Investigating the Systematic Biases on Intraseasonal Time-Scale in NCEP CFSv2 Simulated Indian Summer Monsoon – an Effort of Improvement through Superparameterization Technique.

Thursday, 18 December 2014
Bidyut Bikash Goswami1, Medha S Deshpande2, R Phani Murli Krishna2, Parthasarathi Mukhopadhyay2, Suryachandra Rao Anguluri2, Raghuram G Murtugudde3, Marat Khairoutdinov4 and B. N. Goswami2, (1)University of Victoria, Victoria, BC, Canada, (2)Indian Institute of Tropical Meteorology, Pune, India, (3)Univ of MD--ESSIC, College Park, MD, United States, (4)Stony Brook University, School of Marine & Atmospheric Sciences, Stony Brook, NY, United States
We have evaluated the simulation of Indian summer monsoon and its intraseasonal oscillations in the National Centers for Environmental Prediction (NCEP) climate forecast system model (CFS) version 2 (CFSv2). The dry bias over the Indian landmass in the mean monsoon rainfall is one of the major concerns.

Our analysis shows a possible bias in the co-evolution of convection and sea surface temperature in CFSv2 over the equatorial Indian Ocean. It is also found that the simulated large scale vertical heat source (Q1) and moisture sink (Q2) over the Indian region are biased relative to observational estimates. We posit a possible explanation for the dry precipitation bias over the Indian landmass in the simulated mean monsoon on the basis of the biases associated with the simulated ocean-atmospheric processes and the vertical heating structure.

We further run the CFSv2 model in Superparameterized framework (in T62 resolution) to explore the response of the systematic biases observed in a convection parameterized CFSv2 climate simulations. The incorporation of the superparameterization technique shows reduction of a few systematic biases observed in the traditional convection parametrized CFSv2 model; the highlights are the reduction of the cold tropospheric temperature bias, dry bias of ISM precipitation and underestimation of the synoptic scale variance .

Based on the reduction of systematic biases in the superparameterized framework as compared to convection parameterized CFSv2, we propose superparameterization technique as one of the possible routes towards better simulation of the global climate.