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Indian summer monsoon (ISM) inter-annual variability on spatial and temporal scale is evaluated with CMIP6 models based on historical simulations. We have selected the best ten models based on the model's ability to represent mean seasonal precipitation, annual cycle of precipitation, and coefficient of variation. For these selected models, historical simulation of JJAS (June–September) total precipitation is also evaluated. We found that CMIP6 models depict better pattern correlation (with observation) in representing mean monsoon. Ensemble mean of models has greater skill. Further, total precipitation bifurcation of convective and large-scale rain is also investigated. Model depicts overestimation of convective precipitation and underestimation of stratiform precipitation. Ensemble mean is also depicting overestimation of convective precipitation although it is better than individual models. Model simulated total cloud fraction depict pattern correlation of above 0.6 with respect to observation. In case of multi-model ensemble, pattern correlation of total cloud fraction reaches to 0.85. Further, selected CMIP6 models are used for the estimation of future projections of rainfall in the warming scenario (1 percent increase in CO2 concentration: 1pctCO2). Models depict increased precipitation over Indian land region in future scenarios, and variability is also supposed to be increased due to projected increase in standard deviation of precipitation. [ABSTRACT FROM AUTHOR]

Cloud microphysical processes and rainfall over the Indian summer monsoon (ISM) region are unique because of the strong interaction among clouds, thermodynamics, and dynamics. The heating and presence of water vapor during ISM help for the formation of cloud particles in stratiform and convective clouds. In this study, we have analyzed the role of cloud microphysical processes behind the ISM rainfall (ISMR) and their inter-annual and sub-seasonal variability from the Goddard Earth Observing System (GEOS) model data (i.e., MERRA reanalysis). The spatial distribution of microphysical process rates (e.g., auto-conversion, freezing, accretion of rain and snow) over the Indian subcontinent are in line with the rainfall distribution. Besides, the interannual variability of these cloud microphysical process rates is coupled to that of the ISMR. It is revealed that these microphysical processes are in line with the spatial distribution of more (less) rainfall over the Indian subcontinent during active (break) spells. They also have significant sub-seasonal variability like ISMR over the ISM region. The variance is more in the synoptic scale than in quasi-biweekly mode (QBM) and monsoon intraseasonal oscillation (MISO) scales. Further, the sub-seasonal variances of microphysical process rates are well correlated with the mean ISMR. Results also reveal the teleconnection of cloud microphysical processes over the ISM region with the ENSO phenomena. We hope that the understanding of detailed microphysical processes during ISM will help the development of a climate model for depicting the mean monsoon. It may then enhance the skill of seasonal prediction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 problem, which is short‐lived local phenomenon. Here we show that on seasonal time scale, lightning over the major global hotspot regions is strongly tied with slowly varying global predictors (e.g., El Niño 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 modes in shaping the local land‐atmosphere interactions, which eventually affects seasonal lightning variability. It is shown that seasonal predictability of lightning over the hotspot is comparable to that of seasonal rainfall, opens up an avenue for reliable seasonal forecasting of lightning for special awareness and preventive measures. Plain Language Summary: Lightning, atmospheric hazards have an impact on the loss of human life, forest fire, health, agriculture, and economy across the globe. However, due to its chaotic nature, the tendency of seasonal forecasting of lightning is considered not viable as the understanding of the predictability of lightning is still incomplete. Here, we have explored the possibility of seasonal forecasting of lightning activity and provided a scientific basis as the lightning flashes are found to be tied with slowly varying remote forcings (e.g., El Niño and Southern Oscillation, or other global predictors). Correlation of flash count with different indices (Nino, Pacific decadal oscillation, North Atlantic oscillation, and Extra tropics, etc.) demonstrate the potential of seasonal forecasting of lightning. The multiple regression analysis enhances the skill. The climatology of lightning flash density from the Goddard Earth observing system model is compared with observation. The pattern correlation between observation and model is very high (∼0.7) over global tropics hints at the predictability of lightning flashes on a seasonal time scale. Therefore, better climate models that capture crucial couplings between ocean, atmosphere, and land processes could make skillful predictions of lightning and opens up a possibility for lightning forecast in one season advance. Key Points: Possibility of reliable seasonal forecasting of lightning over global hotspots is demonstratedLightning flashes are found to be tied with slowly varying remote global predictors (e.g., El Niño and Southern oscillation, Atlantic multidecadal oscillation, North Atlantic oscillation, Pacific decadal oscillation, and extra tropics)Multiple regression analysis demonstrates that seasonal lightning and associated rainfall is predictable during pre‐monsoon and monsoon [ABSTRACT FROM AUTHOR]

Skillful prediction of the Indian Summer Monsoon Rainfall (ISMR) has been a bottleneck problem for more than 100 years. The low seasonal predictability is attributed primarily to the chaotic nature of the subseasonal variability. Here, we show that these subseasonal variabilities rather have significant predictable contributions to the seasonal ISMR, varying on the interannual to multidecadal timescale. The subseasonal modes being the building blocks of the monsoon, their net linear contribution may approximate the predictability limit of the ISMR. It is estimated that an average of about 76% (R ∼ 0.87) of the ISMR variance predictable around the 1960s is decreased to about 64% (R ∼ 0.79) in the recent past four decades. It is suggested that improvements in the simulation of subseasonal, particularly the synoptic variability will be key to further improve the seasonal ISMR forecast skill. Plain Language Summary: Skillful prediction of seasonal (June‐to‐September) Indian Summer Monsoon Rainfall (ISMR) has been always a big challenge for the forecasters due to its complex nature of feedback. From the perspective of seasonal prediction, the subseasonal variability of the monsoon rainfall is primarily considered as noise (e.g., Webster et al., 1998). However, using 118 years of observed rainfall data, we show that subseasonal modes of monsoon (i.e., synoptic, supersynoptic, and intraseasonal oscillation) linked with the primary global predictors, contribute significantly to the seasonal anomaly of the ISMR. The complex association of subseasonal modes with the global predictors, which vary on interannual to the multidecadal timescale, shapes the predictability of the ISMR. As a result, the prediction of seasonal ISMR is more accurate in some decades than the others. It is estimated that an average of about 76% (R ∼ 0.87) of the interannual ISMR variance was predictable around the 1960s and that has now decreased to about 64% (R ∼ 0.79) in the past four decades. It is suggested that a better understanding of the mechanisms of subseasonal modes and consequently their reasonable simulation (space‐time statistics, particularly the synoptic variability) is the key to further improve the seasonal ISMR forecast. Key Points: The subseasonal variability, generally considered as noise, is found to have a significant predictable contribution to the seasonal Indian Summer Monsoon Rainfall (ISMR)The synoptic variability contributes a maximum to the ISMR predictability followed by supersynoptic and Monsoon Intraseasonal OscillationsA complex phase relationship between predictors and subseasonal variance modulates the ISMR predictability on decadal to multidecadal timescale [ABSTRACT FROM AUTHOR]

Swenson et al. (2020, https://doi.org/10.1029/2020JD033037) (hereafter SN20) raise some technical issues on observed correlations between the synoptic variances and the seasonal mean of area averaged (all‐India or central India) Indian summer monsoon rainfall (ISMR) reported in Saha et al. (2019, https://doi.org/10.1029/2018JD030082) (hereafter SA19). SN20 did not comment on the other major finding of SA19 on model‐based prediction and predictability of the ISMR. Therefore, we focus in this reply only on the observational part of SA19 even though the modeling part is closely related to the observations. While we disagree with SN20 in all three aspects of their comments, we present additional analysis to clarify the scientific basis for our selection of area averaging, the selection of 30‐year period for the study and argue that the relevant probability density function of daily rainfall is not that of the individual stations (or grid points) but that of the daily averaged rainfall over a comparable area. On the debate of small area averaging (major point of SN20), we argue that it is physically meaningless in the context of ISMR. Key Points: The synoptic variability has a maximum contribution to the seasonal ISMR anomalyFor the choice of area average of ISMR index, the domain should encompass size of a typical monsoon low‐pressure systemWhile synoptic systems always have strong positive association with ISMR, for super‐synoptic systems, it may be either positive or negative [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Using the Two‐Dimensional Video Disdrometer measurements, we investigated the fall velocity of raindrops at Mahabaleshwar (17.92°N, 73.6°E, ~1.4 km above mean sea level, AMSL), a tropical site on the Western Ghats of India. The analysis is for different seasons during 2012–2015. To increase the reliability and accuracy of analysis, the raindrops with diameters in the range 0.5–2 mm and horizontal wind speed < 2 m s−1 are considered. The observed raindrop fall velocities have been corrected for the effect of air density. The ratio between the observed velocity and calculated terminal velocity is considered for estimating superterminal (positively skewed; higher than terminal velocity) and subterminal (negatively skewed; lower than terminal velocity) raindrops. The distribution of these skewed raindrops and its relationship with rain rate, drop diameter, and axis ratio for different seasons has been examined. Results indicate the presence of superterminal and subterminal raindrops in definite proportion in all the season with a majority of the drops have a diameter less than 1 mm. It is found that most of the superterminal raindrops occur at rain rates below 5 mm hr−1, while subterminal raindrops are relatively higher above this rain rate. It is observed that the superterminal raindrops are usually small in size and prolate in shape, while the subterminal raindrops are relatively large and oblate in shape. The diurnal variation of these raindrops during different seasons is also studied. Key Points: 1. For the first time, the super‐ and sub‐terminal raindrops are investigated over Western Ghats, India using long‐term dataset2. The relationships of super‐ and sub‐terminal drops with precipitation features like raindrop diameter, and axis ratio, etc. are examined3. The present study on raindrops size and terminal velocity will be helpful for a better understanding of cloud modelling activities [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Eurasian snow is one of the slowly varying boundary forcings known to have significant influences on the mean and variability of the Indian summer monsoon rainfall (ISMR). A multilayer complex snow scheme, incorporated into the state‐of‐the‐art coupled Climate Forecast System version 2 (CFSv2) showed significant improvements in the simulation of mean ISMR, snow, and Northern Hemisphere surface and tropospheric temperature. Here we show that a realistic simulation of high‐latitude snow decreases the north–south temperature gradient, which in turn decreases the meridional transport of energy from the Equator to the Pole, consequently affecting the tropical sea surface temperature (SST) and air–sea interactions. The global teleconnections of the ISMR with SST and 2 m temperature over land are also improved considerably in association with improved simulation of the oceanic natural modes of variability. Our findings provide new insights for the relationship between the winter Eurasian snow and the following ISMR, namely that the same relationship may be understood through a framework of meridional atmospheric energy transport and its effects on the tropical air–sea interactions. The improvements in the global teleconnection in the modified version of CFSv2 may have implications in the ISMR predictability and prediction skill. [ABSTRACT FROM AUTHOR]

Large socioeconomic impact of the Indian summer monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather low potential predictability (PP) of the seasonal ISM, contributed significantly by "internal," interannual variability was considered insurmountable. Here we show that the internal variability contributed by the ISM subseasonal (synoptic + intraseasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g., El Niño and Southern Oscillation). This provides a scientific basis for predictability of the ISM rainfall beyond the conventional estimates of PP. We establish a much higher actual limit of PP (r∼0.82) through an extensive reforecast experiment (1,920 years of simulation) by improving two major physics in a global coupled climate model, which raises a hope for a very reliable dynamical seasonal ISM forecasting in the near future. Key Points: The observed link between synoptic variability and predictable modes suggest a high predictability of the ISMRCFSv2 with improved physics shows ISMR prediction skill higher than current estimate of potential predictabilityModel shows ∼70% of interannual variability of ISMR is predictable, which is much higher than earlier estimates (∼45%) [ABSTRACT FROM AUTHOR]

The Indian summer monsoon (ISM) is a climate coupled system where cloud processes play a significant role in modulating the thermodynamics and dynamics of the atmosphere. The realistic representation of cloud by coupled model becomes indispensable for the simulation of the rainfall variability dictating the extreme ISM rainfall years. This study pinpoints the importance of correct representation of cloud processes and different types of rainfall in coupled climate model for the realistic simulation of monsoon extreme rainfall years. The study has used historical runs of Coupled Model Intercomparison Project Phase 5 (CMIP5) models. It provided best opportunity to investigate the ISM rainfall characteristics in case of anomalous monsoon and associated physical processes responsible for the modulation of large‐scale circulation. The study has identified the mechanism behind the better simulation of anomalous years. Relatively more cloud condensate at upper level and middle level is present in excess monsoon years. Pertaining to phase changes and thermodynamical processes, there will be relatively more tropospheric temperature gradient and it will be conducive for strong monsoon. Proper representation of convective and stratiform rain is seen during extreme years. Precipitable water is also relatively more during excess monsoon years. As a result, total rainfall also enhances and it leads to excess monsoon. In contrast, during deficient monsoon relatively less cloud condensate at upper level and middle level is present. Therefore, the correct representation of cloud condensate vertical profile and stratiform/convective rain are crucial for the better simulation of excess and deficient monsoon, which may further improve the variability of ISM in climate model. This study connotes the urgent need of the improvement of cloud parameterization in coupled climate models for the betterment of monsoon variability and realistic representation of deficient/excess monsoon rainfall patterns over Indian subcontinent. Total mean rainfall (June–September: JJAS; unit: mm/day) for CMIP5 models (a–g) and GPCP (h). [ABSTRACT FROM AUTHOR]

ABSTRACT: This study explores the possible causes of rainfall distribution over the two major oceanic raining regions of the north Bay of Bengal (nBoB) and the east equatorial Indian Ocean (eEIO). Despite 17% difference in vertically averaged humidity, there is almost 34% difference in mean rainfall over these two regions. The climatological seasonal [June–September (JJAS)] mean (standard deviation) rainfall over nBoB region is always higher (lower) than that over the eEIO region in all the independent data used. The eEIO region has a much larger percentage of low stratiform and convective rainfall (<5 mm day−1) distribution as compared to nBoB, which is totally opposite in case of moderate stratiform and convective rainfall (>5 mm day−1) distribution. This is further substantiated by a much lower values of outgoing long‐wave radiation (OLR) in nBoB (<200 W m−2) as compared to the eEIO (217 W m−2) region. Mean Hadley circulation along with relative vorticity/divergence profile supports more intense (gentle) updrafts over nBoB (eEIO) region. Latent heat (LH) is almost three times at the upper level (∼8 km) in case of nBoB as compared to eEIO; however, at the lower level (∼3 km) LH is marginally higher over eEIO region. Microphysical variables, namely cloud ice optical thickness and cloud ice water path, are in much larger quantities over nBoB as compared to eEIO. Furthermore, the cold (warm) rain processes dominate among other microphysical processes over nBoB (eEIO) region. Thus, the interplay among large‐scale dynamics, thermodynamics and microphysics is very crucial in the formation of deep clouds and convective rain over the nBoB region and similarly shallow clouds and stratiform rain over the eEIO region. This study will be very useful to guide present‐day coupled models for proper representation of different rain components over the nBoB and eEIO region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Noah version 2.7.1 is a moderately complex land surface model (LSM), with a single layer snowpack, combined with vegetation and underlying soil layer. Many previous studies have pointed out biases in the simulation of snow, which may hinder the skill of a forecasting system coupled with the Noah. In order to improve the simulation of snow by the Noah, a multilayer snow scheme (up to a maximum of six layers) is introduced. As Noah is the land surface component of the Climate Forecast System version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP), the modified Noah is also coupled with the CFSv2. The offline LSM shows large improvements in the simulation of snow depth, snow water equivalent (SWE), and snow cover area during snow season (October to June). CFSv2 with the modified Noah reveals a dramatic improvements in the simulation of snow depth and 2 m air temperature and moderate improvements in SWE. As suggested in the previous diagnostic and sensitivity study, improvements in the simulation of snow by CFSv2 have lead to the reduction in dry bias over the Indian subcontinent (by a maximum of 2 mm d−1). The multilayer snow scheme shows promising results in the simulation of snow as well as Indian summer monsoon rainfall and hence this development may be the part of the future version of the CFS. [ABSTRACT FROM AUTHOR]

Modification of the vertical structure of non-adiabatic heating by significant abundance of the stratiform rain in the tropics has been known to influence the large-scale circulation. However, the role of the stratiform rain on the space-time evolution of the observed Boreal summer monsoon intraseasonal oscillations (MISO) has so far been ignored. In the present study, we unravel a feedback mechanism through which the stratiform component of the rain leads to aggregation (organization) of rain on the MISO scale, making it an indispensable component of the MISO evolution dynamics. Using TRMM 3A25 monthly mean data (between 1998 and 2013), the ratio between convective and stratiform rain (RCS) is shown to be strongly related to the total rainfall. Further, composites of rainfall and circulation anomalies corresponding to high (low) values of RCS over the Central India or over the Equatorial Indian Ocean show spatial structures remarkably similar to that associated with the MISOs. Analyzing lead-lag relationship between the convective rain, the stratiform rain and the large scale moisture convergence with respect to peak active (break) spells from daily modern era retrospective-analysis for research and applications data, we unravel that the initial isolated convective elements spawn the stratiform rain which in turn modifies the vertical distribution of heating and leads to stronger large scale moisture convergence thereby producing more convective elements and more stratiform rain ultimately leading to aggregation of rain on the MISO scale. Our finding indicates that large and persisting systematic biases in simulating the summer monsoon rainfall over the Asian monsoon region by climate models are likely to be related to the systematic biases in simulating the MISOs which in turn are related to the serious underestimation of stratiform rain in most climate models. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 Niño 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. [ABSTRACT FROM AUTHOR]

This study evaluates the actual and potential prediction skill of the global monsoon rainfall using hindcast simulations by NCEP CFSv2 at zero to three lead forecast months (L0-L3). It is shown that the model has moderate skill in global monsoon rainfall (GMR) prediction, where the boreal summer monsoon rainfall forecast is more skillful than that of the austral summer. In general, the prediction skill of the GMR (actual and potential) increases with the decrease in lead forecast time, which is true for the all major regional monsoons, except the Australian monsoon. Over the Australian monsoon region, both actual and potential prediction skills in rainfall increase with increase in lead forecast. The forecast skill of tropical SST during austral summer is a maximum at 3 months lead forecast (i.e. July initial conditions) and that is associated with spring predictability barrier. Using partial least square (PLS) regression method, it is shown that the major predictor (first latent vector) of the boreal and austral summer monsoon rainfall variability is ENSO, and the influence of ENSO on rainfall variability is much stronger in the model as compared to the observation. The second PLS regression mode is associated with the non-ENSO variability like tropical Atlantic, Indian, subtropical northwest Pacific Ocean variability, midlatitude interactions etc. However, the model has very poor skill in reproducing the second mode, particularly during the boreal summer monsoon season. It is also shown that a significant part of the Indian summer monsoon rainfall variability is controlled by other than ENSO variability and the model has limited success in capturing that. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cloud fraction, which varies greatly among general circulation models, plays a crucial role in simulation of Indian summer monsoon rainfall (ISMR). The NCEP Climate Forecast System version 2 (CFSv2) model is evaluated in terms of its simulation of cloud fraction, cloud condensate, outgoing longwave radiation (OLR), and tropospheric temperature (TT). Biases in these simulated quantities are computed using observations from CALIPSO and reanalysis data from MERRA. It is shown that CFSv2 underestimates (overestimates) high- (mid-) level clouds. The cloud condensate is also examined to see its impact on different types of clouds. The upper-level cloud condensate is underestimated, particularly during the summer monsoon period, which leads to a cold TT and a dry precipitation bias. The unrealistically weak TT gradient between ocean and land is responsible for the underestimation of ISMR. The model-simulated OLR is overestimated which depicts the weaker convective activity. A large underestimate of precipitable water is also seen along the cross-equatorial flow and particularly over the Indian land region collocated with a dry precipitation bias. The linkages among cloud microphysical, thermodynamical, and dynamical processes are identified here. Thus, this study highlights the importance of cloud properties, a major cause of uncertainty in CFSv2, and also proposes a pathway for improvements in its simulation of the Indian summer monsoon. [ABSTRACT FROM AUTHOR]

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-Niño 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 Niño3.4 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. [ABSTRACT FROM AUTHOR]

The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the sensitivity of the potential predictability of the ISMR to the initial condition through analysis of variance technique (ANOVA), information-based measure, including relative entropy (RE), mutual information (MI), and classical perfect model correlation. In general, the all methods show an increase in potential predictability with a decrease in lead time. Predictability is large over the Pacific Ocean basin as compared to that of the Indian Ocean basin. However, over the Indian land region the potential predictability increases from lead-4 to lead-2 and then decreases at lead-1 followed by again increase at lead-0. While the actual ISMR prediction skill is highest at lead-3 forecast (second highest at lead-1), the potential predictability is highest at lead-2. It is found that highest and second highest actual prediction skill of the ISMR in CFSv2 is due to the combined effects of initial Eurasian snow and SST over Indian, west Pacific and eastern equatorial Pacific Ocean region. While the teleconnection between the ISMR and El Niño-Southern Oscillation is too strong, the ISMR and Indian Ocean dipole have completely out of phase relation in the model as compared to the observation. Furthermore, the actual prediction skill of the ISMR is now very close to the potential predictability limit. Therefore, in order to improve the ISMR prediction skill further, development of model physics as well as improvements in the initial conditions is required. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Role of the cloud parameterization scheme and critical relative humidity (RHcrit) for large-scale precipitation is examined for simulating Indian summermonsoon (ISM) by theNational 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 themodified 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. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 Niño 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. [ABSTRACT FROM AUTHOR]

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 an improvement of rainfall bias over the Asian summer monsoon (ASM) region. CMIP6-MME can better reproduce mean TCF and have reduced dry (wet) rainfall bias on land (ocean) over the ASM region. Model bias with respect to seasonal mean rainfall, TCF, and outgoing longwave radiation (OLR) in CMIP6-MME are improved over the Indian Summer Monsoon (ISM) region by ~40%, ~45%, and ~ 31%, respectively, as compared to CMIP5-MME. Further, CMIP6-MME demonstrates a better spatial correlation with observation/reanalysis. 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. The results establish the credibility of the CMIP6 models and provide a scientific basis for improving the seasonal prediction of ISM. • The total cloud fraction (TCF) over the ISM region is strongly associated with global predictors. • The seasonal mean biases of TCF and rainfall have seen improvement from CMIP5-MME to CMIP6-MME. • Improvement from CMIP5 to CMIP6 models may be attributed to the better depiction of the observed global teleconnections. [ABSTRACT FROM AUTHOR]

Indian summer monsoon circulation can be characterized by mean tropospheric temperature (TT) gradient between ocean and land. Two major heat sources, one near the Myanmar Coast and the other near the Western Ghats play seminal role in defining this TT gradient. While both regions are characterized by very similar orographic features, there are significant differences in frequency of occurrence of precipitating clouds and their characteristics even when the amount of rain in June-July months is almost same in the two regions. Deeper (shallower) clouds appear more frequently over the Myanmar Coast (the Western Ghats). There is a sharp decrease in amount of rainfall from June-July to August-September in both the areas. Rather counter intuitively, during the June-July-August-September season, low and moderate rains contribute more to the total rain in the Myanmar Coast while heavy rains contribute more to the total rain in the Western Ghats. Western Ghats also gets more intense rains but less frequently. With significant differences in moisture availability, updraft, amount and characteristics of cloud condensate in the two regions, this study proposes that the nontrivial differences in features between them could be explained by linkages between cloud microphysics and large scale dynamics. Presence of more cloud liquid water and the role of giant cloud condensation nuclei reveals dominance of warm rain process in the Western Ghats whereas more cloud ice, snow and graupel formation in the Myanmar Coast indicates stronger possibility of cold rain coming from mixed phase processes. Stronger heating caused by mixed phase process in the mid and upper troposphere in the Myanmar Coast and its feedback on buoyancy of air parcel explains the appearance of deeper clouds. Thus, our study highlights importance of mixed phase processes, a major cause of uncertainty in GCMs. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 (2 m 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 Niño3 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. [ABSTRACT FROM AUTHOR]

Abstract The Weather Research and Forecasting (WRF) model coupled with chemistry (WRF-Chem) is used to simulate rainfall pattern over the Indian landmass during a normal monsoon year (June–September 2010). Sensitivity analysis is performed using three different microphysical (MP) schemes (Thompson, Morrison, and Lin) to simulate the rainfall pattern and distribution over India. A significant difference in the rainfall amount, as well as distribution, is observed among the MP schemes, despite using the same model configuration and the meteorological and chemical initial and boundary condition. Lin MP scheme performed better than other schemes in simulating the rainfall over the Indian domain. Heavy rainfall along the foothills of the Himalayas, the mountainous coastal region of western India, and the northeastern part of India and sparse rainfall over northwest India are captured realistically in the model, which agrees well with the observed rainfall pattern over India. The spatial distribution of aerosol optical depths retrieved from the MODIS satellites is broadly well simulated by the model. The model simulated convective and non-convective rainfall characteristics over the Indian landmass are found to be consistent with the monsoon precipitation climatology. It is shown that the change in both convective and non-convective rainfall due to aerosols and chemistry is non-negligible over India during monsoon season. The total rainfall is found to increase significantly over the entire Western Ghats and some part of northeast India, while it is diminished over the northwest, north and east India when aerosol chemistry effects are considered in the model simulation. Additional analysis is performed to simulate the perturbations in convective and non-convective rainfall due to aerosols (e.g., dust, sea salt, black carbon etc.). Results show that the effect of aerosols on convective and non-convective rainfall distribution over India is non-linear. Depending on the meteorological conditions, topography, and the type of aerosol, the aerosol impact and feedback on monsoon rainfall can be positive, negative, or neutral. Highlights • Sensitivity analysis is performed using three different microphysical to simulate rainfall over India. • Lin MP scheme performed better in simulating the rainfall over the Indian domain. • Convective and non-convective rainfall is consistent with the monsoon precipitation climatology. • It is found that aerosol impact and feedback on monsoon rainfall can be positive, negative, or neutral. [ABSTRACT FROM AUTHOR]

The Indian subcontinent is a major hotspot of aerosol loading and several studies have demonstrated the importance of interaction between Asian dust aerosols and the Indian Summer Monsoon Rainfall (ISMR). However, a few studies have suggested a declining trend in the pre-monsoon dust loading over the Indian region. The present study investigates how the decrease in dust emissions influences the intensity of the monsoon rainfall over the Indian region using a fully coupled Weather Research and Forecasting model coupled with chemistry (WRF-Chem). We have conducted two sets of simulation experiments for the June-July-August-September (JJAS) season of 2017: (1) a control experiment with the default dust aerosol emissions, and (2) a sensitivity experiment by reducing dust emissions by 60%. Our analyses show that the reduction in dust emissions significantly alters the spatial distribution of seasonal mean monsoon rainfall with enhanced rainfall over the Bay of Bengal (BoB) and eastern coastal regions, and decreased rainfall over the lower Indo-Gangetic Plain (IGP), and parts of Central India (CI). It is found that the low aerosol loading conditions alter the large-scale circulation patterns along the tropical/sub-tropical region centred between 15°N and 35°N and 40°E −130°E, and through dynamical feedback, strengthens the convective rainfall over the BoB region. As a result, the 10–20 days sub-seasonal mode of the ISMR strengthens, facilitating enhanced westward propagation of rain-bearing systems formed over northern BoB which provides substantial rainfall over vast regions of the Indian subcontinent. • The impact of dust emissions on Indian Summer Monsoon Rainfall (ISMR) is examined. • Reduction in dust emissions significantly alters spatial rainfall distribution. • Strengthening of cyclonic circulation and increase in convective activity are noted over Bay of Bengal in reduced dust scenario. • Significant impact of reduced dust emissions is noted in the 10–20 days sub-seasonal variability of ISMR. • Westward propagating systems are strengthened in the reduced dust scenario. [ABSTRACT FROM AUTHOR]

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. • Chemistry-weather interaction on Indian Summer Monsoon Rainfall (ISMR) is examined. • Inclusion of chemistry plays a role in altering the spatial distribution of ISMR. • Mean rainfall decreases by 3 mm/day after inclusion of chemistry in the model. • Mean bias in rainfall improves in case of chemistry inclusive simulation. • Chemistry interacts with cloud microphysics by modulating cloud hydrometeor paths. [ABSTRACT FROM AUTHOR]