Abstract

The seasonal prediction skill of the Asian summer monsoon is assessed using retrospective predictions (1982–2009) from the ECMWF System 4 (SYS4) and NCEP CFS version 2 (CFSv2) seasonal prediction systems. In both SYS4 and CFSv2, a cold bias of sea-surface temperature (SST) is found over the equatorial Pacific, North Atlantic, Indian Oceans and over a broad region in the Southern Hemisphere relative to observations. In contrast, a warm bias is found over the northern part of North Pacific and North Atlantic. Excessive precipitation is found along the ITCZ, equatorial Atlantic, equatorial Indian Ocean and the maritime continent. The southwest monsoon flow and the Somali Jet are stronger in SYS4, while the south-easterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFSv2 relative to the reanalysis. In both systems, the prediction of SST, precipitation and low-level zonal wind has greatest skill in the tropical belt, especially over the central and eastern Pacific where the influence of El Nino-Southern Oscillation (ENSO) is dominant. Both modeling systems capture the global monsoon and the large-scale monsoon wind variability well, while at the same time performing poorly in simulating monsoon precipitation. The Asian monsoon prediction skill increases with the ENSO amplitude, although the models simulate an overly strong impact of ENSO on the monsoon. Overall, the monsoon predictive skill is lower than the ENSO skill in both modeling systems but both systems show greater predictive skill compared to persistence.

Highlights

  • The global monsoon (Webster et al 1998; Wang et al 2011a) is a major component of global climate system, affecting the global climate and weather such as floods, droughts and other climate extremes

  • The southwest monsoon flow and the Somali Jet are stronger in System 4 (SYS4), while the southeasterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFS version 2 (CFSv2) relative to the reanalysis

  • This study has examined the seasonal predictive skill of the Northern Hemisphere (NH) summer using retrospective predictions by the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 and NCEP CFS version 2

Read more

Summary

Introduction

The global monsoon (Webster et al 1998; Wang et al 2011a) is a major component of global climate system, affecting the global climate and weather such as floods, droughts and other climate extremes. It has been shown that the sensitivity in monsoon prediction/simulation depends on model features, primarily on the presence of ocean–atmosphere coupling, model resolution and improvement of the model physics (Kang et al 2002; Wang et al 2005a, and many others). These model improvements are providing substantial advances in seasonal prediction. 6. ECMWF System 4 (hereafter SYS4) and NCEP CFSv2 (hereafter CFSv2) are fully coupled atmosphere–ocean forecast systems that provide operational seasonal predictions together with reforecast data to evaluate and calibrate the models. The CFSR is a major improvement over the first generation NCEP reanalyses (NCEP R1 and R2) and is the product of a coupled ocean–atmosphere–land system at higher spatial resolution (Saha et al 2010)

Seasonal mean bias and prediction skill
The Asian summer monsoon
ENSO and the monsoon prediction
Discussion
Summary
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call