To better represent organized convection in the Climate Forecast System version 2 (CFSv2), a stochastic multicloud model (SMCM) parameterization is adopted and a 15 year climate run is made. The last 10 years of simulations are analyzed here. While retaining an equally good mean state (if not better) as the parent model, the CFS‐SMCM simulation shows significant improvement in the synoptic and intraseasonal variability. The CFS‐SMCM provides a better account of convectively coupled equatorial waves and the Madden‐Julian oscillation. The CFS‐SMCM exhibits improvements in northward and eastward propagation of intraseasonal oscillation of convection including the MJO propagation beyond the maritime continent barrier, which is the Achilles Heel for coarse‐resolution global climate models (GCMs). The distribution of precipitation events is better simulated in CFSsmcm and spreads naturally toward high‐precipitation events. Deterministic GCMs tend to simulate a narrow distribution with too much drizzling precipitation and too little high‐precipitation events.

A stochastic multicloud model (SMCM) convective parameterization, which mimics the interactions at subgrid scales of multiple cloud types, is incorporated into the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), model (CFSsmcm) in lieu of the preexisting simplified Arakawa–Schubert (SAS) cumulus scheme. A detailed analysis of the tropical intraseasonal variability (TISV) and convectively coupled equatorial waves (CCEW) in comparison with the original (control) model and with observations is presented here. The last 10 years of a 15-yr-long climate simulation are analyzed. Significant improvements are seen in the simulation of the Madden–Julian oscillation (MJO) and most of the CCEWs as well as the Indian summer monsoon (ISM) intraseasonal oscillation (MISO). These improvements appear in the form of improved morphology and physical features of these waves. This can be regarded as a validation of the central idea behind the SMCM according to which organized tropical convection is based on three cloud types, namely, the congestus, deep, and stratiform cloud decks, that interact with each other and form a building block for multiscale convective systems. An adequate accounting of the dynamical interactions of this cloud hierarchy thus constitutes an important requirement for cumulus parameterizations to succeed in representing atmospheric tropical variability. SAS fails to fulfill this requirement, which is evident in the unrealistic physical structures of the major intraseasonal modes simulated by CFSv2 as documented here.

Based on extensive analysis of observations and a series of climate model experiments, here we establish that slow variations of northern hemispheric extratropical sea-surface temperature (SST) anomalies can augment seasonal predictability of the south Asian monsoon. The SST conditions and performance of the south Asian monsoon during 2013 boreal summer months (June-September) led us to hypothesize that the strong extratropical SST anomalies in the North Pacific and North Atlantic in conjunction with weak tropical SST anomalies (weak La Niña) were responsible for the above-normal rainfall over India during 2013. We also argue that the 2013 SST pattern and above-normal monsoon condition are not unique but occurred on several occasions in the past. Further, we show that there is a complementary pattern of strong extratropical SST and weak tropical SST that is associated with below-normal south Asian monsoon rainfall. We also show that the extratropical SST pattern in the Northern Hemisphere is associated with a low-frequency interdecadal mode of variability indicating potential predictability associated with such extratropical SST forcing. Extensive experiments with an atmospheric general circulation model forced by such SST conditions elucidate the mechanism through which the extratropical SSTs influence the Indian monsoon. The SST anomalies affect the north-south temperature gradient and lead to a local displacement of the jet stream, setting up a quasi-stationary wave. Such a stationary wave, in turn, affects the tropospheric temperature (TT) over southern Eurasia, influencing the north-south TT gradient in the region and thereby the Indian monsoon. Our discovery of this additional source of potential predictability together with the fact that the new-generation coupled ocean-atmosphere models are capable of predicting the extratropical SST anomalies brightens the prospect of south Asian monsoon prediction.

A comparative analysis of fourteen 5 year long climate simulations produced by the National Centers for Environmental Predictions (NCEP) Climate Forecast System version 2 (CFSv2), in which a stochastic multicloud (SMCM) cumulus parameterization is implemented, is presented here. These 5 year runs are made with different sets of parameters in order to figure out the best model configuration based on a suite of state‐of‐the‐art metrics. This analysis is also a systematic attempt to understand the model sensitivity to the SMCM parameters. The model is found to be resilient to minor changes in the parameters used implying robustness of the SMCM formulation. The model is found to be most sensitive to the midtropospheric dryness parameter (MTD) and to the stratiform cloud decay timescale (τ30). MTD is more effective in controlling the global mean precipitation and its distribution while τ30 has more effect on the organization of convection as noticed in the simulation of the Madden‐Julian oscillation (MJO). This is consistent with the fact that in the SMCM formulation, midtropospheric humidity controls the deepening of convection and stratiform clouds control the backward tilt of tropospheric heating and the strength of unsaturated downdrafts which cool and dry the boundary layer and trigger the propagation of organized convection. Many other studies have also found midtropospheric humidity to be a key factor in the capacity of a global climate model to simulate organized convection on the synoptic and intraseasonal scales.

Heat waves over India occur during the months of March-June. This study aims at the real-time monitoring and prediction of heat waves using a multi-model dynamical ensemble prediction system developed at Indian Institute of Tropical Meteorology, India. For this, a criterion has been proposed based on the observed daily gridded maximum temperature (Tmax) datasets, which can be used for real-time prediction as well. A heat wave day is identified when either (1) Tmax (a)≥ its climatological 95 th percentile (calculated from daily values during March-June and for 1981–2010), (b) >36 °C, and (c) its departure from normal is >3.5 °C, Or, (2) when the Tmax >44 °C. Three heat wave prone regions, namely, northwest, southeast and northwest-southeast regions are recognized and heat wave spells of minimum consecutive six days are identified objectively for each region during 1981–2018. It is noticed that the prediction system has reasonable skill in predicting the heat waves over heat wave prone regions of India. Forecast verification of heat wave spells during 2003–2018 reveals that the prediction system has great potential in providing overall indication about the onset, duration and demise of the forthcoming heat wave spell with sufficient lead time albeit with some spatio-temporal error.