all-India Summer Monsoon Rainfall Research Articles

Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India

The role of ocean variability is at a focal point in improving the weather and climate forecasts at different spatial and temporal scales. We study the effect of antecedent southwestern Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) as a proxy to upper ocean heat capacitance on all India summer monsoon rainfall (AISMR) during 1993–2019. SSTA and MSLA over the southwestern Indian Ocean (SWIO) have been influenced by El Niño-Southern Oscillation (ENSO), the impact of ENSO-induced SWIO variability was low on rainfall variability over several homogeneous regions. Rainfall over northeast (NE) and North India (EI) has been modulated by ENSO-induced SSTA and MSLA over SWIO, thus effecting the total AISMR magnitude. The ENSO-induced changes in heat capacitance (SSTA and MSLA) over SWIO during antecedent months has less impact on west coast of India, central India and North India (NI) rainfall variability. The long-term trend in pre-monsoonal SSTA and MSLA over SWIO shows decreasing rainfall trend over NI, NE, and EI in the recent time. Furthermore, the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period. While SSTA and MSLA are increasing in the SWIO, large-scale variability of these parameters during preceding winter and pre-monsoon months combined with surface winds could impact the inter-annual AISMR variability over homogeneous regions of India. Similarly, from an oceanic perspective, the antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability.

Simulation of Indian summer monsoon rainfall, interannual variability and teleconnections: evaluation of CMIP6 models

We analyse the performance of global climate models of 6 $$\mathrm{th}$$ generation of Coupled Model Intercomparison Project (CMIP6) in simulating climatological summer monsoon rainfall over India, interannual variability (IAV) of all-India summer monsoon rainfall (ISMR) and its teleconnections with rainfall variability over equatorial Pacific and Indian Oceans. The multimodel ensemble mean (MME) of 61 CMIP6 models shows the best skill in simulating mean monsoon rainfall over India compared to the MMEs of 6 $$\mathrm{th}$$ generation atmosphere-only models (AMIP6) and the previous generations of Atmospheric and Coupled Model Intercomparison Projects (AMIPs and CMIPs). Systematic improvement and reduction in bias are evident from lower to higher AMIPs/CMIPs. Still, there exists dry bias over a narrow region of the monsoon zone of central India besides wet and cold bias over the surrounding oceans. The persistence of errors in atmosphere-only models hints that the source of errors could be with atmosphere models. Fifteen CMIP6 models selected through objective criteria, perform the best in simulating mean monsoon, IAV of ISMR, the strong inverse relationship between ISMR and Boreal summer El Niño-Southern Oscillation (ENSO), and the inverse relationship between all-India rainfall and north–west tropical Pacific rainfall in June. Several models reproduce the dipole structure of Equatorial Indian Ocean Oscillation (EQUINOO) with the centres over western and eastern equatorial Indian Ocean. But, ISMR-EQUINOO relationship in many of them is opposite to the observed. Our analysis implies the need for capturing ISMR-EQUINOO link to improve the simulation of IAV of ISMR which is crucial for reliable monsoon prediction and projection.

Recent unprecedented weakening of Indian summer monsoon in warming environment

All India summer monsoon rainfall (AISMR) shows a declining trend since 1953. Hence, the period 1953–2015 is considered for this study. The deviations of the summer monsoon (SM) rainfall in the recent period (1983–2015) from that of the previous period (1953–1982) have been evaluated for individual months and whole SM season using India Meteorological Department (IMD) gridded rainfall. The changes in the SM rainfall in the recent 3 decades with respect to the changes in the thermal, moisture, and circulation patterns over India and neighborhood are studied. The relationships between the winds over the Somali jet core region and the SM rainfall over India are quantified. Enhancement of rainfall activity is seen over a few subdivisions mainly Jammu & Kashmir and parts of Northwest India where the western disturbance interaction and the meridional wind component of low level jet (LLJ) have been pronounced. The decline in rainfall over major part of India in the recent 33 years is associated with weakened low level circulation, decrease in the relative humidity (RH) over the Bay of Bengal, less frequency of cyclonic disturbances formed in the Bay of Bengal, decrease in the genesis potential parameter as well as north-south sea level pressure gradient between Northern and Southern Hemispheres. The inconsistency of warming rates between Northern India and Southern India and its adjoining areas in the layer, 600–200 hPa induces reduced SM circulation. Further, the Walker and Hadley circulations specify a decline in the convective activity in SM season over the Indian mainland.

High-resolution mid-Holocene Indian Summer Monsoon recorded in a stalagmite from the Kotumsar Cave, Central India

The Indian Summer Monsoon (ISM), a significant part of the global monsoon system, is driven by several climate forcing parameters. With the growing pace of global climate change scenario, there is need to focus on generating more high-resolution records of past monsoon. The ISM reconstructions from Core Monsoon Zone (CMZ) of India, which represents all-India Summer Monsoon Rainfall, are useful for a better understanding of its past variability. Such reconstructions from the CMZ are rather sparse and require detailed study based on various climate proxies. Here, we focus on the reconstruction of ISM variability during the mid-Holocene, based on stalagmite oxygen isotope ratios from the Kotumsar cave, Central India. We show that with decreasing insolation, monsoon started declining at the beginning of the mid-Holocene from 8.5 to 6.5 ka BP, which is also observed in the previous ISM reconstructions with coarser resolutions. However, a gradual increase in the rainfall is observed from 6.5 to 5.6 ka BP, a feature which is also noted in the East Asian Monsoon reconstruction from the Dongge cave. Our record mainly emphasizes on the occurrence of several abrupt weak monsoon events throughout the mid-Holocene. The occurrence of 8.2 and 5.9 ka abrupt weak monsoon events suggest that ISM variability is tightly bound to North-Atlantic Oscillation (NAO). We also demonstrate that ISM during the mid-Holocene was partly sensitive to El-Nino Southern Oscillation (ENSO), and displayed an inverse relationship.

Latitudinal variation in summer monsoon rainfall over Western Ghat of India and its association with global sea surface temperatures

The Western Ghats (WG) of India are basically north-south oriented mountains having narrow zonal width with a steep rising western face. The summer monsoon winds during June to September passing over the Arabian Sea are obstructed by the WG and thus orographically uplift to produce moderate-to-heavy precipitation over the region. However, it is seen that characteristic features of rainfall distribution during the season vary from north to south. Also its correlation with all-India summer monsoon rainfall increases from south to north. In the present study, an attempt is also made to examine long-term as well as short-term trends and variability in summer monsoon rainfall over different subdivisions of WG using monthly rainfall data for the period 1871–2014. Konkan & Goa and Coastal Karnataka show increase in rainfall from 1871 to 2014 in all individual summer monsoon months. Short-term trend analysis based on 31-year sliding window indicates that the trends are not monotonous, but has epochal behavior. In recent epoch, magnitudes of negative trends are consistently decreasing and have changed its sign to positive during 1985–2014.It has been observed that Indian Ocean Dipole (IOD) plays a dominant positive role in rainfall over entire WG in all summer monsoon months, whereas role of Nino regions are asymmetric over WG rainfall. Indian summer monsoon is known for its negative relationship with Nino SST. Negative correlations are also seen for WG rainfall with Nino regions but only during onset and withdrawal phase. During peak monsoon months July and August subdivisions of WG mostly show positive correlation with Nino SST.

Multiscale characterization and prediction of monsoon rainfall in India using Hilbert–Huang transform and time-dependent intrinsic correlation analysis

In this paper, the Hilbert–Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time–frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Nino Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989–2012 including the four extreme events that have occurred during this period.

Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution

AbstractThis study compares the simulation and prediction skill of the Indian summer monsoon at two different horizontal resolutions viz., T126 (~100 km) and T382 (~38 km) using 28 years of hindcast runs of the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) model. It is found that the simulation of the mean state of the South Asian summer monsoon, its variance, and prediction skill of the all India summer monsoon rainfall (AISMR) are better represented in the high‐resolution configuration (T382) of the CFSv2 compared to the low‐resolution (T126) configuration. In the high‐resolution run, the systematic bias in the teleconnection between the AISMR and Indian Ocean Dipole (IOD) has considerably reduced and the teleconnections between the AISMR and El Niño–Southern Oscillation (ENSO) remained same. We hypothesize that the better simulation of mean climate and IOD‐AISMR teleconnection in high‐resolution configuration (T382) of CFSv2 are responsible for the improved prediction skill of AISMR in T382 configuration. Although the T382 configuration of CFSv2 has shown a significant improvement in the simulation and prediction of Indian summer monsoon as compared to the T126 configuration, several parallel efforts are still essential to understand the processes controlling some of the systematic biases of CFSv2 and those efforts are underway as part of the Monsoon Mission project.

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65–75 cm) AISMR in the year 2015.

Indian summer monsoon prediction and simulation in CFSv2 coupled model

AbstractUsing carefully designed coupled model experiments, we have demonstrated that the prediction skill of the all India summer monsoon rainfall (AISMR) in Climate Forecast System version 2 (CFSv2) model basically comes from the El‐Niňo Southern Oscillation‐Monsoon teleconnection. On the other hand, contrary to observations, the Indian Ocean coupled dynamics do not have a crucial role in controlling the prediction skill of the AISMR in CFSv2. We show that the inadequate representation of the Indian Ocean coupled dynamics in CFSv2 is responsible for this dichotomy. Hence, the improvement of the Indian Ocean coupled dynamics is essential for further improvement of the AISMR prediction skill in CFSv2.

Simulation of Indian summer monsoon using the Japan Meteorological Agency’s seasonal ensemble prediction system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June–September (JJAS) by using hindcast of the Japan Meteorological Agency (JMA) seasonal ensemble prediction system (EPS) model, based on five ensembles of March, April and May initial states for a period of 32 years (1979–2010).The hindcast climatology during JJAS simulates the mean monsoon circulation at lower and upper tropospheres very well in JMA model using March, April and May ensembles with a more realistic simulation of Webster and Yang’s broad scale monsoon circulation index. The JMA hindcast climatology during JJAS simulates the rainfall maxima over the west-coast of India and the head Bay of Bengal reasonably well, although, the latter is slightly shifted southwestward. Associated with better forecast skills of El Nino in the JMA model, the interannual variability of All India Summer Monsoon Rainfall (AISMR) during the 32-year period has also been very well simulated with a high significant (99% level) correlation in April ensemble followed by that of March and May ensembles. Thus, the present analysis indicates that the JMA seasonal forecast model can prove to be a useful tool for the dynamical seasonal forecast of AISMR.

Comparison of TMPA-3B42 Versions 6 and 7 Precipitation Products with Gauge-Based Data over India for the Southwest Monsoon Period

Abstract The upgraded version 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products is available to the user community. In this paper, two successive versions of the TMPA-3B42 research monitoring product, version 6 (V6) and V7, at the daily scale are evaluated over India during the southwest monsoon with gauge-based data for a 13-yr (1998–2010) period. Over typical monsoon rainfall zones, biases are improved by 5%–10% in V7 over the regions of higher rainfall like the west coast, northeastern, and central India. A similar reduced bias is seen in V7 over the rain-shadow region located in southeastern India. In terms of correlation, anomaly correlation, and RMSE, a marginal improvement is seen in V7. Additionally, in all-India summer monsoon rainfall amounts, mean, interannual values, and standard deviation show an overall improvement in V7. Different skill metrics over typical subregions within India show an improvement of the monsoon rainfall representation in V7. Rainfall frequency in different categories also indicates an overall improvement in V7 across all scales and subregions. Over central India regions associated with the monsoon transients, the sign of the bias has changed toward a positive bias. Even if the bias in the frequency of the occurrence of light rain has improved in V7, the values still show a large difference compared to observations. Though both V6 and V7 are able to represent the anomalous dry/wet regions during contrasting monsoon years, V7 shows some improvement in amplitude of those anomalies over V6. In general, V7 has considerably improved over V6 and will continue to be in demand from various sectors of observed rainfall data users.

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.

Long-Lead Prediction Skill of Indian Summer Monsoon Rainfall Using Outgoing Longwave Radiation (OLR): an Application of Canonical Correlation Analysis

There is a statistical linkage between tropical outgoing longwave radiation (OLR) and all-India summer monsoon rainfall (AISMR). A positive and significant correlation is observed over the surrounding areas of northeast Australia and the Arafura Sea during the months of January and February (J&F) which drops down as the lead month decreases. The OLR index as an area average over the surrounding areas of northeast Australia and the Arafura Sea is found to have 0.4 correlation with AISMR. The index is also found to be strongly correlated with the Indian monsoon index. In view of the teleconnection pattern, the OLR index is used for the development of statistical models using the concept of linear regression (LR) and canonical correlation analysis (CCA). Potential of CCA over LR is observed for the prediction of seasonal rainfall over the northwest, west central and over the whole country as well. The seasonal rainfall predictability basically comes from the months of June and September.

On the association between pre-monsoon aerosol and all-India summer monsoon rainfall

Summer monsoon rainfall which gives 75–90% of the annual rainfall plays vital role in Indian economy as the food grain production in India is very much dependent on the summer monsoon rainfall. It has been suggested by recent studies that aerosol loading over the Indian region plays significant role in modulating the monsoon circulation and consequent rainfall distribution over the Indian sub-continent. Increased industrialization and the increasing deforestation over past few decades probably cause a gradual increase in the aerosol concentration. A significant negative relationship between pre-monsoon (March–May i.e. MAM) aerosol loading over BOB and IGP regions and the forthcoming monsoon rainfall have been observed from the thorough analysis of the fifteen years (1997–2011) monthly Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) and All-India Summer Monsoon Rainfall (AISMR) data. Composite analysis revealed that AI anomalies during pre-monsoon season are negative for excess year and positive for deficient monsoon years over the Indian subcontinent, with strong variation over Bay of Bengal (BOB) and Indo-Gangetic Plain (IGP) regions from the month of March onwards. The correlation coefficients between AISMR and pre-monsoon AI over BOB and IGP regions are found to be negative and significant at 5% level. The study clearly brings out that the pre-monsoon aerosol loading over the BOB and IGP regions has a significant correlational link with the forthcoming monsoon intensity; however a further study of the aerosol properties and their feedback to the cloud microphysical properties is asked for establishing their causal linkage.

Performance of all-India southwest monsoon seasonal rainfall when monthly rainfall reported as deficit/excess

Performance of all-India summer monsoon rainfall (June to September) has been studied when June or July or June + July rainfall is reported as being in excess/deficit, using monthly and seasonal rainfall data for the period 1871–2010. The study shows that seasonal rainfall was above/below the long term mean in almost all the years when rainfall of June or July or June + July was in excess/deficit. The probabilities of occurrence of seasonal rainfall below the long term mean are 77, 83, and 92% respectively when June, July, and June + July rainfalls are in deficit. The years in which the rainfalls of June and July were deficit, were mostly El Nino years and their corresponding seasonal rainfalls were below the mean. The deficiencies in June/July or June + July rainfall during El Nino years thus give an indication of seasonal rainfall being below the mean, and vice versa for La Nina years. The study has brought out an important result that rainfall during June gives a first signal of the performance of seasonal rainfall. In order to explain the relationship between June excess/deficit rainfall and seasonal above/below mean rainfall, an hypothesis is proposed which states that the circulation anomalies developed over the Indian region during an excess/deficit June persist throughout the season. The hypothesis has been proved by examining the composites of excess/deficit June wind anomalies at 850 and 200 hPa and observing their subsequent persistence from July to September. Copyright © 2013 Royal Meteorological Society

On the predictability of Indian summer monsoon rainfall in general circulation model at different lead time

The objective of this present study is to analyze the predictability of all India summer monsoon rainfall (AISMR) and its dependence on lead time using general circulation model (GCM) output. For the purpose, six GCMs for the hindcast run from 1982 to 2008 are used at three different initializations viz. April (lead 2), May (lead 1), and June (lead 0) for seasonal mean rainfall of June–July–August–September (JJAS). Among these models, four of them are the coupled ocean–atmosphere GCMs (CGCMs) and the remaining two are the atmospheric GCMs (AGCMs). The analysis is made on the basis of statistical measures of predictability including climatology, interannual variability, root mean square error, correlation, signal to noise ratio, potential model predictability and index of agreement. On the basis of these measures it is found that all the GCM having the minimum prediction skill is at lead 2 compare to lead 1 and lead 0. It is also noticed that higher predictability in the lead-1 forecasts is found in coupled models whereas, the predictability of atmospheric models exhibit high in lead 0. Rather than rainfall, teleconnection of rainfall with large scale features (such as sea surface temperature, zonal wind at 850hPa) and monsoon dynamic index (Indian monsoon index (IMI)) are also examined in GCMs. The results depicted that there is not much variation in the teleconnection pattern in two leads (lead 0 and lead 1) whereas; the dynamic index being predicted closer to the observed value at lead 1 in the CGCMs. The GCMs are also examined during four typical monsoon (excess/deficit) years, among which 1983 and 1988 are excess and 1987 and 2002 are deficit. Results indicate that the coupled (atmospheric) models capture the extreme rainfall signal in lead 1 (lead 0). The probabilistic prediction skill of GCM predicted rainfall is also evaluated which supports our initial analysis and results.

Sensitivity of the GCM driven summer monsoon simulations to cumulus parameterization schemes in nested RegCM3

The regional climate model (RegCM3) from the Abdus Salam International Centre for Theoretical Physics has been used to simulate the Indian summer monsoon for three different monsoon seasons such as deficit (1987), excess (1988) and normal (1989). Sensitivity to various cumulus parameterization and closure schemes of RegCM3 driven by the National Centre for Medium Range Weather Forecasting global spectral model products has been tested. The model integration of the nested RegCM3 is conducted using 90 and 30-km horizontal resolutions for outer and inner domains, respectively. The India Meteorological Department gridded rainfall (1° × 1°) and National Centre for Environment Prediction (NCEP)–Department of Energy (DOE) reanalysis-2 of 2.5° × 2.5° horizontal resolution data has been used for verification. The RegCM3 forced by NCEP–DOE reanalysis-2 data simulates monsoon seasons of 1987 and 1988 reasonably well, but the monsoon season of 1989 is not represented well in the model simulations. The RegCM3 runs driven by the global model are able to bring out seasonal mean rainfall and circulations well with the use of the Grell and Anthes–Kuo cumulus scheme at 90-km resolution. While the rainfall intensity and distribution is brought out well with the Anthes–Kuo scheme, upper air circulation features are brought out better by the Grell scheme. The simulated rainfall distribution is better with RegCM3 using the MIT-Emanuel cumulus scheme for 30-km resolution. Several statistical analyses, such as correlation coefficient, root mean square error, equitable threat score, confirm that the performance of MIT-Emanuel scheme at 30-km resolution is better in simulating all-India summer monsoon rainfall. The RegCM3 simulated rainfall amount is more and closer to observations than that from the global model. The RegCM3 has corrected its driven GCM in terms of rainfall distribution and magnitude over some parts of India during extreme years. This study brings out several weaknesses of the RegCM model which are documented in this paper.

Dynamical-Empirical forecast for the Indian monsoon rainfall using the NCEP Coupled Modelling System – Application for real time monsoon forecast

The performance of the National Centre for Environmental Prediction’s (NCEP) operational coupled modeling system known as the Climate Forecast System (CFS) is evaluated for the prediction of all India summer monsoon rainfall (AISMR) during June to September (JJAS). The evaluation is based on the hindcast initialized during March, April and May with 15 ensemble members each for 25 years period from 1981 to 2005.The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of observed climatology with both the rainfall maxima (over the west-coast of India and over the head Bay of Bengal region) well captured, with a signification correlation coefficient between the forecast and observed climatology over the Indian monsoon region (bounded by 50°E-110°E and 10°S-35°N) covering Indian land mass and adjoining oceanic region. Although the CFS forecast rainfall is overestimated over the Indian monsoon region, the land only rainfall amount is underestimated compared to observation. The skill of the prediction of monsoon rainfall over the Indian land mass is found to be relatively weak, although it is significant at 95% with a correlation coefficient (CC) of 0.44 with April ensembles.By using CFS predicted JJAS rainfall over the regions of significant CCs, a hybrid dynamical-empirical model is developed for the real time prediction of AISMR, whose skill is found to be much higher (CC significant above 99% level) than the raw CFS forecasts. The dynamical-empirical hybrid forecast applied on real time for 2009 and 2010 monsoons are found to be much closer to the observed AISMR. Thus, when the hybrid model is used there is a correction not only to the sign of the actual forecast as in the case of 2009 monsoon but also to its magnitude and hence can be used as a better tool for the real time prediction of AISMR.

Association of the Indian summer monsoon rainfall variability with the geophysical parameters over the Arctic region

AbstractA study is carried out to understand whether the Indian summer monsoon rainfall variability is associated with the geophysical parameters over the Arctic region. The correlation analyses of the satellite‐derived sea ice data for 29 years indicate that out of 9 sectors of the Arctic region, the Kara and Barents Seas sector's Sea Ice Extent (KBS SIE) during October has a strong relationship with the All‐India Summer Monsoon Rainfall (AISMR) in the following year. This relationship is more pronounced for the extreme cases, which are identified as the drought or the excess monsoon years. Moreover, the composites of certain geophysical parameters over the Arctic also behave in tandem with the monsoon rainfall. In order to test the relationship of these geophysical parameters with the monsoon rainfall, a case of the recent drought of 2009 is independently evaluated. The results obtained in this study bring out that KBS SIE and some other parameters of the Arctic region can be used as potential predictors in the long‐range forecasting of AISMR with a lead period of more than six months. A table indicating the qualitative forecast of the monsoon rainfall is presented on the basis of some parameters of the Arctic region. The mean sea level pressure anomaly tendency over northwest Europe during winter, which is one of the predictors used for forecasting the AISMR, is significantly correlated with the KBS SIE during the preceding October. As such, with the knowledge of October KBS SIE, this parameter can also be foreseen a few months in advance. Copyright © 2011 Royal Meteorological Society

Seasonal behaviour of NCEP-NCAR longwave cloud radiative forcing and its relationship with all-India summer monsoon rainfall

Using National Center for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) longwave cloud radiative forcing (LWCRF) reanalysis at the top of the atmosphere (TOA) for the period 1949–2006, the seasonal behaviour of the LWCRF and its relationship with the all-India summer monsoon rainfall (AISMR) during the winter (December–January–February, DJF), pre-monsoon (March–April–May, MAM) and summer monsoon (June– July–August–September, JJAS) seasons has been examined. The LWCRF over the Bay of Bengal region (15–20° N and 87.5–92.5° E) during the pre-monsoon season (MAM) is found to be significantly related to AISMR. The correlation coefficient (CC) between AISMR and LWCRF is 0.419, significant at the 1% level. The composite anomalies for excess minus deficient rainfall years of the LWCRF during MAM over the same region strongly support a significant relationship between LWCRF and AISMR. Thus, the LWCRF over the Bay of Bengal region in the pre-monsoon season appears to be a good indicator of the forthcoming monsoon rainfall.