Many parts of the Earth’s surface and two-thirds of the global population are influenced by the monsoon. This paper reviews the current state of knowledge of climate change and its impacts on the global monsoon and its regional components, including recent results from phase 6 of the Coupled Model Intercomparison Project (CMIP6) that were reported at a World Meteorological Organization/World Weather Research Programme workshop held in Zhuhai in early December 2019. The review’s primary focus is on monsoon rainfall, both mean and extremes, whose variability has tremendous economic and societal impacts. Due to the large body of literature on this broad topic, only a fraction can be cited in this concise review.
The global monsoon (GM) is a defining feature of the Earth’s climate and a forced response of the coupled climate system to the annual cycle of solar insolation. For clarity, we define the monsoon domain primarily based on rainfall contrast in the solstice seasons (Fig. 1). The North American monsoon (NAM) domain covers western Mexico and Arizona but also Central America and Venezuela, and is larger than that traditionally recognized by many scientists working on the NAM. We aim to encompass the range of literature marrying together global monsoon, regional monsoon, and paleoclimate monsoon perspectives and therefore reach a compromise. Equatorial Africa and the Maritime Continent also feature annual reversal of surface winds, although the former has a double peak in the equinoctial seasons and the latter is heavily influenced by complex terrain (Chang et al. 2005).
The GM precipitation domain (green) defined by the local summer minus winter precipitation rate exceeding 2 mm day−1, and the local summer precipitation exceeding 55% of the annual total (Wang and Ding 2008). Summer denotes May–September for the NH and November–March for the SH. The dry regions (yellow) are defined by local summer precipitation being less than 1 mm day−1. The arrows show August minus February 925-hPa winds. The blue (red) lines indicate the ITCZ position in August (February). Adopted from P.-X. Wang et al. (2014).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
The GM precipitation domain (green) defined by the local summer minus winter precipitation rate exceeding 2 mm day−1, and the local summer precipitation exceeding 55% of the annual total (Wang and Ding 2008). Summer denotes May–September for the NH and November–March for the SH. The dry regions (yellow) are defined by local summer precipitation being less than 1 mm day−1. The arrows show August minus February 925-hPa winds. The blue (red) lines indicate the ITCZ position in August (February). Adopted from P.-X. Wang et al. (2014).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
The GM precipitation domain (green) defined by the local summer minus winter precipitation rate exceeding 2 mm day−1, and the local summer precipitation exceeding 55% of the annual total (Wang and Ding 2008). Summer denotes May–September for the NH and November–March for the SH. The dry regions (yellow) are defined by local summer precipitation being less than 1 mm day−1. The arrows show August minus February 925-hPa winds. The blue (red) lines indicate the ITCZ position in August (February). Adopted from P.-X. Wang et al. (2014).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Our goal is to outline past changes of the monsoon and identify the key drivers of these changes, assess the roles and impacts of natural and anthropogenic forcings and regional variability, and discuss the limitations and difficulties of current climate models in representing monsoon variability. We will also attempt to summarize projected future changes both globally and in various monsoon regions using recent model results. Due to the inherent uncertainties and model limitations, the degree of confidence in the results varies. A section on model issues and outlook is devoted to discussing challenges of present and future monsoon modeling.
Global monsoon
Detection and attribution of observed changes.
Wang and Ding (2006) found a decreasing trend of global land monsoon precipitation from the 1950s to 1980, mainly due to the declining monsoon in the Northern Hemisphere (NH). After 1980, GM precipitation (GMP) intensified due to a significant upward trend in the NH summer monsoon (Wang et al. 2012). Extended analysis of the whole twentieth-century NH land monsoon rainfall indicates that short-period trends may be part of multidecadal variability, which is primarily driven by forcing from the Atlantic [Atlantic multidecadal variation (AMV)] and the Pacific [interdecadal Pacific oscillation (IPO)] (Zhou et al. 2008; Wang et al. 2013, 2018; Huang et al. 2020a). On the other hand, there is evidence that anthropogenic aerosols have influenced decreases of NH land monsoon precipitation in the Sahel and South and East Asia during the second half of the twentieth century (Polson et al. 2014; Giannini and Kaplan 2019; Zhou et al. 2020b). It should be noted that this long-term decrease in precipitation could be, in part, due to natural multidecadal variations of the regional monsoon precipitation (Sontakke et al. 2008; Jin and Wang 2017; Huang et al. 2020b). It remains a major challenge, however, to quantify the relative contributions of internal modes of variability versus anthropogenic forcing on the global scale.
Projected long-term changes.
The CMIP5 results suggest that GM area, annual range, and mean precipitation are likely to increase by the end of the twenty-first century (Kitoh et al. 2013; Hsu et al. 2013; Christensen et al. 2013). The increase will be stronger in the NH, and the NH rainy season is likely to lengthen due to earlier or unchanged onset dates and a delayed retreat (Lee and Wang 2014). The increase in GM precipitation was primarily attributed to temperature-driven increases in specific humidity, resulting in the “wet get wetter” pattern (Held and Soden 2006).
Analysis of 34 CMIP6 models indicates a larger increase in monsoon rainfall over land than over ocean in all four core shared socioeconomic pathways (SSPs) (Fig. 2; Lee et al. 2019). The projected GMP increase over land by the end of the twenty-first century relative to 1995–2014 in CMIP6 is about 50% larger than in CMIP5. Models with high (>4.2°C) equilibrium climate sensitivity (ECS) account for this larger projection. The causes of CMIP6 models’ high ECS has been discussed in Zelinka et al. (2020). Note that the forced signal of GMP over land shows a decreasing trend from 1950 to the 1980s, but the trend reversed around 1990, which is consistent with the CMIP5 results (Lee and Wang 2014). During 1950–90, the temperature-driven intensification of precipitation was likely masked by a fast precipitation response to anthropogenic sulfate and volcanic forcing, even though the warming trend due to greenhouse gas (GHG) since the preindustrial period (1850–1900) is 3 times larger than the cooling due to aerosol forcing (Lau and Kim 2017; Richardson et al. 2018). The recent upward trend may signify the emergence of the greenhouse gas signal against the rainfall reduction due to aerosol emissions. However, the trend during recent decades can be influenced by the leading modes of multidecadal variability of global sea surface temperature (SST) (Wang et al. 2018). The GM land precipitation sensitivity has a median of 0.8% °C‒1 in SSP2–4.5, and a median of 1.4% °C−1 in SSP5–8.5. The latter is slightly higher than that simulated by CMIP5 models under RCP8.5 (Fig. 2). Z. Chen et al. (2020) estimated that in the long-term (2080–2099) relative to 1995-2014, the GM land precipitation will increase by 3.52% (0.47 ∼ 6.58% for the 10th-90th ensemble range) in SSP2-4.5 and 5.75% (–0.17 ∼ 11.68%) in SSP5-8.5.
Past to future changes of annual-mean global monsoon precipitation (mm day−1) over (a) land and (b) ocean relative to the recent past (1995–2014) in the historical simulation (1850–2014) and four core SSPs (2015–2100) obtained from 34 CMIP6 models. Pink and blue shading indicate the 5%–95% likely range of precipitation change in the low-emission (SSP1–2.6) and high-emission (SSP5–8.5) scenarios, respectively. The mean change during 2081–2100 relative to the recent past is also shown, with the boxplot on the right-hand side obtained from four SSPs in 34 CMIP6 models compared to RCP8.5 in 40 CMIP5 models. The solid dot in the boxplot for SSP5–8.5 indicates individual model’s ECS.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Past to future changes of annual-mean global monsoon precipitation (mm day−1) over (a) land and (b) ocean relative to the recent past (1995–2014) in the historical simulation (1850–2014) and four core SSPs (2015–2100) obtained from 34 CMIP6 models. Pink and blue shading indicate the 5%–95% likely range of precipitation change in the low-emission (SSP1–2.6) and high-emission (SSP5–8.5) scenarios, respectively. The mean change during 2081–2100 relative to the recent past is also shown, with the boxplot on the right-hand side obtained from four SSPs in 34 CMIP6 models compared to RCP8.5 in 40 CMIP5 models. The solid dot in the boxplot for SSP5–8.5 indicates individual model’s ECS.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Past to future changes of annual-mean global monsoon precipitation (mm day−1) over (a) land and (b) ocean relative to the recent past (1995–2014) in the historical simulation (1850–2014) and four core SSPs (2015–2100) obtained from 34 CMIP6 models. Pink and blue shading indicate the 5%–95% likely range of precipitation change in the low-emission (SSP1–2.6) and high-emission (SSP5–8.5) scenarios, respectively. The mean change during 2081–2100 relative to the recent past is also shown, with the boxplot on the right-hand side obtained from four SSPs in 34 CMIP6 models compared to RCP8.5 in 40 CMIP5 models. The solid dot in the boxplot for SSP5–8.5 indicates individual model’s ECS.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Wang et al. (2020) examined the ensemble-mean projection from 15 early-released CMIP6 models, which estimates that under SSP2–4.5 the total NH land monsoon precipitation will increase by about 2.8% °C−1 in contrast to little change in the Southern Hemisphere (SH; −0.3% °C−1). In both hemispheres, the annual range of land monsoon rainfall will increase by about 2.6% °C−1, with wetter summers and drier winters (Zhang et al. 2019). In addition, the projected land monsoon rainy season will be lengthened in the NH (by about 10 days) due to late retreat, but will be shortened in the SH due to delayed onset; the interannual variations of GMP will be more strongly controlled by ENSO variability (Wang et al. 2020). In monsoon regions, increases in specific humidity are spatially uniform (Fig. 4b), but the rainfall change features a robust NH–SH asymmetry and an east–west asymmetry between enhanced Asian–African monsoons and weakened NAM (Fig. 4a), suggesting that circulation changes play a crucial role in shaping the spatial patterns and intensity of GM rainfall changes (Wang et al. 2020; Jin et al. 2020). GHG-induced horizontally differential heating results in a robust “NH warmer than SH” pattern (Fig. 4c), which enhances NH monsoon rainfall (Liu et al. 2009), especially in Asia and northern Africa, due to an enhanced thermal contrast between the large Eurasia–Africa landmass and adjacent oceans (Endo et al. 2018). Those CMIP models that project a stronger interhemispheric thermal contrast generate stronger Hadley circulations, more northward positions of the ITCZ, and enhanced NH monsoon precipitation (Wang et al. 2020). The GHG forcing also induces a warmer equatorial eastern Pacific (Fig. 4c), which reduces NAM rainfall by shifting the ITCZ equatorward (Huang et al. 2013; Wang et al. 2020). Climate models on average predict weakening ascent under global warming (Endo and Kitoh 2014), which tends to dry monsoon regions. Weakening monsoon ascent has been linked to the slowdown of the global overturning circulation (Held and Soden 2006). However, a definitive theory for why monsoon circulations broadly weaken with warming remains elusive.
Land monsoon rainfall (LMR) provides water resources for billions of people; an accurate prediction of its change is vital for the sustainable future of the planet. Regional land monsoon rainfall exhibits very different sensitivities to climate change (Fig. 3). The annual mean LMR in the East Asian and South Asian monsoons shows large positive sensitivities with means of 4.6% °C−1, and 3.9% °C−1, respectively, under SSP2–4.5. The LMR likely increases in NAF, but decreases in NAM, and remains unchanged in the Southern Hemisphere monsoons (Jin et al. 2020).
Projected regional land monsoon precipitation sensitivity under the SSP2–4.5, i.e., the percentage change (2065–99 relative to 1979–2013) per 1°C global warming (% °C−1), derived from 24 CMIP6 models for (a) local summer, (b) local winter, and (c) annual mean land monsoon precipitation for each region. Local summer means JJAS in NH and DJFM for SH, and local winter means the opposite. The upper edge of the box represents the 83rd percentile and the lower edge is the 17th percentile, the box contains 66% of the data. The horizontal line within the box is the median. Red circle is the mean. The vertical dashed line segments represent the range of non-outliers (5%–95%).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Projected regional land monsoon precipitation sensitivity under the SSP2–4.5, i.e., the percentage change (2065–99 relative to 1979–2013) per 1°C global warming (% °C−1), derived from 24 CMIP6 models for (a) local summer, (b) local winter, and (c) annual mean land monsoon precipitation for each region. Local summer means JJAS in NH and DJFM for SH, and local winter means the opposite. The upper edge of the box represents the 83rd percentile and the lower edge is the 17th percentile, the box contains 66% of the data. The horizontal line within the box is the median. Red circle is the mean. The vertical dashed line segments represent the range of non-outliers (5%–95%).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Projected regional land monsoon precipitation sensitivity under the SSP2–4.5, i.e., the percentage change (2065–99 relative to 1979–2013) per 1°C global warming (% °C−1), derived from 24 CMIP6 models for (a) local summer, (b) local winter, and (c) annual mean land monsoon precipitation for each region. Local summer means JJAS in NH and DJFM for SH, and local winter means the opposite. The upper edge of the box represents the 83rd percentile and the lower edge is the 17th percentile, the box contains 66% of the data. The horizontal line within the box is the median. Red circle is the mean. The vertical dashed line segments represent the range of non-outliers (5%–95%).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Projected near-term change.
The interplay between internal modes of variability, such as IPO, AMV, and SH annular mode (Zheng et al. 2014), and anthropogenic forcing is important in the historical record and for the near-term future (Chang et al. 2014). Huang et al. (2020a) used two sets of initial condition large ensembles to suggest that internal variability linked to the IPO could overcome the forced upward trend in the South Asian monsoon rainfall up to 2045. Using twentieth-century observations and numerical experiments, Wang et al. (2018) showed that the hemispheric thermal contrast in the Atlantic and Indian Oceans and the IPO can be used to predict the NH land monsoon rainfall change a decade in advance. The significant decadal variability of monsoon rainfall leads to considerable uncertainties in climate projections for the next 30 years; thus, improvements in predicting internal modes of variability could reduce uncertainties in near-term climate projections.
Regional monsoon changes
South Asian monsoon.
The South Asian summer monsoon (SASM) circulation experienced a significant declining trend from the 1950s together with a weakening local meridional circulation and notable precipitation decreases over north-central India and the west coast that are associated with a reduced meridional temperature gradient (e.g., Krishnan et al. 2013; Roxy et al. 2015). This trend was attributed to effects of anthropogenic aerosol forcing (e.g., Bollasina et al. 2011; Salzmann et al. 2014; Krishnan et al. 2016) and equatorial Indian Ocean warming due to increased GHG (e.g., Sabeerali and Ajayamohan 2017). However, it could potentially be altered by multidecadal variations (Shi et al. 2018) arising from internal modes of climate variability such as the IPO and AMV (e.g., Krishnan and Sugi 2003; Salzmann and Cherian 2015; Jiang and Zhou 2019). The processes by which aerosols affect monsoons were reviewed by Li et al. (2016). Aerosols can also have a remote impact on regional monsoons (Shawki et al. 2018).
CMIP models consistently project increases in the mean and variability of SASM precipitation, despite weakened circulation at the end of the twenty-first century relative to the present (e.g., Kitoh et al. 2013; B. Wang et al. 2014), though some models disagree (Sabeerali and Ajayamohan 2017). The uncertainty in radiative forcing from aerosol emissions in CMIP5 causes a large spread in the response of SASM rainfall (Shonk et al. 2020). However, this is not the case in CMIP6 projections (Fig. 3).
East Asian monsoon.
During the twentieth century, East Asian summer monsoon (EASM) exhibited considerable multidecadal variability with a weakened circulation and a south flood–north drought pattern since the late 1970s (Zhou et al. 2009; Ding et al. 2009). The south flood–north drought pattern has been predominantly attributed to internal variability, especially the phase change of the IPO (Li et al. 2010; Nigam et al. 2015; Li and Wang 2018; Ha et al. 2020a), and aided by GHG-induced warming (Zhu et al. 2012), and increased Asian aerosols emissions from the 1970s to 2000s (Dong et al. 2019). Since 1979, both SST and atmospheric heating over Southeast Asia and adjacent seas have increased significantly (Li et al. 2016), which may have led to decreased rainfall over East Asia, South Asia, (Annamalai et al. 2013) and the Sahel region (He et al. 2017).
Analysis of 16 CMIP6 models indicates that, under the SSP2–4.5 scenario, EASM precipitation will increase at 4.7% °C−1 (Ha et al. 2020b), with dynamic effects more important than thermodynamic effects (Oh et al. 2018; Li et al. 2019). EASM duration is projected to lengthen by about 5 pentads due to earlier onset and delayed retreat (Ha et al. 2020b), which is comparable to previous assessment results (Endo et al. 2012; Kitoh et al. 2013; Moon and Ha 2017).
African monsoon.
West Africa rainfall totals in the Sahel have been increasing since the 1980s, which helped regreening (Taylor et al. 2017). Much of the increase in seasonal rainfall is owed to positive trends in mean intensity (Lodoun et al. 2013; Sarr et al. 2013), rainfall extremes (Panthou et al. 2014; Sanogo et al. 2015), and the frequency of intense mesoscale convective systems (Taylor et al. 2017). Several West African countries have experienced trends toward a wetter late season and delayed cessation of the rains (Lodoun et al. 2013). All the above changes are qualitatively consistent with the CMIP5 response to GHG (Marvel et al. 2020). Preliminary results from CMIP6 confirm that the Sahel will become wetter, except for the west coast, and the rainy season will extend later (Fig. ES1 in the online supplemental material). Yet, the range of simulated variability has not improved, and large quantitative uncertainties in the projections persist. In spite of the large spread, the CMIP6 models project that NAF land monsoon rainfall will likely increase (Fig. 3).
In East Africa, observed increases in the boreal fall short rains are more robust (e.g., Cattani et al. 2018) than negative trends in the spring long rains (e.g., Maidment et al. 2015). Regionality is pronounced, and there is sensitivity to Indian Ocean SSTs and Pacific variability (Liebmann et al. 2014; Omondi et al. 2013). Selected CMIP6 models project little agreement on how East African rainfall will change (Fig. ES2), while some regional models suggest enhanced rainfall during the short rains and a curtailed long-rains season (Cook and Vizy 2013; Han et al. 2019). In the Congo Basin, observed precipitation trends are inconclusive (Zhou et al. 2014; Cook and Vizy 2019), but one study reports earlier onset of the spring rains (Taylor et al. 2018). A preliminary analysis finds overall improvement in CMIP6 models in the overestimation of Congo Basin rainfall, though projections of changes under the SSP2–4.5 scenario are inconsistent (Fig. ES3).
The CMIP6 models project that under SSP2–4.5 scenario and by the latter part of twenty-first century, the SAF land monsoon rainfall will likely increase in summer but considerably reduce in winter, so that the annual range will amplify but the annual mean rainfall will not change significantly (Fig. 3)
Australian monsoon.
Observations show increasing trends in mean and extreme rainfall over northern, especially northwestern Australia since the early 1970s (Dey et al. 2019). Although Australian summer monsoon rainfall has exhibited strong decadal variations during the twentieth and early twenty-first century, making detection and attribution of trends challenging, the recent upward trend since 1970s has been attributed to direct thermal forcing by increasing SST in the tropical western Pacific (Li et al. 2013) and to aerosol and GHG forcing (Rotstayn et al. 2007; Salzmann 2016).
Australian monsoon rainfall is projected to increase by an average of 0.4% °C−1 in 33 CMIP5 models (Dey et al. 2019), although there is a large spread in the magnitude and even the direction of the projected change. By selecting the best-performing models for the Australian monsoon, Jourdain et al. (2013) found that 7 of 10 “good” CMIP5 models indicate a 5%–20% increase in monsoon rainfall over northern (20°S) Australian land by the latter part of the twenty-first century, but trends over a much larger region of the Maritime Continent are more uncertain. The range in Australian monsoon projections from the available CMIP6 ensemble is substantially reduced compared to CMIP5, however, models continue to disagree on the magnitude and direction of change. The CMIP6 models project that summer and annual mean LMR changes are insignificant under SSP2–4.5; but the winter LMR will likely decrease (Fig. 3) due to the enhanced Asian summer monsoon. By the end of the twenty-first century, the Madden–Julian oscillation (MJO) is anticipated to have stronger-amplitude rainfall variability (Maloney et al. 2019), but the impact on Australian summer monsoon intraseasonal variability is uncertain (Moise et al. 2020).
North American monsoon.
Observed long-term twentieth-century rainfall trends are either negative or null, but the trends can vary substantially within this region (Pascale et al. 2019). During the period of 1950–2010 the monsoonal ridge was strengthened and shifted the patterns of transient inverted troughs making them less frequent in triggering severe weather (Lahmers et al. 2016). Recent observational and modeling studies show an increase in the magnitude of extreme events in NAM and Central American rainfall under anthropogenic global warming (Aguilar et al. 2005; Luong et al. 2017).
Climate models suggest an early-to-late redistribution of the mean NAM precipitation with no overall reduction (Seth et al. 2013; Cook and Seager 2013), and a more substantial reduction for Central American precipitation (Colorado-Ruiz et al. 2018). However, there is low confidence in these projections, since both local biases (the models’ representation of vegetation dynamics, land cover and land use, soil moisture hydrology) and remote biases (current and future SST) may lead to large uncertainties (Bukovsky et al. 2015; Pascale et al. 2017). Confidence in mean precipitation changes is lower than in the projection that precipitation extremes are likely to increase due to the changing thermodynamic environment (Luong et al. 2017; Prein et al. 2016).
Figure 5 schematically sums up the factors that are likely to be determinant in the future behavior of the NAM: the expansion and northwestward shift of the NAM ridge, and the strengthening of the remote stabilizing effect due to SST warming, and more intense MCS-type convection. More uncertain remains the future of the NAM moisture surges and the track of the upper-level inverted troughs, which are key synoptic processes controlling convective activity.
South American monsoon.
A significant positive precipitation trend since the 1950s till the 1990s was observed in southeast South America, and has been related to interdecadal variability (Grimm and Saboia 2015), ozone depletion and increasing GHG (Gonzalez et al. 2014; Vera and Diaz 2015). The trend in the tropical South American monsoon is less coherent due to the influence of the tropical Atlantic and the tendency to reverse rainfall anomalies from spring to summer in central and eastern South America due to land–atmosphere interactions (Grimm et al. 2007). In recent decades the dry season has been lengthened and become drier, especially over the southern Amazonia, which has significant influences on vegetation and moisture transport to the SAM core region (Fu et al. 2013).
The CMIP6 models-projected future precipitation changes resemble the anomalies expected for El Niño: little change of annual mean precipitation, with drier winter/spring and increased peak monsoon rainfall (Figs. 3 and 4). This is consistent with El Niño impacts (Grimm 2011) and CMIP5 projections, which show delay and shortening of the monsoon season, but intensification in its peak (Seth et al. 2013), and prolonged dry spells between the rainy events (Christensen et al. 2013). However, intermodel discrepancies are large (Yin et al. 2013). CMIP5 models also likely underestimate the climate variability of the South American monsoon and its sensitivity to climate forcing (Fu et al. 2013). Bias-corrected projections generally show a drier climate over eastern Amazonia (e.g., Duffy et al. 2015; Malhi et al. 2008). Thus, the risk of strong climatic drying and potential rain forest die-back in the future remains real.
Changes in the annual mean (a) precipitation, (b) 850-hPa specific humidity, and (c) surface air temperature. Changes are measured by the SSP2–4.5 projection (2065–99) relative to the historical simulation (1979–2013) in the 15 models’ MME. The color-shaded region denotes the changes are statistically significant at 66% confidence level (likely change). Stippling denotes areas where the significance exceeds 95% confidence level (very likely) by Student’s t test.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Changes in the annual mean (a) precipitation, (b) 850-hPa specific humidity, and (c) surface air temperature. Changes are measured by the SSP2–4.5 projection (2065–99) relative to the historical simulation (1979–2013) in the 15 models’ MME. The color-shaded region denotes the changes are statistically significant at 66% confidence level (likely change). Stippling denotes areas where the significance exceeds 95% confidence level (very likely) by Student’s t test.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Changes in the annual mean (a) precipitation, (b) 850-hPa specific humidity, and (c) surface air temperature. Changes are measured by the SSP2–4.5 projection (2065–99) relative to the historical simulation (1979–2013) in the 15 models’ MME. The color-shaded region denotes the changes are statistically significant at 66% confidence level (likely change). Stippling denotes areas where the significance exceeds 95% confidence level (very likely) by Student’s t test.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Schematic main features related to (left) present and (right) future changes for the NAM. The expansion and northwestward shift of the NAM ridge, the southward shift of the upper-level inverted troughs (IVs) track, and the strengthening of the remote stabilizing effect due to SST warming are shown. Larger clouds in the right panel are suggestive of more intense MCS-type convection. The question marks in the right panel indicate uncertainty in the response, as is the case, for example, for the local mechanisms associated with atmosphere–land interaction, NAM moisture surges, and a southward shift the tropical easterly waves (TEWs) track.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Schematic main features related to (left) present and (right) future changes for the NAM. The expansion and northwestward shift of the NAM ridge, the southward shift of the upper-level inverted troughs (IVs) track, and the strengthening of the remote stabilizing effect due to SST warming are shown. Larger clouds in the right panel are suggestive of more intense MCS-type convection. The question marks in the right panel indicate uncertainty in the response, as is the case, for example, for the local mechanisms associated with atmosphere–land interaction, NAM moisture surges, and a southward shift the tropical easterly waves (TEWs) track.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Schematic main features related to (left) present and (right) future changes for the NAM. The expansion and northwestward shift of the NAM ridge, the southward shift of the upper-level inverted troughs (IVs) track, and the strengthening of the remote stabilizing effect due to SST warming are shown. Larger clouds in the right panel are suggestive of more intense MCS-type convection. The question marks in the right panel indicate uncertainty in the response, as is the case, for example, for the local mechanisms associated with atmosphere–land interaction, NAM moisture surges, and a southward shift the tropical easterly waves (TEWs) track.
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Extreme precipitation events in summer monsoons
Past changes and attribution.
Over the past century, significant increases in extreme precipitation in association with global warming have emerged over the global land monsoon region as a whole, and annual maximum daily rainfall has increased at the rate of about 10%–14% °C−1 in the southern part of the South African monsoon, about 8% °C−1 in the South Asian monsoon, 6%–11% °C−1 in the NAM, and 15%–25% °C−1 in the eastern part of the South American monsoon (Zhang and Zhou 2019). At Seoul, South Korea, one of the world’s longest instrumental measurements of daily precipitation since 1778 shows that the annual maximum daily rainfall and the number of extremely wet days, defined as the 99th percentile of daily precipitation distribution, all have an increasing trend significant at the 99% confidence level (Fig. 6). In the central Indian subcontinent, a significant shift toward heavier precipitation in shorter duration spells occurred from 1950 to 2015 (Fig. 7) (Goswami et al. 2006; Roxy et al. 2017; Singh et al. 2019). In East Asia, the average extreme rainfall trend increased from 1958 to 2010, with a decreasing trend in northern China that was offset by a much larger increasing trend in southern China (Chang et al. 2012). Over tropical South America, extreme indices such as annual total precipitation above the 99th percentile and the maximum number of consecutive dry days display more significant and extensive trends (Skansi et al. 2013; Hilker et al. 2014).
Time series of extreme precipitation events observed at Seoul, South Korea, since 1778. Running 5-yr means of the summer highest 1-day precipitation amount (green, mm day−1 on the left y axis), the number of extremely wet days (blue, right y axis), and the precipitation amount falling in the extremely wet days (red, mm day−1 on the left y axis). The extremely wet days are calculated as the 99th percentile of the distribution of the summer daily precipitation amount in the 227-yr period. Also shown are the corresponding trends obtained by least squares regression for the green curve and by logistic regression for the blue and red curves. Adopted from Wang et al. (2006).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Time series of extreme precipitation events observed at Seoul, South Korea, since 1778. Running 5-yr means of the summer highest 1-day precipitation amount (green, mm day−1 on the left y axis), the number of extremely wet days (blue, right y axis), and the precipitation amount falling in the extremely wet days (red, mm day−1 on the left y axis). The extremely wet days are calculated as the 99th percentile of the distribution of the summer daily precipitation amount in the 227-yr period. Also shown are the corresponding trends obtained by least squares regression for the green curve and by logistic regression for the blue and red curves. Adopted from Wang et al. (2006).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Time series of extreme precipitation events observed at Seoul, South Korea, since 1778. Running 5-yr means of the summer highest 1-day precipitation amount (green, mm day−1 on the left y axis), the number of extremely wet days (blue, right y axis), and the precipitation amount falling in the extremely wet days (red, mm day−1 on the left y axis). The extremely wet days are calculated as the 99th percentile of the distribution of the summer daily precipitation amount in the 227-yr period. Also shown are the corresponding trends obtained by least squares regression for the green curve and by logistic regression for the blue and red curves. Adopted from Wang et al. (2006).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Frequency of extreme rain events (number of grid cells exceeding 150 mm day−1 yr−1) over the central Indian subcontinent (19°–26°N, 75°–85°E) for the summer monsoon (June–September) during 1950–2015. The trend lines are significant at 95% confidence level. The smoothed curves on the time series analyses represent 10-yr moving averages. Adopted from Roxy et al. (2017).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Frequency of extreme rain events (number of grid cells exceeding 150 mm day−1 yr−1) over the central Indian subcontinent (19°–26°N, 75°–85°E) for the summer monsoon (June–September) during 1950–2015. The trend lines are significant at 95% confidence level. The smoothed curves on the time series analyses represent 10-yr moving averages. Adopted from Roxy et al. (2017).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Frequency of extreme rain events (number of grid cells exceeding 150 mm day−1 yr−1) over the central Indian subcontinent (19°–26°N, 75°–85°E) for the summer monsoon (June–September) during 1950–2015. The trend lines are significant at 95% confidence level. The smoothed curves on the time series analyses represent 10-yr moving averages. Adopted from Roxy et al. (2017).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Attribution studies show that global warming has already increased the frequency of heavy precipitation since the mid-twentieth century. An optimal fingerprinting analysis shows that anthropogenic forcing has made a detectable contribution to the observed shift toward heavy precipitation in eastern China (Ma et al. 2017). Simulations with all and natural-only forcing show that global warming increased the probability of the 2016 Yangtze River extreme summer rainfall by 17%–59% (Yuan et al. 2018). A large ensemble experiment also showed that historical global warming has increased July maximum daily precipitation in western Japan (Kawase et al. 2019).
Another anthropogenic forcing is urbanization. A significant correlation between rapid urbanization and increased extreme hourly rainfall has been detected in the Pearl River Delta and Yangtze River Delta of coastal China (Fig. 8) (Wu et al. 2019; Jiang et al. 2020). The increasing trends are larger in both extreme hourly rainfall and surface temperature at urban stations than those at nearby rural stations. The correlation of urbanization and extreme rainfall is due to the urban heat island effect, which increases instability and facilitates deep convection. Large spatial variability in the trends of extreme rainfall in India due to urbanization and changes in land use and land cover has also been detected (Ali and Mishra 2017).
The surface air temperature and extreme hourly rainfall trends for urban stations (red) and rural stations (blue) in the Yangzi River Delta, calculated from changes from 1975–96 to 1997–2018, during MJJAS. The thick crosses are averages of the station values. Adapted from Figs. 1 and 11 in Jiang et al. (2020).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
The surface air temperature and extreme hourly rainfall trends for urban stations (red) and rural stations (blue) in the Yangzi River Delta, calculated from changes from 1975–96 to 1997–2018, during MJJAS. The thick crosses are averages of the station values. Adapted from Figs. 1 and 11 in Jiang et al. (2020).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
The surface air temperature and extreme hourly rainfall trends for urban stations (red) and rural stations (blue) in the Yangzi River Delta, calculated from changes from 1975–96 to 1997–2018, during MJJAS. The thick crosses are averages of the station values. Adapted from Figs. 1 and 11 in Jiang et al. (2020).
Citation: Bulletin of the American Meteorological Society 102, 1; 10.1175/BAMS-D-19-0335.1
Landfalling tropical cyclones (TCs) make large contributions to heavy precipitation in coastal East Asia. In the last 50 years, the decreasing frequency of incoming western North Pacific (WNP) TCs more than offsets the increasing TC rainfall intensity, resulting in reduced TC-induced extreme rainfall in southern coastal China, so the actual increase in non-TC extreme rainfall is even larger than observed (Chang et al. 2012). Evidence in the WNP, and declining TC landfall in eastern Australia (Nicholls et al. 1998), suggest that this poleward movement reflects greater poleward TC recurvature.
Future projection.
One of the most robust signals of projected future change is the increased occurrence of heavy rainfall on daily-to-multiday time scales and intense rainfall on hourly time scales. Heavy rainfall will increase at a much larger rate than the mean precipitation, especially in Asia (Kitoh et al. 2013; Kitoh 2017). Unlike mean precipitation changes, heavy and intense rainfall is more tightly controlled by the environmental moisture content related to the Clausius–Clapeyron relationship and convective-scale circulation changes. On average, extreme 5-day GM rainfall responds approximately linearly to global temperature increase at a rate of 5.17% °C−1 (4.14%–5.75% °C−1) under RCP8.5 with a high signal-to-noise ratio (Zhang et al. 2018). Regionally, extreme precipitation in the Asian monsoon region exhibits the highest sensitivity to warming, while changes in the North American and Australian monsoon regions are moderate with low signal-to-noise ratio (Zhang et al. 2018). CMIP6 models project changes of extreme 1-day rainfall of +58% over South Asia and +68% over East Asia in 2065–2100 compared to 1979–2014 under the SSP2–4.5 scenario (Ha et al. 2020b). Model experiments also indicate a threefold increase in the frequency of rainfall extremes over the Indian subcontinent under future projections for global warming of 1.5°–2.5°C (Bhowmick et al. 2019). Meanwhile, light-to-moderate rain events may become less frequent (Sooraj et al. 2016).
Changes in the variability of monsoon rainfall may occur on a range of time scales. Brown et al. (2017) found increased rainfall variability under RCP8.5 f