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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]

The ability of an ensemble of five regional climate models (hereafter RCMs) under Coordinated Regional Climate Downscaling Experiments-South Asia (hereafter, CORDEX-SA) in simulating the key features of present day near surface mean air temperature (Tmean) climatology (1970-2005) over the Himalayan region is studied. The purpose of this paper is to understand the consistency in the performance of models across the ensemble, space and seasons. For this a number of statistical measures like trend, correlation, variance, probability distribution function etc. are applied to evaluate the performance of models against observation and simultaneously the underlying uncertainties between them for four different seasons. The most evident finding from the study is the presence of a large cold bias (−6 to −8 °C) which is systematically seen across all the models and across space and time over the Himalayan region. However, these RCMs with its fine resolution perform extremely well in capturing the spatial distribution of the temperature features as indicated by a consistently high spatial correlation (greater than 0.9) with the observation in all seasons. In spite of underestimation in simulated temperature and general intensification of cold bias with increasing elevation the models show a greater rate of warming than the observation throughout entire altitudinal stretch of study region. During winter, the simulated rate of warming gets even higher at high altitudes. Moreover, a seasonal response of model performance and its spatial variability to elevation is found. [ABSTRACT FROM AUTHOR]

An assessment of the projection of near surface air temperature over the Himalayan region from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out for different Representative Concentration Pathway (RCP) scenarios. The purpose of this study is to assess the probable future changes in the mean temperature climatology and its long term trend for different seasons under greenhouse gas forcing scenarios for different seasons till the end of 21st century. A number of statistical measures such as changes in mean climatology, long term trend and probability distribution function have been used in order to detect the signals of changes in climate. Moreover, the associated uncertainties among different model experiments and their ensemble in space, time and different seasons in particular have been quantified. Despite of strong cold bias in the model experiments over Himalayan region (Nengker et al., 2017), statistically significant strong rate of warming (0.03–0.09 °C/year) across all the seasons and RCPs have been projected by all the models and their ensemble. Season specific response towards the warming is indicated by ensemble under future climate while ON season shows comparable magnitude of warming than DJF. Such warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios from RCP2.6 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the trend magnitude between different models describes the uncertainty associated with the model projections and scenarios. A substantial seasonal response to warming with respect to elevation was also found, as DJF season followed by ON portrays highest rate of warming, specifically at higher elevation sites such as western Himalayas and northern part of central Himalayas. The different elevation classes respond differently to the projected future warming under different RCPs and seasons. Such higher warming during DJF may have consequences as changes in the fractional distribution of the solid and liquid precipitation as well as the melting of the glacial deposits with ultimately affecting the streamflow response and water resources in the downstream areas. [ABSTRACT FROM AUTHOR]

An assessment of the projection of minimum and maximum air temperature over the Indian Himalayan region (IHR) from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out under two different Representative Concentration Pathway (RCP) scenarios. The major aim of this study is to assess the probable future changes in the minimum and maximum climatology and its long-term trend under different RCPs along with the elevation dependent warming over the IHR. A number of statistical analysis such as changes in mean climatology, long-term spatial trend and probability distribution function are carried out to detect the signals of changes in climate. The study also tries to quantify the uncertainties associated with different model experiments and their ensemble in space, time and for different seasons. The model experiments and their ensemble show prominent cold bias over Himalayas for present climate. However, statistically significant higher warming rate (0.23–0.52 °C/decade) for both minimum and maximum air temperature (T min and T max ) is observed for all the seasons under both RCPs. The rate of warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios starting from RCP4.5 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the magnitude of trend between different models describes the uncertainty associated with the model projections and scenarios. The projected rate of increase of T min may destabilize the snow formation at the higher altitudes in the northern and western parts of Himalayan region, while rising trend of T max over southern flank may effectively melt more snow cover. Such combined effect of rising trend of T min and T max may pose a potential threat to the glacial deposits. The overall trend of Diurnal temperature range (DTR) portrays increasing trend across entire area with highest magnitude under RCP8.5. This higher rate of increase is imparted from the predominant rise of T max as compared to T min . [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]

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.

In this study, output of the Hadley Centre Regional Circulation Model (RCM) (HadRM3P, 0.44° × 0.44° resolution) was used as input to the Canadian Forest Fire Weather Index (FWI) for the present and 2 future IPCC climate scenarios (Special Report on Emissions Scenarios [SRES], A2 and B2 scenarios). The aim was to investigate the effects of climate change on fire risk (number of days with fire risk, length of fire risk season, etc.) for the EU Mediterranean countries. Results indicated a general increase in fire risk in both future scenarios over the whole study area, The increase in fire risk was mainly due to 3 components: (1) increase in the number of years with fire risk; (2) increase in the length of the season with fife risk; (3) increase of extreme events (e.g. total number of days with FWI >45 and episodes with FWI >45 for 7 consecutive days) during the fire season. As expected, A2 scenario showed a greater increase in risk than B2 scenario. These general increases in fire risk may have a very strong impact in areas where forest land cover is high (e.g. the Alps region in Italy, the Pyrenees in Spain and mountains of the Balkan region). [ABSTRACT FROM AUTHOR]