Climate Prediction: ENSO, MJO and Teleconnections
Abstract

Abstract Author: Mingyue Chen, Wanqiu Wang, and Arun Kumar

Abstract Title: Predictability of monthly precipitation and temperature associated with atmospheric/land initial conditions and sea surface temperatures

Abstract: Predictability of the monthly prediction of precipitation and surface air temperature associated with initial atmosphere and land surface information, and sea surface temperatures (SSTs) are analyzed based on retrospective forecasts from the National Centers for Environmental Prediction (NCEP) coupled climate forecast system (CFS), which is initialized with atmospheric/land/ocean analyses, and AMIP simulations with 5 atmospheric general circulation models (GFS, CCM3, ECHAM, NSIPP, and SFM), which are forced with observed SSTs. The work focuses on analyzing the following aspects: (1) the CFS dependence of forecast skill on lead time; (2) the extent to which the forecast benefits from the use of analyzed initial conditions for the atmosphere and land surface; and (3) seasonal and spatial variations of the forecast skill. Our preliminary analysis shows that the initial memory from atmosphere and land surface in the CFS plays an important role in enhancing the forecast skill for lead time up to 30-40 days. The CFS monthly forecasts within this range of lead time are superior to the AMIP simulations which do not include the initial information in the atmosphere and land surface. Beyond this time range, the coupled model forecast becomes not as good as the AMIP simulations due to the loss of memory from initial condition and inaccuracy of SST forecasts. Further analysis for the CFS forecast and AMIP simulations is being conducted. Detailed results will be presented at the workshop.