Simulation of Indian summer monsoon features by latest coupled model of National Centers for Environmental Prediction (NCEPs) Climate Forecast System version 2 (CFSv2) is attempted in its long run. Improvements in the simulation of Indian summer monsoon as compared with previous version (CFSv1) is accessed and areas which still require considerable refinements are introduced. It is found that, spatial pattern of seasonal mean rainfall and wind circulations are more realistic in CFSv2 as compared with CFSv1. Variance and northward propagation of intraseasonal oscillation (ISO), which also contribute to the seasonal mean rainfall are remarkably improved. However, the central Indian dry bias still persists and amplified. Pervasive cold bias in surface (2 m air temperature) as well as in the whole troposphere is further increased in CFSv2. These cold biases may be partly attributed to the lack of model's ability to realistically simulate the ratio of convective and stratiform rainfall. Sea-surface temperature (SST) over the Indian Ocean is underestimated in CFSv2. However, CFSv1 shows east-west dipole structure in the bias. The teleconnection of El Nino Southern Oscillation (ENSO) and Indian summer monsoon rainfall (ISMR) in terms of Niño3 SST and monsoon rainfall correlation is more realistic in the latest version of the model. Overall, there are substantial improvements in CFSv2 as compared with CFSv1, but it has to evolve further to realistically simulate the mean and variability of ISMR.

Observations have shown that the Indian Ocean is consistently warming and its warm pool is expanding, particularly in the recent decades. This paper attempts to investigate the reason behind these observations. Under global warming scenario, it is expected that the greenhouse gas induced changes in air-sea fluxes will enhance the warming. Surprisingly, it is found that the net surface heat fluxes over Indian Ocean warm pool (IOWP) region alone cannot explain the consistent warming. The warm pool area anomaly of IOWP is strongly correlated with the sea surface height anomaly, suggesting an important role played by the ocean advection processes in warming and expansion of IOWP. The structure of lead/lag correlations further suggests that Oceanic Rossby waves might be involved in the warming. Using heat budget analysis of several Ocean data assimilation products, it is shown that the net surface heat flux (advection) alone tends to cool (warm) the Ocean. Based on above observations, we propose an ocean-atmosphere coupled positive feedback 710 Climatic Change (2012) 110:709-719 mechanism for explaining the consistent warming and expansion of IOWP. Warming over IOWP induces an enhancement of convection in central equatorial Indian ocean, which causes anomalous easterlies along the equator. Anomalous easterlies in turn excite frequent Indian ocean Dipole events and cause anti-cyclonic wind stress curl in south-east and north-east equatorial Indian ocean. The anomalous wind stress curl triggers anomalous downwelling oceanic Rossby waves, thereby deepening the thermocline and resulting in advection of warm waters towards western Indian ocean. This acts as a positive feedback and results in more warming and westward expansion of IOWP.

Large socioeconomic impact of the Indian summer monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather low potential predictability (PP) of the seasonal ISM, contributed significantly by “internal,” interannual variability was considered insurmountable. Here we show that the internal variability contributed by the ISM subseasonal (synoptic + intraseasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g., El Niño and Southern Oscillation). This provides a scientific basis for predictability of the ISM rainfall beyond the conventional estimates of PP. We establish a much higher actual limit of PP (r∼0.82) through an extensive reforecast experiment (1,920 years of simulation) by improving two major physics in a global coupled climate model, which raises a hope for a very reliable dynamical seasonal ISM forecasting in the near future.

In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

No abstract