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Indian summer monsoon: Extreme events, historical changes, and role of anthropogenic forcings

Overview

Deepti Singh, Subimal Ghosh, Mathew K. Roxy, Sonali McDermid

Published Online: Jan 24 2019

DOI: 10.1002/wcc.571

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The South Asian summer monsoon is a complex coupled human‐natural system that poses unique challenges for understanding its evolution alongside increasing anthropogenic activities. Rapid and substantial changes in land‐use, land‐management and industrial activities over the subcontinent, and warming in the Indian Ocean, have influenced the South Asian summer monsoon. These might continue to be significant drivers in the near‐term along with rising global greenhouse gas emissions. Deciphering the region's vulnerability to climate change requires an understanding of how these anthropogenic activities, acting on a range of spatial scales, have shaped the monsoon spatially and temporally. This review summarizes historical changes in monsoon rainfall characteristics, associated mechanisms, and the role of anthropogenic forcings, focusing on subseasonal variability and extreme events. Several studies have found intensified subseasonal extremes across parts of India and an increase in spatial variability of rainfall despite an overall weakening of seasonal rainfall in the monsoon core. However, understanding these changes remains challenging because of uncertainties in observations and climate models. The mechanisms and relative influences of various anthropogenic activities, particularly on subseasonal extremes, remain relatively underexplored. Large biases in the representation of relevant processes in global climate models limit the ability to attribute historical changes and make reliable projections. Nevertheless, recent advances in modeling these processes using higher‐resolution modeling frameworks provide new tools to investigate the Indian summer monsoon's response to various anthropogenic forcings. There is an urgent need to understand how these forcings interact to shape climate variability and change in this vulnerable region. This article is categorized under: Paleoclimates and Current Trends > Earth System Behavior

Annual number of deaths associated with flooding events in India. Data from EM‐DAT: The CRED/OFDA International Database (Guha‐Sapir, Below, & Hoyois, )

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Summary of established and potential pathways by which anthropogenic forcings can influence seasonal and subseasonal rainfall characteristics

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Same as in Figure but for linear trends from 1981 to 2013. Gray dots indicate significance of trends at the 5% level Note: Depiction of political boundaries in these maps may not be authoritative/accurate

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Comparison of the climatological patterns of different rainfall characteristics from five datasets (IMD: 1° (1951–2013), IMD: 0.25° (1901–2015), APHRODITE: −0.25° (1951–2015), CPC: −0.5° (1979–present), CHIRPS: 0.05° (1981–present)). The climatology is calculated over 1981–2007 Note: Depiction of political boundaries in these maps may not be authoritative/accurate

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(a) Trend in the spatial variability of the mean monsoonal rainfall over the Indian region with a 30‐year overlapping moving window. The blue line indicates the spatial variability of the mean rainfall, whereas the black solid line shows the fitted trend (linear) line. The mentioned trend value was computed with the modified Mann–Kendall approach. (b) Trend in the spatial variability of the extreme rainfall (corresponding to a 50‐year return period) with the blue and black lines representing analyses similar to those shown in panel (a). The trends in both panels are significant at the 5% significance level (Reprinted with permission from Ghosh et al. (). Copyright Creative Commons Attribution License)

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(a) Station locations used in the constructing of area‐averaged time series of the IITM‐HIMR dataset (1871–2012), for different homogeneous rainfall regions. Each color represents one particular region, whereas “All India data” includes all stations. IITM‐HIMR data consist of area‐averaged monthly rainfall amounts from 1871 to 2012. We have built the time series of annual ISM rainfall from these data by taking the average over JJAS months for each year. (b–g) Markers are the percentage trend in each quantile over the 142‐year period; marker colors and shape correspond to different geographic regions and the error bars represent the 95% confidence intervals obtained using method of bootstrapping on residuals. Red dotted lines indicate the linear trend in the mean of the respective time series over the 142 years, independent of quantiles. Black heavy lines act as a reference for no change/no trend. Quantile values are indicated by τ (horizontal axes). Brown‐shaded vertical bands highlight the lower quantiles, that is, trends for τ ∈ [0.1,0.3]. Blue‐shaded vertical bands highlight the higher quantiles, that is, trends for τ ∈ [0.75,0.95]. Observe the low values of the quantile trends for τ ∈ [0.1,0.3] for west central, central northeast, northeast, northwest, and All India indicating intensification of droughts in these regions (Reprinted with permission from Malik et al. (). Copyright American Geophysical Union)

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Time series of wet (blue) and dry (red) spell frequency and intensity over the core‐monsoon domain. Missing links in the time series are years with no wet or dry spells. Trend lines are estimated using the nonparametric locally estimated scatterplot smoothing (LOESS) regression technique; shading represents the 90% confidence intervals of the estimated trends. p‐values are obtained from testing the difference in means of the distributions of each variable between 1951–1980 and 1981–2011 using the nonparametric moving block bootstrap test. Colors indicate the significance level of the p‐values (Reprinted with permission from D. Singh, Tsiang et al., (). Copyright Springer Nature; Nature Climate Change)

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(a) JJAS rainfall from 1861 to 2005 averaged over South Asia (10–35°N, 70–90°E) in the CMIP5 all‐forcings historical experiment (blue), greenhouse gas (GHG)‐only historical experiment (red) and the aerosol‐only historical experiment (black). The thick lines show the multi‐model ensemble means with a 21‐year running mean applied, while the pale envelope indicates the range from the mean. Only eight models are used in constructing this figure (see Section 2): CanESM2, CCSM4, CSIRO‐Mk3.6.0, GFDL‐CM3, GFDL‐ESM2M, HadGEM2‐ES, IPSL‐CM5A‐LR and NorESM1‐M. The curves are centered around zero by removing the mean rainfall from pre‐industrial control runs (piControl) of the same models. Units are mm/day. (b) Same as (a) but for the global mean and annual mean land–sea surface temperature contrast from 1861 to 2005. Units are K (Reprinted from Guo et al., () under Creative Commons Attribution 3.0 License)

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Schematic illustration of changes in the Indian summer monsoon (Roxy, ) (Reprinted with permission from Roxy (). Copyright Springer Nature; Nature Climate Change)

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Summer monsoon (June–September) rainfall departure (in %) over central India (75–85°E, 19–26°N), during 1901–2018. Wet years (above 10% departure) are marked in dark blue colors and drought years (below −10% departure) are marked in red colors. El Niño and La Niña conditions for the same season are marked using red and blue dots, respectively. Rainfall data is from the Indian Meteorological Department

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Time series of 5‐year running means of the Indian summer monsoon precipitation anomalies (mm/day) relative to the 1950–2013/2014 base period. The precipitation is area‐averaged over northern central India (20–28°N, 76–87°E). The various colors represent various datasets, that is, colors of black, blue, green, purple, orange, red, and cyan corresponding to the datasets of CRU, GPCC, PREC/L, GPCP, TRMM, IMD, and the ensemble mean, respectively. The legends of the figure are in the lower box and the numbers in the first and third columns of the box represent the linear trends of precipitation in 1950–2002 and 2002–2013/2014, respectively. The numbers in the box followed by a star ‘*’ denote trends significant at the 95% confidence level (Reprinted with permission from Jin & Wang (). Copyright Springer Nature; Nature Climate Change)

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Midlatitude–tropical interactions. Synoptic maps showing the 700‐hPa precipitable water anomalies (mm) and mean 700‐hPa winds (m/s) during the Uttarakhand flooding disaster in 2013, (a) 13 June, (b) 15 June, and (c) 17 June 2013. (d)–(f) As in (a)–(c), but for synoptic maps of 850‐hPa height anomalies (m) and mean 850‐hPa winds (m/s) (Reprinted with permission from Houze et al., . Copyright American Meteorological Society)

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Tracks (blue lines) of low pressure systems formed during the JJAS monsoon season in the period 1888–2003. The genesis (red dots) and termination (black dots) locations are represented. (b) Seasonal (JJAS) climatological mean of mean sea level pressure (hPa) for the period of 1948–2003 (Reprinted with permission from Krishnamurthy & Ajayamohan (2010). Copyright American Meteorological Society)

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Composite precipitation anomalies from the July–August 1951–2011 mean for all extreme wet/dry spells in 1951–1980. Wet/dry spells are defined as events with rainfall anomalies over Central India exceeding ±1σ for a minimum of three consecutive days (Reprinted with permission from D. Singh, Tsiang, Rajaratnam & Diffenbaugh (). Copyright 2014 Springer Nature; Nature Climate Change) Note: Depiction of political boundaries in these maps may not be accurate

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External forcings affecting South Asia (a) land‐use and land‐cover map of India for 2005 based on multiple satellite remote sensing data and extensive ground truthing, and following the International Geosphere Biosphere Program classification scheme (Reprinted from Roy et al. (2015) under Creative Commons Attribution 4.0 International License.); (b) percent irrigated area in 2015 (Reprinted from Ambika et al. (2016) under Creative Commons Attribution 4.0 International License.); and (c) MODIS‐derived annual mean mid‐visible aerosol optical depth (Reprinted with permission from Srivastava (). Copyright John Wiley and Sons)

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