Regional Climate Model Research Articles

The influence of moisture on precipitation patterns across the Western Tibetan Plateau and its response to sea surface temperature warming

Abstract The distribution of water resources in sub-basins across the Western Tibetan Plateau (WTP) is of critical importance due to not only ecological vulnerability resulting from the extremely arid climatology but also the political sensitivities surrounding the international rivers. In this study, we utilize an advanced water vapor tracer (WVT) embedded in the widely used regional climate model – Weather and Research Forecast (WRF), to quantify moisture contributions from four main sources towards precipitation over the WTP region. We also analyze influences on other sub-basins in the TP for comparison purposes. We examine how changes in sea surface temperature (SST) during 2010s compared to 1980s have influenced precipitation patterns and moisture contributions over recent decades. Our findings indicate that terrestrial moisture sources contribute more than oceanic sources towards the endorheic TP region. Recycling processes originating from highlands area are revealed to play a greater role in contributing moisture over WTP compared to those from lowlands areas. Furthermore, our results demonstrate stronger agreements between wetting distribution patterns and distributions of liquid/solid hydrometeors rather than water vapor distribution itself, highlighting condensation/freezing as critical factors. Notably, we observe different responses within Amu Dayra basin compared to the main WTP when subjected to SST changes. This study focuses on delineating distinct roles of terrestrial and oceanic moisture sources in driving precipitation changes over WTP, while specifically emphasizing condensation process’ contribution to inner TP’s precipitation and highlighting moisture transport form oceans’ influence on precipitation patterns over Amu Dayra basin.

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

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

Considerations in designing climate change assessments for complex, non-linear hydrological systems

Bias correction of climate model simulations is vital to allow climate change impacts to be assessed for water resources systems. However, there has been limited research to date on the implications of system non-linearity on bias correction approaches. Here we bias correct regional climate model simulations of precipitation and evapotranspiration and use the output to force hydrological and water balance models of five small, interconnected lakes located south west of Sydney. We show that substantial, non-linear storage within the lakes amplifies biases that are not evident when the climate forcing or even the hydrological model simulations are evaluated using daily distributions of the climate variables and streamflow.The non-linearity in the stage-storage relationships of the lakes means that each lake responds differently to the same climate forcings. For example, ensemble mean projections for one lake suggest increases in water level across the full distribution of lake levels, whilst other lakes are projected to have decreasing water levels up to the median of the distribution, but increases during wetter conditions. These differences are explained by the varying influence of potential evapotranspiration increases depending on the surface area of the lakes at different depths. Using bottom up climate change assessments, we further explore these non-linear responses of the lakes to different climate forcings. We show that bottom up climate change assessments can provide information on the relative role of potential evapotranspiration changes compared to precipitation changes, providing more guidance to ecosystem managers than just using bias corrected climate model simulations alone. The paper discusses opportunities for future work to improve representation of climate attributes important for storage dominated water resource and natural ecosystems

Partitioning the drivers of Antarctic glacier mass balance (2003–2020) using satellite observations and a regional climate model

We investigate the mass changes of Antarctic glaciers from 2003 to 2020, partitioning them into the contributions of surface mass balance (SMB) and ice discharge, using high-resolution ice mass change estimates derived from the combination of two different types of satellite observations (gravimetry and altimetry) and outputs from a regional climate model. Our analysis indicates that changes in ice discharge have played a dominant role in ongoing ice mass trends and their accelerations, especially in glaciers near the Amundsen and Bellingshausen Seas in West Antarctica. In particular, mass losses of the Thwaites and Pine Island Glaciers have been mostly (>90%) controlled by ice discharge, while the contribution of SMB has been relatively minor. In East Antarctica, SMB accounts for significant portions (>50%) of ice mass imbalances of glaciers in e.g., Dronning Maud Land and Wilkes Land. Ice discharge has also played a notable role in overall mass gain in the region. While our ice discharge estimates agree well with previous estimates from satellite imagery in West Antarctica, notable differences are found in glaciers of East Antarctica and the Antarctic Peninsula. This highlights the need for more observations and improved numerical models to refine these estimates.

Atmospheric CO2 fertilization effect on cereal yields in Morocco using the CARAIB dynamic vegetation model

Climate change and rising atmospheric CO2 levels are critical factors influencing agricultural productivity, particularly in Morocco, where cereal crops are essential for food security. The primary objective of this study is to evaluate the combined effects of atmospheric CO2 variations and climatic changes on cereal yields up to 2099 using the CARAIB dynamic vegetation model. This evaluation is driven by four future scenarios based on the Euro-CORDEX initiative's regional climate models under the Representative Concentration Pathway 8.5. As part of this evaluation, the study also validates the CARAIB model for Morocco’s major cereal crops: soft wheat, durum wheat, and barley, in order to ensure the model's accuracy in simulating crop responses under projected environmental conditions. The CARAIB model effectively simulated historical cereal yield trajectories across major farming regions in Morocco from 2000 to 2016, demonstrating its robust predictive capability. Our future projections suggest that elevated CO2 levels might initially sustain cereal yields at approximately 90 % of current levels until 2050. This trend indicates that the increase in atmospheric CO2 may exert a moderating influence on the negative impacts of other environmental stressors on crop yields. However, despite this initial buffering effect, the overall yield trend from the present until 2099 indicates a decrease for most combinations of crop, zone, and climate model, even with the CO2 fertilization effect, except in some cases, the model exhibits slight increases or stabilization in yields. Additionally, the CARAIB model predicts potential yield shocks of 10–35 % below current levels from the 2080 s onwards, primarily due to periodic droughts. This variation underscores the complexity of the interplay between CO2 fertilization and climatic changes, emphasizing the urgent need for Morocco to develop adaptive agricultural strategies for long-term sustainability in the face of climatic challenges.

A dataset of high-resolution climate change projections over South America with bias correction

Accurate and detailed datasets are crucial for assessing climate change impacts. Regional climate models provide high-resolution simulations and are key tools but often exhibit systematic biases. Therefore, this paper presents a dataset derived from bias-corrected Eta regional model simulations and projections driven by four global CMIP5 models. The correction applied to daily precipitation, potential evapotranspiration, actual evapotranspiration, and 2-m air temperature, was conducted on a 0.2° x 0.2° grid over South America. The dataset covers two periods: 1976-2005 (baseline) and 2006-2099 (future) under RCP4.5 and RCP8.5 scenarios. Empirical quantile mapping was used to adjust the Eta model outputs to better match observational data. This method modified the accumulated probability curves of the Eta model outputs to align with observational curves for both baseline and future climates. Two observational datasets were used for correction and evaluation. The new dataset shows that bias correction significantly reduced the errors in the Eta simulations, especially for the frequent values of precipitation, potential evapotranspiration, and 2-m temperature, and also corrected the annual cycle and frequency distribution of these variables, approaching the observations. The pattern of extreme precipitation indices from the bias-corrected Eta dataset also reduced error. Bias correction was applied to future projections. The comparison against the raw Eta dataset showed that the trends of changes were preserved, but in general, the peaks of the changes were smoothed. As in the raw Eta dataset, the RCP8.5 scenario showed a higher change rate than RCP4.5. This work also revealed the large uncertainty of the observational dataset; some of the remaining errors after the bias correction were mostly due to differences between the two correction and evaluation observational datasets. The described dataset is freely available from the CNPq LattesData repository at the following link: https://doi.org/10.57810/lattesdata/WAVGSL.

The asymmetric distribution of rainfall frequency and amounts in India

Studies of rainfall usually focus on the total amount precipitating throughout a certain period. Compared to rain rates associated with extreme events, the rain rates associated with the most frequent events is understudied. In this study, the characteristics of daily precipitation in India are explored using two metrics − rain frequency peak (the most frequent non-zero rain rate) and rain amount peak (the rain rate at which the most amount of rain falls). These metrics are computed over India using local and global datasets to investigate the characteristics of typical daily precipitation accumulations. These values are sensitive to the dataset used for this analysis since the temporal and spatial resolution of the rainfall data will influence the rain frequency peak and rain amount peak. Our study reveals the rain frequency peak is highest during the summer monsoon, while the winter season exhibits lower values, particularly at higher latitudes. Similarly, the rain amount distribution indicates dominance of heavy rain rates during the monsoon and post-monsoon seasons, leading to high total precipitation. The maximum rain frequency peak for any region in India reaches up to a value of 35 mm/day while the maximum rain amount peak reaches up to a value as high as 90 mm/day. These metrics would be useful in systematically evaluating typical daily precipitation in regional climate models and assessing downstream impacts of uneven precipitation such as lower crop yields, flood-drought alterations, and fluctuating water availability.

Wildfire Burnt Area and Associated Greenhouse Gas Emissions under Future Climate Change Scenarios in the Mediterranean: Developing a Robust Estimation Approach

Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required to update wildfire policies. We present a robust methodology for estimating regional wildfire BA and GHG emissions under future climate change scenarios in the Mediterranean. The Fire Weather Index, selected drought indices, and meteorological variables were correlated against BA/GHG emissions data to create area-specific statistical projection models. State-of-the-art regional climate models (horizontal resolution: 12 km), developed within the EURO-CORDEX initiative, simulated data under three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) up to 2070. These data drove the statistical models to estimate future wildfire BA and GHG emissions in three pilot areas in Greece, Montenegro, and France. Wildfire BA is projected to increase by 20% to 130% up to 2070, depending on the study area and climate scenario. The future expansion of fire-prone areas into the north Mediterranean and mountain environments is particularly alarming, given the large biomass present here. Fire-smart landscape management may, however, greatly reduce the projected future wildfire BA and GHG increases.

Convection Permitting Regional Climate Modelling Over the Carpathian Region

Convection Permitting Regional Climate Modelling Over the Carpathian Region

Evaluation of Wind Speed Accuracy Enhancement in South Asia Through Terrain-Modified Wind Speed (Wt) Adjustments of High-Resolution Regional Climate Modeling

Evaluation of Wind Speed Accuracy Enhancement in South Asia Through Terrain-Modified Wind Speed (Wt) Adjustments of High-Resolution Regional Climate Modeling

Does Applying Subsampling in Quantile Mapping Affect the Climate Change Signal?

Bias in regional climate model (RCM) data makes bias correction (BC) a necessary pre-processing step in climate change impact studies. Among a variety of different BC methods, quantile mapping (QM) is a popular and powerful BC method. Studies have shown that QM may be vulnerable to reductions in calibration sample size. The question is whether this also affects the climate change signal (CCS) of the RCM data. We applied four different QM methods without subsampling and with three different subsampling timescales to an ensemble of seven climate projections. BC generally improved the RCM data relative to observations. However, the CCS was significantly modified by the BC for certain combinations of QM method and subsampling timescale. In conclusion, QM improves the RCM data that are fundamental for climate change impact studies, but the optimal subsampling timescale strongly depends on the chosen QM method.

First results of the polar regional climate model RACMO2.4

Abstract. The next version of the polar Regional Atmospheric Climate Model (referred to as RACMO2.4p1) is presented in this study. The principal update includes embedding of the package of physical parameterizations of the Integrated Forecast System (IFS) cycle 47r1. This constitutes changes in the precipitation, convection, turbulence, aerosol and surface schemes and includes a new cloud scheme with more prognostic variables and a dedicated lake model. Furthermore, the standalone IFS radiation physics module ecRad is incorporated into RACMO, and a multilayer snow module for non-glaciated regions is introduced. Other updates involve the introduction of a fractional land–ice mask, new and updated climatological data sets (such as aerosol concentrations and leaf area index), and the revision of several parameterizations specific to glaciated regions. As a proof of concept, we show first results for Greenland, Antarctica and a region encompassing the Arctic. By comparing the results with observations and the output from the previous model version (RACMO2.3p3), we show that the model performs well regarding the surface mass balance, surface energy balance, temperature, wind speed, cloud content and snow depth. The advection of snow hydrometeors strongly impacts the ice sheet's local surface mass balance, particularly in high-accumulation regions such as southeast Greenland and the Antarctic Peninsula. We critically assess the model output and identify some processes that would benefit from further model development.

Towards an Improved Representation of Dust Aerosol–Rainfall Relationship Influenced by 2018 Dust Event over Indian Region by Using Regional Climate Model: Impact of Horizontal Resolution

Towards an Improved Representation of Dust Aerosol–Rainfall Relationship Influenced by 2018 Dust Event over Indian Region by Using Regional Climate Model: Impact of Horizontal Resolution

Characteristics of Precipitation and Mesoscale Convective Systems Over the Peruvian Central Andes in Multi 5‐Year Convection‐Permitting Simulations

AbstractUsing the Weather Research and Forecasting model with two planetary boundary layer schemes, ACM2 and MYNN, convection‐permitting model (CPM) regional climate simulations were conducted for a 6‐year period, including a one‐year spin‐up period, at a 15‐km grid spacing covering entire South America and a nested convection‐permitting 3‐km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4‐km simulation covering South America produced by National Center for Atmospheric Research (NCAR), three gridded precipitation products, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian central Andes region. Results show that all km‐scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low‐level jet and terrain‐induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests model improvements and better model configurations for future regional climate projections.

Evaluating input data sources for isotope‐enabled rainfall‐runoff models

AbstractIsotope‐enabled models provide a means to generate robust hydrological simulations. However, daily isotope‐enabled rainfall‐runoff models applied to larger spatial scales (>100 km2) require more input data than conventional non‐isotope models in the form of precipitation isotope time series, which are difficult to generate even with point station measurements. Spatially distributed isotope data can be circumvented by isotope‐enabled climate models. Here, we evaluate the hydrological simulations of the J2000‐isotope enabled hydrological model driven with data from corrected and un‐corrected isotope‐enabled global and regional climate models (isotope‐enabled global spectral model [IsoGSM] and isotope‐enabled regional spectral model [IsoRSM], respectively) compared with 1 year of measured reference station and a yearly average precipitation isotope input for a pilot site, the data‐scarce sub‐humid Eerste River catchment in South Africa. The models driven by all input products performed well for upstream and downstream discharge gauges with Nash Sutcliffe efficiency (NSE) from 0.58 to 0.85 and LogNSE of 0.66 to 0.93. The simulated δ2H stream isotopes using the reference J2000‐iso and J2000‐isoRSM were good for the main river with a stream Kling Gupta efficiency (KGE) of between 0.4–0.9 and the top 100 Monte Carlo simulations varying by around 5‰ for δ2H. For smaller tributaries the model was unable to capture the measured stream isotopes due to biased precipitation isotope inputs. Adjusting the J2000‐iso with a bias corrected IsoRSM improved the stream and groundwater isotope simulation and outperformed the model driven by an average yearly precipitation isotope input. Differences in simulated hydrological processes were only evident between the models when evaluating percolation with unrealistic simulations for the standard J2000 model. While the regional climate model is computationally more intensive than its global counterpart, it provided better stream isotope simulations and improvements to simulated percolation. Our results indicate that isotope‐enabled climate models can provide useful input data in data scarce regions for hydrological models, where improved water management to address climate change impacts is needed.

Are Our Climate Data Fit for Your Purpose?

Abstract This paper describes the vision and objectives for evaluation and quality control of datasets available on the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). The CDS is a cloud service providing reliable, open, and free access to climate datasets from many different sources, including observations and derived climate data records, global and regional reanalysis products, and output from global and regional climate models. CDS data and information are widely used to monitor climate change, for policy development, environmental impact studies, business plans, public awareness, and many other purposes. CDS users include policymakers, technical consultants, and data scientists who are climate literate but not necessarily specialized in climate science. A key objective for C3S is to simplify data discovery and access and to ensure that all information provided is fit for purpose. The role of evaluation and quality control is to generate useful statements about the technical and scientific quality of the datasets available on the CDS, presented in a form that can be mapped directly to user requirements associated with specific application contexts. The ambition is to enable users to select, locate, access, and process climate data that are fit for purpose, simply by expressing their requirements in their own words.

Future projections of storm surge in Hurricane Katrina and sensitivity to meteorological forcing resolution

In this study, we investigated whether and how the storm surge induced by Hurricane Katrina could change if it occurs in a future warmer climate, and the sensitivity of the changes to atmospheric forcing resolution. Climate model simulations of Hurricane Katrina at 27 km, 4.5 km, and 3 km resolutions were used to drive storm surge simulations in historical and future climates using the ADvanced CIRCulation (ADCIRC) model. We found that peak surge height increased significantly in the future with all forcing resolutions. However, the future projection is 22% greater in the 3 km forcing, typical of regional climate models, compared to the 27 km forcing, typical of state-of-the-art global climate models. Additionally, the spatial extent of the future change is highly sensitive to forcing resolution, extending most broadly under the 27 km forcing. Furthermore, we found that storm surge duration decreases in the future with all forcing resolutions due to increasing TC translation speed and decreasing ocean lifetime. However, the future change in the surge duration is sensitive to the forcing resolution, decreasing by 31% in the 27 km forcing and 6% in the 3 km forcing.

Estimation of suspended sediment load to the Volta Lake under changing climate using empirical discharge-sediment equations

ABSTRACT Reliable estimates of sediment fluxes into rivers provide valuable information for current and future water resource development and management. In this study, empirical equations relating discharge to suspended sediment loads were developed for the three major tributaries (Black Volta, White Volta, and Oti) of the Volta Lake. The empirical equations were fed with projected future river discharges for the three tributaries, to estimate the suspended sediment fluxes into the Volta Lake for the future (2051–2080) relative to a baseline period (2014–2016). The Soil and Water Assessment Tool (SWAT) was used to model the projected discharges. The SWAT model was driven by downscaled climate projections from two regional climate models (RCA4 and RACMO22 T) forced by two emission scenarios (RCP 4.5 and RCP 8.5) from the IPCC Fifth Assessment. Compared to the baseline, the estimated future sediment fluxes show average increments of about 19% and 28%, respectively under RCP 4.5 and RCP 8.5. This has negative consequences as less water is stored for hydropower generation and the lake water becomes more turbid, which inhibits zooplankton and fishery production. The result underscores the need to improve the management of the Lake's watershed by ensuring proper landcover/use planning, afforestation and enforcing regulations on land degradation and general environmental conservation.

Evaluation of the Effects of Climate Variability on Water Resources and Agriculture: Insights from Regional Climate Models

Climate variability and change present significant challenges to global food and water security, particularly in vulnerable regions like East and Southern Africa. This review synthesizes current research on the impact of climate drivers such as temperature and precipitation variability—on water resources and agricultural productivity in this region. Analyzing over four decades of data and recent findings, this paper discusses the complex interactions between climate phenomena, including El Niño Southern Oscillation (ENSO), and their effects on regional agriculture and water systems. The review highlights the limitations of Regional Climate Models (RCMs) in accurately predicting climate patterns and emphasizes the need for improved modeling techniques. Recommendations for future research and policy interventions are provided to enhance climate resilience in these critical sectors.

Evaluation of future wind climate over the Eastern Mediterranean Sea

The aim of this study is to evaluate wind climate characteristics in the Eastern Mediterranean Sea until the end of the 21st century using the wind fields simulated by the EURO-CORDEX Regional Climate Models (RCM), namely the Rossby Centre regional atmospheric model (version RCA4). Long-term averages, long-term trends, and long-term variability in wind characteristics under two Representative Concentration Pathway (RCP 4.5 and 8.5) scenarios were assessed for two future periods: the near future for the years 2021–2060 and the middle future for the years 2061–2100. Relative to the historical period, the most remarkable changes in the mean wind speeds were projected under the RCP 8.5 scenario in the middle future in the central and southern Aegean Sea (exceeding 5 % increase) and in the eastern and northern Levantine basin (up to 5 % decrease), compatible with the definition of the climate scenarios as the RCP8.5 scenario assumes a rising radiative forcing until 2100. The changes in extreme wind characteristics are more noteworthy in agreement with the indicators of climate change (i.e., an increase in the intensity and the frequency of extreme events). 100-year return period wind speed increases by about 29 % in the western Levantine basin under the RCP 8.5 scenario while the most drastic decrease in 100-year extreme wind speed was found in the central Levantine basin with a reduction of exceeding −29 % for the middle future period under the RCP 8.5 scenario. These decreasing and increasing behaviors in extreme wind speeds are expected in the study area as the Mediterranean is affected by many climate systems.