Indian Monsoon Rainfall Research Articles

Performance of August–September Indian monsoon rainfall when June–July rainfall is reported as being in deficit/excess

The Indian summer monsoon season (June to September) has been divided into two sub-seasons, viz., June + July (JJ) and August + September (AS). The performance of all-India AS rainfall has been examined when JJ rainfall is reported as being in excess/deficit, during the period 1871–2012. The study shows that the performance of AS rainfall is higher/lower than the JJ rainfall when JJ rainfall is in deficit/excess. The mean values of JJ and AS rainfalls are computed when JJ rainfall is in deficit/excess. The difference between the mean values is statistically significant. Sea surface temperature (SST) data over the Arabian Sea and Bay of Bengal for the period 1941–2011 have been used to examine the relationship between JJ SST and JJ, AS and AS − JJ (AS minus JJ) rainfalls. A statistically significant negative correlation is observed between JJ SST and JJ rainfall while significant positive correlation is observed between JJ SST and AS − JJ rainfall. The sub-seasonal relationship has been explained through the changes in the surface meridional temperature gradient over the Indian land and Oceanic regions in the JJ sub-season due to the performance of rainfall during the JJ sub-season. Wind data at 850 and 200 hPa levels for the period 1949–2012 have been analyzed to examine anomalies during deficit and excess JJ years. During deficit JJ rainfall years, easterly wind anomalies are observed at lower tropospheric levels over the Arabian Sea, which influence SST distributions. The SST distribution strengthens the meridional temperature gradient for developing the strong monsoon circulation during the AS sub-season thereby increasing the AS rainfall. A reverse situation is observed during excess JJ sub-seasons.

Solar radiation shapes Indian monsoon rainfall

Solar radiation shapes Indian monsoon rainfall

Spatial and temporal analysis of rainfall and temperature trend of India

Climate change is a serious issue resulting in global variation in the temperature and precipitation pattern. In this study, changes in rainfall trend in India for 141 years (1871–2011) and temperature trend for 107 years (1901–2007) were analysed. The annual, seasonal and monthly changes in different regions of India were investigated to see the climate change in different parts of the country, and the net excess or deficit of rainfall and temperature in India was obtained. Statistical non-parametric tests were performed to see the trend magnitude with the Mann-Kendall (MK) test and Sen’s slope. Mann-Whitney-Pettitt (MWP) test was used for probable break point detection in the series, and change percentage was calculated over 30 sub-divisions and 7 broad regions. The results indicate decreasing annual and monsoon rainfall of India in most of the sub-divisions, and temperature fluctuations were observed in all the places. Temperatures (minimum, maximum and mean) were showing a significant increase, particularly in the winter and post-monsoon time. Wide variation was noticed all over India in the case of the minimum temperature. Variation was also observed at different spatial scales of sub-divisions and regions. This study gives the net impact of climate change in India which shows net excess of temperature and net deficit of rainfall.

Non-stationary and non-linear influence of ENSO and Indian Ocean Dipole on the variability of Indian monsoon rainfall and extreme rain events

The El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are widely recognized as major drivers of inter-annual variability of the Indian monsoon (IM) and extreme rainfall events (EREs). We assess the time-varying strength and non-linearity of these linkages using dynamic linear regression and Generalized Additive Models. Our results suggest that IOD has evolved independently of ENSO, with its influence on IM and EREs strengthening in recent decades when compared to ENSO, whose relationship with IM seems to be weakening and more uncertain. A unit change in IOD currently has a proportionately greater impact on IM. ENSO positively influences EREs only below a threshold of 100 mm day−1. Furthermore, there is a non-linear and positive relationship between IOD and IM totals and the frequency of EREs (>100 mm day−1). Improvements in modeling this complex system can enhance the forecasting accuracy of the IM and EREs.

Indian summer monsoon rainfall: Dancing with the tunes of the sun

There is strong statistical evidence that solar activity influences the Indian summer monsoon rainfall. To search for a physical link between the two, we consider the coupled cloud hydrodynamic equations, and derive an equation for the rate of precipitation that is similar to the equation of a forced harmonic oscillator, with cloud and rain water mixing ratios as forcing variables. Those internal forcing variables are parameterized in terms of the combined effect of external forcing as measured by sunspot and coronal hole activities with several well known solar periods (9, 13 and 27days; 1.3, 5, 11 and 22years). The equation is then numerically solved and the results show that the variability of the simulated rate of precipitation captures very well the actual variability of the Indian monsoon rainfall, yielding vital clues for a physical understanding that has so far eluded analyses based on statistical correlations alone. We also solved the precipitation equation by allowing for the effects of long-term variation of aerosols. We tentatively conclude that the net effects of aerosols variation are small, when compared to the solar factors, in terms of explaining the observed rainfall variability covering the full Indian monsoonal geographical domains.

Global warming and the weakening of the Asian summer monsoon circulation: assessments from the CMIP5 models

The evolution of the Asian summer monsoon (ASM) in a global warming environment is a serious scientific and socio-economic concern since many recent studies have demonstrated the weakening nature of large-scale tropical circulation under anthropogenic forcing. But, how such processes affect the ASM circulation and rainfall is still a matter of debate. This study examines the climate model projections from a selected set of Coupled Model Inter-comparison Project 5 (CMIP5) models to provide a unified perspective on the future ASM response. The results indicate a robust reduction in the large-scale meridional gradient of temperature (MGT) at upper levels (200 hPa) over the ASM region, associated with enhanced ascendance and deep tropospheric heating over the equatorial Pacific in the future climate. The differential heating in the upper troposphere, with concomitant increase (decrease) in atmospheric stability (MGT), weakens the ASM circulation, promotes a northward shift of the monsoon circulation and a widening of the local Hadley cell in the eastern Indian sector. An examination of the water vapour budget indicates the competing effects of the thermodynamic (moisture convergence) and dynamics processes (monsoon circulation) on future ASM rainfall changes. The former component wins out over the later one and leads to the intensification of Indian monsoon rainfall in the CMIP5 projections. However, the diagnostics further show a considerable offset due to the dynamic component.

Observed changes in southwest monsoon rainfall over India during 1901–2011

ABSTRACTThe trends and epochal variability of southwest monsoon over the country as a whole and four homogeneous regions are examined using monthly rainfall data (1901–2011) of 640 political districts of India. The district rainfall data is computed from station rainfall data. The same station data is used to analyse the trends in the frequency of rainfall events of different intensities for examining extreme rainfall events. The existence of the multidecadal epochal variability of rainfall is clearly established in the all‐India monsoon rainfall as well as monsoon rainfall over the four homogenous regions. However, over different homogenous regions, the phases of multidecadal variability are found to be different. Principal component analysis brings out Northeast India (NEI) rainfall as more dominant mode for all‐India rainfall. Significant decrease in southwest monsoon rainfall over NEI is observed during the post 1950 period. Decreasing trends are also observed over the monsoon core region during the post‐1950 period. Over these regions, monsoon rainfall has increased significantly during the pre‐1950 period. It has been shown that the decreasing trend in monsoon rainfall during the post 1950 period is the result of multidecadal epochal variability. Geographical regions that experienced significant changes in the frequency of days of rainfall with different intensities are also identified. Significant change/turning points are also detected in the southwest monsoon rainfall. Frequency of moderate rainfall events (5 mm ≤ daily rainfall < 100 mm) decreased significantly during the period 1951–2010 over the monsoon core region of India whereas no significant changes are observed in the frequencies of heavy (daily rainfall >100 mm) or very heavy rain (daily rainfall >150 mm) during the southwest monsoon season. Climatic shift or change point in monsoon rainfall in India is also detected by an established statistical test.

A hybrid model based on latest version of NCEP CFS coupled model for Indian monsoon rainfall forecast

AbstractThe forecast skill of all India summer monsoon rainfall (AISMR) during June to September (JJAS) in the new version of the National Centers for Environmental Prediction (NCEP)'s Climate Forecast System version 2 (CFSv2) is analyzed by considering 28 years (1982–2009) retrospective forecasts. The spatial patterns of JJAS mean rainfall and its interannual variability is more realistic over the Indian monsoon region in CFSv2 as compared to previous version of NCEP's CFS. A hybrid (dynamical–empirical) model based on the forecast variables of CFSv2 is developed for AISMR, which shows correlation that is highly significant with observed AISMR. The hybrid model correctly predicted the observed AISMR departure of 2013.

Combining regional moist static energy and ENSO for forecasting of early and late season Indian monsoon rainfall and its extremes

AbstractWe exploit El Niño–Southern Oscillation (ENSO) indices and moist static energy of surface air over the Indian subcontinent and surroundings as predictors of monsoon rainfall over India during early and late seasons, defined here as 20 May to 15 June and 20 September to 15 October, respectively. Although these seasons contribute only ~22% of the entire seasonal rainfall, they clearly affect planning of agriculture and water resources. A simple, nonlinear, statistical model applied to these predictors accounts for ~40% and 45% of the observed variance of early and late season rainfall, respectively, and similar fractions for 3 day maximum rainfall intensity. Forecasted average and 3 day maximum rainfall at grid points covering India show greatest success over central India during the early season and over west central, northwestern, and northern India during the late season, regions where agriculture dominates land use. These predictors, however, offer virtually no predictability of peak season rainfall.

Long Range Forecast on South West Monsoon Rainfall using Artificial Neural Networks based on Clustering Approach

The purpose of this study is to forecast Southwest Indian Monsoon rainfall based on sea surface temperature, sea level pressure, humidity and zonal (u) and meridional (v) winds. With the aforementioned parameters given as input to an Artificial Neural Network (ANN), the rainfall within 1 0 x1 0 grids of southwest Indian regions is predicted by means of one of the most efficient clustering methods, namely the Kohonen Self- Organizing Maps (SOM). The ANN is trained with input parameters spanning for 36 years (1960-1995) and tested and validated for a period of 9 years (1996-2004). It is further used to predict the rainfall for 6 years (2005-2010). The results show reasonably good accuracy for the summer monsoon periods June, July, August and September (JJAS) of the validation years.

Desert dust and monsoon rain

For centuries, inhabitants of the Indian subcontinent have know that heavy dust events brought on by strong winds occur frequently in the pre-monsoon season, before the onset of heavy rain. Yet scientists have never seriously considered the possibility that natural dust can affect monsoon rainfall. Up to now, most studies of the impacts of aerosols on Indian monsoon rainfall have focused on anthropogenic aerosols in the context of climate change. However, a few recent studies have show that aerosols from antropogenic and natural sources over the Indian subcontinent may affect the transition from break to active monsoon phases on short timescales of days to weeks. Writing in Nature Geoscience, Vinoj and colleagues describe how they have shown that desert dust aerosols over the Arabian Sea and West Asia can strenghten the summer monsoon over the Indial subcontinent in a matter of days.

Role of Ocean in the Variability of Indian Summer Monsoon Rainfall

Asian summer monsoon sets in over India after the Intertropical Convergence Zone moves across the equator to the northern hemisphere over the Indian Ocean. Sea surface temperature (SST) anomalies on either side of the equator in Indian and Pacific oceans are found related to the date of monsoon onset over Kerala (India). Droughts in the June to September monsoon rainfall of India are followed by warm SST anomalies over tropical Indian Ocean and cold SST anomalies over west Pacific Ocean. These anomalies persist till the following monsoon which gives normal or excess rainfall (tropospheric biennial oscillation). Thus, we do not get in India many successive drought years as in sub-Saharan Africa, thanks to the ocean. Monsoon rainfall of India has a decadal variability in the form of 30-year epochs of frequent (infrequent) drought monsoons occurring alternately. Decadal oscillations of monsoon rainfall and the well-known decadal oscillation in SST of the Atlantic Ocean (also of the Pacific Ocean) are found to run parallel with about the same period close to 60 years and the same phase. In the active–break cycle of the Asian summer monsoon, the ocean and the atmosphere are found to interact on the time scale of 30–60 days. Net heat flux at the ocean surface, monsoon low-level jetstream (LLJ) and the seasonally persisting shallow mixed layer of the ocean north of the LLJ axis play important roles in this interaction. In an El Nino year, the LLJ extends eastwards up to the date line creating an area of shallow ocean mixed layer there, which is hypothesised to lengthen the active–break (AB) cycle typically from 1 month in a La Nina to 2 months in an El Nino year. Indian monsoon droughts are known to be associated with El Ninos, and long break monsoon spells are found to be a major cause of monsoon droughts. In the global warming scenario, the observed rapid warming of the equatorial Indian ocean SST has caused the weakening of both the monsoon Hadley circulation and the monsoon LLJ which has been related to the observed rapid decreasing trend in the seasonal number of monsoon depressions.

The changing characteristics of monsoon rainfall in India during 1971-2005 and links with large scale circulation

This study examines the changes in monsoon rainfall of India using a suite of extreme indices defined by the Expert Team on Climate Change Detection Monitoring and Indices (ETCCDMI) to make the Indian results comparable internationally, in addition to the use of some relevant indices particularly developed for the Indian climate. To this end, the recently developed high resolution daily gridded (0.5° × 0.5°) rainfall dataset for the period 1971–2005 has been analysed using robust nonparametric techniques. Despite the high interannual variability and spatial diversity of the Indian climatology, the results reveal signal of changes for several extreme rainfall indices, generally consistent with the simulated outcome of an intensified Indian monsoon rainfall in the context of global warming. A predominant decrease in wet days, moderate and total rainfall is observed in the high rainfall regions of northeast, central and southwest India. In the active monsoon months of July and August, the dry spells defined by the maximum length of consecutive dry days (CDD) have increased significantly over the north and central regions of India, suggesting a serious threat to the Indian agriculture. Simultaneously, the extreme rainfall indices, based on the percentile and absolute values, show increasing trends over large parts of the country. The probability density function (PDF) of several indices show noticeable changes since the 1990s over the homogeneous central and northeast parts of India. The indices representing the total monsoon rainfall and dry spells are better correlated with the El Nino–Southern Oscillation (NINO3.4), compared to that of the Indian Ocean Dipole Mode Index (IODMI). The mapping of the observed rainfall trends and their correspondence to the large scale circulation modes is expected to assist the policy makers to prioritize the mitigation and adaptation strategies.

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.

Gridded daily Indian monsoon rainfall for 14 seasons: Merged TRMM and IMD gauge analyzed values

Indian monsoon is an important component of earth’s climate system. Daily rainfall data for longer period is vital to study components and processes related to Indian monsoon. Daily observed gridded rainfall data covering both land and adjoining oceanic regions are required for numerical model validation and model development for monsoon. In this study, a new gridded daily Indian rainfall dataset at 1°×1° latitude/longitude resolution covering 14 monsoon seasons (1998–2011) are described. This merged satellite gauge rainfall dataset (NMSG) combines TRMM TMPA rainfall estimates with gauge information from IMD gridded data. Compared to TRMM and GPCP daily rainfall data, the current NMSG daily data has more information due to inclusion of local gauge analysed values. In terms of bias and skill scores this dataset is superior to other daily rainfall datasets. In a mean climatological sense and also for anomalous monsoon seasons, this merged satellite gauge data brings out more detailed features of monsoon rainfall. The difference of NMSG and GPCP looks significant. This dataset will be useful to researchers for monsoon intraseasonal studies and monsoon model development research.

Glacial fluctuations of the Indian monsoon and their relationship with North Atlantic climate: new data and modelling experiments

Abstract. Several paleoclimate records such as from Chinese loess, speleothems or upwelling indicators in marine sediments present large variations of the Asian monsoon system during the last glaciation. Here, we present a new record from the northern Andaman Sea (core MD77-176) which shows the variations of the hydrological cycle of the Bay of Bengal. The high-resolution record of surface water δ18O dominantly reflects salinity changes and displays large millennial-scale oscillations over the period 40 000 to 11 000 yr BP. Their timing and sequence suggests that events of high (resp. low) salinity in the Bay of Bengal, i.e. weak (resp. strong) Indian monsoon, correspond to cold (resp. warm) events in the North Atlantic and Arctic, as documented by the Greenland ice core record. We use the IPSL_CM4 Atmosphere-Ocean coupled General Circulation Model to study the processes that could explain the teleconnection between the Indian monsoon and the North Atlantic climate. We first analyse a numerical experiment in which such a rapid event in the North Atlantic is obtained under glacial conditions by increasing the freshwater flux in the North Atlantic, which results in a reduction of the intensity of the Atlantic meridional overturning circulation. This freshwater hosing results in a weakening of the Indian monsoon rainfall and circulation. The changes in the continental runoff and local hydrological cycle are responsible for an increase in salinity in the Bay of Bengal. This therefore compares favourably with the new sea water δ18O record presented here and the hypothesis of synchronous cold North Atlantic and weak Indian monsoon events. Additional sensitivity experiments are produced with the LMDZ atmospheric model to analyse the teleconnection mechanisms between the North Atlantic and the Indian monsoon. The changes over the tropical Atlantic are shown to be essential in triggering perturbations of the subtropical jet over Africa and Eurasia, that in turn affect the intensity of the Indian monsoon. These relationships are also found to be valid in additional coupled model simulations in which the Atlantic meridional overturning circulation (AMOC) is forced to resume.

Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models

Abstract. The possibility of an impact of global warming on the Indian monsoon is of critical importance for the large population of this region. Future projections within the Coupled Model Intercomparison Project Phase 3 (CMIP-3) showed a wide range of trends with varying magnitude and sign across models. Here the Indian summer monsoon rainfall is evaluated in 20 CMIP-5 models for the period 1850 to 2100. In the new generation of climate models, a consistent increase in seasonal mean rainfall during the summer monsoon periods arises. All models simulate stronger seasonal mean rainfall in the future compared to the historic period under the strongest warming scenario RCP-8.5. Increase in seasonal mean rainfall is the largest for the RCP-8.5 scenario compared to other RCPs. Most of the models show a northward shift in monsoon circulation by the end of the 21st century compared to the historic period under the RCP-8.5 scenario. The interannual variability of the Indian monsoon rainfall also shows a consistent positive trend under unabated global warming. Since both the long-term increase in monsoon rainfall as well as the increase in interannual variability in the future is robust across a wide range of models, some confidence can be attributed to these projected trends.

Decadal scale oscillations and trend in the Indian monsoon rainfall

The emerging need for extended climate prediction requires a consideration of the relative roles of climate change and low-frequency natural variability on decadal scale. Addressing this issue, this study has shown that the variability of the Indian monsoon rainfall (IMR) consists of three decadal scale oscillations and a nonlinear trend during 1901–2004. The space–time structures of the decadal oscillations are described. The IMR decadal oscillations are shown to be associated with Atlantic Multidecadal Oscillation (AMO), Atlantic tripole oscillation and Pacific Decadal Oscillation (PDO). The sea surface temperatures (SSTs) of the North Pacific and North Atlantic Oceans are also resolved as nonlinear decadal oscillations. The SST AMO mode has high positive correlation with IMR while the SST tripole mode and SST PDO have negative correlation. The trend in IMR increases during the first half of the period and decreases during the second half. The IMR trend is modified when combined with the three decadal oscillations.

Documentary reconstruction of monsoon rainfall variability over western India, 1781–1860

Investigations into the climatic forcings that affect the long-term variability of the Indian summer monsoon are constrained by a lack of reliable rainfall data prior to the late nineteenth century. Extensive qualitative and quantitative meteorological information for the pre-instrumental period exists within historical documents, although these materials have been largely unexplored. This paper presents the first reconstruction of monsoon variability using documentary sources, focussing on western India for the period 1781–1860. Three separate reconstructions are generated, for (1) Mumbai, (2) Pune and (3) the area of Gujarat bordering the Gulf of Khambat. A composite chronology is then produced from the three reconstructions, termed the Western India Monsoon Rainfall reconstruction (WIMR). The WIMR exhibits four periods of generally deficient monsoon rainfall (1780–1785, 1799–1806, 1830–1838 and 1845–1857) and three of above-normal rainfall (1788–1794, 1813–1828 and 1839–1844). The WIMR shows good correspondence with a dendroclimatic drought reconstruction for Kerala, although agreement with the western Indian portion of the tree-ring derived Monsoon Asia Drought Atlas is less strong. The reconstruction is used to examine the long-term relationship between the El Nino-Southern Oscillation (ENSO) and monsoon rainfall over western India. This exhibits peaks and troughs in correlation over time, suggesting a regular long-term fluctuation. This may be an internal oscillation in the ENSO-monsoon system or may be related to volcanic aerosol forcings. Further reconstructions of monsoon rainfall are necessary to validate this. The study highlights uncertainties in existing published rainfall records for 1817–1846 for western India.

A common mode of variability of African and Indian monsoon rainfall at decadal timescale

There are many factors and mechanisms capable of influencing and perturbing rainfall in both African and Indian monsoon regions. Using observed data and ensembles of Atmospheric General Circulation Model simulations, evidence is presented that an association between the two systems exists on decadal timescales and the mechanism responsible for this common mode is suggested. Decadal variability of rainfall in the two monsoon systems results from a large scale forcing induced by an interplay of different ocean basins. The emerging pattern is characterized by warmer (cooler) equatorial and cooler (warmer) extratropical regions, more visible in the northern hemisphere. This large scale forcing pattern leads to an upper-level pressure gradient between the equator and the monsoon regions which modifies also the Tropical Easterly Jet, thus providing a potential link between the African and Indian monsoon. The response is baroclinic, therefore at low levels, the pressure gradient reverses and leads to increased (reduced) pressure over the Saharan and Indian region, both being favourable for a weakening (strengthening) of the respective monsoons. Therefore, the predictability of the monsoon trends depends mainly on how well the sea surface temperature modes, which modulate the monsoons variability, can be predicted.