Abstract
The impact of initial land-surface states on monthly to seasonal prediction skill of the Indian summer monsoon (June- September) is investigated using a suite of hindcasts made with the Climate Forecast System version 2 (CFSv2) operational forecast model. The modern paradigm of land-atmosphere coupling is applied to quantify biases in different components of the land-atmosphere coupled system and their effect on systematic errors. Three sets of hindcasts are performed for the period spanning 1982-2009 initialized at the start of April, May and June. For a particular initial date of a given year, one member (Control run) has the analyzed land initial state consistent with the atmosphere, sea ice and ocean states for that year; the other 27 members have land states taken from each of the remaining 27 years. There is significant improvement in the deterministic prediction skill of near surface temperature and soil moisture on monthly and seasonal time scales due to realistic land initial conditions. The improvement occurs in those areas where the land-atmosphere coupling is strongest. Improvements in the prediction skill of precipitation are confined to relatively small areas. The pattern of skill differences resembles patterns of land-atmosphere coupling strength, while biases in the representation of land-atmosphere coupling affect the skill of temperature and rainfall. The re-emergence of skill in temperature and precipitation towards the end of the season over northwest India within April and June IC hindcasts may be attributed to better simulation of the withdrawal phase of the monsoon as well as increased land-atmosphere coupling. For May IC hindcasts, increased skill in air temperature on the sub-seasonal time scales could be due to other large-scale factors. Errors in the parameterization of radiation, convection, boundary layer processes, surface moisture fluxes and the representation of vegetation contribute to decay in potential predictability and skill attributable to land initial conditions. Furthermore, incorrect representation of daily and sub-daily precipitation statistics over land also likely lead to errors in land-atmosphere coupling. Above all, the importance of accurate land surface initialization and land-atmosphere coupling in improving the Indian summer monsoon prediction on sub-seasonal to seasonal time scales is emphasized.
Highlights
Does land surface initialization matter for the forecast of the Indian summer monsoon rainfall (ISMR) and temperature on sub-seasonal to seasonal time scales? That is the question being addressed
We investigate the impact of initial land states and land-atmosphere coupling on the predictability and prediction skill of Indian summer monsoon
There is significant deterministic skill improvement in the interannual anomalies of monthly and seasonal (JJAS) near surface temperatures due to realistic land surface initialization
Summary
Does land surface initialization matter for the forecast of the Indian summer monsoon rainfall (ISMR) and temperature on sub-seasonal to seasonal time scales? That is the question being addressed. Though state-of-the-art dynamical models have demonstrated improvement in the prediction skill of the ISMR in the last decade (Kumar et al, 2005; DelSole and Shukla, 2012; Kim et al, 2012; Rajeevan et al, 2012; Nanjundiah et al, 2013; Sperber et al, 2013), much of the skill has derived from improved forecasts of sea surface temperature (SST) (DelSole and Shukla, 2012), and the strong relationship that the ISMR bears with SST over different ocean basins (Saji et al, 1999; Gadgil et al, 2003; Goswami et al, 2006; Kumar et al, 2006).
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