Improved Simulation of the East Asian Summer Monsoon Rainfall with Satellite-Derived Snow Water Equivalent Data*

Abstract

The effect of Eurasian spring snow amount on the summer monsoon rainfall over East Asia has been studied both observationally and numerically. The results indicate that the Eurasian spring snow amount could be important for seasonal prediction of East Asian summer monsoon (EASM) rainfall. Therefore, accurately initializing snow could be critical to improving seasonal prediction of EASM rainfall by numerical models. An attempt has been made in this study to initialize snow in a regional climate model using snow water equivalent (SWE) data derived from a microwave imager. Results from an ensemble seasonal prediction experiment for the 2005 EASM show that the satellite-derived SWE data can be effectively used to initialize a dynamical seasonal prediction model, which leads to improved seasonal prediction of EASM rainfall. Possible effects of snow anomalies over the Tibetan Plateau on EASM rainfall were also studied through a comparative ensemble simulation in which snow was initialized by spinning up the same model from the previous winter. It is found that the anomalous snow amount over the Tibetan Plateau could lead to cooling of the surface and lower troposphere not only over the Tibetan Plateau but also in the surrounding areas because of the reduced net surface shortwave radiation associated with the high snow albedo. This would result in positive anomalies in geopotential height and weaken the cyclonic monsoon circulation in the lower troposphere in East Asia, causing a rainfall increase in South China but a reduction in the Yangtze River Valley in early summer (May-June). The difference in rainfall in midsummer (July-August) was not significant when compared with that in early summer. The surface heat budget indicates that the reduced net surface shortwave radiation is largely balanced by the reduced surface sensible heat flux.

A comparison between the effects of snow albedo and infiltration of melting water of Eurasian snow on East Asian summer monsoon rainfall

“…In order to understand the heat balance at the land surface in different simulations, a heat budget for the land surface is conducted [see Souma and Wang , 2009]. The land surface heat budget equation can be written as where Nsw is the net shortwave radiation, Nlw is the net longwave radiation, G is the sum of ground heat flux and latent heat of snowmelt, Le is the surface latent heat flux, and Hs is the surface sensible heat flux, respectively.…”

“…[20] In order to understand the heat balance at the land surface in different simulations, a heat budget for the land surface is conducted [see Souma and Wang, 2009]. The land surface heat budget equation can be written as…”

“…As given by Souma and Wang [2009], the model domain covers the area of 2.5–50.0°N, 65.0–145.0°E (Figure 1) with horizontal grid spacing of 0.5°. The USGS high‐resolution topographic data set (0.0833° × 0.0833°) was used to obtain the model envelope topography (contours in Figure 1).…”

“…Recent studies indicate that the Eurasian snow anomalies in winter and spring, especially those over the Tibetan Plateau (TP), may have a considerable effect on the variability of South Asian and East Asian summer monsoon (EASM) rainfall [ Hsu and Liu , 2003; Wu and Qian , 2003; Zhang et al , 2004; Wu and Kirtman , 2007; Zhao et al , 2007]. Recently, Souma and Wang [2009] have demonstrated that the satellite‐derived snow water equivalent (SWE) data can be used to improve the simulation of EASM rainfall.…”

California reanalysis downscaling at 10 km using an ocean‐atmosphere coupled regional model system

“…Since the first successful demonstrations of regional climate modeling by Dickinson et al [1989] and Giorgi and Bates [1989], regional climate models (RCMs) have been widely used to downscale global reanalysis and general circulation model (GCM) outputs. This dynamical downscaling allows high resolution results to be obtained from coarse resolution driving data for various purposes, such as long‐term reanalysis downscaling [ Liang et al , 2004; Kanamitsu and Kanamaru , 2007], the downscaling of projected climate scenarios [ Giorgi et al , 1994; Leung et al , 2004; Stowasser et al , 2007; Hong et al , 2010], seasonal prediction [ Sun et al , 2006], process studies identifying soil moisture atmosphere interactions [ Kanamitsu and Mo , 2003], analyses on the effects of snow on the Asian summer monsoon [ Seol and Hong , 2009; Souma and Wang , 2009], and an evaluation of physics parameterization schemes [ Yhang and Hong , 2008; Koo and Hong , 2010]. Such an approach is also useful to isolate regional feedback imbedded within evolving large‐scale features [ Hong and Kalnay , 2000].…”