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Regional climate models (RCMs) serve as an important tool to perform high resolution climate simulations for a limited area. However, there remains a crucial issue regarding the efficacy of RCMs that whether they are able to produce added value (AV) in representing the regional climatic features compared to their coarse scale driving Global climate models (GCMs). The present study aims at assessing the AV of a set of RCM simulations performed under the Coordinated Regional Climate Downscaling Experiments‐South Asia (CORDEX‐SA) compared with their respective driving GCMs in representing the Indian summer monsoon (ISM) precipitation and its dynamical features. For this purpose, the performance of RCMs and GCMs is compared for the present day climate (1970–2005) against observations and reanalysis datasets in terms of various ISM features, that is, the spatial distribution of precipitation, the evolution of vertical shear of zonal wind and the onset phase of ISM in terms of delayed or early arrival of monsoon. The RCMs show a clear improvement upon their corresponding driving GCMs in simulating spatial features of precipitation distribution that are characteristically associated with ISM. Regionally, the RCMs also show skill in capturing the precipitation amount as a reduction in bias is found compared with their driving GCMs. There is a significant improvement in the simulation of onset dates of ISM by only some of the experiments particularly, RegCM4 driven with IPSL‐CM5A‐LR. The study concludes that the present set of CORDEX‐SA RCM simulations do indeed add value to their driving GCMs for a number of features associated with the ISM precipitation but this value varies with region, driving GCM and the particular feature of ISM under focus. Simulated climatology (1970–2005) of JJAS mean precipitation (mm day−1) by (A) IMD observation, (a1–l1) GCMs and their corresponding (a2–l2) CORDEX‐SA RCMs grouped together in a box. The orographic feature in spatial variability of ISM is evident in IMD—a characteristic which seems to be better captured here by RCMs as compared to their parent GCMs. [ABSTRACT FROM AUTHOR]

Regional climate model (RCM) simulations are being increasingly used for climate change impact assessments, but their application is challenging due to considerable biases inherited from global climate model (GCM) simulations and generated from dynamical downscaling processes. This study assesses the biases in NARCliM (NSW and ACT regional climate modelling) simulations and quantifies the consequence of the climate biases in the downstream assessment of climate change impact on wheat crop system, using the Agricultural Production System sIMulator (APSIM). Results showed that post‐processing bias‐corrected temperature and rainfall data from NARCliM had small annual mean biases but large biases in the crop growing season (CGS). During the CGS, the mean bias error of rainfall was generally positive for rainfall probability and negative for intensity, which subsequently resulted in APSIM simulating negative biases for runoff and deep drainage and positive bias in soil evaporation. Bias in soil water balance and water availability resulted in less plant transpiration and less N uptake, ultimately, leading to large negative biases in crop yields. A simple bias correction of the simulated crop yield driven by RCMs could result in a largely consistent distribution with those generated with APSIM simulations forced by observed climate. Our results showed that RCM simulation biases could confound with the climate change signal and produced an unreliable estimate of the effects of the changes in climate and farm management variables on crop yields. The results suggested that RCM simulations with the current bias correction on the RCM‐simulated outputs applied on an annual basis were inadequate for climate change assessments which involve biophysical models. Our study highlights the need for improved RCM simulations by eliminating the systemic biases associated with rainfall characteristics, although suitable post‐processing bias correction on a seasonal or monthly basis may result in improved RCM simulations for agricultural impacts of climate change. RCM simulations with post‐processing bias correction of RCM outputs on an annual basis were inadequate for climate change assessments with biophysical models. Our study highlights the need for improved RCM simulations by eliminating the systemic biases associated with rainfall characteristics, although suitable post‐processing bias correction on a seasonal or monthly basis may result in improved RCM simulations for agricultural impact analysis of climate change. [ABSTRACT FROM AUTHOR]

The performance of eight National Center for Environmental Prediction (NCEP2) reanalysis-driven regional climate models (RCMs), seven from the North American Regional Climate Change Program (NARCCAP) and one from the Coordinated Regional Downscaling Experiment (CORDEX), in simulating the 1980_2004 climate of western Canada was assessed at a number of spatial and temporal scales. Results indicated that the RCMs were more successful at capturing the seasonal spatial distribution of mean temperature than precipitation and that inaccuracies in the spatial distribution of the summer climate moisture index were likely due to the errors in precipitation distribution and amount. All RCMs performed less well in simulating summer precipitation, most likely due to continued problems with the simulation of convective precipitation. At the grid box scale, quantile_quantile (q_q) plots for temperature indicated that all RCMs showed very similar distributions to observed but with warm or cold biases, and errors in the simulation of a number of temperature-based extremes indices were related to these biases. For precipitation, q_q plots indicated that most RCMs overestimated precipitation totals, and while tending to follow the observed quantiles at smaller precipitation amounts, they diverged at larger precipitation totals. Performance in simulating the precipitation-based extremes indices depended largely on whether or not a RCM over-or under-estimated precipitation totals _ with those RCMs simulating too much precipitation underestimating the number of consecutive dry days and dry day persistence, and vice versa. Despite improvements in RCM resolution and parameterisation schemes, this work indicates that the simulation of precipitation in particular is still problematic in western Canada. This implies that scenarios of climate change constructed from RCM output require some form of bias correction to be of most use in impacts studies. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, version 4 of the International Centre for Theoretical Physics (ICTP) regional climate model named RegCM4 is used to perform 6 years simulation including 1 year for spin-up (from January 2001 to December 2006) over Central Africa using four convective schemes: The Emmanuel scheme ( MIT), the Grell scheme with Arakawa-Schulbert closure assumption ( GAS), the Grell scheme with Fritsch-Chappell closure assumption ( GFC) and the Anthes-Kuo scheme (Kuo). We have investigated the ability of the model to simulate precipitation, surface temperature, wind and aerosols optical depth. Emphasis in the model results were made in December-January-February ( DJF) and July-August-September ( JAS) periods. Two subregions have been identified for more specific analysis namely: zone 1 which corresponds to the sahel region mainly classified as desert and steppe and zone 2 which is a region spanning the tropical rain forest and is characterized by a bimodal rain regime. We found that MIT scheme shows a better index of agreement in temperature, but it generally has a tendency to an overestimation of other simulated parameters. Overall, GAS scheme is more suitable to downscale the aforementioned parameters except temperature, as well as the diurnal cycle of precipitation everywhere over the study domain irrespective of the seasons. In JAS, model results are similar in the representation of regional wind circulation. Apart from the MIT scheme, all the convective schemes give the same tendencies in aerosols optical depth simulations. Additional experiment reveals that the use of BATS instead of Zeng scheme to calculate ocean flux appears to improve the quality of the model simulations. [ABSTRACT FROM AUTHOR]

The article focuses on the analysis of the skills of seven regional climate models (RCM) in the reproduction of mean daily temperatures for the Central England region. It notes that three airflow indices were used during the examination of RCM's ability to reproduce. Comparison among the frequency distribution of each index, the relationships between the daily airflows indices and temperature, as well as the ability of models to reproduce is discussed.