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.

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[1] Annual, seasonal, and monthly trends in surface air temperature were examined over India during the period . Besides this, annual and seasonal trends were also scrutinized in view of global warming concerns during the 2 non-global (1901-1909 and 1946-1975) and global (1910-1945 and 1976-2003) À1 has been observed in maximum (T max ), minimum (T min ), mean (T mean ) temperatures, and diurnal temperature range (DTR; T max À T min ), respectively during the period 1901-2003. The annual temperatures (T mean , T min , and T max ) show a cooling (warming) tendency during the non-global (global) warming periods, apart from the second non-global warming period of T max . The seasonal trends in T min and T mean also show similar behavior; whereas, T max shows warming in all sub-periods, excluding the first non-global warming period of the pre-monsoon and monsoon. Seasonal analysis depicts that, both post-monsoon and winter seasons are getting warmer with regard to T max and T min . During the analysis as well as in non-global and global warming periods, annual DTR has increased. DTR increases in all seasons, with the largest increase in winter and the smallest in post-monsoon; whereas monthly analysis reveals that all the months, except March, October, and November are contributing significantly to the annual increase of DTR. The partial correlation analysis reveals that the total cloud cover along with the secondary factors like precipitation and soil-moisture are responsible for increase in DTR over India during the period 1948-2003.