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.
Along with good prediction skill for major SST boundary forcings such as El Niño and IOD, their appropriate teleconnection spatial patterns also need to be captured well for the better prediction of the land precipitation like Indian summer monsoon rainfall. Here in the study, even though majority of the models has better skill for Nino3.4 index and IOD index, their spatial teleconnection pattern is higher for CFSv2‐T382 (pattern correlation of 0.8) and also has less bias in tropical region. Thus as seen in the figure, it has better Indian summer monsoon rainfall (ISMR)–SST relationship (PCC = 0.6) compared to all other models and hence CFSv2‐T382 has better skill (0.55) for ISMR, while skill is less than 0.1 for the models with PCC values very less. Spatial pattern of correlation between ISMR and seasonal SST anomalies from (a) observations, (b)–(p) individual model hindcasts and (o) MME of all models. Statistically significant (90% confidence level) correlations are stippled.
Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2–3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.
Indian Summer Monsoon (ISM) synoptic scale systems (low‐pressure systems, LPS) are known to produce increased rainfall over central India (CI). Fidelity of the Climate Forecast System version 2 (CFSv2) at simulating the LPS and their characteristics is evaluated in this study using a feature tracking algorithm. The model is able to reproduce the clustering of LPS by monsoon intraseasonal oscillations and the associated precipitation over eastern‐central India. It is found that mean biases in circulation and moisture stem from cold sea surface temperature (SST) bias in the model which results in weak LPS linked rainfall events over central India. Two sensitivity experiments were carried out to study the effect of coupled dynamics of tropical basins on LPS. Suppression of active dynamics of the tropical Indian Ocean in CFSv2 causes a reduction in cold SST bias and enhanced cyclogenesis in the northern Bay of Bengal. The reduced low‐level anticyclonic bias and enhanced moisture availability result in a better simulation of LPS structure, and associated precipitation over CI. Suppression of active ocean dynamics in tropical Pacific Ocean causes a perennial El‐Niño type bias which restricts LPS propagation over the Indian landmass, possibly due to time‐mean subsidence induced by remote El‐Niño forcing. Sensitivity experiments indicate the need for improvements in the representation of tropical Indian Ocean coupled dynamics as well as convective parameterization schemes in the model for subsequent improvements in the simulation of ISM at various time scales.
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