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The evaluation and usefulness of lightning prediction for the Indian subcontinent are demonstrated. Implementation of the lightning parameterizations based on storm parameters, in the Weather Research and Forecasting (WRF) Model, with different microphysics schemes are carried out. With the availability of observed lightning measurements over Maharashtra from the lightning detection network (LDN), lightning cases have been identified during the pre-monsoon season of 2016–18. Lightning parameterization based on cloud top height defined by a reflectivity threshold factor of 20 dBZ is chosen. Initial analysis is carried out for 16 lightning events with four microphysical schemes for the usefulness in lightning prediction. Objective analysis is carried out and quantitative model performance (skill scores) is assessed based on observed data. The skills are evaluated for 10- and 50-km2 boxes from the 1-km domain. There is good POD of 0.86, 0.82, 0.85, and 0.84, and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from WSM6, Thompson, Morrison, and WDM6, respectively. There is an overestimation in lightning flash with a spatial and temporal shift. The fractional skill score is evaluated as a function of spatial scale with neighborhoods from 25 to 250 km. These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast over India. The skill for 2019 and 2020 pre-monsoon are calculated to address the predictability of operational lightning prediction over India. Significance Statement A high-resolution model, namely, the Weather Research and Forecasting (WRF) Model, with multiple microphysics parameterization schemes and lightning parameterization is used here. The objective analysis is carried out for the lightning cases over India and the quantitative performance is assessed. The results highlight that there is fairly good probability of detection (POD) of 0.86, 0.82, 0.85, and 0.84 and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from four different microphysical schemes (WSM6, Thompson, Morrison, and WDM6, respectively). These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast. The validation of lightning forecast system deployed over India for five pre-monsoon months in real time is carried out, which gives POD of 0.90, FAR of 0.64, hit rate of 0.57, and POFD of 0.50 for the whole Indian region.

The teleconnection studies regarding Indian summer monsoon (ISM) clouds are not focused on detail from both observational and modeling aspects. This is despite the fact that clouds play a seminal role in governing rainfall variability through the modulation of heating and induced circulation. Therefore, we find it essential to explore whether the inter-annual variability of ISM clouds is also remotely influenced by the slowly varying predictable component e.g. Sea Surface Temperature (SST). The findings reveal the linkage of observed TCF (and rainfall) over the ISM region with slowly varying forcing (e.g., global SST). The observed/reanalysis teleconnection pattern of TCF-SST is almost similar to that of rainfall-SST.In the long-term period, TCF and SST show a strong and positive correlation with Extra-Tropics (R ~ 0.41), NAO (R ~ 0.51), and AMO (R ~ 0.41) SST regions, in addition to canonical ENSO teleconnection (R ~ −0.39). This is better captured in CMIP6-MME than in CMIP5-MME. The representation of the global teleconnection pattern has been significantly improved in participating models from CMIP5 to CMIP6. The teleconnection with extra-tropics and north Atlantic mode of variability is markedly enhanced in CMIP6-MME compared to CMIP5-MME. The present study has also shown the lag correlations in the teleconnection analysis, i.e., the correlation of June–September (JJAS) mean of rainfall/TCF with October–December (OND) SST from observation/reanalysis, CMIP5-MME, and CMIP6-MME. The CMIP6-MME performs better than CMIP5-MME as compared to observation/reanalysis. Thus, the improved understanding of the teleconnection of cloud variables with ENSO and other predictors (ET, NAO, and AMO) will help researchers take up the challenges of improving the ISMR skill far ahead using the new generation coupled climate models. This may facilitate reliable seasonal ISM forecasting.Keywords: Indian Summer Monsoon, Clouds, Teleconnection, CMIP5, CMIP

The study explores the role of ice-phase microphysics and convection for the better simulation of Indian summer monsoon rainfall (ISMR) and monsoon intraseasonal oscillation (MISO). Sensitivity experiments have been performed with coupled climate model- CFSv2 using different microphysics (with and without ice phase processes) and convective [Simple Arakawa Schubert (SAS), new SAS (NSAS)] parameterization schemes. Results reveal that the ice phase microphysics parameterization scheme performs better in the simulation of active and break composites of the ISMR as compared to ice-free runs. The difference between ice (ICE) and ice-free run (NOICE) can be attributed to the availability of copious cloud condensate at the upper level. Better representation of upper-level cloud condensate in ICE run (i.e., with ice phase microphysics) leads to correct representation of specific humidity in active and break spells. Proper depiction of upper-level cloud condensate further leads to realistic modulation of atmospheric circulation and better simulation of convection (as represented by OLR) in active and break spells of ICE run. As a result, better simulation of active and break occurs in the ICE run. In contrast, NOICE run (i.e., with warm phase microphysics) fails to depict upper-level cloud condensate in the active phase. It leads to an improper representation of specific humidity. Circulation features are also unrealistic, and convection is suppressed in the active phase. As a result, the active phase is not adequately simulated in the NOICE run. NOICE run composites during active spells depict the overestimation of the ascending branch of Hadley circulation as compared to MERRA reanalysis, which is relatively better in ICE run. NOICE run composites during active spells depict the overestimation of the ascending branch of Walker circulation as compared to MERRA reanalysis, which is further improved in ICE runs. The north–south space–time spectra of daily rainfall anomaly are also better captured by ICE run as compared to NOICE run. Results indicate that ice-phase processes are more important for capturing the difference between active and break composites, while convection parameterization is relatively more important for the intraseasonal variance analyses. Further improvements in ice microphysics parameterization with better convection schemes in models will be helpful for the betterment of MISO and will lead to the improved simulation of monsoon.

Skillful seasonal prediction of lightning is crucial over several global hotspot regions, as it causes severe damages to infrastructures and losses of human life. While major emphasis has been given for predicting rainfall, prediction of lightning in one season advance remained uncommon, owing to the nature of the problem, which is short-lived local phenomenon. Here we show that on the seasonal time scale, lightning over the major global hot-spot regions is strongly tied with slowly varying global predictors (e.g., El Nino and Southern Oscillation). Moreover, the sub-seasonal variance of lightning is highly correlated with global predictors, suggesting a seminal role played by the global climate mode in shaping the local land-atmosphere interactions, which eventually affects seasonal lightning variability. It is shown that the seasonal predictability of lightning over the hotspot is comparable to that of seasonal rainfall, which opens up an avenue for reliable seasonal forecasting of lightning for special awareness and preventive measures. Keywords: Lightning, Seasonal forecasting, SST, Global predictors

The seasonal prediction of the Indian summer monsoon (ISM) and Monsoon Intraseasonal Oscillations (MISO), as well as the Madden Julian Oscillations (MJO) that strongly modulate MISO, is of much importance to the agriculture sectors of the Indian Sub‐continent. This study highlights the importance of selecting proper “autoconversion” coefficients for precipitation formation in Coupled Global Climate Model's convective and microphysical schemes in simulating MISO and MJO. With a series of sensitivity experiments, we systematically examined the convective autoconversion and microphysical autoconversion coefficients in the National Centers for Environmental Prediction Coupled Forecast System version 2 (CFSv2). The mean and intraseasonal features of MISO and MJO are improved with a proper combination of autoconversion coefficients, which not only provide a better partition of cloud water and ice but also better feedback between the large‐scale condensation and convective parameterization. The results demonstrate a road map for the improvement of MISO as well as MJO simulation using a coupled climate model.

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.

A major sub-seasonal variability of the tropics and sub-tropics, the quasi-biweekly mode (QBM), is known to have significant influence on the seasonal mean of the south Asian monsoon rainfall. A coupled Atmosphere–Ocean General Circulation Model (AOGCM) being essential for seasonal prediction, the ability of the AOGCMs in simulating the space–time characteristics with fidelity is critical for successful seasonal prediction of the south Asian monsoon in particular and seasonal prediction in the tropics in general. However, strength and weaknesses in simulating the QBM by AOGCMs have remained poorly investigated so far. Here, we examine the simulation of the QBM in AOGCM and show that improvement of parameterizations of both convection and microphysics is required to improve the simulation of the QBM. While the standard version of the model overestimates the variance of QBM and simulates a smaller scale Rossby waves (n = 1), the modified version of the model where the simple Arakawa–Schubert (SAS) convection parameterization is combined with a new improved microphysics parameterization (MCMv.1) proposed by us, simulates a more realistic space–time characteristics of the QBM. In yet another version of the model, we combine the new SAS with the new improved microphysics parameterization. Interestingly, this version of the model also simulates the space–time structure poorly with poor westward propagation and fragmented organization, but it simulates a reasonable variance. These results indicate that a synergy among the convective parameterization and microphysics parameterizations is critical in simulating the QBM in particular and equatorial waves in general. We show that most the biases in simulating the QBM may be related to the biases of the model in simulating the stratiform fraction of precipitation. While the simulation of the space–time characteristics of QBM is better simulated in the MCMv.1, the convective coupling is still too strong as compared to observations, an area for future improvement of the model.

We have analyzed the teleconnection of total cloud fraction (TCF) with global sea surface temperature (SST) in multi-model ensembles (MME) of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). CMIP6-MME has a more robust and realistic teleconnection (TCF and global SST) pattern over the extra-tropics (R ~0.43) and North Atlantic (R ~0.39) region, which in turn resulted in improvement of rainfall bias over the Asian summer monsoon (ASM) region. CMIP6-MME can better reproduce the mean TCF and have reduced dry (wet) rainfall bias on land (ocean) over the ASM region. CMIP6-MME has improved the biases of seasonal mean rainfall, TCF, and outgoing longwave radiation (OLR) over the Indian Summer Monsoon (ISM) region by ~40%, ~45%, and ~31%, respectively, than CMIP5-MME and demonstrates better spatial correlation with observation/reanalysis. Results establish the credibility of the CMIP6 models and provide a scientific basis for improving the seasonal prediction of ISM., 12 pages, 4 main figures, 2 supplementary figures

IITM provides seasonal monsoon rainfall forecast using modified CGCM CFSv2. The present operational CFSv2 initilized with the INCOIS-GODAS ocean analysis based on MOM4p0d and 3DVar assimilation schemes. Recently new Ocean analysis GODAS-Mom4p1 using Moduler Ocean Model (MOM) upgraded physical model MOM4p1 is generated. This analysis has shown improvement in terms of subsurface temperature, salinity , current as well as sea surface temperature (SST), sea surface salinity (SSS) and surface currents over the Indian Ocean domain with respect to present operational INCOIS-GODAS analysis (Rahaman et al. 2017;Rahman et al. 2019). This newly generated ocean analysis is used to initialize NCEP Climate Forecast System (CFSv2) for the retrospective run from 2011 to 2018. The simulated coupled run has shown improvement in both oceanic as well atmospheric parameters. The more realistic nature of coupled simulations across the atmosphere and ocean may be promising to get better forecast skill.

The Indian subcontinent is a major hotspot of enhanced aerosol loading and emission of various chemical compounds due to extensive anthropogenic activities that profoundly impact the hydrological cycle of this region. Using coupled regional atmospheric chemistry transport model, WRF-Chem (Weather Research and Forecasting model coupled with chemistry) with fully interactive chemistry and dynamics, we carried out two sets of 122-days simulation experiments: (1) a control experiment without embedding chemistry and (2) a sensitivity experiment with embedding fully coupled MOZART-MOSAIC chemistry, to examine the impact of chemistry on the Indian Summer Monsoon Rainfall (ISMR) of 2017. The results show that inclusion of chemistry in the model plays a role in altering the spatial distribution of ISMR, reflected by a reduced mean rainfall by about 3 mm/day over the Indian subcontinent. The mean bias of model simulated rainfall reduces from 2.73 mm/day (excluding chemistry) to 1.6 mm/day (including chemistry), and from 1.2 mm/day (excluding chemistry) to 0.81 mm/day (including chemistry) when compared with IMD (India Meteorological Department) and TRMM (Tropical Rainfall Measuring Mission) rainfall data, respectively. Additionally, we found that inclusion of interactive chemistry causes a decrease in the daily convective rainfall by about 3 mm that is contributing largely to the mean rainfall differences. Further, analysis of hydrometeors indicates that the chemistry interacts with the cloud microphysics by modulating the hydrometeor paths of different cloud hydrometeors that consequently impacts the simulation of ISMR over this region. Results indicate that the virtue of detailed and more realistic representation of chemistry in the model has led to more realistic simulation and cannot be ignored in numerical forecasting of ISMR.

The overall yearly seasonal performance of Indian southwest monsoon rainfall (ISMR) for the whole Indian land mass is presently expressed by the India Meteorological Department (IMD) by a single number, the total quantum of rainfall. Any particular year is declared as excess/deficit or normal monsoon rainfall year on the basis of this single number. It is well known that monsoon rainfall also has high interannual variability in spatial and temporal scales. To account for these aspects in ISMR, we propose two new spatial and temporal indices. These indices have been calculated using the 115 years of IMD daily 0.25° × 0.25° gridded rainfall data. Both indices seem to go in tandem with the in vogue seasonal quantum index. The anomaly analysis indicates that the indices during excess monsoon years behave randomly, while for deficit monsoon years the phase of all the three indices is the same. Evaluation of these indices is also studied with respect to the existing dynamical indices based on large-scale circulation. It is found that the new temporal indices have better link with circulation indices as compared to the new spatial indices. El Nino and Southern Oscillation (ENSO) especially over the equatorial Pacific Ocean still have the largest influence in both the new indices. However, temporal indices have much better remote influence as compared to that of spatial indices. Linkages over the Indian Ocean regions are very different in both the spatial and temporal indices. Continuous wavelet transform (CWT) analysis indicates that the complete spectrum of oscillation of the QI is shared in the lower oscillation band by the spatial index and in the higher oscillation band by the temporal index. These new indices may give some extra dimension to study Indian summer monsoon variability.

This study estimates the seasonal mean (June–September) recycled rainfall and investigates its dominant modes of variability over the continental regions of the Indian summer monsoon. A diagnostic method based on the basic atmospheric water vapor budget equation is employed in order to partition the observed rainfall into recycled and advected components. The global teleconnections with the recycled (advected) rainfall are found to be weak (strong), which is consistent with the basic assumptions of the sources of atmospheric water vapor. It is shown that the mean recycled rainfall over the Indo-Gangetic Plain, central India, and western Himalayas ranges between 10% and 40% of the total rainfall. While EOF1 (38.5%) of the recycled rainfall reveals covariability between the regional and external influences, EOF2 (14%) shows a mode independent to the external influences (i.e., advected rainfall), prevailing over the Indo-Gangetic Plain. Furthermore, a strong decreasing trend in PC2 over the last 36 years suggests a change in the local feedback (land, atmosphere), which in turn may have also contributed to the decreasing trend in the observed monsoon rainfall over central and northern India.

Simulation of the spatial and temporal structure of the monsoon intraseasonal oscillations (MISOs), which have effects on the seasonal mean and annual cycle of Indian summer monsoon (ISM) rainfall, remains a grand challenge for the state-of-the-art global coupled models. Biases in simulation of the amplitude and northward propagation of MISOs and related dry rainfall bias over ISM region in climate models are limiting the current skill of monsoon prediction. Recent observations indicate that the convective microphysics of clouds may be critical in simulating the observed MISOs. The hypothesis is strongly supported by high fidelity in simulation of the amplitude and space-time spectra of MISO by a coupled climate model, when our physically based modified cloud microphysics scheme is implemented in conjunction with a modified new Simple Arakawa Schubert (nSAS) convective parameterization scheme. Improved simulation of MISOs appears to have been aided by much improved simulation of the observed high cloud fraction and convective to stratiform rain fractions and resulted into a much improved simulation of the ISM rainfall, monsoon onset and the annual cycle.

The quest for one of the most dominant processes controlling the large-scale circulations in the tropics is unraveled. The impact of cloud microphysical processes is known to have effects on rainfall and local atmospheric thermodynamics, however, its effect on the prevailing mean circulations is not yet studied. Two sets of coupled global climate model experiments (ICE and NO ICE microphysics) reveal that ice microphysics improves the strength of the Hadley circulation with respect to observation. Results pinpoint that ICE simulation enhances high cloud fraction (global tropics: ~59 %, India: ~51 %) and stratiform rain (global tropics: ~5 %, India: ~15 %) contribution. ICE and NO ICE cloud microphysics impacts differently on the outgoing longwave radiation (OLR), tropospheric temperature, surface shortwave and longwave radiation. The effect of ice microphysics reduces OLR, which signifies deeper convection in the ICE run. The global annual average of the net radiation flux (shortwave and longwave) at the surface in ICE run (108.1 W/m2) is close to the observation (106 W/m2), which is overestimated in NO ICE run (112 W/m2). The result of apparent heat source term over the land and ocean surface eventually modifies regional Hadley circulation. Thus, the effect of ice microphysics in the global coupled model is important not only because of microphysics but also due to the radiation feedbacks. Therefore, better ice-phase microphysics is required in the new generation of climate forecast model, which may lead to improvements in the simulation of monsoon.

The study mainly investigates sea surface temperature (SST) and outgoing longwave radiation (OLR) relationships in coupled climate model. To support the analysis, high-level cloud and OLR relationship is also investigated. High-level cloud and OLR relationship depicts significant negative correlation over the entire monsoon regime. Coupled climate model is able to produce the same. SST and OLR relationship in observation also depicts significant negative relationship, in particular, over the Equatorial Eastern Indian Ocean (EIO) region. Climate Forecast System version 2 (CFSv2) is able to portray the negative relationship over EIO region; however, it is underestimated as compared to observation. Significant negative correlations elucidate that local SSTs regulate the convection and further it initiates Bjerknes feedback in the central Indian Ocean. It connotes that SST anomalies during monsoon period tend to be determined by oceanic forcing. The heat content of the coastal Bay of Bengal shows highest response to EIO SST by a lag of 1 month. It suggests that the coastal region of the Bay of Bengal is marked by coastally trapped Kelvin waves, which might have come from EIO at a time lag of 1 month. Sea surface height anomalies, depth at 20 °C isotherms and depth at 26 isotherms also supports the above hypothesis. Composite analysis based on EIO index and coupled climate model sensitivity experiments also suggest that the coastal Bay of Bengal region is marked by coastally trapped Kelvin waves, which are propagated from EIO at a time lag of 1 month. Thus, SST and OLR relationship pinpoints that the Bay of Bengal OLR (convection) is governed by local ocean–atmospheric coupling, which is influenced by the delayed response from EIO brought forward through oceanic planetary waves at a lag of 1 month. These results have utmost predictive value for seasonal and extended range forecasting. Thus, OLR and SST relationship can constitute a pivotal role in investigating the atmosphere–ocean interaction.

Thunderstorms source of lightning discharge is a major hazard to humans. In India, the loss of human life due to thunderstorms is high because of frequent lightning during pre-monsoon season (March-May). Therefore, simulation of lightning flash counts based on various lightning parameterization schemes and Lightning Potential Index (LPI) in Weather Research and Forecasting (WRF) model are evaluated over Maharashtra, India. The offline diagnostic methods for calculating lightning flashes are also evaluated from the model generated storm parameters. The observation from Maharashtra Lightning Detection Network (LDN) is used to validate the simulated total lightning flash for four events. The lightning flashes calculations by following Price and Rind (1992) based on cloud top height (PR92CTH) and vertically integrated ice water path (IWP) simulated the spatial pattern comparably well. The lightning parameterization based on cloud top height defined by the radar reflectivity factor threshold of 20 dBZ (DLP2) has performed better as compared to observation. The results show (i) better spatial pattern and frequency distribution of lightning flashes, (ii) accumulated rainfall, maximum reflectivity and time evolutions are in good agreement with flashes, (iii) correlation between simulated flash and hydrometeors are higher, (iv) the number of matching grid boxes due to randomness is also higher, 74.9%, 56.5%, 68.1% and 82.7% of matching grid boxes for the four cases and (v) the results based on LPI are also in consistent with the results of DLP2. The study highlights the robustness of DLP2 and indicates a promising future for the operational forecast of lightning prediction.

This study examines the relationship between clouds and sea surface temperatures (SSTs) during Indian Summer Monsoon (ISM). Observation reveals dominance of high-level clouds in the monsoon region. NCEP Climate Forecast System version 2 (CFSv2) is able to replicate the observed high-level cloud fractions although it underestimates the magnitude. Cloud–SST relationship for observation depicts dominance of positive correlation over equatorial Indian Ocean region which is well recapitulated in CFSv2 simulations. To investigate the most dominating patterns of interannual variability of rainfall, SST and clouds, empirical orthogonal function (EOF) analysis is performed on seasonal JJAS (June–September) dataset. EOF analysis signifies that high-level clouds are highly correlated with rainfall for observations during ISM season. This study has also investigated relationship of clouds and El Nino Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD) during ISM period. Interannual variations of clouds connote significant correlation with ENSO index (Nino 3/Nino 3.4). First principal component (PC1) of high-level clouds and ENSO index indicate significant negative correlation. EOF analysis based on observation also connotes that first mode (second) of EOF analysis is associated with ENSO (IOD). It is also confirmed by maximum covariance analysis (MCA). CFSv2 is also able to depict the significant negative correlation between PC1 of high-level clouds and ENSO index. Observation based second principal component (PC2) of high-level clouds and IOD index exhibits significant positive correlation. It gives indication that PC2 of observed high-level clouds can be associated with IOD. In contrast, PC2 of CFSv2 simulated high-level cloud and IOD index are not correlated. EOF analysis based on CFSv2 shows that the first mode of EOF analysis is associated with ENSO; however, second mode of EOF is not related with IOD. MCA analysis also supports these findings. It means that CFSv2 has good ability to represent ENSO as compared with IOD.

The southward penetration of mid-latitude westerlies and their interaction with monsoon current are harbinger of intense rainfall activity over northern and central India. Such synoptic condition prevailed during June 14–17, 2013, and unleashed relentless rainfall over the state of Uttarakhand. Observational aspects of this event have been explored using surface, satellite and reanalysis data. Precipitation features have been explored using data from Precipitation Radar Onboard TRMM satellite in conjunction with TRMM-TB42, Automatic Weather Station and Automatic Rain Gauge Station data. The ERA interim dataset has been used to explore prevalent synoptic conditions, and Modern-Era Retrospective Analysis for Research and Applications reanalysis fields revealed that prevalent synoptic features led to moisture flux convergence in lower troposphere. Vertically integrated water vapor transport over the India and adjoining oceanic region is evaluated to gain an insight into the dynamical mechanism of rainfall activity over Uttarakhand. Results reveal that interplay between movement of monsoon low along the monsoon trough resulting in strong low-level convergence and constant feeding of moisture from Arabian Sea and Bay of Bengal and strong upper-level divergence owing to southward intrusion of mid-latitude westerly trough resulted in heavy rainfall activity over Uttarakhand.

The budget analysis of microphysical process rates based on Modern Era Retrospective-analysis for Research and Applications (MERRA) products are presented in the study. The relative importance of different microphysical process rates, which is crucial for GCMs, is investigated. The autoconversion and accretion processes are found to be vital for Indian Summer Monsoon (ISM). The map-to-map correlations are examined between observed precipitation and MERRA reanalysis. The pattern correlations connote the fidelity of the MERRA datasets used here. Results of other microphysical parameters (e.g. ice water content from CloudSat, high cloud fraction from CALIPSO and MODIS, latent heating from TRMM, cloud ice mixing ratio from MERRA) are presented in this study. The tropospheric temperature from reanalysis product of MERRA and NCEP are also analyzed. Furthermore, the linkages between cloud microphysics production rates and dynamics, which are important for North–South tropospheric temperature gradient for maintaining the ISM circulation, are also discussed. The study demonstrates the microphysical process rates, which are actually responsible for the cloud hydrometeors and precipitation formation on the monsoon intraseasonal oscillations timescale. Cloud to rain water auto-conversion and snow accretion rates are the dominant processes followed by the rain accretion. All these tendency terms replicates the similar spatial patterns as that of precipitation. The quantification of microphysical process rates and precipitation over different regions are shown here. The freezing rate is also imperative for the formation of cloud ice as revealed by the observation. Freezing rates at upper level and snow accretion at middle level may have effect on latent heating release. Further it can modulate the north–south temperature gradient which can influence the large-scale monsoon dynamics. The rain water evaporation is also considered as a key aspect for controlling the low level moisture convergence (source of water vapor) in ISM. This study has highlighted the importance of detailed microphysical production rates for warm and mixed-phase cloud processes, which is a major source of uncertainty in the climate models. Better understanding of these processes will definitely add value to the present generation climate models. Therefore the hypothesis/pathway emerged from the present study may be helpful for the future model development research.

A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5–L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project, CPC Merged Analysis of Precipitation and the India Meteorological Department precipitation dataset respectively for L3. Despite significant El-Nino Southern Oscillation (ENSO) spring predictability barrier at L3, the ISMR skill score is highest at L3. Further, large scale zonal wind shear (Webster–Yang index) and SST over Nino3.4 region is best at L1 and L0. This implies that predictability aspect of ISMR is controlled by factors other than ENSO and Indian Ocean Dipole. Also, the model error (forecast error) outruns the error acquired by the inadequacies in the initial conditions (predictability error). Thus model deficiency is having more serious consequences as compared to the initial condition error for the seasonal forecast. All the model parameters show the increase in the predictability error as the lead decreases over the equatorial eastern Pacific basin and peaks at L2, then it further decreases. The dynamical consistency of both the forecast and the predictability error among all the variables indicates that these biases are purely systematic in nature and improvement of the physical processes in the CFSv2 may enhance the overall predictability.

This study explores the role played by ocean processes in influencing Indian summer monsoon rainfall (ISMR) and compares the observed findings with National Centers for Environmental Prediction (NCEP)-coupled model Climate Forecast System, version 2 (CFSv2). The excess and deficit ISMR clearly brings out the distinct signatures in sea surface height (SSH) anomaly, thermocline and mixed layer depth over north Indian Ocean. CFSv2 is successful in simulating SSH anomalies, especially over Arabian Sea and Bay of Bengal region. CFSv2 captures observed findings of SSH anomalies during flood and drought (e.g., Rossby wave propagation which reaches western Bay of Bengal (BoB) during flood years, Rossby wave propagation which did not reach western BoB during drought). It highlights the ability of CFSv2 to simulate the basic ocean processes which governs the SSH variability. These differences are basically generated by upwelling and downwelling caused by the equatorial and coastal Kelvin and Rossby waves, thereby causing difference in SSH anomaly and thermocline, and subsequently modifying the convection centers, which dictates precipitation over the Indian subcontinent region. Since the observed SSH anomaly and thermal structure show distinct characteristic features with respect to strong and weak ISMR variability, the assimilation of real ocean data in terms of satellite products (like SSHA from AVISO/SARAL) bestow great promise for the future improvement.

The present study explores the impact of two different microphysical parameterization schemes (i.e. Zhao and Carr, Mon Wea Rev 125:1931-1953, 1997:called as ZC; Ferrier, Amer Meteor Soc 280-283, 2002: called as BF) of National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) on Indian summer monsoon (ISM). Critical relative humidity (RHcrit) plays a crucial role for the realistic cloud formation in a general circulation model (GCM). Hence, impact of RHcrit along with microphysical scheme on ISM is evaluated in the study. Model performance is evaluated in terms of simulation of rainfall, lower and upper tropospheric circulations, cloud fraction, cloud condensate and outgoing longwave radiation (OLR). Climatological mean features of rainfall are better represented by all the sensitivity experiments. Overall, ZC schemes show relatively better rainfall patterns as compared to BF schemes. BF schemes along with 95 % RHcrit (called as BF95) show excess precipitable water over Indian Ocean basin region, which seems to be unrealistic. Lower and upper tropospheric features are well simulated in all the sensitivity experiments; however, upper tropospheric wind patterns are underestimated as compared to observation. Spatial pattern and vertical profile of cloud condensate is relatively better represented by ZC schemes as compared to BF schemes. Relatively more (less) cloud condensate at upper level has lead to relatively better (low) high cloud fraction in ZC (BF) simulation. It is seen that OLR in ZC simulation have great proximity with observation. ZC (BF) simulations depict low (high) OLR which indicates stronger (weaker) convection during ISM period. It implies strong (weak) convection having stronger (weaker) updrafts in ZC (BF). Relatively more (less) cloud condensate at upper level of ZC (BF) may produce strong (weak) latent heating which may lead to relatively strong (weak) convection during ISM. The interaction among microphysics, thermodynamics, and dynamics works in tandem through a closed feedback loop.

A possible link between preonset land atmospheric conditions and the Indian summer monsoon rainfall (ISMR) is explored. It is shown that, the preonset positive (negative) rainfall anomaly over northwest India, Pakistan, Afghanistan, and Iran is associated with decrease (increase) in ISMR, primarily in the months of June and July, which in turn affects the seasonal mean. ISMR in the months of June and July is also strongly linked with the preonset 2 m air temperature over the same regions. The preonset rainfall/2 m air temperature variability is linked with stationary Rossby wave response, which is clearly evident in the wave activity flux diagnostics. As the predictability of Indian summer monsoon relies mainly on the El Nino–Southern Oscillation (ENSO), the found link may further enhance our ability to predict the monsoon, particularly during a non-ENSO year.

Role of the cloud parameterization scheme and critical relative humidity (RHcrit) for large-scale precipitation is examined for simulating Indian summer monsoon (ISM) by the National Centers for Environmental Prediction (NCEP) climate forecast system version 2 (CFSv2). The major biases of the model simulations namely dry bias over the major continents, cold tropospheric temperature (TT) bias and cold sea surface temperature (SST) bias are related to biases in distribution of clouds. This study evaluates the role of variable RHcrit to get better simulation of high level clouds and reduce TT bias and cloud microphysical parameterization to improve the meridional gradient of TT towards achieving better simulation of south Asian monsoon precipitation. Sensitivity experiments of CFSv2 with the modified RHcrit and cloud microphysical scheme compared to the control simulation show that while the RHcrit leads to some development of the cloud distribution and contributes to some progress of the dry bias over India, the cloud microphysics changes lead to a significant improvement of the cloud simulations. Particularly, revised cloud microphysics scheme coupled with modified RHcrit results in a much improved global distribution of cloud fraction with zonal mean cloud fraction being close to observation. This leads to significant improvement in the meridional gradient of TT leading to rainfall over south Asian monsoon region. The dry bias is not only reduced over the Indian subcontinent but also over other regions of global tropics such as the central Africa and the northern South America. The annual cycle of all India rainfall is in good agreement with observation not only in amount but also in the onset and withdrawal phases. Thus, modifications in the cloud microphysical parameterization scheme in CFSv2 have played a vital role in simulation of the ISM in particular. The sensitivity experiments demonstrate the betterment of the mean monsoon and may lead to help improve monsoon forecasts.

Large socio-economic impact of the Indian Summer Monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather estimated low potential predictability limit (PPL) of seasonal prediction of the ISM, contributed significantly by 'internal' interannual variability was considered insurmountable. Here we show that the 'internal' variability contributed by the ISM sub-seasonal (synoptic + intra-seasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g. El Nino and Southern Oscillation). This provides a scientific basis for predictability of the ISM rainfall beyond the conventional estimates of PPL. We establish a much higher actual limit of predictability (r~0.82) through an extensive re-forecast experiment (1920 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.

The improvements in representing the Indian summer monsoon features are assessed using the latest coupled global reanalysis, the climate forecast system reanalysis (CFSR) from National Centre for Environmental Predictions (NCEP) with respect to widely used NCEP-reanalysis 2 (NCEP2). To validate the reanalysis product corresponding observations are also used. CFSR is more realistic in terms of spatial pattern of seasonal mean rainfall, first and second empirical orthogonal function (EOF) modes of interannual variability as compared with NCEP2 reanalysis. Percentage of total variance was explained by northward and westward propagating intraseasonal oscillation modes in CFSR that are better captured than those from NCEP2 reanalysis. Northward propagating mode is well depicted in CFSR as compared with that of NCEP2 and may be attributed to the strong vertical shear of zonal wind and strong meridional gradient of specific humidity in CFSR. Distribution of the north–south and east–west spectra of CFSR suggests much better conformity with observation as compared with NCEP2. Surface wind patterns of CFSR are having relatively small errors compared to Quick Scatterometer (QuikSCAT) winds. CFSR has some success in simulating observed sea surface temperature (SST)–rainfall relationship as well as in depicting the coupled ocean-atmosphere phenomena of El Nino and Southern Oscillation (ENSO) in terms of Indian summer monsoon rainfall (ISMR) teleconnections. Overall monsoon features in CFSR are much closer to the observation as compared with NCEP2, and these may be because of the realistic representation of ocean–atmosphere feedback owing to the coupled model-based reanalysis. This study will help in establishing the credibility of the CFSR in terms of realistic representation of Indian summer monsoon as compared with widely used atmospheric only reanalysis.

The present study was intended to develop a Water Quality Index (WQI) for the coastal water of Visakhapatnam, India from multiple measured water quality parameters using different multivariate statistical techniques. Cluster analysis was used to classify the data set into three major groups based on similar water quality characteristics. Discriminant analysis was used to generate a discriminant function for developing a WQI. Discriminant analysis gave the best result for analyzing the seasonal variation of water quality. It helped in data reduction and found the most discriminant parameters responsible for seasonal variation of water quality. Coastal water was classified into good, average, and poor quality considering WQI and the nutrient load. The predictive capacity of WQI was proved with random samples taken from coastal areas. High concentration of ammonia in surface water during winter was attributed to nitrogen fixation by the phytoplankton bloom which resulted due to East India Coastal Current. This study brings out the fact that water quality in the coastal region not only depends on the discharge from different pollution sources but also on the presence of different current patterns. It also illustrates the usefulness of WQI for analyzing the complex nutrient data for assessing the coastal water and identifying different pollution sources, considering reasons for seasonal variation of water quality.

Improvement in the simulation of the Indian summer monsoon (ISM) rainfall by modifying the existing microphysical scheme in Atmospheric General Circulation Model (AGCM), ECHAM5, is of great interest in this present study. New formulations for the cloud ice formation have been implemented and tested in this sensitivity studies. Core monsoon zone rainfall is better captured by modification experiments as compared to the control simulations. Modification experiments give indication of improvement in cloud ice production. The overestimation of total cloud cover over the oceanic region is improved a lot in modification experiments. The major improvement appears in the realistic simulations of the proper ratio of convective and stratiform rain products. It depicts better resemblance with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations. The changes in cloud ice production arise due to different formulations used in this study, and it is manifested in the monsoon rainfall by the production of proper convective to total rain ratio. Ice nucleation which is a function of supersaturation (called as experiment MOD1) performs better for the Indian monsoon region. A new formulation used in this study provides promise for the better simulation of monsoon and it will further accelerate the future development of a reliable prediction system of ISM using dynamical models.

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 (2m 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 NiA±o3 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.

This study investigates the El Nino Southern Oscillation (ENSO) teleconnections to tropical Indian Ocean (TIO) and their relationship with the Indian summer monsoon in the coupled general circulation model climate forecast system (CFS). The model shows good skill in simulating the impact of El Nino over the Indian Oceanic rim during its decay phase (the summer following peak phase of El Nino). Summer surface circulation patterns during the developing phase of El Nino are more influenced by local Sea Surface Temperature (SST) anomalies in the model unlike in observations. Eastern TIO cooling similar to that of Indian Ocean Dipole (IOD) is a dominant model feature in summer. This anomalous SST pattern therefore is attributed to the tendency of the model to simulate more frequent IOD events. On the other hand, in the model baroclinic response to the diabatic heating anomalies induced by the El Nino related warm SSTs is weak, resulting in reduced zonal extension of the Rossby wave response. This is mostly due to weak eastern Pacific summer time SST anomalies in the model during the developing phase of El Nino as compared to observations. Both eastern TIO cooling and weak SST warming in El Nino region combined together undermine the ENSO teleconnections to the TIO and south Asia regions. The model is able to capture the spatial patterns of SST, circulation and precipitation well during the decay phase of El Nino over the Indo-western Pacific including the typical spring asymmetric mode and summer basin-wide warming in TIO. The model simulated El Nino decay one or two seasons later, resulting long persistent warm SST and circulation anomalies mainly over the southwest TIO. In response to the late decay of El Nino, Ekman pumping shows two maxima over the southern TIO. In conjunction with this unrealistic Ekman pumping, westward propagating Rossby waves display two peaks, which play key role in the long-persistence of the TIO warming in the model (for more than a season after summer). This study strongly supports the need of simulating the correct onset and decay phases of El Nino/La Nina for capturing the realistic ENSO teleconnections. These results have strong implications for the forecasting of Indian summer monsoon as this model is currently being adopted as an operational model in India.

Cloud dynamics played a fundamental role in defining Indian summer monsoon (ISM) rainfall during drought of 2009. The anomalously negative precipitation was consistent with cloud properties. Although, aerosols inhibited the growth of cloud effective radius in the background of sparse water vapor, their role is secondary. The primary role, however, is played by the interactive feedback between cloud microphysics and dynamics owing to reduced efficient cloud droplet growth, lesser latent heating release and shortage of water content. Cloud microphysical processes were instrumental for the occurrence of ISM drought 2009.

This study has identified probable factors that govern ISMR predictability. Furthermore, extensive analysis has been performed to evaluate factors leading to the predictability aspect of Indian Summer Monsoon Rainfall (ISMR) using uncoupled and coupled version of National Centers for Environmental Prediction Coupled Forecast System (CFS). It has been found that the coupled version (CFS) has outperformed the uncoupled version [Global Forecast System (GFS)] of the model in terms of prediction of rainfall over Indian land points. Even the spatial distribution of rainfall is much better represented in the CFS as compared to that of GFS. Even though these model skills are inadequate for the reliable forecasting of monsoon, it imparts the capacious knowledge about the model fidelity. The mean monsoon features and its evolution in terms of rainfall and large-scale circulation along with the zonal and meridional shear of winds, which govern the strength of the monsoon, are relatively closer to the observation in the CFS as compared to the GFS. Furthermore, sea surface temperature–rainfall relation is fairly realistic and intense in the coupled version of the model (CFS). It is found that the CFS is able to capture El Nino Southern Oscillation ISMR (ENSO-ISMR) teleconnections much strongly as compared to GFS; however, in the case of Indian Ocean Dipole ISMR teleconnections, GFS has the larger say. Coupled models have to be fine-tuned for the prediction of the transition of El Nino as well as the strength of the mature phase has to be improved. Thus, to sum up, CFS tends to have better predictive skill on account of following three factors: (a) better ability to replicate mean features, (b) comparatively better representation of air–sea interactions, and (c) much better portrayal of ENSO-ISMR teleconnections. This study clearly brings out that coupled model is the only way forward for improving the ISMR prediction skill. However, coupled model’s spurious representation of SST variability and mean model bias are detrimental in seasonal prediction.

The latest version of the state-of-the-art global land–atmosphere–ocean coupled climate forecast system of NCEP has shown considerable improvement in various aspects of the Indian summer monsoon. However, climatological mean dry bias over the Indian sub-continent is further increased as compared to the previous version. Here we have attempted to link this dry bias with climatological mean bias in the Eurasian winter/spring snow, which is one of the important predictors of the Indian summer monsoon rainfall (ISMR). Simulation of interannual variability of the Eurasian snow and its teleconnection with the ISMR are quite reasonable in the model. Using composite analysis it is shown that a positive snow anomaly, which is comparable to the systematic bias in the model, results into significant decrease in the summer monsoon rainfall over the central India and part of the Equatorial Indian Ocean. Decrease in the summer monsoon rainfall is also found to be linked with weaker northward propagation of intraseasonal oscillation (ISO). A barotropic stationary wave triggered by positive snow anomaly over west Eurasia weakens the upper level monsoon circulation, which in turn reduces the zonal wind shear and hence, weakens the northward propagation of summer monsoon ISOs. A sensitivity experiment by reducing snow fall over Eurasian region causes decrease in winter and spring snow depth, which in turn leads to decrease in Indian summer monsoon rainfall. Results from the sensitivity experiment corroborate with those of composite analysis based on long free run. This study suggests that further improvements in the snow parametrization schemes as well as Arctic sea ice are needed to reduce the Eurasian snow bias during winter/spring, which may reduce the dry bias over Indian sub-continent and hence predictability aspect of the model.

An attempt has been made to explore all the facets of Evaporation-Precipitation (E-P) distribution and variability over the Indian Ocean (IO) basin using Objectively Analyzed air-sea Fluxes (OAFlux) data and subsequently a thorough assessment of the latest version of National Centers for Environment Prediction (NCEP) Climate Forecast System (CFS) version-2 is done. This study primarily focuses on two fundamental issues, first, the core issue of pervasive cold SST bias in the CFS simulation in the context of moisture flux exchange between the atmosphere and the ocean and second, the fidelity of the model in simulating mean and variability of E-P and its elemental components associated with the climatic anomalies occurring over the Indian and the Pacific ocean basin. Valuation of evaporation and precipitation, the two integral component of E-P, along with the similar details of wind speed, air-sea humidity difference ( $$\Updelta Q$$ ) and Sea Surface Temperature (SST) are performed. CFS simulation is vitiated by the presence of basin wide systematic positive bias in evaporation, $$\Updelta Q$$ and similar negative bias in wind speed and SST. Bifurcation of the evaporation bias into its components reveals that bias in air humidity ( $$\hbox{Q}_{a}$$ ) is basically responsible for the presence of pervasive positive evaporation bias. The regions where CFS does not adhere to the observed wind-evaporation and $$\hbox{Q}_{a}$$ -evaporation relation was found to lie over the northern Arabian Sea (AS), the western Bay of Bengal (BoB) and the western Equatorial IO. Evaporation bias is found to control a significant quantum of cold SST bias over most of the basin owing to its intimate association with SST in a coupled feedback system. This area is stretched over the almost entire north IO, north of $$15^{\circ}\hbox{S}$$ excluding a small equatorial strip, where the evaporation bias may essentially explain 20–100 % of cold SST bias. This percentage is maximum over the western IO, central AS and BoB. The CFS simulation comply the distinct feature of the observed mean annual cycle of evaporation and precipitation, but with the additive systematic bias over most of the region. El Nino and negative Indian Ocean Dipole (NIOD) seems to have much better control over the interannual variability of evaporation in the CFS simulation, contrary to the observation where El Nino and positive Indian Ocean Dipole (PIOD) has the larger say. Both El Nino and PIOD (La Nina and NIOD) have the negative (positive) influence on the basin wide evaporation with the exception over a limited region and this relation holds for the twain. The seasonal (JJA and SON) locking of El Nino and PIOD to evaporation and precipitation is displayed by the north-south and east-west asymmetric correlation pattern respectively and this is much perspicuous in the observation as compared to the CFS. The conjoined influence of El Nino and PIOD on evaporation (precipitation) reveals the dominance of PIOD (PIOD + El Nino) response in case of both the observation as well as the CFS. This study will lead a way forward to rectify the ubiquitous cold SST bias in the CFS simulation and help in establishing the credibility of the CFS in terms of seasonal predictability.