Evaluation of the added value of a high‐resolution regional climate model simulation of the South Asian summer monsoon climatology

Abstract

The South Asian summer monsoon (SASM) is a continental scale weather phenomenon, which fluctuates at a range of temporal and spatial scales. Although majority of global climate models are broadly able to simulate the large scale characteristics of the SASM, they generally have major deficiencies such as constraints in reproducing observed mean precipitation. It is generally anticipated that higher resolution regional climate models (RCMs) would be able to simulate an improved mean state owing to their capacity to better simulate fine temporal and spatial scale features and variability. Here, we analyse SASM simulations using a contemporary Hadley Centre RCM, forced by ERA‐Interim reanalysis and observed sea surface temperature, at medium (0.44°) and high (0.11°) horizontal resolutions. Evaluation of the results show that, compared to the medium resolution RCM, the high resolution RCM is able to better resolve the interaction of the low level monsoon flow with the Himalayan orography leading to added value in simulating many aspects of SASM precipitation such as the seasonal mean, relative frequency distribution of daily precipitation, and various metrics of precipitation extremes. In contrast to many previous studies, maximum added value is note along the Indo‐Gangetic plain rather than over the complex Himalayas, and the added values of up to 5 mm day−1 and 50 days are noted for mean precipitation and number of wet days, respectively over the region. Similarly, added values of up to 15 and 3 mm day−1 are noted for 95th percentile of daily precipitation and simple daily intensity index, respectively over central India and the Himalayan range. These results suggest that higher resolution RCMs have the potential to add more value when downscaling global climate model climate change projections.

Added value of CORDEX‐SA experiments in simulating summer monsoon precipitation over India

“…However, there are five experiments in which AV is found which indicates an improvement in the representation of spatial patterns of precipitation by the RCMs. These results further corroborate with the previous findings that AV shown by an RCM varies significantly in space over a region (Di Luca et al, ; Karmacharya et al, ; Rummukainen, ) and, hence, considering the spatial pattern over entire India the improvement may not be seen. A similar finding for the AV of CORDEX‐SA RCMs over India was made by Choudhary et al .…”

“…() was recently used by Karmacharya et al . () for determining AV of a high resolution RCM simulation of ISM. This method is chosen because of the simplicity of computation and direct connection of AV with the reduction in magnitude of bias.…”

“…The issue of AV of RCMs has gathered a lot of attention in the recent years (Castro et al, ; Sotillo et al, ; Duffy et al, ; Feser ; Kanamitsu and Kanamaru, ; Rauscher et al, ; Sanchez‐Gomez et al, ; Winterfeldt and Weisse, ; De Sales and Xue, ; Prömmel et al, ; Paeth and Mannig, ; Di Luca et al, , , ; Karmacharya et al, ). The AV of an RCM implies that the additional information given by the RCM is reliable and/or has some utility (Rummukainen, ).…”

The effect of modeling choices on updating intensity-duration-frequency curves and stormwater infrastructure designs for climate change

“…Unfortunately, these models also rarely agree on the magnitude of future change, a phenomenon classified as "uncertainty" in model results (Walker et al 2013). Uncertainty can be inherited from GCMs due to factors such as internal variability, modeling assumptions, and greenhouse gas emissions scenarios (Kirtman et al 2013), and can also arise from the spatial resolution of the RCM used in impact assessment (Leung and Qian 2003;Rauscher et al 2010Rauscher et al , 2016Prein et al 2016a;Mendoza et al 2016;Karmacharya et al 2017) and the MOS technique used in downscaling (Tryhorn and DeGaetano 2011;Gudmundsson et al 2012;Bürger et al 2013;Chen et al 2013;Srivastav et al 2014a;Sunyer et al 2014;Cannon et al 2015).…”

Quantifying the added value of convection-permitting climate simulations in complex terrain: a systematic evaluation of WRF over the Himalayas

“…Such vulnerability and exposure of the mountainous communities to the adverse socioeconomic impacts of changing climate and its induced disasters greatly highlights the importance of understanding their drivers and their subsequent impacts on various sectors of life for devising local-scale adaptation strategies. This requires, above all, fine-scale climatic information, particularly of surface air temperature (hereinafter temperature) and precipitation, which are also amongst the essential inputs to impact assessment models (Böhner and Lehmkuhl, 2005;Karmacharya et al, 2016;Gerlitz et al, 2016).…”