Systematic errors in Monsoon simulation: importance of the equatorial Indian Ocean processes

H. Annamalai1, B. Taguchi2, J.P. McCreary1, J. Hafner1, M. Nagura2, and T. Miyama2

International Pacific Research Center, University of Hawaii, USA Application Laboratory, JAMSTEC, Japan In climate models, simulating the monsoon precipitation climatology remains a grand challenge. Compared to CMIP3, the multi-model-mean (MMM) errors for Asian-Australian monsoon (AAM) precipitation climatology in CMIP5, relative to GPCP observations, have shown little improvement. One of the implications is that uncertainties in the future projections of time-mean changes to AAM rainfall may not have reduced from CMIP3 to CMIP5. Despite dedicated efforts by the modeling community, the progress in monsoon modeling is rather slow. This leads us to wonder: Has the scientific community reached a "plateau" in modeling mean monsoon precipitation? Our focus here is to better understanding of the coupled air-sea interactions, and moist processes that govern the precipitation characteristics over the tropical Indian Ocean where large-scale errors persist. A series idealized coupled model experiments are performed to test the hypothesis that errors in the coupled processes along the equatorial Indian Ocean during inter-monsoon seasons could potentially influence systematic errors during the monsoon season. Moist static energy budget diagnostics has been performed to identify the leading moist and radiative processes that account for the large-scale errors in the simulated precipitation. As a way forward, we propose three coordinated efforts, and they are: (i) idealized coupled model experiments; (ii) process-based diagnostics and (iii) direct observations to constrain model physics. We will argue that a systematic and coordinated approach in the identification of the various interactive processes that shape the precipitation basic state needs to be carried out, and high-quality observations over the data sparse monsoon region are needed to validate models and further improve model physics.