Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

Abstract

Abstract The usefulness of dynamical downscaling of seasonal reforecasts of Indian Monsoon is explored to address the seasonal mean biases in the reforecasts. Almost all the current generation global coupled models, including the Climate Forecast System version 2 (CFSv2, T126 ∼110 km), exhibit systematic mean dry bias over the central Indian region during the summer monsoon season. Cold sea surface temperature (SST) biases in the Indian Ocean and a weak monsoon circulation due to a colder tropospheric temperature contribute to this dry bias. Such systematic biases restrict the use of skillful forecasts from these models in climate applications (such as agriculture or hydrology). Dynamical downscaling of seasonal forecasts (∼110 km resolution) using the Weather Research and Forecasting (WRF) model coupled to a simple ocean mixed layer model (OML; WRFOML) at 38 km resolution significantly reduces the majority of the systematic biases reported earlier. The seasonal mean dry bias reduces to 16% in WRFOML as compared to 44% (33%) in the CFSv2‐T126 (WRFCTL) over the Indian land region. Warmer SSTs in the Indian Ocean and a more robust monsoon circulation emanating from a realistic simulation of the tropospheric temperature reduced the systematic biases in WRFOML compared to CFSv2‐T126 and WRFCTL. Additionally, category‐wise rainfall distributions are also improved drastically in the downscaled simulations (WRFOML). Downscaled reforecasts with reduced systematic biases have better suitability for climate applications.

Keywords

• Indian Monsoon
• sea surface temperature
• dynamical downscaling
• systematic bias