15A.1 Process-Oriented Diagnostics of Seasonal Influence of Initialization on Indian Summer Monsoon Rainfall and Large-Scale Atmospheric Circulation in NCEP CFSv2

Thursday, 11 January 2018: 1:30 PM
406 (Hilton) (Austin, Texas)
Ravi Shukla, George Mason Univ., Fairfax, VA; and B. Huang, J. L. Kinter III, C. S. Shin, and L. Marx

The cryosphere, notably snow cover, has important variations on seasonal–to-interannual time scales that interact with the atmosphere and underlying land surface. A significant portion of Eurasia and North America is covered with snow during the winter months. This affects the surface radiation budget, primarily by altering surface albedo, and influences soil moisture through a delayed hydrologic effect by snowmelt. Previous studies have demonstrated the effects of Eurasian snow anomalies on large-scale atmospheric circulation over middle and high latitudes in the Northern Hemisphere, North American winter temperature, the Arctic Oscillation, the North Atlantic Oscillation, monsoon precipitation and El Niño–Southern Oscillation.

Recently, the Center for Ocean-Land-Atmosphere Studies (COLA) completed a 400-year simulation using a revised version of NCEP CFSv2 and 30-year reforecasts (1979-2008) initialized with initial conditions from January to May. The re-forecasts were made using ocean initial conditions (OIC) from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), the NCEP Global Ocean Data Assimilation System (GODAS), the ECMWF Ocean Reanalysis System 3 (ORA-S3), and NEMOVAR. To sample uncertainty in the atmosphere and land surface state, four ensemble members were generated for each OIC by using atmospheric and land surface initial conditions taken from the instantaneous fields at 0000 UTC of the first 4 days in January and May of each year. The total number of ensemble members is 16 = 4 OICs X 4 AIC/LICs – for both January and May cases. We found that CFSv2 overestimates the Eurasian snow-covered region and its surface albedo. The snow-melting rate is also slow in the simulation and reforecasts. These errors are associated with bias in the surface energy budget and may be an important cause of the intense cold bias at the surface in Eurasia, which causes significant biases in geopotential heights in the mid-troposphere and in turn contributes to an upper level large-scale atmospheric circulation bias. Some CMIP5 Earth system models depict similar biases. Persisting from winter to early summer, these biases have serious effects on monsoon predictions. Therefore, parameterization of snow and surface albedo is important for climate simulation and seasonal prediction.

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