Inter-comparison of two regional climate models (RegCM and WRF) in downscaling CFSv2 for the seasonal prediction of Indian summer monsoon

Abstract

The e cacy of two latest versions of regional climate models (RegCM and WRF) for simulating the Indian summer monsoon (JJAS) is tested in this study. The CFSv2 hindcast outputs are downscaled over the Indian monsoon domain for 11 contrasting monsoon seasons using the regional models. The April start ensembles of the CFSv2 are averaged to generate the initial and lateral boundary conditions for driving the WRF and RegCM. The regional models perform better in simulating the Indian summer monsoon features better than the parent CFSv2 model. The rainfall pattern as well as the intensities are improved with the dynamical downscaling and the errors in the rainfall are minimized over the GCM hindcast. On comparing the two regional models, the RegCM overestimates the rainfall during the excess and normal monsoon seasons. The RCMs improve the skill of rainfall prediction as compared to the GCM and WRF shows better skill in particular. One peculiar nding of this study is that the daily rainfall biases averaged over all the years of simulation shows that the two RCMs show similar biases with RegCM showing stronger biases occasionally. It may be implied that the errors from GCM in the form of the ICBC might be in uencing the simulation in the RCMs. The upper air and surface parameters analysis shows that the WRF performs better in representing the semi-permanent features of the Indian summer monsoon which may be helping in improving the rainfall over the RegCM. The wind pattern as well as the relative humidity along the vertical column of the atmosphere are captured better in the WRF model. Diagnostics of CAPE & vertically integrated moisture transport supports the nding of the rainfall being simulated better in the WRF model.

Dynamic changes and impacts of accumulated temperature on the Tibetan plateau from 1980 to 2018 based on the fusion of simulation and observation data

“…The development of numerical models compensates for the shortcomings of the two aforementioned data sources and provides spatiotemporally continuous data for related research [ 15 , 16 ]. However, due to bias in driving data and imperfections in model theory, there is a large degree of deviation in the simulation data of numerical models [ 17 ]. Moreover, the resolution of global or even regional climate model outputs is usually too coarse to accurately resolve physical and dynamic processes [ 18 ].…”

Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

“…Maurya et al, [47] made a point that the grid spacing can be made finer but up to a certain extent beyond which the skill deteriorates further. Mohanty & Mohanty [51] compared the skill of two RCMs, RegCM4 and WRF4 in simulating the Indian summer monsoon using CFSv2 initial conditions and found that both the RCMs improved the rainfall pattern and intensities over the parent GCM.…”