Abstract

This paper analyzes the role of the Indian Ocean (IO) and the atmosphere biases in generating and sustaining large-scale precipitation biases over Central India (CI) during the Indian summer monsoon (ISM) in the climate forecast system version 2 (CFSv2) hindcasts that are produced by initializing the system each month from January 1982 to March 2011. The CFSv2 hindcasts are characterized by a systematic dry monsoon bias over CI that deteriorate with forecast lead-times and coexist with a wet bias in the tropical IO suggesting a large-scale interplay between coupled ocean–atmosphere and land biases. The biases evolving from spring-initialized forecasts are analyzed in detail to understand the evolution of summer biases. The northward migration of the Inter Tropical Convergence Zone (ITCZ) that typically crosses the equator in the IO sector during April in nature is delayed in the hindcasts when the forecast system is initialized in early spring. Our analyses show that the delay in the ITCZ coexists with wind and SST biases and the associated processes project onto the seasonal evolution of the coupled ocean–atmosphere features. This delay in conjunction with the SST and the wind biases during late spring and early summer contributes to excessive precipitation over the ocean and leading to a deficit in rainfall over CI throughout the summer. Attribution of bias to a specific component in a coupled forecast system is particularly challenging as seemingly independent biases from one component affect the other components or are affected by their feedbacks. In the spring-initialized forecasts, the buildup of deeper thermocline in association with warmer SSTs due to the enhanced Ekman pumping in the southwest IO inhibits the otherwise typical northward propagation of ITCZ in the month of April. Beyond this deficiency in the forecasts, two key ocean–atmosphere coupled mechanisms are identified; one in the Arabian Sea, where a positive windstress curl bias in conjunction with warmer SSTs lead to a weakening of Findlater jet and the other in the east equatorial IO where a remote forcing by the predominantly westerly bias in the western-central equatorial IO in the summer strengthen the seasonal downwelling Kelvin wave that in turn deepens the thermocline in the eastern IO. The equatorial Kelvin wave continues as a coastal Kelvin wave and disperses as Rossby waves off Sumatra and induces positive SST and precipitation biases in the eastern and southern Bay of Bengal. This study shows that the biases that first appear in winds lead to a cascade of coupled processes that exacerbate the subsequent biases by modulating the evolution of seasonal processes such as the annual Kelvin and Rossby waves and the cross-equatorial vertically integrated moisture transport. While this analysis does not offer any particular insights into improving the ISM forecasts, it is a foundational first step towards this goal.

Highlights

  • A skillful forecast of the Indian summer monsoon rainfall (ISMR) is pivotal to the economy, agriculture and water-resources for more than a billion people

  • We explore the role of ocean and atmosphere biases on precipitation biases over Central India (CI) during the boreal summer as forecasted by climate forecast system version 2 (CFSv2) at different lead months

  • The CFSv2 forecasts are characterized by a systematic dry precipitation bias over the CI for June, July and August target months for forecasts initialized individually from various previous months (Fig. 1)

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Summary

Introduction

A skillful forecast of the Indian summer monsoon rainfall (ISMR) is pivotal to the economy, agriculture and water-resources for more than a billion people. Lee et al (2010) examined the biases in a suite of coupled forecast systems and found that the seasonal prediction skill is positively correlated with the ability to reproduce the mean-state and the annual cycle. The CGCM forecast systems are known to exhibit systematic biases in capturing the seasonal features of the global circulation and the process-based understanding of the evolution of biases involving interactions among the system components should lead to an improved understanding for their causes. We extend this approach by focusing on the evolution of the biases leading up to the target of the analysis, i.e., the dry continental bias in the ISM in CFSv2 hindcasts

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