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State-of-the-art coupled models have several limitations in representing the phase and amplitude characteristics of monsoon intra-seasonal oscillations (MISO). Specifically, the models' deficiencies in predicting stronger active spells have been widely reported in earlier studies. In the present study, we endeavour to overcome this limitation by better representing the diurnal cycle of the sea surface temperature and the associated feedback processes. In the present study, we demonstrate that resolving the diurnal cycle rectification in a state-of-the-art global coupled model improves the simulation of MISO. The present analysis showcases how rectification can modulate the oceanic, atmospheric and interfacial properties so that the coupled model can better simulate stronger active monsoon spells. The essential requirements for the coherent northward propagation mechanism of MISOs are pronounced in the presence of intra-seasonal rectification by diurnal SSTs.

Seasonal prediction of Indian summer monsoon rainfall has been considered as one of the important factors to decide the social and economic aspect of India because of its multi-sectorial dependencies. This study evaluates the performance of seven state-of-the-art GCMs in simulating the summer monsoon rainfall on a seasonal scale over the period of 1982–2008 using the GCM reforecasts. The rainfall simulated by the models is compared with the IMD observed rainfall dataset at 0.25° × 0.25°. Preliminary analysis of spatial pattern and statistics shows that the models IITM-CFSv2, NCEP-CFSv2 and ECMWF are some of the prominent models that capture the seasonal rainfall pattern and possess good skill. ECMWF performs very well in simulating the rainfall pattern as well as the rainfall intensities. Comprehensive statistical analysis such as standard deviation ratio and skill scores concludes that the IITM-CFSv2 produces the rainfall pattern as well as the variability of the summer monsoon better than its counterparts. The multi-model simple mean also tends to improve with the addition of IITM-CFSv2. Though the rainfall trend and variance simulated by IITM-CFSv2 is quite in agreement with the observed, there lies a significant dry bias over the north-west India. The mean simulated rainfall is quite less with the CFSv2 models. Though the IITM-CFSv2 simulates lesser rainfall at all the four-lead times, it is quite capable in capturing the rainfall variability. The models ECMWF and GFDLA04 are well performers in terms of mean rainfall estimates whereas the models CFSv2 is better in terms of reproducing the rainfall variability.

The Tropical Atmosphere Ocean (TAO) and Triangle Trans-Ocean Buoy Network (TRITON) buoys deployed in the tropical Pacific play a significant role in monitoring tropical Pacific conditions such as El-Nino/La-Nina in real-time. Earlier studies have illustrated the importance as well as irrelevance of moored buoy observations towards the prediction of sea surface temperature in the tropical Pacific Ocean. With the advent of Argo floats, have these moored buoy observations become redundant? In the present study, we address this question in the context of Indian summer monsoon rainfall (ISMR) prediction. The extreme monsoon year of 2018 was selected to test the above hypothesis. Without moored buoy observations being assimilated, the oceanic initial conditions and the seasonal forecasts had large oceanic temperature errors and forecasted a normal monsoon instead of a below-normal monsoon during 2018. Therefore, moored buoy observations are essential and should be assimilated to get realistic Pacific sea surface temperature (SST) and accurate ISMR predictions.

Increased frequency of droughts in the recent decade (2002, 2004, 2009, 2014, 2015 being drought years) and the severity of their impact makes drought prediction an inherent component of forecasts for drought mitigation and preparedness. The research and development activities cannot prove their full potential unless they can produce a long lead skillful prediction of extreme conditions like flood or drought. During 2014 and 2015, India experienced deficit monsoon for two successive years. The present study explores the various factors responsible for the droughts during 2014 and 2015 and how well the state-of-the-art coupled model capture the consecutive droughts. The study shows that not only tropical sea surface temperatures (SSTs) but also extratropical SSTs can influence the rainfall over India. Extratropical SSTs impacted the rainfall during 2014 by modulating the strength and location of subtropical jet and tropospheric temperature gradient. On the other hand, tropical SSTs over Pacific Ocean influenced the rainfall during 2015 by modulating the atmospheric teleconnections via Walker circulation. The present-day models considered in this study could not capture consecutively the Indian monsoon droughts and the associated atmosphere and ocean conditions.

Present study analyses the role of tropical Pacific sea surface temperature (SST) biases in simulating the mean and inter-annual variability of Indian summer monsoon rainfall (ISMR) using the hindcasts from Monsoon Mission CFSv2 (MMCFS) and ECMWF-SYSTEM4 (ES4) prediction systems. ES4 simulates the mean and annual cycle of ISMR better than MMCFS when initialized with February initial conditions (3-month lead, Feb IC). At the same time, interannual variability (IAV) and skill (correlation between the ensemble mean hindcast and observations) are the highest (least) for MMCFS (ES4). May IC (0-month lead) hindcasts of both the models have similar mean, annual cycle, IAV and skill. Further analysis shows that ES4_Feb IC hindcasts exhibit a strong cold bias (~ 2 °C) in the equatorial central Pacific, which is close to zero for MMCFS_Feb IC. Meanwhile, the 0-month lead (May IC) hindcast has a similar cold bias (~ 1.5 °C) in the equatorial Pacific for both the model hindcasts. Thus, model hindcasts with a better mean ISMR are having a strong cold bias in the equatorial Pacific and that having very less SST bias has higher IAV and skill for ISMR simulations. Additional investigation using North American National Multi-Model Ensemble Project (NMME) models is carried out and the following conclusion on the different roles of Pacific mean state bias is drawn. Hindcasts with strong cold SST bias in the tropical Pacific (ES4 hindcast and May IC hindcasts of MMCFS) tend to mimic the teleconnections associated with La Nina conditions reducing the dry bias over India, resulting in mean ISMR closer to the observed value, still less than the observed mean. At the same time, due to the same strong cold bias, the El Nino-Southern Oscillation (ENSO) induced rainfall and circulation pattern in the Pacific are weak and extended further northwestward. This weakens the ENSO induced heat sources over the Indian Ocean and monsoon region, resulting in reduced ENSO-ISMR teleconnections. These factors result in a poor simulation of interannual variability and skill of ISMR. On the other hand, model hindcasts with less cold bias in the equatorial Pacific (MMCFS_Feb IC) suffer from strong dry bias over India; however, the skill of ISMR is higher as a result of strong ENSO-Monsoon teleconnections. Thus the study confirms the differential role of SST bias in the tropical Pacific in simulating mean and IAV of ISMR in seasonal prediction models. A close to observed mean SST in the tropical Pacific and proper ENSO-Monsoon teleconnection is essential for the better skill of ISMR in the present generation seasonal prediction models.

Reasonable seasonal prediction skill for the Indian summer monsoon rainfall has been achieved using the Monsoon Mission (MM) Seasonal Forecast model, at a lead time of 3 months. The ensembles in the MM model are generated by utilizing lagged initial conditions. The possibility of enhancing the lead time is explored by using the burst ensemble approach. Comprehensive seasonal hindcast experiments carried out in this study reveal that the two methods exhibit similar skill scores for the major tropical phenomenon which govern ISMR variability. In general, the model forecasts are slightly under-dispersive but satisfactorily represent the spread-error relationship for major tropical oceanic climate modes. The ratio between the spread and RMSE is small for ISMR forecasts. Though the skill scores for the majority of indices are similar, the monsoon teleconnections seem to be quite sensitive to the initialization strategy. It is found that the burst initialization method provides a gain of 1-month lead time compared to lagged initialization strategy employed in previous studies without compromising the prediction skill. The gain of a months’ lead time with the burst ensemble approach is a tempting and useful proposition, which can be crucial for the policy- and decision-makers.

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.

The sensitivity of seasonal predictions of the Indian summer monsoon (ISM) to convection parameterization schemes (CPS) is studied using 37 years of hindcast experiments. The predictions are quite sensitive to changes in these schemes and improve the skill by 18–28%. Though the mean state circulation and rainfall over India improves, the sea surface temperature (SST) biases increase in the sensitivity experiments compared to the control run. The ability of the model to realistically capture the teleconnections associated with monsoon such as the El-Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) also appears to change with different CPS. It is found that the suitability of a CPS for ISM in the Climate Forecast System version 2 (CFSv2) stems from its ability to capture cloud fractions realistically and keep the SST biases to a minimum. The revised Simplified-Arakawa–Schubert (SAS2, Han and Pan in Weather Forecast 26:520–533. https://doi.org/10.1175/waf-d-10-05038.1 , 2011) scheme gives better prediction skill for ISM compared to the skill score obtained from SAS2 with shallow convection (SAS2sc) primarily because it simulates realistic clouds, without aggravating the SST biases, particularly in the tropical Pacific Ocean, and captures the Indian Ocean teleconnections realistically. SAS2sc significantly under-estimates the low-level clouds over global equatorial region, despite simulating better mid and high-level clouds, higher Nino 3.4 skill, and better inter-annual variability of ISM. The cold SST bias in the tropical basins is large in SAS2sc. Therefore, to exploit the merits of SAS2sc, unrealistic suppression of low clouds needs to be addressed, and the cold SST biases need to be minimized.

The Indian Institute of Tropical Meteorology (IITM) has generated seasonal and extended range hindcast products for 1981-2008 and 2003-2016 respectively using the IITM-Climate Forecast System (IITM-CFS) coupled model at various resolutions and configurations. Notably, our observational analysis suggests that for the 1981-2008 period, the tropical Indo-Pacific drivers, namely, the canonical El Niño-Southern Oscillation (ENSO), ENSO Modoki, and Indian Ocean Dipole (IOD) are significantly associated with the observed Kharif rice production (KRP) of various rice-growing Indian states. In this paper, using the available hindcasts, we evaluate whether these state-of-the-art retrospective forecasts capture the relationship of the KRP of multiple states with the local rainfall as well as the tropical Indo-Pacific drivers, namely, the canonical ENSO, ENSO Modoki and the IOD. Using techniques of anomaly correlation, partial correlation, and pattern correlation, we surmise that the IITM-CFS successfully simulate the observed association of the tropical Indo-Pacific drivers with the local rainfall of many states during the summer monsoon. Significantly, the observed relationship of the local KRP with various climate drivers is predicted well for several Indian states such as United Andhra Pradesh, Karnataka, Odisha, and Bihar. The basis seems to be the model's ability to capture the teleconnections from the tropical Indo-Pacific drivers such as the IOD, canonical and Modoki ENSOs to the local climate, and consequently, the Kharif rice production.

The high propensity of deficient monsoon rainfall over the Indian sub-continent in the recent 3 decades (seven deficient monsoons against 3 excess monsoon years) compared to the prior 3 decades has serious implications on the food and water resources in the country. Motivated by the need to understand the high occurrence of deficient monsoon during this period, we examine the change in predictability of the Indian summer monsoon (ISM) and its teleconnections with Indo-Pacific sea surface temperatures between the two periods. The shift in the tropical climate in the late 1970s appears to be one of the major reasons behind this. We find an increased predictability of the ISM in the recent 3 decades owing to reduced ‘internal’ interannual variability (IAV) due to the high-frequency modes, while the ‘external’ IAV arising from the low-frequency modes has remained largely the same. The Indian Ocean Dipole–ISM teleconnection has become positive during the monsoon season in the recent period thereby compensating for the weakened ENSO–ISM teleconnection. The central Pacific El-Nino and the Indian Ocean (IO) warming during the recent 3 decades are working together to realise enhanced ascending motion in the equatorial IO between 70°E and 100°E, preconditioning the Indian monsoon system prone to a deficient state.

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 intra-seasonal 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 anti-cyclonic 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-Nino type bias which restricts LPS propagation over the Indian landmass, possibly due to time-mean subsidence induced by remote El-Nino 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.

Seasonal prediction of Indian summer monsoon rainfall is a challenging task for the modeling community and predicting seasonal mean rainfall at smaller regional scale is much more difficult than predicting all India averaged seasonal mean rainfall. The regional scale prediction of summer monsoon mean rainfall at longer lead time (e.g., predicting 3–4 months in advance) can play a vital role in planning of hydrological and agriculture aspects of the society. Previous attempts for predicting seasonal mean rainfall at regional level (over 5 Homogeneous regions) have resulted with limited success (anomaly correlation coefficient is low, ACC ≈ 0.1–0.4, even at a short lead time of one month). The high resolution Climate Forecast System, version 2 (CFSv2) model, with spectral resolution of T382 (∼38 km), can predict the Indian summer monsoon rainfall (ISMR) at lead time of 3–4 months, with a reasonably good prediction skill (ACC ≈ 0.55). In the present study, we have investigated whether the seasonal mean rainfall over different homogenous regions is predictable using the same model, at 3–4 months lead time? Out of five homogeneous regions of India three regions have shown moderate prediction skill, even at 3 months lead time. Compared to lower resolution model, high resolution model has good skill for all the regions except south peninsular India. High resolution model is able to capture the extreme events and also the teleconnections associated with large scale features at four months lead time and hence shows better skill (ACC ≈ 0.45) in predicting the seasonal mean rainfall over homogeneous regions.

The climate shift in 1976–1977 sets a clear distinction in the teleconnection between Indian summer monsoon rainfall (ISMR) and sea surface temperatures (SST) in the tropical oceans, particularly in the tropical Indian Ocean (TIO). Before the climate shift (Pre-77), the observed correlation between ISMR and SST was positive in the eastern equatorial Indian Ocean (EIO) and negative correlation prevailed in the western equatorial Indian Ocean (WIO), whereas after the climate shift (Post-77), the correlation pattern has reversed (i.e. positive correlation in WIO and negative correlation in EIO). The major reason for this shift is the consistent warming of SSTs throughout the tropics and frequent occurrence of IOD events. Further, it is noticed that during the earlier decades, the Indian Ocean was responding to the atmospheric forcing, particularly along the axis of strong cross equatorial flow, whereas in the recent decades, Indian Ocean dynamics are playing a crucial role in determining the SST in major parts of the Indian Ocean particularly in the EIO. Seasonal re-forecasts from six state-of-the-art global coupled atmosphere–ocean climate models which participated in ENSEMBLES faithfully capture the pre-77 dipole structure of correlations. But the post-77 correlation pattern is not simulated by any of the six models considered in this study. Two possible reasons are pointed out for the models' inability to capture the reversal of teleconnection: (1) in the coupled models, unchanged mean state of the winds inhibits ISMR to be governed by coupled dynamics in the TIO; (2) the strong, local SST–rainfall relationship in models even in post-77 period limits the influence of the teleconnections on the ISMR.

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.

India experienced deficient monsoon rainfall in 2015 that followed the deficient monsoon of 2014. India Meteorological Department (IMD) correctly predicted the large rainfall deficiency (86% of long period average) in 2015. Incidentally, this was the first ever deficient monsoon forecast issued by IMD, though it had earlier issued below-normal rainfall forecasts in the previous two deficient monsoon years (2009 and 2014) and was partially correct. The fact that there were only three previous occasions of consecutive two deficient monsoon years during the last 114 years (1901-2014) was itself a challenge to IMD to issue the forecast in 2015. It may be mentioned that IMD persisted with the deficient monsoon forecast for 2015, even though there were predictions from private agencies for a normal monsoon and apprehensions from the press and media about the low probability of two consecutive deficient years. IMD was also able to correctly predict the regional distribution of seasonal rainfall during the season. IMD's first deficient monsoon forecast was based on the state-of-the-art operational statistical forecasting system, which was introduced in 2007. IMD was further confident for a deficient monsoon due to the clear indications of a strong El Nino event by June itself. Forecasts from high-resolution coupled forecasting system (CFS) developed by the Indian Institute of Tropical Meteorology, Pune, under the Monsoon Mission also suggested a deficient monsoon in 2015. In this article we provide details of the operational forecasting models and verification of these forecasts. Brief description about the experimental CFS developed under the Monsoon Mission and CFS forecast for the 2015 southwest monsoon season is also presented.