The role of northern Arabian Sea surface temperature biases in CMIP5 model simulations and future projections of Indian summer monsoon rainfall
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Many climate models have problems simulating Indian summer monsoon rainfall and its variability, resulting in considerable uncertainty in future projections. Problems may relate to many factors, such as local effects of the formulation of physical parametrisation schemes, while common model biases that develop elsewhere within the climate system may also be important. Here we examine the extent and impact of cold sea surface temperature (SST) biases developing in the northern Arabian Sea in the CMIP5 multi-model ensemble, where such SST biases are shown to be common. Such biases have previously been shown to reduce monsoon rainfall in the Met Office Unified Model (MetUM) by weakening moisture fluxes incident upon India. The Arabian Sea SST biases in CMIP5 models consistently develop in winter, via strengthening of the winter monsoon circulation, and persist into spring and summer. A clear relationship exists between Arabian Sea cold SST bias and weak monsoon rainfall in CMIP5 models, similar to effects in the MetUM. Part of this effect may also relate to other factors, such as forcing of the early monsoon by spring-time excessive equatorial precipitation. Atmosphere-only future time-slice experiments show that Arabian Sea cold SST biases have potential to weaken future monsoon rainfall increases by limiting moisture flux acceleration through non-linearity of the Clausius–Clapeyron relationship. Analysis of CMIP5 model future scenario simulations suggests that such effects are small compared to other sources of uncertainty, although models with large Arabian Sea cold SST biases may suppress the range of potential outcomes for changes to future early monsoon rainfall.
KeywordsIndian summer monsoon SST bias Model systematic error Future projections Arabian Sea Climate model CMIP5
The authors acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. ECMWF ERA-Interim data used in this study have been obtained from the ECMWF data server. RCL and GMM were supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), the NERC Changing Water Cycle (CWC) SAPRISE project (reference NE/I022841/1) and the European Commission’s 7th Framework Programme, under Grant Agreement number 282672, EMBRACE project. AGT is funded by a NERC Fellowship (NE/H015655/1), SAPRISE project (NE/I022469/1), and the CWC Hydroflux-India project (NE/I022485/1). The authors thank two anonymous reviewers for suggestions which significantly helped to improve the manuscript.
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