet or dry spells resulting in shorter dry spells and slightly longer wet spells. Modeling uncertainties are accounted for a bigger contribution to the bias than internal variability since the multimodel ensemble dispersion is the largest. Throughout the 21st century, both ensembles project significant trends in winter at higher latitudes resulting in increasing wet day frequency, increasing number of wet spell, longer wet spells and shorter dry spells. For other seasons, internal variability of the CRCM5‐LE and differences among the various NA‐CORDEX simulations seems to overwhelm the climate change signal. Wet spell intensities increases are projected for all seasons over almost the entire domain. Globally, wetter climate with potential significant hydrological impacts are expected in many regions.