Indian Monsoon Rainfall Research Articles

Spatial variability and moisture tracks of Indian monsoon rainfall and extremes

Spatial variability and moisture tracks of Indian monsoon rainfall and extremes

Deep learning approach for predicting monsoon dynamics of regional climate zones of India

The complex interplay of various complicated meteorological and oceanic processes has made it more difficult to accurately predict Indian monsoon rainfall. A future-oriented and one of the most potential methods for predictive analytics is deep learning. The proposed work exploits empirical Mode Decomposition-Detrended Fluctuation Analysis (EMD-DFA) and long short-term memory (LSTM) deep neural networks (EMD-LSTM) to build novel predictive models and analyze predictability effectively. The time series data of each homogeneous monsoon zone are decomposed into different empirical time series components known as intrinsic mode functions (IMFs). The proposed work's obtained results report that the EMD-LSTM hybrid strategy consistently outperforms other methods in terms of accuracy. Furthermore, we examined possible relationships between each homogeneous monsoon zone and multiple climate drivers, shedding light on the complicated relationships that influence monsoon patterns. This study presents a unique way of predicting complex monsoon rainfall in homogenous regions of India and marks the first application of the EMD-LSTM technique for this purpose to the best of our knowledge which is necessary for improving water conservation and distribution at different climate zones of India.

Indian ocean warming, extreme positive Indian Ocean Dipole events, and their impact on monthly Indian Monsoon rainfall from June to November in NMME models

Indian Ocean (IO) warming, the frequency of extreme positive Indian Ocean Dipole (EPIOD) events, and its impact on Indian monsoon rainfall (IMR) are more prominent in the recent era. A proper understanding of the relationship between the EPIOD and IMR is very important because two thirds of the people in India are dependent on agriculture and related economy. The primary focus of this study is on three main aspects: (i) analysing the monthly trend of sea surface temperature (SST) in the IO; (ii) the spatial variability of SST anomalies (SSTA) linked to the EPIOD and its impact on the IMR; and (iii) the factor responsible for the intensification of the EPIOD. These aspects are thoroughly assessed using 12 NMME models, comparing them with observations and reanalysis datasets from 1990 to 2020. The analysis reveals a warming trend in the central IO during June and July, with significant increases observed from August to November. Over the past 31 years, the central IO has experienced an approximate rise of 0.9 to 1 °C from June to November. Among the 12 models scrutinised, GFDL_FLOR_A, FLOR_B, and RSMAS_CCSM4 closely align with observed spatial trends. In contrast, models such as GFDL_SPEAR and NASA_GEOSS tend to overestimate these spatial trends. An increase in easterlies over the equatorial eastern IO was identified as a possible driver for the rising SST in the central IO during October and November. In response to EPIOD on the monthly spatial rainfall distribution over India, all NMME models often underestimate rainfall over the west coast and exhibit wet biases over the leeward side of the Western Ghats. Grid-wise skill scores for accumulated monthly rainfall demonstrated variations among NMME models, with GFDL_SPEAR, RSMAS_CCSM4, and other IC3 models showing higher skill. The interhemispheric pressure gradient (IHPG) has been identified as a precursor to EPIOD events. A positive IHPG from March to June is correlated with EPIOD occurrences from October to December. It is observed that half of the NMME models are capable of simulating a positive relationship between IOD and IHPG, consistent with the observed data.

Interference of significant sub-decadal periodicities in Indian monsoon rainfall

Interference of significant sub-decadal periodicities in Indian monsoon rainfall

A CNN Bidirectional LSTM framework for predicting monsoon rainfall in India

A CNN Bidirectional LSTM framework for predicting monsoon rainfall in India

Contrasting controls on convection at latitude zones near and away from the equator for the Indian summer monsoon

Understanding controls on convection on various timescales is crucial for improved monsoon rainfall forecasting. Although the literature points to vertically homogeneous vorticity signatures preceding rainfall during the Indian summer monsoon, we show using reanalysis data that, for rainfall associated with northward propagating intraseasonal oscillations (ISOs), different controls are present at different latitude zones. For the latitude zone close to the equator (5∘N–14∘N) and including the southern Indian region, a conventional dynamical control on rainfall exists with barotropic vorticity leading ISO rainfall by about five days. In contrast, for the latitude zone away from the equator (15∘N–24∘N; covering the central Indian region), thermodynamic fields control ISO rainfall, with barotropic vorticity following rainfall by two days on average. Over central India, the pre-moistening of the boundary layer (BL) yields maximum moist static energy (MSE) about four days prior to ISO rainfall. Analyzing the statistics of individual events verifies these observations. Similar thermodynamic control is also present for the large-scale extreme rainfall events (LEREs) occurring over central India. These high rainfall events are preceded by positive MSE anomalies arising from the moisture preconditioning of the BL. The resulting convection then leads to a maximum in barotropic vorticity 12 h after the rainfall maximum. Characterizing these influences on convection occurring over various timescales can help identify the dominant mechanisms that govern monsoon convection. This can help reduce climate model biases in simulating Indian monsoon rainfall.

The relationship between central Indian terrestrial vegetation and monsoon rainfall distributions in different hydroclimatic extreme years using time-series satellite data

The relationship between central Indian terrestrial vegetation and monsoon rainfall distributions in different hydroclimatic extreme years using time-series satellite data

On drastic change in the relationship between snow cover and Indian monsoon rainfall

On drastic change in the relationship between snow cover and Indian monsoon rainfall

Intermodel Spread of Historical Indian Monsoon Rainfall Change in CMIP6: The Role of the Tropical Pacific Mean State

Abstract Robust projections of the Indian summer monsoon rainfall (ISMR) are critical as it provides 80% of the annual precipitation to more than 1 billion people who are very vulnerable to climate change. However, even over the historical period, state-of-the-art climate models have difficulties in reproducing the observed ISMR trends and are affected by a large intermodel spread, which questions the reliability of ISMR projections. Such uncertainty could come from internal variability or model biases. Here, we study the impact of the latter on the historical forced change of ISMR in 34 models from CMIP6. First, we show that models’ biases over India do not significantly impact how they simulate the historical change of ISMR. However, we do find statistically significant relationships between ISMR historical forced changes and remote rainfall and temperature biases within the tropics by using a maximum covariance analysis (MCA). Our results highlight the key role of tropical Pacific sea surface temperature (SST) mean state biases as an important source of intermodel spread in the ISMR change. The physical mechanisms underlying these statistical relationships between ISMR change and the intermodel spread of Pacific SST biases are finally explored. We found that models having El Niño/La Niña–like mean SST bias in the Pacific tend to exhibit El Niño/La Niña–like changes over the historical period, impacting ISMR through a shift in the Walker circulation and Rossby wave propagation across the Pacific.

High resolution dynamical downscaling of global products using spectral nudging for improved simulation of Indian monsoon rainfall

Societal wellbeing and risk management demand the evolution of rainfall during the Indian summer monsoon (ISM) at various spatio-temporal scales. The ISM rainfall (ISMR) is simulated for 1982–2018 using the Weather Research and Forecasting (WRF) model at 15 km grid-spacing by forcing with ERA5 reanalysis data. Two experiments are conducted without spectral-nudging (NSN) and with spectral-nudging (SN) in which the wind and temperature fields are updated. The seasonal mean rainfall over India and its homogeneous regions has realistically captured in SN experiment with less root mean square error (4.5 mm day−1) and high correlation (0.62) than the NSN run (6.0 mm day−1 and 0.35), with significant improvements in the first 60-days' simulation. Very-light, light, moderate, and heavy rain categories are well detected in the SN in 5 homogeneous regions out of 6. About 57–89% of years match the observed contribution of these rainfall categories in the SN experiment's seasonal rainfall, while 21–54% in the NSN. Better simulation of moderate rain from SN run in 24 years leads to improved contribution to the seasonal rainfall. NSN runs mostly underestimate the rainrate contribution in recent years. High threat scores (0.2–0.6) for these rainfall categories indicate the consistent performance of SN run in all monsoon months than the NSN (0.1–0.45). The SN could capture the interannual variability of observed ISMR, which aids in detecting the normal, excess, and deficit years at ∼75%, ∼86%, and ∼ 70% rate with high threat scores (0.60, 0.55, and 0.58, respectively), unlike NSN run. The SN experiment is superior in capturing frequent rainfall in extreme years, improving performance at monthly and seasonal scales. While NSN mainly underestimates rainfall in all extreme years. Thus, the study highlights the importance of spectral-nudging to high-resolution downscaling for improved long-range simulations.

Rainfall Variability and Rice Sustainability: An Evaluation Study of Two Distinct Rice-Growing Ecosystems

The inconsistency of the Indian monsoon has constantly threatened the country’s food production, especially key food crops such as rice. Crop planning measures based on rainfall patterns during the rice-growing season can significantly improve the sustainable water usage for water-intensive crops such as rice. This study examines the variability of Indian monsoonal rainfall in rainfed and irrigated rice-cultivating regions to improve rainfall utilization and irrigation water-saving practices. Two distinct rice-growing conditions in southern peninsular India are chosen for this study. The preliminary seasonal rainfall analysis (1951–2015) showed anomalies in the Sadivayal (rainfed rice) region compared to the Karaikal (irrigated rice). The dry-spell analysis and weekly rainfall classification suggested shifting the sowing date to earlier weeks for the Thaladi season (September–February) and Kar season (May–September) to avoid exposure to water stress in Sadivayal. Harvesting of excess rainwater during the wet weeks is proposed as a mitigation strategy for Karaikal during the vegetative stage of the Kuruvai season (June–October) and Late Thaladi season (October–February), where deficit rainfall is expected. Results showed that an adaptation strategy of early sowing is the most sustainable measure for rainfed rice cultivation. However, harvesting the excess rainwater is an ideal strategy to prevent water stress during deficient rainfall periods in irrigated rice farming. This comparative study proposes a comprehensive rainfall analysis framework to develop sustainable water-efficient rice cultivation practices for the changing rainfall patterns.

Climate and sea level forcing of terrigenous sediments input to the eastern Arabian Sea since the last glacial period

Analyses of mass accumulation rate (MAR), laser grain-size, clay mineralogy, and SrNd isotopic compositions of <2 μm and < 63 μm grain-size fractions have been applied to sediments from the International Ocean Discovery Program (IODP) Site U1456, which span the last 31 ka, in order to constrain (i) past changes in the relative proportions of the fine-grained detrital sediments derived from the Indus River and the Deccan Traps and the dynamics of sediment transport to the Laxmi Basin and (ii) past changes in Indian monsoon rainfall and latitudinal migration of Intertropical Convergence Zone since the last glacial period. Sr and Nd isotopes results combined with clay mineral assemblages suggest that clay-sized sediments at IODP Site U1456 derive mainly from the Indus River (70–90%), followed by a minor contribution from rivers draining the volcanic province of the Deccan Traps (10–30%). During the Last Glacial Maximum, clay mineralogy was dominated by illite and chlorite primarily derived from the highlands of the Indus River basin. The combination of lower temperatures and lower monsoonal rainfall during glacial times led to a reduction of smectite formation rates in the floodplains, while glacial erosion accelerated in the Himalayan and Tibetan highlands leading to the release of large quantities of illite and chlorite minerals into the Arabian Sea. In addition, glacial low sea-level stands allowed a connection between river mouths and the heads of deep-sea channels favoring the southward transfer of sediments to IODP Site U1456 by turbidity currents. Each maximum of the summer insolation curve, associated with a northward shift of the ITCZ and an intensification of summer monsoon rainfall (from 31 to 25 ka BP and from 12 to 8 cal ka BP), was associated with enhanced rainfall in the Indus floodplain which in turn led to an increase in the physical erosion and weathering of plain soils, which were rich in pedogenic minerals (such as smectite), and/or a higher production rate of smectite in the soils of the Indus floodplain. A compilation of three Nd isotopic composition records obtained at sites located on a N-S latitudinal transect in the eastern Arabian Sea have allowed us to establish the impact of sea level variations and latitudinal migration of the ITCZ over time on the sedimentation of the eastern Arabian Sea.

Effect of ENSO on Seasonal Temperature Over Tamil Nadu

Increased concentration of greenhouse gases is expected to alter the radiative balance of atmosphere, causing increase in temperature and changes in precipitation patterns. Climate variability has been principal source of fluctuations in Indian food production. Even though there is no long-term trend, inter - annual variability of Indian monsoon rainfall leading to frequent droughts and floods has profound influence on agriculture and intern, national economy. It is well recognized that El Niño / Southern Oscillation (ENSO) is the dominant mode of climate variability on seasonal to inter-annual scales and its impacts are felt worldwide. ENSO often affects seasonal temperature, precipitation and thus crop yields in many regions, however, the overall impacts of ENSO on global yields are uncertain Maximum and minimum average temperature was calculated for different seasons such as Cold Weather Period (CWP), Hot Weather Period (HWP), South West Monsoon (SWM), North East Monsoon (NEM) for each El Niño, La Niña and neutral years at various districts of Tamil Nadu. Relation between ENSO and temperature was analyzed by computing the anomaly in temperature at different districts of Tamil Nadu. The temperature deviation between the ENSO and neutral phases was not significant as the deviation was within + 0.3oC in all the seasons.

Sub‐Annual to Interannual Arabian Sea Upwelling, Sea Surface Temperature, and Indian Monsoon Rainfall Reconstructed Using Congruent Micrometer‐Scale Climate Proxies

AbstractThe Indian monsoon system impacts over 1 billion people, and rainfall from its southwest monsoon is critical for agriculture. Cool sea surface temperatures (SSTs) in the Arabian Sea have been associated with reduced monsoon rainfall and vice versa, although this relationship is difficult to examine due to scarce and short instrumental records. We utilize laminated Pakistan Margin sediments to reconstruct southwest monsoon rainfall and Arabian Sea SSTs at sub‐annual to multi‐annual resolution for the twentieth century. We applied paired sub‐mm mapping techniques to intact sediment core surfaces to measure elemental abundances and lipid biomarkers. The first principal component of the elemental results (PC1) explains 47% of the elemental variance and is driven by detrital lithogenic elements K, Ti, and Fe. PC1 is positively correlated with homogeneous India June‐September rainfall departures from 1903 to 1928 and 1949–1970 on a multi‐annual scale. Correlations in the remaining timespans are either negative or absent, likely driven by a combination of dam and barrage construction on the Hub and Indus Rivers and negative rainfall departures. We propose PC1 as a proxy for southwest monsoon rainfall on multi‐annual timescales prior to ∼1930. A dampened annual SST cycle is resolved with the alkenone paleothermometer, which also captures the interannual‐decadal trends of twentieth century Arabian Sea SSTs. We propose the crenarchaeol‐caldarchaeol tetraether index, sourced from planktonic Thaumarchaeota, as a delayed upwelling proxy for the western Arabian Sea. Our reconstructions corroborate the established relationship of reduced Arabian Sea upwelling leading to warmer SSTs and increased monsoon rainfall, and vice versa.

Dominant Anomalous Circulation Patterns of Tibetan Plateau Summer Climate Generated by ENSO-Forced and ENSO-Independent Teleconnections

Abstract The interannual variability of Tibetan Plateau (TP) summer climate has tremendous impacts on both regional hydrological cycles and global climate. In this study, we extract four dominant modes of the summertime large-scale circulation over the TP and surrounding areas from both the observation and simulations by a coupled general circulation model, CAS-FGOALS-f3-L. Based on the 10-member tropical Pacific pacemaker experiments, the ENSO-forced and ENSO-independent signals are isolated, each of which is represented by two dominant modes. The two ENSO-forced modes correspond to ENSO developing and decaying summer, respectively. The positive phase of the developing (decaying) ENSO-related mode is characterized by an anomalous baroclinic cyclone (anticyclone) over the western TP excited by the variations of the tropical summer monsoon rainfall. During the El Niño developing summer, the Indian monsoon rainfall variation is driven by an eastward shift of the Walker circulation due to warm anomalies in the equatorial central-eastern Pacific, whereas during the El Niño decaying summer it is caused by the basinwide warming in the tropical Indian Ocean. The two ENSO-independent modes are associated with the summer North Atlantic Oscillation (SNAO) and the circumglobal teleconnection (CGT) pattern, respectively. The positive phases of the SNAO- and CGT-related modes are characterized by an anomalous anticyclone over the western TP and zonal cyclone–anticyclone dipole pattern over the TP, respectively, both of which are associated with mid- and high-latitude stationary Rossby wave trains.

Network-Based Approach and Climate Change Benefits for Forecasting the Amount of Indian Monsoon Rainfall

Abstract Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year’s data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill.

Evaluating the Monsoon (Rainfall) Risk Profile for the Feasibility of Rainfall Derivatives Instrument in India

Indian monsoon rainfall has significant variations and unpredictability. Therefore, there is a need for suitable risk mitigation techniques to manage the monsoon risk. This paper aims to study the statistical properties of the monsoon (rainfall) risk profile of the meteorological subdivisions of India. A thorough examination of the monsoon rainfall's statistical properties will help to understand the monsoon risk problem and the feasibility of rainfall derivatives for India. The study is based on the empirical data of the South-West monsoon rainfall for the past fifty years of all 36 meteorological sub-divisions. The results reveal that the monsoon rainfall data has taken a wide range across years and sub-divisions reflecting that the variability has increased in recent years. This is particularly useful to design rainfall derivatives’ contracts. Therefore, the creation of the monsoon (rainfall) risk market similar to the temperature risk markets holds the key to effective management of monsoon risk in India.

Penultimate deglaciation Asian monsoon response to North Atlantic circulation collapse

During glacial terminations, massive iceberg discharges and meltwater pulses in the North Atlantic triggered a shutdown of the Atlantic Meridional Overturning Circulation (AMOC). Speleothem calcium carbonate oxygen isotope records (δ18OCc) indicate that the collapse of the AMOC caused dramatic changes in the distribution and variability of the East Asian and Indian monsoon rainfall. However, the mechanisms linking changes in the intensity of the AMOC and Asian monsoon δ18OCc are not fully understood. Part of the challenge arises from the fact that speleothem δ18OCc depends on not only the δ18O of precipitation but also temperature and kinetic isotope effects. Here we quantitatively deconvolve these parameters affecting δ18OCc by applying three geochemical techniques in speleothems covering the penultimate glacial termination. Our data suggest that the weakening of the AMOC during meltwater pulse 2A caused substantial cooling in East Asia and a shortening of the summer monsoon season, whereas the collapse of the AMOC during meltwater pulse 2B (133,000 years ago) also caused a dramatic decrease in the intensity of the Indian summer monsoon. These results reveal that the different modes of the AMOC produced distinct impacts on the monsoon system.

Inter-annual variability of Indian monsoon rainfall in the JMA’s seasonal ensemble prediction system in relation to ENSO and IOD

Inter-annual variability of Indian monsoon rainfall in the JMA’s seasonal ensemble prediction system in relation to ENSO and IOD

Identifying and Using Suitable Oceanic Domains for Prediction of the Indian Monsoon Rainfall over Tamil Nadu

Rainfall prediction are vital for agriculture which is one of the primary sectors greatly affected by climate variability and extremes. Agriculture plays a vital role in shaping the economy of India which is often affected by monsoon. Sea surface temperature (SST) plays a vital role in rainfall predictability over the land surface. A total of twelve different domains of oceanic influences of SST on monsoon rainfall over Tamil Nadu were selected for analysis. The SST of different lead times (February, March, April, and May for southwest monsoon (SWM) and June, July, August, and September for northeast monsoon (NEM) from the ERSSTv5 and ECMWF-SEAS5 model with the Canonical Correlation Analysis (CCA) were used in the Climate Predictability Tool (CPT) to identify the best predictor domains for the prediction of SWM and NEM rainfall over Tamil Nadu. The model training utilized the first 40 years (1981-2020) SST and rainfall data and prediction was done for the 2021 seasons. The results of the study revealed from Kendall tau goodness index and CCA score, the predictor domains comprised of a combination of oceanic domains, this were the Indian, Arabian, Bay of Bengal, and Pacific Oceans recorded the best CCA score and the goodness index. Is therefore recommended that, these domains which have the highest overall predictability can be used by the National meteorological services to early warning and monsoon rainfall information over Tamil Nadu.