Indian Summer Monsoon Rainfall Research Articles

Multiproxy studies on the spatially distinct surface samples to reconstruct palaeoecology and palaeoclimate from the Core Monsoon Zone of India

Palaeoclimatic reconstructions necessitate an understanding of the various biotic and abiotic responses to develop a modern analogue. The interpretation and calibration of the modern data allow us to check the lead-lag effect in different proxy parameters, which could be applied for robust palaeoclimatic reconstructions. We, in the present study, analysed pollen and diatom (biotic proxies), as well as grain size, magnetic susceptibility and geochemistry (abiotic components) of the modern soil samples, collected from the states of Madhya Pradesh and Chhattisgarh, the Core of the Monsoon Zone (CMZ) in central India. The weathered materials of the Palaeocene Cretaceous extrusive rocks and sedimentary rocks of the Late Triassic to the Upper Carboniferous are underlying the soil cover in these areas, respectively. The study revealed that the overall pollen and diatom preservation is comparatively good in the areas where the Palaeocene Cretaceous extrusive rocks are found except for the areas of human settlements, whereas the preservation of pollen and diatom was comparatively poor in areas where sedimentary rocks of the Late Triassic to Upper Carboniferous are found. The most plausible reason for this difference is the availability of nutrients which are supplied more abundantly by the easily weatherable Deccan basalt rocks compared to their sedimentary counterparts. The present study will serve as baseline information about biotic-abiotic interactions operating in the central Indian Core Monsoon Zone (CMZ). Since the intensity and duration of the rainfall in the CMZ are largely governed by the annual Indian summer monsoon rainfall (AISMR), therefore, the present study could help trace the weak or intense monsoon periods (break and active spells, respectively) of the past hundreds to thousands of years by studying the sediment profiles/cores.

Optimization of CMIP6 models for simulation of summer monsoon rainfall over India by analysis of variance

AbstractThe advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.

Applicability of seasonal forecasts from dynamical models for reservoir management practices

Despite the availability of reliable seasonal forecasts of Indian Summer Monsoon Rainfall (ISMR), the use of dynamical models driven by these forecasts for reservoir level management is limited. Reservoir water management can specially be useful if it can be done several months in advance, in view of an impending drought/flood scenario. The applicability of seasonal forecasts from the Monsoon Mission (MM) seasonal forecast model for seasonal and monthly inflow forecasts for tropical Indian reservoirs (Mula and Kangsabati) is studied using the Soil and Water Assessment Tool (SWAT) hydrological model, at a lead time of 3 months. Long-term observed inflow datasets are used for calibration and validation of SWAT-Calibration and Uncertainty Procedure (CUP) with Sequential Uncertainty Fitting (SUFI)-2 algorithm using insitumeteorological data. Observed inflows and inflow simulations are compared with simulated inflow using SWAT with same calibrated parameters, but with forcing derived from reforecasts from the MM model. The SWAT-CUP calibrated well with reasonable Nash Sutcliffe Efficiency (NSE) (Mula = 0.75, Kangsabati = 0.79) and Percentage Bias (PBIAS) (Mula = -28%, Kangsabati = 17%) for both reservoirs. The skill scores for streamflow predictions vary from 0.6-0.70 during the monsoon season, indicating reasonable accuracy for these predictions. The SWAT-MM model has a reasonable skill with 0.52-0.53 NSE and 26%-40% PBIAS. Therefore, SWAT-MM-based model has a good potential to forecast monthly and seasonal reservoir inflow for various agro-climatic zones of India. These forecasts when used in real-time, can serve as a guideline for managing the reservoir storage and release, and hence proving to be of great socio-economic importance.

Evaluating the climatic state of Indian Summer Monsoon during the mid-Pliocene period using CMIP6 model simulations

The Indian Summer Monsoon Rainfall (ISMR) plays a critical role in agriculture, thereby significantly affecting the economy of India. Yet, there is a large spread in the ISMR variability for future projections (by the end of 21st century) as simulated by coupled general circulation models. Gaining insight into the variations of the ISMR during warm periods could enhance our ability to understand ISMR variability in the future. In this study, we have selected the mid-Pliocene warm period from 3.0 to 3.3 million years ago (Ma), which has similar external forcing (orbital parameters) comparable to the end of the 21st century. To evaluate the ISMR mean state during the mid-Pliocene, we have used six available Coupled Model Intercomparison Project phase 6 (CMIP6) model simulations and their multi-model ensemble mean. Our analysis suggests that the ensemble of CMIP6 models is better than individual models in capturing the ISM rainfall patterns and its characteristics for the historical period of 1914–2013. During the mid-Pliocene, we find an increase in the JJAS rainfall over most parts of India in comparison to the pre-industrial period with an increase of 34% in seasonal precipitation. This higher precipitation conditions during the mid-Pliocene is accompanied by thermo dynamical (higher CO2 forcing led to higher tropospheric temperature and higher precipitable water) and dynamical (larger tropospheric temperature gradient between Indian landmass and southern Indian Ocean corresponds to enhanced moisture transport, enhanced low-level cross-equatorial flow and intensified Monsoon Hadley Circulation) aspects.

How is the relationship between rainfall and water vapor in the Indian monsoon influenced by changes in lapse rate during global warming?

Most climate models have shown that the Indian Summer Monsoon Rainfall (ISMR) will increase on account of global warming. The primary reason is the increase in column water vapor (CWV). The rainfall increase is not, however, proportional to the increase in column water vapor; for a given amount of CWV, rainfall will be lower in the future, according to model simulations. This suggests that other there are factors are at play. We have used a diagnostic model based on moist static energy (MSE) and moisture conservation to examine the impact of changes in the vertical lapse rate of temperature. Our results indicate that on decadal time scales, changes in ISMR are mediated by changes in CWV and temperature lapse rates. This is consistent with previous studies that showed the impact of column-mean temperature on the rainfall-CWV relationship. Findings are significant for climate model projections of ISMR, as correct estimation of lapse rate changes in models is necessary to predict ISMR changes accurately.

Hydrological impacts of altered monsoon rain spells in the Indian Ganga basin: a century-long perspective

The Indian Ganga basin (IGB) is one of the most valuable socioeconomic regions in the Indian subcontinent. The IGB supports more than half a billion people due to an abundant supply of freshwater for agro-industrial purposes, primarily through Indian Summer Monsoon (ISM) rainfall contributions (∼85%). Any alterations in ISM characteristics would significantly affect freshwater availability, and as a result, socioeconomic activities would be affected. Therefore, in this study, we have attempted to assess how the monsoon rain spell characteristics, i.e. peak, volume, and duration, altered historically between 1901 to 2019. We further analyzed the specific IGB regions where monsoon rain spell changes are more prominent and their hydrological implications. Our estimates reveal that short-duration high-magnitude rain spells have significantly increased across the major regions of the IGB after 1960, which implies the increased probabilities of flash flood hazards. At the same time, the rain spell volumes have been depleted across the IGB after 1960, especially in the eastern Indo-Gangetic plains and southern IGB regions, indicating increased drought frequencies. Further, Himalayan regions, i.e. upper Ganga, upper Yamuna, and upper Ghaghra, have demonstrated increasing magnitudes of rain spell peaks, volume, and duration post-1960. In addition, the continuous warming and anthropogenic alterations might further exaggerate the current situation. Thus, these inferences are helpful for river basin management strategies to deal with the extreme hydrological disasters in the IGB.

Unusual subseasonal variability of Indian summer monsoon rainfall in 2020

AbstractAbove‐normal all‐India summer monsoon (ISM) rainfall was received in 2020 with 109% of its long‐period average. According to the India Meteorological Department (IMD), ISM 2020 experienced robust month‐to‐month rainfall variations; in particular, the peak monsoon month of July 2020 witnessed deficit rainfall. The present study investigated the possible mechanisms that are responsible for the July 2020 deficit rainfall. Analysis revealed that the strong low‐level moisture divergence caused by the westward‐propagating atmospheric Rossby wave, induced by suppressed western North Pacific (WNP) convection, is primarily responsible for the July rainfall deficit. The WNP suppressed convection is linked to anomalous low‐level anticyclonic circulation. It is noted that the intense WNP anticyclone is maintained by the tropical Indian Ocean (TIO) warming‐induced atmospheric Kelvin waves and strong low‐level convergence over the Meiyu‐Baiu rainband. Unlike July, the strength of the WNP anticyclone and TIO warming are weaker in June and August. In addition to the observational analysis, the skill of the Climate Forecast System version‐2 (CFSv2) model in predicting the July rainfall at a lead of about two months is examined. Strong positive rainfall anomalies over India are predicted by the model in July, which is in contrast to the observations. Despite predicting the TIO warming, the model failed to capture the westward extension of the WNP anticyclone, which impacted the July rainfall prediction. To understand the importance of the WNP anticyclone on ISM rainfall, a CFSv2 sensitivity experiment is carried out by imposing strong negative sea surface temperature (SST) anomalies over the WNP region to excite the anticyclone. Both the westward extension of the WNP anticyclone and suppressed rainfall over the core monsoon region in July are captured by the model when low‐level circulation is imposed. The model sensitivity experiment strongly supports the role of the WNP anticyclone and its westward extension in inducing reduced rainfall in July 2020 over the monsoon trough region.

The recent trends in the Indian summer monsoon rainfall

The recent trends in the Indian summer monsoon rainfall

Revisiting the Linkage Between the Pacific–Japan Pattern and Indian Summer Monsoon Rainfall: The Crucial Role of the Maritime Continent

AbstractThe Pacific–Japan (PJ) pattern traditionally refers to the meridional dipole mode of rainfall and the low‐level circulation over the tropical western North Pacific and mid‐latitude East Asia. However, recent studies have reported that the PJ pattern can also affect the Indian summer monsoon (ISM) via the anomalous circulation over the North Indian Ocean. We summarize the currently available PJ indices and re‐examine the linkage between the PJ pattern and the ISM. We found that the only PJ indices that are significantly correlated with rainfall in southern India are the two indices containing signals of the Maritime Continent. The Maritime Continent rainfall can also stimulate circulation anomalies in the North Indian Ocean, thereby strengthening the PJ–ISM linkage. When the signals associated with the Maritime Continent are removed, the PJ–ISM linkage becomes weak and insignificant. The PJ indices should be chosen carefully when studying the climatic impacts of the PJ pattern.

Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations

Abstract. We present the Monsoon Mission Coupled Forecast System version 2.0 (MMCFSv2) model, which substantially upgrades the present operational MMCFSv1 (version 1) at the India Meteorology Department. The latest 25 years (1998–2022) of retrospective seasonal coupled hindcast simulations of the Indian summer monsoon with April initial conditions from Coupled Forecast System Reanalysis are discussed. MMCFSv2 simulates the tropical wind, rainfall, and temperature structure reasonably well. MMCFSv2 captures surface winds well and reduces precipitation biases over land, except over India and North America. The dry bias over these regions remained like in MMCFSv1. MMCFSv2 captures significant features of the Indian monsoon, including the intensity and location of the maximum precipitation centers and the large-scale monsoon circulation. MMCFSv2 improves the phase skill (anomaly correlation coefficient) of the interannual variation of Indian summer monsoon rainfall (ISMR) by 17 % and enhances the amplitude skill (normalized root mean square error) by 20 %. MMCFSv2 shows improved teleconnections of ISMR with the equatorial Indian and Pacific oceans. This 25-year hindcast dataset will serve as the baseline for future sensitivity studies of MMCFSv2.

Exploring evolutionary patterns in the teleconnections between Indian summer monsoon rainfall and Indian Ocean dipole over decades

Exploring evolutionary patterns in the teleconnections between Indian summer monsoon rainfall and Indian Ocean dipole over decades

Artificial intelligence predicts normal summer monsoon rainfall for India in 2023

Inaccuracy in the All Indian Summer Monsoon Rainfall (AISMR) forecast has major repercussions for India's economy and people's daily lives. Improving the accuracy of AISMR forecasts remains a challenge. An attempt is made here to address this problem by taking advantage of recent advances in machine learning techniques. The data-driven models trained with historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole values outperform the traditional physical models, and the best-performing model predicts that the 2023 AISMR will be roughly 790 mm, which is typical of a normal monsoon year.

Impact of urban land use on mean and heavy rainfall during the Indian summer monsoon

Abstract. Northern India has undergone intense urbanization since the middle of the 20th century. The impact of such drastic land-use change on the regional weather and climate remains to be assessed. In this work, we study the impact of the modification of land use – from vegetation to urban – on the Indian summer monsoon rainfall as well as on other meteorological variables. We use the regional Meso-scale Non-Hydrostatic (Meso-NH) model coupled with an urban module (the Town Energy Balance model) to perform monthlong sensitivity simulations centered around Kolkata, the most urbanized area in northeastern India. Paired simulations, one with and another without urban settings, have been performed to identify the impacts related to urbanization through both thermodynamic and kinetic effects. We find that the perturbation induced by urban land use enhances the mean rainfall over the model domain, principally by intensifying the convective activity through thermodynamic perturbation, leading to a 14.4 % increase in the monthly mean rainfall. The urban area also induces a 15.0 % rainfall increase during two modeled periods of heavy precipitation caused by low-pressure systems. In addition, the modeling results demonstrate that the urban area not only generally acts as a rainfall enhancer, particularly during nighttime, but also induces the generation of a specific storm in one modeled case that would not have formed in the absence of the urban area. The initiation of this storm over the city was primarily due to the urban terrain's disturbance of the near-surface wind flow, leading to a surge in dynamically produced turbulent kinetic energy (TKE). The thermal production of TKE over the nighttime urban boundary layer, on the other hand, serves as a contributing factor to the storm formation.

Combined influence of El Niño, IOD and MJO on the Indian Summer Monsoon Rainfall: Case Study for the years 1997 and 2015

To find out how El Niño, the positive Indian Ocean Dipole (IOD), and the Madden-Julian Oscillation (MJO) affect the Indian Summer Monsoon Rainfall (ISMR), this study looks at two different monsoon years: 1997 (normal ISMR) and 2015 (below-normal ISMR). These two years were ideal for examining their combined effects on the performance of ISMR because they both experienced super El Niño conditions over the Pacific Ocean and positive IOD over the Indian Ocean. In these two years, spatial diversity associated with sea surface temperature (SST) was found over the Indian Ocean as well as the Pacific Ocean. The analysis of the large-scale circulation showed a distinct difference: the below-normal monsoon in 2015 was due to weak southwesterly flow toward the Indian landmass as well as anomalous subsidence compared to 1997. Analysis of rainy and dry days shows that 2015 had a greater (less) number of dry (rainy) days compared to 1997, contributing to a below-normal ISMR in 2015. The study also looked at the role of MJO in the observed rainfall anomalies over India during these two years, when there was both El Niño and positive IOD. This was done by looking at the composite of rainfall anomalies for different MJO phases. In 1997, during the monsoon season, MJO showed prolonged activity over the Indian Ocean (favorable MJO phases), which contributed to normal ISMR. However, in 2015, MJO was found active for many days over the western Pacific (unfavorable MJO phases), which contributed to below-normal ISMR. Overall, this case study highlights the complex interactions between El Niño, IOD, MJO, and monsoon dynamics, emphasizing the need for a comprehensive study of these factors to understand the behaviour of ISMR.

The combined influence of west Pacific subtropical high and tropical cyclones over the northwest Pacific on Indian summer monsoon rainfall

AbstractWe examined the interrelationship between the Indian summer monsoon rainfall (ISMR), the west Pacific subtropical high (WPSH), and tropical cyclone (TC) activity over the western north Pacific (WNP) Ocean during the peak monsoon season in 1951–2019. When WNP TCs are inactive (active), there is a noticeable 10° westward (eastward) shift observed along with intensification (weakening) of the WPSH. The WPSH and WNP TC activities are divided into four categories—an eastward shift of WPSH with active TCs (EA) and inactive TCs (EI), and a westward shift of WPSH with active TCs (WA) and inactive TCs (WI)—in order to investigate the combined influence of the WPSH and WNP TCs on ISMR during non‐El Niño–Southern Oscillation (non‐ENSO), El Niño, and La Niña phases. In the EA non‐ENSO cases, a surplus (deficit) rainfall was noticed over central (southern peninsular) India as a result of moisture convergence (low convective available potential energy, abnormal moisture divergence and downdraft); however, the rainfall is above normal over the Indian subcontinent. During the EI non‐ENSO case, an excess (deficit) regional rainfall activity was observed over east‐central India and parts of north India (southern peninsular India) through regional changes in atmospheric circulations and midtropospheric vertical velocity. In contrast, in WI (WA) non‐ENSO cases, the majority of India (southern peninsular and northeast India) experiences excessive precipitation while east‐central India (east and west central India) undergoes a deficit rainfall. This pattern is consistent with the spatial distribution of moisture flux convergence, convective available potential energy, and midtropospheric vertical velocity. In the case of non‐ENSO, the westward shift of the WPSH associated with inactive TCs is one of the favourable conditions for ISMR. In general, the effect of an eastward (westward) shift of the WPSH along with more (less) TCs during the non‐ENSO case is favourable to ISMR. On the other hand, rainfall patterns during ENSO are significantly associated with the circulation changes teleconnected to El Niño and La Niña conditions.

Role of Improved Ocean Initial State in the Seasonal Prediction of Indian Summer Monsoon: A Case Study

This case study has made an effort to show the impact of improved ocean initial conditions (ICs) in a coupled forecast system (CFSv2) simulation on the seasonal prediction of Indian summer monsoon rainfall (ISMR). CFSv2 is used as an operational dynamical model for the seasonal prediction of ISMR. Here, we show an improved ISMR skill by initializing the ocean component of CFSv2 using new improved ocean ICs based on Global Ocean Data Assimilation System (GODAS) analysis. This new analysis is better than the NCEP GODAS, which uses the earlier-generation ocean model MOM4p0d and assimilates observed temperature and synthetic salinity using the 3DVar assimilation scheme. However, the new, improved GODAS analysis uses the MOM4p1 ocean model and assimilates observed salinity instead of synthetic salinity. We performed twin sets of nearly identical model experiments differing only in their ICs, with one set using NCEP ICs and the other using the new ICs (NIC). The NIC experiment consistently shows better El Niño–Southern Oscillation prediction skill than the NCEP IC experiment. This advancement leads to improvement in the ISMR skill. We found that the substantial improvements in both oceanic and atmospheric variables in a coupled feedback system contributed to the improved ISMR skills. The enhanced ISMR skill score of the NIC experiment might be the result of improved teleconnections, better depiction of large-scale monsoon circulations, and reduced model drift.

Redefining landslide susceptibility under extreme rainfall events using deep learning

The apparent changes in the Indian summer monsoon rainfall pattern and the nature of extreme rainfall events (EREs) in the southern Western Ghats (WG) caused widespread landslides across the region. Landslide susceptibility maps generated using the past landslide inventory are one of the efficient tools that can help mitigate the deleterious effects of landslides. Landslide susceptibility maps produced using the landslide inventory of normal and extreme rainfall years by deep neural network (DNN) modelling vary in terms of their degree of susceptibility, and the differences are prominent in the moderate and high susceptibility zones. The DNN model trained by landslide inventory of normal rainfall years is capable of predicting landslides during EREs (with Area Under the Curve (AUC) value >0.90). The inclusion of the landslides that occurred during recent EREs (since 2018) into the existing landslide inventory provides a more accurate and refined prediction of landslide susceptibility, which facilitates risk-informed landscape planning and development of the region. In addition, the result reveals that 13 % of the Kerala state is extremely susceptible for landslide occurrence, among this Idukki, Palakkad, Malappuram, Pathanamthitta, and Wayanad districts are highly vulnerable to the occurrence of landslides. Besides, the study also shows an increase of 3.46 % area in extreme susceptibility zone after the 2018 ERE. The updated landslide susceptibility map of the region may be used as a vital tool for planning landslide mitigation activities in the wake of recurrent EREs and associated landslide occurrences.

Variability of low-level jet over the Arabian Sea and its association with Indian summer monsoon rainfall

Variability of low-level jet over the Arabian Sea and its association with Indian summer monsoon rainfall

Quantifying the association between Arctic Sea ice extent and Indian precipitation

AbstractThe unprecedented sea ice loss in the Arctic is due to its stronger surface warming than other parts across the globe, resulting in far‐flung effects on weather and climate at different spatial and temporal scales. We investigate how Arctic Sea ice and Indian summer monsoon rainfall (ISMR) are associated at seasonal scale and when preconditioned by Arctic Oscillation (AO) phases. This study also examines the potential predictability using correlation and composite analysis. The results indicate a significant decline in sea ice extent (SIE) at a rate of 0.055 Mkm2/year (p < 0.05). The strong association between SIE and precipitation in the Indian region is confirmed by correlation values ranging from −0.6 to 0.6 (p < 0.05). The spatial patterns of seasonal SIE and precipitation association remain consistent for 1979–2021. We found that the prevailing AO phases influence the association of sea ice with ISMR precipitation. The high correlation between SIE and ISMR anomalies suggests that Arctic Sea ice could be a reliable predictor for ISMR.

Dipole pattern of Indian Summer Monsoon precipitation modulated by the Circumglobal Teleconnection during the Last Deglaciation

Modern observations have demonstrated that the Indian Summer Monsoon (ISM) rainfall shows a west-east dipole pattern influenced by the Circumglobal Teleconnection (CGT). In this study, we investigated the temporal evolution of the ISM rainfall and the dynamic mechanisms using a set of transient simulations (TraCE-21 ka, simulation of Transient Climate Evolution over the past 21,000 years) during the Last Deglaciation. The results show that, the responses of ISM rainfall always present west-east dipole patterns under different forcing factors and such responses are closely related to the CGT-like wave trains in the Northern Hemisphere summer. During the Last Deglaciation, the ISM is strengthened and summer precipitation is increased over northwestern India but decreased over northeastern India. With increased solar insolation and atmospheric CO2, as well as reduced global ice volume, the positive-phase CGT-like wave trains, characterized by an anticyclonic anomaly over west-central Asia (WCA) and a cyclonic anomaly over the Tibetan Plateau (TP), tends to shift the South Asian High (SAH) westward and strengthens (weakens) the divergence in the upper-troposphere over northwestern (northeastern) India. Simultaneously, the intensification of the monsoon trough (MT) due to the local baroclinic atmospheric circulations over Arabian Peninsula-Iranian Plateau leads to the moisture convergence (divergence) over northwestern (northeastern) India in the lower troposphere. For the Younger Dryas (YD) cooling event, the pattern of CGT-like wave trains and Indian summer rainfall do the reverse. Theses rainfall responses highlight the spatial complexity of multi-scale Indian monsoon evolution and it should be cautious when explaining geological proxies.