All India summer monsoon rainfall prediction using an artificial neural network

The prediction of Indian summer monsoon rainfall (ISMR) on a seasonal time scales has been attempted by various research groups using different techniques including artificial neural networks. The prediction of ISMR on monthly and seasonal time scales is not only scientifically challenging but is also important for planning and devising agricultural strategies. This article describes the artificial neural network (ANN) technique with error- back-propagation algorithm to provide prediction (hindcast) of ISMR on monthly and seasonal time scales. The ANN technique is applied to the five time series of June, July, August, September monthly means and seasonal mean (June + July + August + September) rainfall from 1871 to 1994 based on Parthasarathy data set. The previous five years values from all the five time-series were used to train the ANN to predict for the next year. The details of the models used are discussed. Various statistics are calculated to examine the performance of the models and it is found that the models could be used as a forecasting tool on seasonal and monthly time scales. It is observed by various researchers that with the passage of time the relationships between various predictors and Indian monsoon are changing, leading to changes in monsoon predictability. This issue is discussed and it is found that the monsoon system inherently has a decadal scale variation in predictability. Received: 13 March 1999 / Accepted: 31 August 1999     

Indian summer monsoon rainfall predictability and variability associated with Northwest Pacific circulation in a suit of coupled model hindcasts

The Northwest Pacific (NWP) circulation (subtropical high) is an important component of the East Asian summer monsoon system. During summer (June–August), anomalous lower tropospheric anticyclonic (cyclonic) circulation appears over NWP in some years, which is an indicative of stronger (weaker) than normal subtropical high. The anomalous NWP cyclonic (anticyclonic) circulation years are associated with negative (positive) precipitation anomalies over most of Indian summer monsoon rainfall (ISMR) region. This indicates concurrent relationship between NWP circulation and convection over the ISMR region. Dry wind advection from subtropical land regions and moisture divergence over the southern peninsular India during the NWP cyclonic circulation years are mainly responsible for the negative rainfall anomalies over the ISMR region. In contrast, during anticyclonic years, warm north Indian Ocean and moisture divergence over the head Bay of Bengal-Gangetic Plain region support moisture instability and convergence in the southern flank of ridge region, which favors positive rainfall over most of the ISMR region. The interaction between NWP circulation (anticyclonic or cyclonic) and ISMR and their predictability during these anomalous years are examined in the present study. Seven coupled ocean–atmosphere general circulation models from the Asia-Pacific Economic Cooperation Climate Center and their multimodel ensemble mean skills in predicting the seasonal rainfall and circulation anomalies over the ISMR region and NWP for the period 1982–2004 are assessed. Analysis reveals that three (two) out of seven models are unable to predict negative (positive) precipitation anomalies over the Indian subcontinent during the NWP cyclonic (anticyclonic) circulation years at 1-month lead (model is initialized on 1 May). The limited westward extension of the NWP circulation and misrepresentation of SST anomalies over the north Indian Ocean are found to be the main reasons for the poor skill (of some models) in rainfall prediction over the Indian subcontinent. This study demonstrates the importance of the NWP circulation variability in predicting summer monsoon precipitation over South Asia. Considering the predictability of the NWP circulation, the current study provides an insight into the predictability of ISMR. Long lead prediction of the ISMR associated with anomalous NWP circulation is also discussed.     

Seasonal prediction of Indian summer monsoon in NCEP coupled and uncoupled model

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 Niño 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 Niño 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.     

Weakening of Indian summer monsoon rainfall in warming environment

Though over a century long period (1871–2010) the Indian summer monsoon rainfall (ISMR) series is stable, it does depict the decreasing tendency during the last three decades of the 20th century. Around mid-1970s, there was a major climate shift over the globe. The average all-India surface air temperature also shows consistent rise after 1975. This unequivocal warming may have some impact on the weakening of ISMR. The reduction in seasonal rainfall is mainly contributed by the deficit rainfall over core monsoon zone which happens to be the major contributor to seasonal rainfall amount. During the period 1976–2004, the deficit (excess) monsoons have become more (less) frequent. The monsoon circulation is observed to be weakened. The mid-tropospheric gradient responsible for the maintenance of monsoon circulation has been observed to be weakened significantly as compared to 1901–1975. The warming over western equatorial Indian Ocean as well as equatorial Pacific is more pronounced after mid-70s and the co-occurrence of positive Indian Ocean Dipole Mode events and El Nino events might have reinforced the large deficit anomalies of Indian summer monsoon rainfall during 1976–2004. All these factors may contribute to the weakening of ISMR.     

Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models

Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean–atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959–1979) are closer to the ‘perfect model’ (attainable) score but, large differences are observed between ‘actual’ skill and ‘perfect model’ skill in the recent period (1980–2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon.     

Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia

Several observational and modeling studies indicate that the Indian summer monsoon rainfall (ISMR) is inversely related to the Eurasian snow extent and depth. The other two important surface boundary conditions which influence the ISMR are the Pacific sea surface temperature (SST) to a large extent and the Indian Ocean SST to some extent. In the present study, observed Soviet snow depth data and Indian rainfall data for the period 1951–1994 have been statistically analyzed and results show that 57% of heavy snow events and 24% of light snow events over west Eurasia are followed by deficient and excess ISMR respectively. Out of all the extreme monsoon years, care has been taken to identify those when Eurasian snow was the most dominant surface forcing to influence ISMR. During the years of high(low) Eurasian snow amounts in spring/winter followed by deficient(excess) ISMR, atmospheric fields such as temperature, wind, geopotential height, velocity potential and stream function based on NCEP/NCAR reanalyses have been examined in detail to study the influence of Eurasian snow on the midlatitude circulation regime and hence on the monsoon circulation. Results show that because of the west Eurasian snow anomalies, the midlatitude circulations in winter through spring show significant changes in the upper and lower level wind, geopotential height, velocity potential and stream function fields. Such changes in the large-scale circulation pattern may be interpreted as precursors to weak/strong monsoon circulation and deficient/excess ISMR. The upper level velocity potential difference fields between the high and low snow years indicate that with the advent of spring, the winter anomalous convergence over the Indian region gradually becomes weaker and gives way to anomalous divergence that persists through the summer monsoon season. Also the upper level anomalous divergence centre shifts from over the Northern Hemisphere and equator to the Southern Hemisphere over the Indian Ocean and Australia.     

Lag-relationship between mid-tropospheric geopotential heights over the Northern Hemisphere and the Indian summer monsoon rainfall: Implications for forecasting

Summary In this study the relationship between mid-tropospheric geopotential heights over the Northern Hemisphere (20° N to 90° N, around the globe) and Indian summer monsoon rainfall (ISMR: June to September total rainfall) have been examined. For this purpose, the monthly 500 hPa geopotential heights in a 2.5° lat./lon. grid over the Northern Hemisphere and the ISMR data for the period 1958 to 2003 have been used.The analysis demonstrates a dipole structure in the correlation pattern over the East Pacific Ocean in the month of January which intensifies in February and weakens in March.The average 500 hPa geopotential height over the eastern tropical Pacific Ocean during February (index one), has a significant positive relationship (r = 0.72) with the ISMR. In addition, the surface air temperature (SAT) anomaly over North-west Eurasia during January (index two) is found to be strongly related with the subsequent summer monsoon rainfall. These relationships are found to be consistent and robust during the period of analysis and these indices are found to be independent of each other.Hence, using index one and index two, a multiple linear regression model is developed for the prediction of the ISMR and the empirical relationships are verified on independent data. The forecast of the ISMR, using the above model, is found to be satisfactory.The dipole structure in the correlation pattern over the East Pacific region during February weakens once the ENSO (El-Nino and Southern Oscillation) events are excluded from the analysis. This suggests that the dipole type relationship between mid-tropospheric geopotential heights over the East Pacific Ocean and the ISMR may be a manifestation of the ENSO cycle.     

Role of the cloud adjustment time scale in simulation of the interannual variability of Indian summer monsoon

The simulation of precipitation in a general circulation model relying on relaxed mass flux cumulus parameterization scheme is sensitive to cloud adjustment time scale (CATS). In this study, the frequency of the dominant intra-seasonal mode and interannual variability of Indian summer monsoon rainfall (ISMR) simulated by an atmospheric general circulation model is shown to be sensitive to the CATS. It has been shown that a longer CATS of about 5 h simulates the spatial distribution of the ISMR better. El Niño Southern Oscillation–ISMR relationship is also sensitive to CATS. The equatorial Indian Ocean rainfall and ISMR coupling is sensitive to CATS. Our study suggests that a careful choice of CATS is necessary for adequate simulation of spatial pattern as well as interannual variation of Indian summer monsoon precipitation.     

A coupled model study on ENSO, MJO and Indian summer monsoon rainfall relationships

Summary In this paper, we have tried to understand the ENSO, MJO and Indian summer monsoon rainfall relationships from observation as well as from coupled model results. It was the general feeling that El-Niño years are the deficient in Indian monsoon rainfall and converse being the case for the La-Niña years. Recent papers by several authors noted the failure of this relationship. We find that the model output does confirm a breakdown of this relationship. In this study we have seen that a statistically defined modified Indian summer monsoon rainfall (MISMR) index, a linearly regressed ISMR index and dynamical Webster index (WBSI), shows an inverse relationship with ENSO index during the entire period of integration (1987 to 1999). It is also seen from this study that the amplification of the MJO signals were large and the ENSO signals were less pronounced during the years of above normal ISMR. The MJO signal amplitudes were small and ENSO signals were strong during the years of deficient ISMR. It has been noted that here is a time lag between the MJO and ENSO signal in terms of their modulation aspect. If time lag is added with the ENSO signal then both signals maintain the amplitude modulation theory. A hypothesis is being proposed here to define a relationship between MJO and ENSO signals for the entire period between 1987 and 1999.Received September 18, 2002; revised November 22, 2002; accepted December 20, 2002 Published online: May 8, 2003     

New statistical models for long-range forecasting of southwest monsoon rainfall over India

The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June–September) rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5 and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models during the verification period (1981–2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models were also found to be accurate.     

A novel deep neural network model approach to predict Indian Ocean dipole and Equatorial Indian Ocean oscillation indices

Indian Ocean Dipole (IOD) and Equatorial Indian Ocean oscillation (EQUINOO) are important climatic system oscillation events in the Indian Ocean region that affects the Indian summer monsoon rainfall (ISMR). The prime focus of this study is to deliberate the influence of these events on ISMR and an attempt has been made to predict these events for future time scales using a Long short term memory (LSTM) deep learning model. LSTM is a special kind of recurrent neural network (RNN) which specializes in learning long-term dependencies and extracting important features. The features learnt by the model is then ranked using correlational analysis (linear and nonlinear). This approach helps in selecting decisive and imperative set of relevant predictors, which can be employed to predict IOD and EQUINOO. Nonlinear correlational identified predictors are found to forecast with greater precision as to their linear counterparts. The model-calibrated correlation coefficient for IOD and for EQUNIOO was 0.90 and 0.88 respectively at a lead of 5 months. Our proposed model was observed to work at par with the other existing models in terms of various statistical evaluation measures.     

Role of convective and microphysical processes on the simulation of monsoon intraseasonal oscillation

Climate Dynamics - 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...     

A possible mechanism for the weakening of El Niño-monsoon relationship during the recent decade

Summary Observational data are used to explore the relationship between surface air temperature anomaly gradients and Indian summer monsoon rainfall (ISMR). The meridional temperature anomaly gradient across Eurasia during January directed towards equator (pole) is a very good precursor of subsequent excess (deficient) Indian summer monsoon rainfall (ISMR). This gradient directed towards equator (pole) indicates below (above) normal blocking activity over Eurasia, which leads to less (more) than normal southward penetration of dry and cold mid latitude westerlies over the Indian monsoon region, which ultimately strengthens (weakens) the normal monsoon circulation. These findings suggest a mechanism for the weakening of relationship between El Niño and ISMR.Though there is a strong fundamental association between El Niño (warm ENSO) and deficient Indian summer monsoon rainfall (ISMR), this relationship was weak during the period 1921–1940 and the recent decade (1991–1998). During the El Niño years of 1921–1940 and 1901–1998, the meridional temperature anomaly gradient across Eurasia (Eurasian forcing) during January was directed towards equator. On the other hand, during the El Niño years of 1901–1920 and 1941–1990 this gradient was directed towards pole. Thus during 1921–1940 and 1991–1998, the adverse impact of El Niño on Indian monsoon was reduced by the favorable Eurasian forcing resulting in the weak association between El Niño and ISMR. This finding disagrees with the hypothesis of winter warming over the Eurasian continent as the reason for the observed weakening of this relationship during recent decade.     

Four-dimensional structure and sub-seasonal regulation of the Indian summer monsoon multi-decadal mode

A better understanding of the drivers and teleconnection mechanisms responsible for the multi decadal mode (MDM) of variability of the Indian summer monsoon rainfall (ISMR) with major socio-economic impacts in the region through clustering of large-scale floods or droughts is key to improving the poor simulation of ISMR MDM by most climate models. Here, using the longest instrumental record of ISMR available (1813–2006) and longest atmospheric and oceanic re-analyses, the global four dimensional (space–time) structures of atmospheric and oceanic fields of the multi-decadal mode of ISMR and sub-seasonal evolution of the teleconnection mechanism are brought out, essential for understanding underlying drivers but lacking so far. The relationships between the spatial structure of winds, Sea Surface Temperature (SST) and thermocline depth with the ISMR MDM indicate that the tropical ocean over the Indo-Pacific domain is passive responding primarily to the surface winds associated with the mode. A close association between the Atlantic Meridional Overturning Circulation (AMOC), north Atlantic (NA) SST, NA sea surface salinity (SSS) and the ISMR MDM indicate a slow oceanic pathway linking NA SST and the ISMR. In addition to strong correlation (~ 0.9) between global spatial patterns of JJAS SST associated with the MDMs of ISMR, NA SST and AMOC, strong temporal coherence (correlations ~ 0.9) between them is suggestive of regulation of the ISMR MDM (T ~ 65-years) by the NA SST associated with the Atlantic Multidecadal Oscillation (AMO) through a ‘fast’ atmospheric bridge. On a seasonal time scale, the atmospheric bridge manifests in the form of a stationary Rossby wave train generated by an anticyclonic (cyclonic) barotropic vorticity located above positive (negative) SST anomaly over NA in two phases of the AMO. That the AMO SST is the driver of the ISMR MDM is further supported when we unravel the sub-seasonal face of the teleconnection between the two. We show that phase locking of active (break) spells with annual cycle during positive (negative) phases of the ISMR MDM are forced by a similar phase locking of barotropic anticyclonic (cyclonic) vorticity over the NA SST with the annual cycle through the generation of a quasi-stationary Rossby wave train with an anticyclonic (cyclonic) vorticity at upper level over the Indian region with the NA columnar vorticity leading Indian monsoon rainfall by about a week. Our findings provide a basis for enhanced predictability of tropical climate through slow modulation by extra-tropical SST.

     

Characteristics of Eurasian snow depth with respect to Indian summer monsoon rainfall

Earlier studies show a strong negative relationship between Eurasian snow cover/depth and Indian summer monsoon rainfall (ISMR). In such studies, both the parameters snow and rainfall are seasonally averaged over large areas. Indian summer monsoon has its own characteristics of evolution such as onset, active, break and withdrawal phases which have been studied extensively. However, the evolution of Eurasian snow is yet to be examined. Further, it is interesting to explore the characteristics of evolution of snow over the different regions of Eurasia and their relationship with the evolution characteristics of summer monsoon. In this paper, a detailed examination has been done on the starting and the ending dates of snowfall over different regions of Eurasia and attempts have been made to explore any relationship with onset of ISMR. It is observed that the regions where snowfall started early, it also ended late. Further, in those regions maximum snow depth also occurred late. In some years, more snowfall in East Eurasia is followed by less snowfall in West Eurasia. Also snow depths particularly in the northernmost and southwest regions of East Eurasia are opposite in phase. The results of this study indicate a weak relationship between snow starting dates in Eurasia and summer monsoon onset dates in the Kerala coast. However, the relationship between the northernmost Eurasian snow depth and the summer monsoon precipitation in the Peninsular India is significant.     

Dynamical linkage of tropical and subtropical weather systems to the intraseasonal oscillations of the Indian summer monsoon rainfall. Part II: Simulations in the ENSEMBLES project

We assess the ability of individual models (single-model ensembles) and the multi-model ensemble (MME) in the European Union-funded ENSEMBLES project to simulate the intraseasonal oscillations (ISOs; specifically in 10–20-day and 30–50-day frequency bands) of the Indian summer monsoon rainfall (ISMR) over the Western Ghats (WG) and the Bay of Bengal (BoB), respectively. This assessment is made on the basis of the dynamical linkages identified from the analysis of observations in a companion study to this work. In general, all models show reasonable skill in simulating the active and break cycles of the 30–50-day ISOs over the Indian summer monsoon region. This skill is closely associated with the proper reproduction of both the northward propagation of the intertropical convergence zone (ITCZ) and the variations of monsoon circulation in this band. However, the models do not manage to correctly simulate the eastward propagation of the 30–50-day ISOs in the western/central tropical Pacific and the eastward extension of the ITCZ in a northwest to southeast tilt. This limitation is closely associated with a limited capacity of models to accurately reproduce the magnitudes of intraseasonal anomalies of both the ITCZ in the Asian tropical summer monsoon regions and trade winds in the tropical Pacific. Poor reproduction of the activity of the western Pacific subtropical high on intraseasonal time scales also amplify this limitation. Conversely, the models make good reproduction of the WG 10–20-day ISOs. This success is closely related to good performance of the models in the representation of the northward propagation of the ITCZ, which is partially promoted by local air–sea interactions in the Indian Ocean in this higher-frequency band. Although the feature of westward propagation is generally represented in the simulated BoB 10–20-day ISOs, the air–sea interactions in the Indian Ocean are spuriously active in the models. This leads to active WG rainfall, which is not present in the observed BoB 10–20-day ISOs. Further analysis indicates that the intraseasonal variability of the ISMR is generally underrepresented in the simulations. Skill of the MME in seasonal ISMR forecasting is strongly dependent on individual model performance. Therefore, in order to improve the model skill with respect to seasonal ISMR forecasting, we suggest it is necessary to better represent the robust dynamical links between the ISOs and the relevant circulation variations, as well as the proportion of intraseasonal variability in the individual models.     

Influence of Eurasian snow on Indian summer monsoon in NCEP CFSv2 freerun

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.     

Why ensemble mean projection of south Asian monsoon rainfall by CMIP5 models is not reliable?

Future projections of the Indian summer monsoon rainfall (ISMR) and its large-scale thermodynamic driver are studied by using CMIP5 model outputs. While all models project an increasing precipitation in the future warming scenario, most of them project a weakening large-scale thermodynamic driver arising from a weakening of the upper tropospheric temperature (UTT) gradient over south Asian summer monsoon region. The weakening of the UTT gradient under global warming scenarios is related to the increase in sea surface temperature (SST) over the equatorial Indian Ocean (EIO) leading to a stronger increase of UTT over the EIO region relative to the northern Indian region, a hypothesis supported by a series of Atmospheric General Circulation Model (AGCM) experiments forced by projected SSTs. To diagnose the inconsistency between the model projections of precipitation and the large-scale thermodynamic driver, we have examined the rate of total precipitation explained by convective and stratiform precipitations in observations and in CMIP5 models. It is found that most models produce too much (little) convective (stratiform) precipitation compared to observations. In addition, we also find stronger precipitable water—precipitation relationship in most CMIP5 models as compared to observations. Hence, the atmospheric moisture content produced by the model immediately gets converted to precipitation even though the large-scale thermodynamics in models weaken. Therefore, under global warming scenarios, due to increased temperature and resultant increased atmospheric moisture supply, these models tend to produce unrealistic local convective precipitation often not in tune with other large-scale variables. Our results questions the reliability of the ISMR projections in CMIP5 models and highlight the need to improve the convective parameterization schemes in coupled models for the reliable projections of the ISMR.     

Outgoing long-wave radiation over the Tropical Pacific and Atlantic Ocean and Indian summer monsoon rainfall

Summary In this paper, the interannual variability of satellite derived outgoing longwave radiation (OLR) is examined in relation to the Indian summer monsoon rainfall (June to September total rainfall; ISMR). Monthly grid point OLR field over the domain i.e. the tropical Pacific and Atlantic region (30°N to 30°S, 110°E to 10°W) and the ISMR for the period 1974–2001 are used for the study. A strong and significant north–south dipole structure in the correlation pattern is found between the ISMR and the OLR field over the domain during January. This dipole is located over the west Pacific region with highly significant negative (positive) correlations over the South China Sea and surrounding region (around north-east Australia). The dipole weakens and moves northwestward during February and disappears in March. During the month of May, the OLR over the central Atlantic Ocean shows a significant positive relationship with the ISMR. These relationships are found to be consistent and robust during the period of analysis and can be used in the prediction of the ISMR.A multiple regression equation is developed, using the above results, for prediction of the ISMR and the empirical relationships are verified using an independent data set. The results are encouraging for the prediction of the ISMR. The composite annual cycle of the OLR, over the west Pacific regions during extreme ISMR is found to be useful in the prediction of extreme summer monsoon rainfall conditions over the Indian subcontinent.     

Atmospheric circulation characteristics during some El-Nino years in relation to Indian summer monsoon rainfall

Summary Based on observed rainfall data of India Meteorological Department (IMD), correlation coefficients (CCs) have been computed between Indian summer monsoon rainfall (ISMR) and sea surface temperature (SST) anomalies over different Nino regions and standardised pressure difference between Tahiti and Darwin. Significant positive CCs are found between the Southern Oscillation Index (SOI) in winter and subsequent June rainfall over India. Concurrent with and subsequent to Indian summer monsoon, SOI shows significant positive CC with the mean rainfall of July to September (JAS). Significant negative CCs are found between JAS mean rain and the concurrent and following SST anomalies over Nino-3.4 region. On the basis of these correlations, it is proposed that the entire period of summer monsoon from June to September could be divided into two sub-periods such as: early summer (June) and mid-late summer (July to September) monsoon for prediction of ISMR in the extended range.In order to examine the characteristics of atmospheric circulation during some El-Nino years, divergent flow at 200hPa and omega field at 500hPa based on NCEP/NCAR reanalysis have been studied in detail. Major significant southward shift of upper level divergent field from India are related to El-Nino and this shift may be responsible for causing droughts during several El-Nino years over India. Also vertical wind fields at 500hPa show sinking motion over large parts of India and west Pacific and ascending motion over southern Indian Ocean, central and eastern Pacific during major drought years.