Northeast Monsoon Rainfall Research Articles

Influence of mixed Rossby-Gravity waves on the North-East monsoon rainfall over south India

The northeast (NE) monsoon contributes considerable annual rainfall over southern peninsular India. Here, an attempt has been made to evaluate the role of convectively coupled mixed Rossby-Gravity (MRG) waves on the variabilities of NE monsoon rainfall. The gridded rainfall data from 1980 to 2021 from the India Meteorological Department shows large interannual variability in NE monsoon rainfall over southern peninsular India. Wavelet analysis of outgoing longwave radiation (OLR) data shows the dominance of short-period waves over the equatorial Indian Ocean during the years with above-normal NE monsoon rainfall compared to below-normal rainfall years. The space–time spectral analysis further confirms the dominance of short-period westward propagating convectively coupled equatorial waves (CCEW) during above-normal NE monsoon rainfall years. In contrast, the eastward component of CCEWs dominates during the below-normal rainfall years. The empirical orthogonal function (EOF) analysis of MRG waves filtered OLR data shows that the first two leading modes explain more than 14 % of variability over the Indian Ocean domain. Moreover, the vertically integrated moisture flux convergence indicates the role of MRG wave in triggering the extreme rainfall over southern peninsular India and also offers favorable conditions for the formation of cyclogenesis and, thereby, inducing heavy rainfall over the South Indian domain. Thus, the results suggest that the westward propagating MRG waves play a seminal role in modulating the NE monsoon rainfall over southern peninsular India.

Untangling the dynamics of heavy rainfall events (HRE) in the Northeast monsoon season and exploring its connection to the Indian Ocean warming and the Madden Julian Oscillation

Southeastern Peninsular India witnessed heavy rainfall events (HRE) during the recent El Nino year 2015 and La Nina year 2021 in November and early December. Both these events were associated with enhanced easterly wave activity and active Madden Julian Oscillation (MJO) over the Maritime continent. The MJO was on the fourth (MJO-4) phase for 15 and 17 days in November 2015 and 2021 respectively. A weakening in the positive correlation between El Nino and Northeast monsoon rainfall and a strengthening in the positive correlation between the MJO-4 days and HRE is observed in very recent years. This study revealed that the unequal changes in Sea Surface Temperature (SST) in the Indo-Pacific region i.e., warming over the eastern Indian Ocean (IO) and cooling over the western Pacific Ocean can be favourable for the increased occurrence of MJO-4 days as well as HRE over southern Peninsular India. The easterly wind anomalies towards the southern Bay of Bengal (BoB) and southeastern Peninsular India and westerly wind anomalies over the central equatorial Indian Ocean are significantly correlated with HRE in the recent epoch (2001−2021). Warming in eastern IO brings easterlies from the cool western Pacific and westerlies from the western IO, which helps the MJO to remain active over maritime continent and inhibits its further eastward propagation. The HREs in the recent epoch are associated with the low-pressure systems and cyclonic circulation over southern Peninsular India, in which moisture transport is from the Arabian Sea to Peninsular India. The HRE in the early epoch (1981–2000) is related to the anticyclonic circulation over BoB region and the moisture transport is from BoB and southeastern IO. A proposed mechanism for the HRE in the recent epoch is as follows. The SST gradient in the Indo-Pacific Ocean is conducive for the moistening of MJO-4 region and is favourable for the enhanced easterly wave activity and the formation of low-pressure systems, which get strengthened while moving towards southern Peninsular India due to the presence of strong equatorial westerlies over IO. These strengthened low pressure systems leads to strong ascending wind anomalies over southern Peninsular India and strong descending anomalies over Central IO and HREs over southern Peninsular India.

Characterization of the future northeast monsoon rainfall based on the clustered climate zone under CMIP6 in Peninsular Malaysia

The Northeast monsoon (NEM) exerts a significant influence on the climate system of Peninsular Malaysia (PM) and has profound implications for water resource management, flood control, and disaster preparedness in the region. This study employed CHIRPS rainfall data from 1981 to 2022 at a spatial resolution of 0.25° to generate climate zones. The performance of global climate models (GCMs) in replicating monthly rainfall was evaluated using multiple statistical metrics, with the top-ranked ones being used for the generation of future projection downscaled ensemble models. The NEM months (November, December, January, February, and March) were used as input parameters to form clustered homogeneous groups using the Ward hierarchical method. The optimal number of climate zones was determined using the elbow method, silhouette width values, and spatial evaluation. Ultimately, a 7-cluster solution was chosen due to its consistency and meaningful cluster formation. The zones were categorized into two regions: the West Coast and the East Coast, based on their geographical location and distinctive monthly NEM rainfall patterns. The analysis of the probability distribution function (PDF) showed that rainfall in the West Coast region had a more spread-out pattern with varying amounts, while rainfall in the East Coast region was more concentrated and consistent. The Northeast climate zone received the highest rainfall, attributed to its proximity to the South China Sea, mountainous areas, and the NEM convergence zone. In contrast, the Northwest and Lower Central-West climate zones received the lowest rainfall due to the rain shadow effect, limited moisture availability, and distance from major moisture sources. The CMCC-ESM2, CMCC-CM2-SR5, EC-Earth3, and CanESM5 consistently ranked among the highest performing models. After linear scaling bias correction, validated GCMs showed significant improvements, with improvements in MAE, NRMSE, PBIAS, RSR, NSE, MD, CP, R2, and KGE by 11–45%, 15–47%, 96–100%, 15–47%, 10–79%, 5–37%, 68–113%, 11–73%, and 9–51%, respectively. Analysis of NEM season rainfall patterns across climate zones indicated consistent mid-future increases for the Northwest and Lower Central-West zones (12.4–19.8%) and the Upper Central-West and Southwest zones (0.2–5.4%) across all scenarios. In the far-future, the Southeast zone saw an overall increase in rainfall (4.9–11.4%), while the Central-West zone exhibited mixed results (−4.0 to 5.6%). The results also highlighted spatial variability and the need to consider specific climate zones when assessing the impacts of climate change on the NEM, emphasizing the importance of incorporating these projections into long-term planning and decision-making processes.

Evaluation of Historic Trends for Monthly, Seasonal, and Annual Rainfall Series of Tenkasi, Tamil Nadu, India

Analyzing the variability and trends of rainfall plays a major role in water resource planning and management. Changes in rainfall patterns significantly influence the water availability in the irrigation structures and agronomical practices of crops. This study aimed to investigate the trends and estimate the magnitudes of monthly, seasonal, and annual rainfall series in Tenkasi, Tamil Nadu, India, using 49 years of rainfall data from 1971 to 2019. Sen’s Innovative trend analysis (ITA) method, Mann-Kendall (MK), and Simple Linear Regression (SLR) test were applied to assess the trends at 5 and 10 % significance levels. Trend magnitudes were estimated by Sen’s slope estimator (SSE) and SLR method. Changes in rainfall magnitude with mean values in percentage were estimated for all three slope estimation methods. The results revealed that the ITA method detected more significant trends of rainfall series over other methods. Significant downward trends were exhibited by October and north east monsoon (NEM) rainfall series had a trend magnitude of -0.43 mm/year and -0.04 mm/year. The percentage change in magnitude of the trend from mean values for October and the NEM series was -11.5% and -0.39%. Sub-trends within the October and NEM rainfall series showed that the low rainfall sub-series exhibited no trend, medium and high rainfall sub-series showed a downward trend. This study concluded that, in comparison to traditional trend analysis methods, the ITA method demonstrated a more rigorous investigation of trends. The significant decrease in rainfall during the NEM necessitates greater attention to water resources planning and management, serving as valuable scientific information for both crop planning and water resource management.

Regional and remote influences of ocean‐atmospheric processes on northeast monsoon rainfall during 2021 over India

AbstractSevere rainfall during the recent northeast monsoon season (October, November and December) in 2021 caused unexpected disasters (triggered flooding and landslides) in the pilgrim city of Tirupati and the metropolitan city of Chennai over the peninsular of India (PI). The remote and regional influence of several ocean‐atmospheric climate indices on northeast monsoon rainfall (NEMR) during the OND season is investigated to understand the contributory mechanisms of extreme rainfall events over the PI during 2021. The interannual variability of NEMR and its relationship with the OND sea surface temperature (SST) anomaly over the Niño3.4 and Indian Ocean Dipole (IOD) regions are considered. In addition, SST anomalies over the Bay of Bengal (BoB), such as east BoB (EBoB), east equatorial Indian Ocean (EEIO) and their gradients (GSST) connections with NEMR, are also examined. The impact of Siberian High (SH), Western Pacific High (WPH), Arabian Sea Surface Pressure (ASSP), easterly waves (EW) and land surface temperature contrast (LSTC) is also analysed and incorporated to find a specific linkage with NEM 2021. The results show that excess NEMR during OND 2021 is suggestively associated with favourable large‐scale conditions like negative IOD and the La Niña phase over the Indo‐Pacific region. In November 2021, two deep depressions made landfall over Tamil Nadu (within the latitude of 11°–13°N) and over adjoining areas, which caused copious amounts of rainfall (maximum ~90 mm·day−1), leading to flooding conditions over Tirupati and Chennai. During the NEM season, abundant warming of BoB and weak vertical wind shear, along with negative anomalies of ASSP, modulated the cyclonic activities that enhanced the sufficient moisture transports from BoB to the PI. The MSLP anomalies over the ASSP region depict a significant correlation (−0.47) with the interannual variability of NEM as compared to SH and WPH during the OND season of 2021.

Large-scale atmospheric teleconnections and spatiotemporal variability of extreme rainfall indices across India

Identifying trends in hydrometeorological time series during extreme weather events and their interactions with large-scale atmospheric teleconnections is crucial for climate change research. This study evaluates 14 precipitation-based indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) across seven climatic zones of India using gridded daily rainfall data from the India Meteorological Department (IMD) for 120 years (1902–2021) utilised. Trend analysis was carried out using the Mann-Kendall (MK) test, Theil-slope Sen's estimator, Innovative Trend Analysis (ITA), and other statistical tools. Change point detection is established using the Pettitte test and Cumulative Sum algorithm. The relationships between large-scale atmospheric teleconnections and ETCCDI indices are also found, and Multiple Linear Regression (MLR) models are developed between them.The results show significant increasing trends in extreme rainfall indices in India's Ladakh region, located in the arid desert-cold climatic zone. The annual, Southwest Monsoon (SW-Monsoon), Northeast Monsoon (NE-Monsoon), and summer rainfall trends were positive, while winter rainfall had a negative trend across most climatic zones. Significant associations between large-scale atmospheric teleconnections, including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Global Temperature Anomaly (GTA), Southern Oscillation Index (SOI), SST of Niño 3.4 region, Oceanic Niño Index (ONI), and Dipole Mode Index (IOD) and ETCCDI indices were established across multiple climatic zones. Using MLR analysis, this study attempts to establish, for the first time, the relationship between teleconnections and ETCCDI indices across India. Extreme rainfall indices are influenced by climate change during the SW-Monsoon across most of the climatic zones of India. During the previous El Niño event (2014–2016), average annual rainfall decreased by 19.5%, SW-Monsoon rainfall decreased by 25.2%, and NE-Monsoon rainfall decreased by 64.1% in India. The findings may provide valuable insights into mitigation strategies to sustain the adverse effects of extreme weather conditions and enhance climate resilience.

Temporal characteristics of northeast monsoon rainfall and its teleconnection with the large-scale circulation indices

Temporal characteristics of northeast monsoon rainfall and its teleconnection with the large-scale circulation indices

Decoding the enigma of 100-year record-breaking rainfall over Tamil Nadu using wavelet analysis

There have been increasing trends of short-intensive rainstorm events, which lead to urban flood disasters in many coastal areas of India. The relationship between regional rainfall and global large-scale climate indices has shown high teleconnections in terms of recurrence and covariance. We have examined the teleconnection between the November and December 2015 rainstorm events (100-year record breaking rainfall) with the Oceanic Nino Index using the dataset from the Indian Meteorological Organisation (NEMR) and National Oceanic and Atmospheric Administration (ONI). The study has found out not only the existence of the cause-and-effect relationship but also the recurrences of these high rainfall events. The climatological trend of northeast monsoon rainfall (NEMR) and Oceanic Nino Index (ONI) is calculated, and the correlation for a period of 71 years (1951–2021) is quantified using Pearson's correlation method. We observed that Chennai, Nagapattinam, Cuddalore, Kancheepuram, Thiruvarur, and adjoining coastal districts are more prone to torrential rains during El Niño years as compared to La-Niña years which is unusual but not exceptional. Wavelet analysis is performed on the NEMR and ONI signals to decipher the teleconnections at different temporal scales. Wavelet coherence shows an in-phase relationship between the ONI and NEMR signal and high covariance (0.8–0.9 at 5% significance level using Monte Carlo generated noise), between the two in 2015 the year which broke the 100 years rainfall record for Chennai. The study highlights the crucial linkage (cause and effect relationship) between the regional rainstorms and the large-scale global index (ONI), and wavelet analysis can explain the distant relationship in the northeast monsoon. The study provides quantified insights especially in the present era of climate change, where the hydrological extremes are persistent. The study does not only connect ONI with NEMR but also quantifies the in-phase strength of the relationship which can guide the scientific community towards accurate predictions and develop disaster mitigation strategies.

Tracing the Sources of River Waters Using Stable Isotopes (δ18O and δ2H) in Two Mountainous Watersheds, Southern Western Ghats, India

Stable isotopes of oxygen (δ18O) and hydrogen (δ2H) are useful tools to characterize and monitor the hydrological processes in aquatic ecosystems.The present study uses δ18O and δ2H data from two small catchment rivers such as the east-flowing Bhavani river and the west-flowing Thuthapuzha River in southern Western Ghats to get an insight into the water cycle dynamics and sources of water.The Bhavani River drains through the semi-arid, lee-ward side of the Western Ghats and shows a markedly depleted isotopic composition as compared to the Thuthapuzha River which drains through the humid, windward side of the Western Ghats. The isotopic systematics are strongly influenced by the southwest monsoon (SWM) and the northeast monsoon (NEM) rainfalls. However, during the pre-monsoon (PRM) season, the isotopic composition in these river waters is governed mainly by the baseflow and evaporative processes. Both the river basins experience a higher vapour recycling effect in their headwaters. The NEM rainfall contains a considerable amount of recycled vapour, which is evident from the observed high d-excess values. Whereas the SWM rain events show the dominance of the original sea moisture source. There is a distinct difference in the isotopic ratios and d-excess values between the SWM (d-excess: Bhavani River 10.29 ± 1.29‰; Thuthapuzha River 10.52 ± 2.78‰) and the NEM (d-excess: Bhavani River 15.15 ± 0.92‰; Thuthapuzha River 12.67 ± 4.49‰) seasons, with the predominance of lighter isotopes in the latter period. In general, the seasonal and spatial differences in isotopic composition in the Bhavani River (δ18O: PRM−5.42 ± 1.76‰; SWM−4.16 ± 0.77‰; NEM -4.73 ± 0.76‰) and the Thuthapuzha River (δ18O: PRM −4.26 ± 0.67‰; SWM −2.54 ± 0.76‰; NEM −2.68 ± 0.96‰) indicate that precipitation (atmospheric source) as the major source of water in the monsoon (SWM and NEM) season, while the base flow contribution from groundwater has a major stake in PRM season. Keywords: Stable Water Isotopes, d-excess, Climate Gradient, Base Flow Discharge, Western Ghats, Southwest India

Rainfall Precipitation Pattern from 2001-2021 over Andhra Pradesh Region in India

The research focuses on a key meteorological variable, precipitation, in order to analyze the changing trend of rainfall in Andhra Pradesh, India. The State highly depends on rainfall for its agricultural activities, but the occurrence of rainfall is unprecedent and variable, causing adverse implications on the cropping system as well as negative effects on natural water resources. Daily data from the years 2001 to 2021 were analyzed to determine monthly, seasonal and yearly rainfall variability using statistical methods such as mean, standard deviation (SD) and coefficient variability (CV). District wise monthly, seasonal and annual rainfall trends have been drawn using 21-year daily data. The statistical analysis of whole reference time series data reveals that maximum mean South West Monsoon (SWM) rainfall is received in the north and central coastal districts of Srikakulam, Visakhapatnam, Vizianagaram, East Godavari, West Godavari and Krishna. North East Monsoon (NEM) rainfall contributes significantly in mean annual rainfall of southern coastal district and Rayalaseema sub division of the State.

Assessment of WRF microphysics and cumulus parameterizations in simulating heavy rainfall events over Badulu Oya catchment, Sri Lanka

Abstract Extreme rainfall events leading to severe hydrological impacts warrant an accurate prediction of such events not only on time but also in magnitude. Sri Lanka is a South Asian country that is frequently affected by severe tropical storms. The primary aim of this study was to improve heavy rainfall events forecast during the North-East monsoon over the Badulu Oya catchment, Sri Lanka. This aim was accomplished by simulating precipitation for two extreme North-East monsoon rainfall events using the Weather Research and Forecasting (WRF-ARW) model. A detailed comparison was made between the 24-h spatial distribution of model rainfall and observations obtained from rainfall gauges. Verification was evaluated based on three deterministic approaches. Each rainfall event was simulated multiple times using 15 different parameterization scheme combinations including six microphysics and four cumulus schemes at a 3 km grid resolution. The filtered best model combinations were validated using observations from another two heavy North-East monsoon rainfall events. The key finding from these evaluations was that model configurations with WSM5, WSM6, Kessler and WDM6 microphysics, and KF, BMJ and MKF cumulus schemes displayed the overall best performances. Therefore, these combinations have a good potential for operational use in numerical weather prediction over the said catchment.

On the simulation of northeast monsoon rainfall over southern peninsular India in CMIP5 and CMIP6 models

On the simulation of northeast monsoon rainfall over southern peninsular India in CMIP5 and CMIP6 models

Extent of diurnal cycle of rainfall and its intra seasonal variation over coastal Tamil Nadu during north east monsoon season

The diurnal variation of north east monsoon rainfall of coastal Tamil Nadu represented by four coastal stations Chennai Nungambakkam (Nbk), Chennai Meenambakkam (Mbk), Nagapattinam (Npt) and Pamban (Pbn) was studied in detail based on hourly rainfall data of rainy days only, for the period 1 Oct-31 Dec for the 47/48 year period 1969-2016/2017. Mean Octet rainfall and its anomaly were computed for the 8 octets 00-03,…., 21-24 hrs of the day and the anomaly was tested for statistical significance. Various analysis for the individual months of Oct, Nov, Dec and the entire period Oct-Dec were separately conducted. The basic technique of evolutionary histogram analysis supplemented by harmonic analysis of octet mean rainfall anomaly was used to detect the diurnal cycle signal. Two indices named as diurnal variation of rainfall index and coefficient of mean absolute octet rainfall anomaly representing the intensity of diurnal variation in dimensionless numbers were defined, computed and interpreted.
 The analysis based on the above techniques revealed that the diurnal signal which shows an early morning maximum and late afternoon minimum of octet rainfall is well defined in Oct, decreases in Nov and further decreases in Dec for all the 4 stations. Though the diurnal variation manifests a well defined pattern in Dec the signal is not statistically significant in most cases. For Nbk and Mbk there is a weak secondary peak of octet rainfall anomaly occurring in the forenoon and afternoon respectively in Oct and Dec suggesting the presence of semi-diurnal variation of rainfall. Stationwise, the diurnal signal is most well defined for each month/season in Pbn followed by Npt, Nbk and then Mbk.
 
 The physical causes behind the diurnal signal and its decrease as the north east monsoon season advances from Oct to Dec have been deliberated. The well known feature of nocturnal maximum of oceanic convection influencing a coastal station with maritime climate and the higher saturation at the lower levels of the upper atmosphere in the early morning hours have been advanced as some of the causes. For the much more complex feature of decrease of diurnal signal with the advancement of the season, the decrease of minimum surface temperature over coastal Tamil Nadu from Oct to Dec causing an early morning conceptual land breeze has been shown as one of the plausible causes based on analysis of temperature and wind. Scope for further work based on data from automatic weather stations, weather satellites and Doppler Weather Radars has been discussed.

Impact of Sunspot Number, Enso and Long Wave Radiation on the North East Monsoon Rainfall in the Coastal Districts of Tamil Nadu State

Impact of Sunspot Number, Enso and Long Wave Radiation on the North East Monsoon Rainfall in the Coastal Districts of Tamil Nadu State

Simulation of Northeast Monsoon in a coupled regional model framework

The rainfall during Northeast Monsoon (NEM) meets most of the water demand and influences the socioeconomic condition of the population in the southern part of India. The present study focuses to identify a better, coupled land-atmosphere combination using regional downscaling experiments with RegCM4 for representing the mean characteristics of the NEM rainfall. Three suites of model integrations are made using three different land-surface schemes such as Biosphere-Atmosphere Transfer Scheme (BATS), Subgrid-BATS (SUB), and Community Land Model version 4.5 (CLM). The CLM experiment shows a better spatial representation of the rainfall due to the better representation of the moisture flux and convection, whereas the BATS and SUB experiments show strong wet bias (1–6 mm/yr) over Tamilnadu and Kerala. The spatial correlation of the climatological rainfall with respect to observation is maximum (0.5–0.6) for the CLM. However, the model experiments fail to reproduce the interannual variability of rainfall. The better representation of climatological rainfall, evaporation and turbulent fluxes (sensible and latent heat flux) leads to an improved temperature climatology in the CLM experiment. The study illustrates that the CLM land-surface scheme performs better in representing the northeast monsoon as compared to others however there is a need to explore avenues for further improvements in the performance of the coupled model framework.

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.

Role of moisture transport from Western Pacific region on water vapor isotopes over the Bay of Bengal

Bay of Bengal (BoB) region is a major moisture source to both Indian Summer Monsoon (ISM) and North East Monsoon (NEM) rainfall over the surrounding region. Oxygen isotopic ratios (δ18O) in the rainfall over the peripheral region show a decreasing trend towards the later months of ISM and shows minimum during NEM. This was previously explained with local factors of BoB such as isotopically depleted river discharge to BoB, increased cyclonic activity etc., however, the exact reasons remain unclear. δ18O of vapor collected from BoB when there was no cyclonic activity also shows ~2 ‰ depletion in NEM season compared to that during ISM. Here we show, easterly winds passing through a convectively active narrow zonal band (central to western equatorial Pacific and Indonesian region) bring significant amounts (median 64%: interquartile range 30%) of isotopically depleted moisture to BoB region, and results in anomalous depletion of vapor over BoB during NEM. Vapor source-tagged simulations from isotope enabled Community Atmospheric Model show easterly moisture flux from western Pacific region dominates over the BoB surface evaporation flux.

Trend and change point detection of seasonal rainfall for effective crop planning over southern transition zone of Karnataka, India

The significance of the trends was tested by Mann-Kendall test for annual and seasonal rainfall. Among the 14 taluks, only Hassan taluk shows a significant positive trend in annual rainfall while eight taluks have shown non-significant positive trend and remaining five taluks have shown non-significant negative trend. The annual rainfall for the entire zone have shown non-significant positive trend. For the SWM season, Alur taluk shows a significant negative trend and eight taluks have shown non-significant positive trend. However, five taluks and whole zone showed a non-significant negative trend. Southwest monsoon and annual rainfall in Bhadravathi taluk was increased in 2007 (571.9 mm to 785.1 mm and 857.6 mm to 1090.9 mm, respectively) and in Shivamogga, the change in annual rainfall was decreased during 1983 (1497.5 mm to 944.0 mm) and 2011 (944.0 mm to 796.6 mm). The northeast monsoon rainfall was increased during 1992 (134.3 mm to 441.1 mm) and it was decreased during 1994 (441.1 mm to 162.0 mm) in Shikaripura taluk. Similarly, in Hunsur taluk, the SW Monsoon rainfall has increased (701.8 mm to 1010.2 mm) during 1991 and it was decreased during 2001 (1010.2 mm to 723.3 mm), in Periyapatna and Honnali taluk, Northeast monsoon rainfall has decreased during 2012 and 2011, respectively.

Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques

Understanding and quantifying long-term rainfall variability at regional scale is important for a country like India where economic growth is very much dependent on agricultural production which in turn is closely linked to rainfall distribution. Using machine learning techniques viz., cluster analysis (CA) and principal component analysis (PCA), the spatial and temporal rainfall patterns over the meteorological subdivisions in India are examined. Monthly rainfall data of 117 years (1901–2017) from India Meteorological Department over 36 meteorological subdivisions in India is used in this study. Using hierarchical clustering method, six homogeneous rainfall clusters were identified in India. Among the rainfall clusters, Group 1 had 30% dissimilarity with Groups 2, 3, and 4 while Group 5 and Group 6 are highly dissimilar (more than 90% dissimilarity) with the rest of the groups. Rainfall seasons in each group were further classified into dry, wet, and transition periods. The duration of dry period is smaller in group which consists of subdivisions from southern part of the country. The transition period between dry and wet period was found to be smaller for subdivisions in the coastal region. Both CA and PCA showed high rainfall variability in Groups 5 and 6, which comprise subdivisions from north east, Kerala, Konkan, and costal Karnataka and low rainfall variability in Groups 1 and 2 which comprise subdivisions from east, north, and central part of the country. Strong negative trend in annual and Indian summer monsoon rainfall is seen in northeast India and Kerala while positive trend is observed over costal Karnataka and Konkan region. The negative trend in post monsoon rainfall particularly over the peninsular and northeast India indicates weakening of northeast monsoon rainfall in the country.

High‐resolution climate change projection of northeast monsoon rainfall over peninsular India

AbstractIn this study, projected changes in mean northeast monsoon (NEM) rainfall and associated extreme rainfall and temperature events, over peninsular India (PI) and its six subdivisions, are quantified. High‐resolution dynamically downscaled simulations of the Weather Research and Forecasting (WRF) regional climate model driven by the boundary conditions from the Community Climate System Model version 4 (CCSM4) model (WRF‐CCSM4) are compared with statistically downscaled simulations of NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP). Over PI, these downscaled simulations show low bias in mean NEM rainfall (≤ − 0.44 mm·day−1) and high pattern correlation coefficient (≥0.75), giving confidence in their future projections. Under future warming over PI, both downscaled simulations project future significant enhancement in NEM rainfall with WRF‐CCSM4 projecting 1.98 mm·day−1 (83.78% change with respect to the present‐day mean) whereas the multimodel ensemble (MME) of eight NEX‐GDDP models project 0.67 ± 0.58 mm·day−1 (19.78%) by the midddle of the century and 1.42 ± 0.97 mm·day−1 (42.76%) by the end of the century. Analysis of extreme rainfall events shows that WRF‐CCSM4 projects future enhancement (reduction) in extreme rainfall (R95p) days over 91.4% (8.6%) of grid‐points over PI. In future, coastal areas of Karnataka and Andhra Pradesh will likely experience increased extreme rainfall occurrence by more than 25 days and 15–20 days respectively. Projected future enhancement in the mean and extreme NEM rainfall is attributed to the increased precipitable water under a warming climate. Future projection of extreme temperature indices shows an increase in minimum and maximum temperatures over PI during the NEM season. Over PI, future winter nights and days are found to be warmer than those in the present day and the temperature change in future winter nights is found to be larger than that in winter days. This climate change information would help decision‐makers in evaluating existing policies and devising revised policies to reduce risk due to climate change.