Intraseasonal Rainfall Variability Research Articles

Remote influence on regional scale intraseasonal rainfall variability over Vietnam

AbstractRemote influences on intraseasonal anomalous rainfall over regions that encompass North and South Vietnam are explored using a 38‐year (1979–2016) global dataset over the extended summer (May–October). Wet and Dry composites in filtered daily data with lags of up to 2 weeks are assembled for various rainfall indices over the two subregions, including atmospheric reanalysis products and in‐situ rainfall data. On the regional scale, the moisture flux convergence correlates well with reanalysed rainfall. The large‐scale dynamics associated with these composites are described. Rainfall composites of opposing signs show asymmetrical large‐scale precursors and different pathways of influence. Wet and Dry anomalies in North Vietnam are seen to originate from Europe and propagate at high latitudes. The exact nature of the precursors is sensitive to the definition of the composite index. There is also a pathway of influence along the Asian jet, which impacts South Vietnam, especially for Wet composites which often coincide with Dry conditions in the North. South Vietnam is also influenced by tropical divergent precursors, which are again asymmetric between Wet and Dry composites.

The Influence of Southeastern South American Rainfall on Weather Patterns Over the Tropical Atlantic, Northwestern Africa and Western Europe

AbstractDuring boreal winter (December–February), the South American monsoon system (SAMS) reaches its annual maximum when upper‐tropospheric westerly winds prevail over the equatorial Atlantic. Atmospheric dynamic model simulations suggest that Rossby waves generated over South America can propagate to and potentially influence weather patterns in the Northern Hemisphere (NH). However, observational evidence has been absent previously. Here we focus on southeastern South American (SESA) precipitation anomalies, which can characterize intraseasonal rainfall variability of the SAMS. Since tropical “westerly duct” and convective heating are important factors for cross‐equatorial propagation of Rossby wave (CEPRW), we identify two groups of events based on the two factors. By comparing the events associated with both SESA rainfall and tropical westerlies to the events associated with tropical westerlies only, we find that an anomalous Rossby wave train is triggered by precipitation anomalies over SESA, propagates in the southwest–northeast direction, and subsequently crosses the equator. Over a period of 4 days, near‐surface temperature over northwestern Africa and western Europe becomes warmer, accompanied by increased surface downward longwave radiation and precipitable water. The equatorward propagating Eliassen–Palm flux anomalies originated from SESA support the evidence of CEPRW. Simulations using a time‐dependent linear barotropic model forced by prescribed divergence anomalies over SESA further confirm that SESA rainfall can influence the NH weather patterns through CEPRW. Knowledge of this study will help us better understand and model interhemispheric teleconnections over the American–Atlantic–African/European sector.

Impacts of Intra-Seasonal Rainfall Variability and Cropping Practices on Cereal Yields in Sub-Saharan Africa

Impacts of Intra-Seasonal Rainfall Variability and Cropping Practices on Cereal Yields in Sub-Saharan Africa

Intra-seasonal rainfall variability and crop yield in the Upper East Region of Ghana

Intra-seasonal rainfall variability and crop yield in the Upper East Region of Ghana

Convective systems and rainfall in East Africa

AbstractEast Africa is particularly vulnerable to weather extremes, with severe weather linked to thousands of deaths per year. Improved forecasts of convective events in this region are urgently needed, from both nowcasting and numerical weather prediction models. Improving these forecasts requires further knowledge of convection in this region. This study aims to improve understanding of convective events in East Africa, based on a six‐year climatology of convective life cycles and the associated precipitation. Convective systems are identified as contiguous areas of cold cloud in geostationary satellite measurements over East Africa. A tracking algorithm is used to trace the evolution of the properties of these systems through time and space. Matching the systems to surface precipitation obtained from satellite microwave observations provides insight into how the life cycles of these systems relate to precipitation at the surface. Over the region as a whole, 59% of the accumulated precipitation can be attributed to the tracked convective systems. The majority (81%) of heavy precipitation events (10 mmhr−1) are attributable to convective systems, while light rainfall events (1 mmhr−1) are not (1.8%). Most of the tracked convective systems have an area less than 400 km2 and last for less than an hour. However, the less frequent larger longer‐lived systems produce the majority of the regional accumulated precipitation. Composite life cycles of the tracked systems show rapid intensification and the heaviest precipitation initially, followed by a steady increase in area and weakening in intensity before the system decays. Finally, the Madden–Julian oscillation, which plays a key role in intraseasonal rainfall variability in the region, is also linked to the amount of rainfall due to convective systems through changes in the frequency and properties of these systems.

Surface‐driven amplification of Madden–Julian oscillation circulation anomalies across East Africa and its influence on the Turkana jet

AbstractIn semi‐arid environments, rainfall‐driven soil moisture fluctuations exert a strong influence on surface turbulent fluxes. Intraseasonal rainfall variability can therefore impact low‐level atmospheric temperatures and influence regional circulations. Using satellite observations and an atmospheric reanalysis, we investigate whether rainfall variability induced by the Madden–Julian oscillation (MJO) triggers land–atmosphere feedbacks across East Africa.We identify that surface fluxes during the East African wet seasons (March–May and October–December) are sensitive to MJO‐induced precipitation variations across low‐lying regions of South Sudan and highland regions of Uganda and southwest Kenya. For example, during MJO phases 6 to 8, when rainfall is suppressed, surface temperatures and sensible heat fluxes increase, whilst evapotranspiration decreases. Spatial variations in the surface flux response to rainfall variability feed back onto the atmosphere through amplifying MJO‐associated pressure anomalies. During dry MJO events for instance, surface warming across the exit region of the Turkana channel increases the low‐tropospheric along‐channel pressure gradient and intensifies the Turkana jet. We conclude that average surface‐driven temperature fluctuations during a single day are responsible for approximately 12% of MJO‐associated variability of the Turkana jet speed. However, we expect that the accumulation of heat over multiple days to the west of the East African Highlands further amplifies anomalies in the pressure gradient and jet intensity. Modelling experiments are required to quantify the accumulated impact of the surface forcing. Surface‐driven Turkana jet variations influence the East African moisture budget and affect the intensity and inland propagation of coastal convection. Not only is this the first study to investigate the importance of intraseasonal land–atmosphere feedbacks across East Africa, but it is also the first to show that Turkana jet characteristics are partly driven by surface conditions. This work motivates an investigation into whether subseasonal forecast models fully harness the predictability from surface‐induced jet variations.

Bay of Bengal upper-ocean stratification and the sub-seasonal variability in convection: Role of rivers in a coupled ocean-atmosphere model

The Bay of Bengal (BoB) receives a large amount of freshwater from rains and rivers, resulting in large upper-ocean stratification due to the freshening effect. This salinity stratification has been theorized to impact sea-surface temperature (SST) and convection on intra-seasonal time scales by affecting the ocean mixed layer and the barrier layer. This article aims to quantify the impact of salinity stratification on the sub-seasonal variability in SST and convection by using in-situ ocean observations and coupled model experiments. It is shown that monsoon intra-seasonal oscillations (MISOs) exhibit varied levels of intra-seasonal variability in SST and rainfall based on the underlying ocean conditions. The largest intra-seasonal variability in SST does not cause the largest convection variability in the north-western BoB. Instead, moderate variability in SST and rainfall associated with MISOs co-occur with deep mixed layer and thick barrier layer conditions. Realistic representation of river freshwater fluxes in a coupled ocean-atmosphere model leads to improved intra-seasonal SST and rainfall variability. Thick barrier layers in the north-western Bay attenuates the entrainment cooling of the mixed layer, and the high mixed layer heat content provides conducive oceanic conditions for the genesis of monsoon low-pressure systems (LPS), thereby affecting rainfall over India. This study has important implications for operation forecasting using coupled models.

Persistent Wet and Dry Spells of Indian Summer Monsoon Rainfall: A Reexamination of Definitions of “Active” and “Break” Events

Abstract Persisting wet events (PWEs) and persisting dry events (PDEs) over central India (CI), defined by rain spells lasting for 5 days or more above and below climatology, respectively, represent an important component of the Indian summer monsoon’s intraseasonal variability. However, half of such PREs and PDEs that do not overlap with conventionally defined “active” and “break” spells over CI while contributing about 20% to the seasonal mean remained poorly studied. Here we find that, in contrast to more abundant longer (>5 days) wet and dry spells over the CI, the intraseasonal rainfall variability over northeast India (NEI) is characterized by higher abundance of intense shorter spells (<5 days). Physically, the difference is linked to the fact that monsoon intraseasonal oscillations with a 30–60-day time scale dominate subseasonal variability over the CI, whereas the 10–20-day quasi-biweekly mode dominates subseasonal variability over NEI. While non-overlapping PDEs are associated with large-scale but lower-intensity breaks, non-overlapping PWEs are associated with synoptic events with relatively smaller spatial scales rather than large-scale active events. Here, a percentile-based definition of active and break spells as daily rainfall in excess of the 90th percentile and below the 30th percentile, respectively, persisting for more than 3 days is proposed; this encompasses almost all non-overlapping PWEs and PDEs and is expected to be more useful to the users. Contributions of the subseasonal fluctuations to the seasonal mean and their association with predictable drivers indicate that the seasonal mean rainfall over the NEI is significantly less predictable than that over the CI. Significance Statement Farmers eagerly await the extended range projections of “wet” and “dry” phases of Indian monsoon intraseasonal variability for planting, harvesting, and water resource management. The prevailing definitions for “active” and “break” spells, however, ignored the less intense persisting wet or dry events that contribute roughly equally to the seasonal mean rainfall. To facilitate the process, a new percentile-based definition of the Indian summer monsoon rainfall’s intraseasonal spells is proposed that includes nearly all non-overlapping PWEs and PDEs. The subseasonal fluctuations’ contributions to the seasonal mean and its association with predictable drivers suggest that the seasonal mean rainfall over the NEI is much less predictable than that over the CI, and thus illustrates the contrasting features of spells within these two regions.

The Influence of the Madden–Julian Oscillation on the Wet Season Rainfall over Saudi Arabia

The influence of Madden–Julian oscillation (MJO) is examined on intraseasonal rainfall variability during the wet season (November–April) by using the real-time multivariate (RMM) MJO index, ERA5 reanalysis, and daily observed rainfall dataset from 26 stations in Saudi Arabia for the period 1985–2021. The MJO 8 phases are categorized into wet (phases 1, 2, 7 and 8) and dry (phases 3, 4, 5, and 6) based on the Saudi Arabian intraseasonal rainfall characteristics associated with MJO phases. It is observed that 41% (46%) of total (extreme) rainfall events occur during the MJO wet phases, while only 23% (18%) of such events occur during MJO dry phases. The intraseasonal variability signals are isolated from daily dataset by applying a 30- to 90-day period bandpass filter. The analyses are validated by constructing composites of daily filtered precipitation anomalies during MJO 8 phases. The physical mechanism indicates that the significant intraseasonal wetter conditions are linked with enhanced easterly and southeasterly moisture convergence over Saudi Arabia from the Arabian Sea. The atmospheric cyclonic circulation anomalies during the wet phases favor more moisture convergence and vertical moisture advection, which may lead to enhanced convection and rainfall. However, during the dry phases, anticyclonic circulation anomalies enhance moisture divergence and reduce vertical moisture advection and consequently suppress the convection and rainfall activity over Saudi Arabia. The analyses show that the intraseasonal rainfall variability over Saudi Arabia is significantly influenced by the MJO during the wet season. These findings have important implications for sub-seasonal rainfall forecasting in Saudi Arabia.

Intra-seasonal variability of autumn rainfall in the Yangtze River Delta and its related atmospheric circulations

In the densely populated and economically developed Yangtze River Delta (YRD) region, abnormal rainfall in autumn (September–November) has important implications for economic development, food security, and livelihoods. Here we explore the intra-seasonal variability of autumn rainfall in the YRD and pre-evolution of related atmospheric circulations, which can help to improve intra-seasonal rainfall prediction skills in the autumn season. Intra-seasonal variability of autumn rainfall over the YRD exhibits two dominant timescales, a quasi-biweekly oscillation (QBWO) cycle associated with tropical cyclones, and an intra-seasonal oscillation (ISO) cycle controlled by large-scale atmospheric circulation. Considering the more stable and robust ISO than QWBO, the ISO is primarily addressed here. Two dominant ISO modes in autumn rainfall are identified by using an empirical orthogonal function analysis. The first ISO mode is characterized by a monopole with regional consistent rainfall anomalies (MP mode), whereas the second ISO mode has a meridional dipole structure with opposite rainfall anomalies over the south and north part of YRD (DP mode). These two ISO modes are modulated by the ISO propagation of atmospheric circulation in key areas, including the Bay of Bengal, the western North Pacific, and the Okhotsk Sea, from 25-day to 10-day lead when the rainfall mode is enhanced. The MP mode (regional enhanced rainfall) is related to regional active moist convection, strong western Pacific subtropical high (WPSH) and active cold air invasion via Lake Baikal. The DP mode (moist in the south and dry in the north) is accompanied by north-south convective dipoles, weak WPSH and inactive cold air outbreaks.

The role of air–sea coupling on November–April intraseasonal rainfall variability over the South Pacific

We investigate the impact of resolving air-sea interaction on the simulation of the intraseasonal rainfall variability over the South Pacific using the ECHAM5 atmospheric general circulation model coupled with the Snow-Ice-Thermocline (SIT) ocean model. We compare the fully coupled simulation with two uncoupled ECHAM5 simulations, one forced with sea surface temperature (SST) climatology and one forced with daily SST from the coupled model. The intraseasonal rainfall variability over the South Pacific is reduced by 17% in the uncoupled model forced with SST climatology and increased by 8% in the uncoupled simulation forced with daily SST, suggesting the role of air–sea coupling and SST variability. The coupled model best simulates the key characteristics of the two dominant patterns (modes) of intraseasonal rainfall variability over the South Pacific with reasonable propagation and correct periodicity. The spatial structure of the two rainfall modes in all three simulations is very similar, suggesting the dynamics of the atmosphere primarily generate these modes. The southeastward propagation of rainfall anomalies associated with two leading rainfall modes in the South Pacific depends upon the eastward propagating Madden–Julian Oscillation (MJO) signals from the Indian Ocean and western Pacific. Air-sea interaction improves such propagation as both eastward and southeastward propagations are substantially reduced in the uncoupled model forced with SST climatology. The simulation of both eastward and southeastward propagations considerably improved in the uncoupled model forced with daily SST; however, the periodicity differs from the coupled model. Such discrepancy in the periodicity is attributed to the changes in the SST-rainfall relationship with weaker correlations and the nearly in-phase relationship, attributed to enhanced positive latent heat flux feedbacks.

Impacts of the MJO on Rainfall at Different Seasons in Indonesia

The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability of rainfall in Indonesia, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. This study aims to investigate the general impacts of MJO on rainfall at different seasons in Indonesia, particularly during boreal summer. Impacts of the MJO on daily rainfall anomaly during the four climatic seasons: DJF, MAM, JJA, and SON in Indonesia have been evaluated using in-situ data from 86 stations during 1981 - 2012 and remote sensing data using GPM IMERGV06 from 2001 - 2019. The greatest impact of the MJO on rainfall over Indonesia occurs during the DJF and MAM seasons (austral summer), with the magnitude varying across regions. Enhanced rainfall generally occurs over the western parts of Indonesia on phases 2 to 4, central parts of Indonesia on phase 4, and eastern parts of Indonesia on phases 4 to 5. Conversely, suppressed rainfall generally occurs over the western parts of Indonesia on phases 5 to 8, central parts of Indonesia on phases 6 to 8, and eastern parts of Indonesia on phases 1 to 2 and 6 to 8. In addition, the MJO influence during the JJA and SON seasons are slightly less, in terms of intensity, than during the DJF and MAM seasons, which is likely due to the northward shift of ITCZ and, hence, the intraseasonal oscillation convective envelope during boreal summer. Generally, enhanced rainfall occurs over the western and northern parts of Indonesia on phases 2 to 3, and suppressed rainfall on phases 6 to 7. The results indicate that convectively active MJO may increase the possibility of daily extreme rainfall in particular regions in Indonesia at different seasons.

Intraseasonal oscillation of the rainfall variability over Rwanda and evaluation of its subseasonal forecasting skill

Rwanda is a landlocked country in central-eastern Africa. As a country highly dependent on rain-fed agriculture, Rwanda is vulnerable to rainfall variability. Observational data show that there are two rainy seasons in Rwanda, i.e., the long rainy season and the short rainy season. This study mainly focuses on the dominant intraseasonal rainfall mode during the long rainy season (February–May), and evaluates the forecast skill for the intraseasonal variability (ISV) over Rwanda and its surrounding regions in a state-of-the-art dynamic model. During the long rainy season, observational results reveal that the dominant intraseasonal rainfall mode in Rwanda exhibits a significant variability on the 10–25-day time scale. One-point-correlation analysis further unveils that the 10–25-day intraseasonal rainfall variability in Rwanda co-varies with that in its adjacent areas, indicating that the overall 10–25-day rainfall variability in Rwanda and its adjacent regions (8°S–3°N, 29°–37°E) should be considered collectively when studying the dominant intraseasonal rainfall variability in Rwanda. Composite results show that the development of the 10–25-day rainfall variability is associated with the anomalous westerly wind in Rwanda and its surrounding regions, which may trace back to a pair of westward-propagating equatorial Rossby waves. Based on the observational findings, an ISO_rainfall_index and an ISO_wind_index are proposed for quantitatively evaluating the forecast skill. The ECMWF model has a comparable skill in predicting the wind index and the rainfall index, with both indices showing a skill of 18 days.摘要非洲中东部地区的经济主要依靠自给农业支撑, 该地区农业经济对降水的变化尤为敏感. 本文以卢旺达为例, 观测分析指出卢旺达的次季节降雨主要集中在10–25天; 根据次季节尺度降水变率的单点相关方法, 发现卢旺达的次季节降水变率和周围区域变化一致; 进一步合成结果显示该地区次季节降水变率与异常西风有关, 这可追溯到赤道地区西传的赤道Rossby波. 最后, 本文评估了当前动力模式ECMWF对卢旺达地区(即非洲中东部)次季节降水变率的预报能力, 发现EC模式在对该区域降水和相关风场指数的预报技巧都在18天左右, 且预报技巧表现出一定的年际差异, 这可能与热带太平洋的背景海温信号有关. 该工作增进了当前对非洲中东部地区的次季节降水变率和预测水平的认知, 并且对该地区国家粮食安全和防灾减灾具有启示性意义.

Assessing the Impact of Land Cover, Soil, and Climate on the Storage Potential of Dryland Sand Dams

Sand dams, a water-harvesting structure employed by rural communities in drylands have an inconsistent record of effectiveness. While many sand dams are highly functioning, improper siting, siltation, seepage, and high rates of evaporation from shallow sand reservoirs inhibit the water storage capacity of some sand dams. This study examines large-scale drivers of sand dam storage potential through analysis of an integrated surface and subsurface flow model. Multiple simulations were run, and comparative simulation analyses consider the effect of geomorphological factors, intraseasonal rainfall variability, and future climate conditions on sand dam performance criteria. The analyses revealed that a watershed highly cultivated with low water crops actually reduces evapotranspiration below that of natural vegetation and supports higher groundwater recharge. Additionally, intraseasonal variation and volume of rainfall impact sand dam performance less than the prevailing pattern and duration of dry and rainy seasons. Sand dams constructed in watersheds with sandier soils may experience greater connectivity with the stream margins and thus provide additional groundwater recharge. Lastly, climate change may improve some conditions desirable for sand dam performance, such as extending the duration of the rainy season and reducing overall evapotranspiration. However, the interactions between the expected climate change conditions and other geomorphological factors may result in a net decline in sand dam performance. The results of this study may help identify watersheds that are likely to support a sand dam with high potential for capturing and storing water throughout the dry season.

Influence of the strongest positive Indian Ocean Dipole and an El Niño Modoki event on the 2019 Indian summer monsoon

The year 2019 experienced an excess monsoon season over the Indian region, with the seasonal rainfall being 110 % of the long period average (LPA). Several zones across the country suffered multiple extreme rainfall events and flood situations resulting in a massive loss of life and property. The first half of 2019 experienced a moderate El Niño Modoki event that lasted till mid-summer. Another important feature of 2019 was the strongest recorded positive Indian Ocean Dipole (IOD) that lasted approximately seven months from May to November. This study has examined the reasons for the intra-seasonal variability of rainfall over India during the 2019 monsoon using available remote sensing and reanalysis data. Our analysis has shown that the presence of El Niño and the formation of a very severe cyclonic storm (VSCS) in the Arabian Sea were unfavorable for the monsoon onset and its northward advancement during June. However, the Walker circulation associated with El Niño helped strengthen the IOD developed early in the Indian Ocean, much before the monsoon onset. The anomalously strong IOD strengthened the monsoon circulation during July-September and resulted in excess rainfall over India.

IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

AbstractA high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

Trends in Extreme Wet Events during Summer Monsoon Season over India

India is a predominantly agrarian country with most of the agriculture in India being rainfed. Summer monsoon means life-giving rains for the entire Indian subcontinent. In India, the success or failure of the harvest and water scarcity in any year is always considered with the greatest concern. Extreme wet and dry events play a vital role in the intra-seasonal variability of monsoon rainfall. Particularly, in recent times, the occurrences of wet spells over India have led to widespread floods in the country. With changing climatic conditions, the occurrence and prediction of these extreme events have become a major stabilizing factor for the millions inhabiting the country. In this study, using 117 years (1901-2017) of high resolution daily gridded rainfall data, trends in extreme high rainfall events over six regions of India have been examined. The temporal change in extreme rainfall events during the time period 1901-1950 and 1951-2017 in summer monsoon months (June, July, August and September) has also been examined, in order to understand the increasing effect of anthropogenic factors after 1950. Using standardized scores (z-scores), each grid cell is analyzed to identify the number of extreme wet spells experienced during the study period. The analysis reveals that the number of extreme wet events showed an increasing trend in the month of June, as against a decreasing trend in July, after 1950, in most of the regions. In the recent period, the number of wet extreme events also show a decreasing trend in the northeastern region, which is considered as one of the rainiest zones of the world. As the present study tries to evaluate the changing trend of extreme wet events, it will help in formulation of hazard management strategies and policies by government planners.

Confronting CHIRPS dataset and in situ stations in the detection of wet and drought conditions in the Brazilian Midwest

AbstractThe states of Mato Grosso (MT) and Mato Grosso do Sul (MS) are located in Midwest Brazil and are dependent on agribusiness, which makes their water regimes of fundamental importance. However, the existing weather stations in these states are limited, and rainfall products are therefore a valuable alternative. The objectives of this study are: (a) to validate the CHIRPS datasets for the states of MT and MS in the Brazilian Midwest region; (b) to evaluate the intraseasonal variability of regional rainfall via CHIRPS; and (c) to define the drought and wet periods on the decennial scale, based on the annual SPI. Three seasons were defined (rainy, drought, and transition), and the differences showed that the MS rainy season (56.65%) was reduced due to the veranicos that occur in February, with an increase in the percentages of the drought (13.83%) and transition (29.52%) seasons. The highest monthly rainfall occurs in the north of MS (328–390 mm), while intermediate rainfall (109–273 mm) occurs in the south of MS. This satisfactorily represents the formation of a rainfall gradient in a N–S direction during the rainy season, similar to other characterizations around the world. Based on the annual SPI assessed per decade, there were moderate droughts annually in the 1990s, 2000s, and 2010s, but not in the 1980s. There were significant differences between the decades due to the intensity and categorization of the ENSO phases (strong and moderate), and drier decades with moderate rainfall reduction in both states can be defined in the last 40 years. The data obtained via CHIRPS are satisfactory for the spatio‐temporal evaluation of regional rainfall, and for the use of SPI to detect drought and wet periods in the states of MT and MS.

Performance of the RegCM-MITgcm Coupled Regional Model in Simulating the Indian Summer Monsoon Rainfall

An effort is made to evaluate the performance of an atmosphere–ocean coupled regional model in simulating the Indian summer monsoon rainfall during the years 1995–2015. The RegCM and MITgcm models are taken as the atmospheric and oceanic components of the coupled model, respectively. It is found that the coupling helps to improve the SST of the Arabian Sea and Bay of Bengal regions of the model domain. The air–sea coupling also improves the wind speed simulation. The RegCM-MITgcm coupled model correctly represents the intraseasonal variability of the Indian summer monsoon rainfall as compared to the RegCM stand-alone model. It is shown that the very high and extreme rainfall intensity events are correctly simulated by the RegCM-MITgcm coupled model, whereas the RegCM stand-alone model underestimates the precipitation intensities. It is argued that the atmosphere–ocean coupling helps to increase the predictability skill of the Indian monsoon rainfall. For example, the limit of predictability of the Indian monsoon rainfall obtained from the coupled model (22 days) is closer to that obtained from the IMD observation (24 days). The corresponding limit of predictability from the stand-alone model is 20 days.

Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India

Flash droughts cause rapid depletion in root-zone soil moisture and severely affect crop health and irrigation water demands. However, their occurrence and impacts in the current and future climate in India remain unknown. Here we use observations and model simulations from the large ensemble of Community Earth System Model to quantify the risk of flash droughts in India. Root-zone soil moisture simulations conducted using Variable Infiltration Capacity model show that flash droughts predominantly occur during the summer monsoon season (June–September) and driven by the intraseasonal variability of monsoon rainfall. Positive temperature anomalies during the monsoon break rapidly deplete soil moisture, which is further exacerbated by the land-atmospheric feedback. The worst flash drought in the observed (1951–2016) climate occurred in 1979, affecting more than 40% of the country. The frequency of concurrent hot and dry extremes is projected to rise by about five-fold, causing approximately seven-fold increase in flash droughts like 1979 by the end of the 21st century. The increased risk of flash droughts in the future is attributed to intraseasonal variability of the summer monsoon rainfall and anthropogenic warming, which can have deleterious implications for crop production, irrigation demands, and groundwater abstraction in India.