Abstract

The southeastern periphery of the Tibetan Plateau (SEPTP) was hit by an extraordinarily severe drought in the autumn of 2009. Overall, the SEPTP has been gripped by a sustained drought for six consecutive years. To better understand the physical causes of these types of severe and frequent droughts and thus to improve their prediction and enhance the ability to adapt, many research efforts have been devoted to the disastrous droughts in the SEPTP. Nonetheless, whether the likelihood and strength of the severe droughts in the SEPTP, such as that in the autumn of 2009, have been affected by anthropogenic climate change remains unknown. This study first identifies the atmospheric circulation regime responsible for the SEPTP droughts and then explores how human-induced climate change has affected the severe droughts in the SEPTP. It is found that the drought conditions in the SEPTP have been driven by the Indian–Pacific warm pool (IPWP) sea surface temperature (SST) through strengthening of the local Hadley circulation and anomalously cyclonic motion over the South China Sea. Ensemble simulations of climate models demonstrate a robust increase in the dry and warm meteorological conditions seen during the 2009 SEPTP autumn drought due to anthropogenic global warming. Given that warming is expected to continue into the future, these results suggest that it is likely that drought conditions will become more common in the SEPTP.

1. Introduction

Human and natural systems are particularly vulnerable to extreme weather and climate, and thus changes in extremes are expected to be a dominant contributor to change in overall risk into the future (IPCC 2012). Drought usually originates from a deficiency of precipitation over an extended period of time and is identified as one of the extreme aspects of the hydrological cycle (IPCC 2012, 2013). If drought is coupled with extreme heat and low humidity, it can increase not only the intensity and/or duration of the drought itself but also the chance of wildfires in the presence of sufficient fuel (IPCC 2012). Because of its destructive impacts on the human life, agriculture, ecology, and physical systems of affected regions, there is increasing interest in understanding changes in drought under global warming and quantifying the role of human and other external influences on drought (Chen and Sun 2015; Dai et al. 2004; Dai 2013; Dai and Zhao 2017; Greve et al. 2014; Sheffield et al. 2012; Trenberth et al. 2004, 2014; Trenberth 2008; Zhao and Dai 2015, 2017).

Whether the effects of natural decadal-scale variability, such as the interdecadal Pacific oscillation (IPO) and associated Pacific decadal oscillation, on drought can be distinguished from externally driven long-term trends or not can seriously affect the attribution of the causes of drought (Rupp et al. 2012; Sheffield et al. 2012; Dai 2013; Dai and Zhao 2017; Trenberth et al. 2014, 2015). Global warming is expected to fuel increased frequency and severity of droughts in many parts of the globe in the future, mainly as a consequence of increasing evaporation and thus surface drying, as well as by decreasing regional precipitation (Held and Soden 2006; Trenberth et al. 2004; Trenberth 2008; Dai 2011, 2013; Zhao and Dai 2015, 2017). The increased droughts observed in some regions have been linked to SST changes in response to anthropogenic forcing (Lewis et al. 2011; Dai 2011; Williams and Funk 2011; Trenberth et al. 2015). The likelihood and strength of droughts has been altered by human influence on global climate (Rupp et al. 2012, 2013; Trigo et al. 2013; Harrington et al. 2014; Trenberth et al. 2015; Williams et al. 2015). In brief, although increased warmth from anthropogenic forcing may not lead to more frequent droughts, it is expected that when droughts occur they are likely to set in more quickly and be more intense (Trenberth et al. 2015).

Southwestern China (SWC) is located on the southeastern periphery of the Tibetan Plateau (SEPTP). In 2009, it experienced the severest autumn drought on record (CMA 2010). This persistent drought, lasting from September 2009 to March 2010, was characterized by both low precipitation and high temperature. More than 16 million people and 11 million livestock suffered drinking water shortages, over 4 million hectares of crops were devastated, most rivers shrank substantially and some dried up completely, and forest fires were more frequent (CMA 2010). SWC has received less precipitation than normal since 2009 and has been gripped by a sustained drought of six consecutive years (CMA 2014). To better understand the physical causes of such severe and frequent droughts in SWC and thus help improve the prediction of similar events, many previous studies have been devoted to the investigation of the disastrous droughts in SWC (Chen and Sun 2015; Lu et al. 2011; Zhang et al. 2013; Yu et al. 2014; Feng et al. 2014; Wang et al. 2015). For instance, it was suggested that the severe SWC droughts were facilitated by warmer-than-normal surface air temperature through enhancing surface evaporation (Lu et al. 2011), anomalous downward motions induced by enhanced heating over the Maritime Continent during La Niña years through the connection of local Hadley circulation (Feng et al. 2014), and an anomalously warm central equatorial Pacific or tropical northwest Pacific (Zhang et al. 2013; Wang et al. 2015). However, few studies have focused on whether the likelihood and strength of the severe droughts in SWC such as in 2009 autumn were affected by anthropogenic climate change. Whether severe droughts of this type should increase under anthropogenic global warming is of great concern to both the public and policymakers.

Given that droughts are strongly governed by atmospheric circulation and that forced circulation changes are not well established, it is thus easier in the attribution of a severe drought to “regard the corresponding circulation regime as being largely unaffected by climate change and ask whether the impact of this drought was affected by the known changes in the climate systems’ thermodynamic state” (Trenberth et al. 2015, p. 175). In this study, the dominant circulation regime responsible for the severe droughts in the SEPTP, such as the once-in-a-century autumn drought in 2009 (CMA 2010), is investigated. Then, we examine whether the strength and chance of severe drought in the SEPTP is affected by anthropogenic climate change through modification of the thermodynamic states that are closely associated with the drought circulation regime.

2. Data and method

a. Data description

The 0.25° × 0.25° gridded monthly surface air temperatures (SATs) and precipitation observation dataset over China, developed from over 2400 observing stations in China using thin-plate smoothing splines and angular distance weighted interpolation methodology, referred as CN05.1 (Wu and Gao 2013), are used. Monthly vertical velocity at 500 hPa and atmospheric column water vapor flux data obtained from the Japanese 55-Year Reanalysis (JRA-55; Ebita et al. 2011) are used. The monthly global SST data from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003) are also used.

To examine the contribution of anthropogenic warming to the severe droughts in the SEPTP and allow for the different model representations of the climate system, we analyze the monthly vertical velocity at 500 hPa and precipitation and SAT data from the International CLIVAR C20C+ Detection and Attribution Project. The data outputs simulated by the atmospheric general circulation models (AGCMs) CAM5.1 (Angélil et al. 2017) and MIROC5 (Shiogama et al. 2013, 2014) are used. These two models run at a resolution of 1° and 1.4°, respectively. Two sets of ensemble experiments are performed (Table 1): All-Hist and Nat-Hist. In the All-Hist experiment, CAM5.1 and MIROC5 are forced by historical anthropogenic and natural external forcing agents plus observational data of sea surface temperature (SST) and sea ice. In the Nat-Hist scenario, both AGCMs are run under time-variable boundary conditions as in All-Hist, but the anthropogenic contributions to the boundary conditions are removed; that is, anthropogenic SST changes are estimated by calculating a multimodel average of the differences between SST in the all-forcing historical runs and the natural-forcing historical runs of multiple CMIP5 models and subtracting that from observed values (Christidis et al. 2013). The attributable anthropogenic ocean warming is characterized by a globally consistent warming and the warming over the Indian–Pacific warm pool is higher than that over other regions of the tropical ocean (Shiogama et al. 2014). For CAM5.1, both All-Hist and Nat-Hist include 100 realizations, including a 50-member ensemble from January 1959 to June 2015 and a 50-member ensemble from January 1996 to June 2015. For MIROC5, All-Hist includes a 10-member ensemble from January 1950 to December 2014 and a 50-member ensemble from January 2006 to August 2015. Nat-Hist includes a 50-member ensemble from January 2006 to August 2015. Each realization in the scenarios differs from the other only in its initial state. For more details about the experimental design, the reader is referred to http://portal.nersc.gov/c20c/main.html.

Table 1.

Summary of attribution experiments performed with CAM5.1 and MIROC5.

Summary of attribution experiments performed with CAM5.1 and MIROC5.
Summary of attribution experiments performed with CAM5.1 and MIROC5.

To investigate the anthropogenic influence on the thermodynamic state that is closely related to the atmospheric circulation driving the severe drought, and also to reduce the dependence of the attribution results on the experiment design and increase the confidence of the attribution results, the monthly vertical velocity, precipitation, SATs, and SST simulated by the historical, historicalNat, RCP8.5, and piControl experiments from the fifth phase of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012) are analyzed. The historical simulations are forced by natural (solar radiation and volcanic aerosols) and anthropogenic (greenhouse gases, aerosols, ozone, and land use) agents, and the historicalNat simulations are only forced by natural agents. The RCP8.5 simulations are run with projected increases in greenhouse gases and represent the high-emission scenario that is the most representative of global CO2 emissions from 2005 to the present, whereas the piControl (preindustrial) simulations are forced with the external forcing fixed at 1850 levels. Before an assessment of the anthropogenic influence on the SEPTP drought can be made, it is necessary to evaluate the performance of the models utilized. Following the methodologies in previous studies (King et al. 2015; Uhe et al. 2016), the Kolmogorov–Smirnov (KS) test was used to determine whether the model simulations adequately capture observed interannual variability of both SEPTP temperature and precipitation in autumn over the 1960–2005 period. We selected the 17 CMIP5 models, for which no more than one of the historical simulations is significantly different (p < 0.05) from the observed anomalies of both SEPTP temperature and precipitation, and for which historical, historicalNat, RCP8.5, and piControl simulations are available. The selected CMIP5 models and the number of the ensemble members of individual experiments used are shown in Table 2. To examine the anthropogenic influence on the likelihood of severe droughts in the SEPTP such as the autumn drought in 2009, both the historicalNat and piControl simulations were used as indicators of variability without any anthropogenic impact.

Table 2.

Details of the 17 CMIP5 models used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

Details of the 17 CMIP5 models used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)
Details of the 17 CMIP5 models used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

To potentially minimize the effect of individual model biases and internal variability, the external forcing signals are estimated as the multimodel ensemble mean of CMIP5 simulations and multiensemble mean of CAM5.1 and MIROC5 simulations. To include the possible influence of internal climate variability, spreads among the ensembles are also calculated.

b. Method

Unless specifically claimed, anomalies for all variables are calculated as the deviation from the climatological mean of 1961–90. Trends in all variables are estimated using least squares linear regression. All statistical significance tests are performed using the two-tailed Student’s t test assuming uncorrelated Gaussian noise.

To quantify the intensity of drought in the observations and simulations, the climatic drought index known as the standard precipitation evapotranspiration index (SPEI; Vicente-Serrano et al. 2010) is used. The SPEI index is calculated based on the observational and simulated precipitation and temperature as below:

 
formula

In Eq. (1), P is monthly precipitation (mm). PET is potential evapotranspiration (mm) and is estimated via the method of Thornthwaite, which has the advantage of only requiring monthly temperature (°C) (Thornthwaite 1948). Also, D is the difference between precipitation and PET and k (months) is the time scale of the aggregation (in this study, we use 3 months); n is the calculation month. SPEI values are calculated by first fitting a Pearson type III distribution to monthly series of D and then being normalized.

To evaluate whether the chance of severe drought in the SEPTP, as in autumn 2009, has been altered by anthropogenic influence on climate, following previous studies (Stott et al. 2004; Pall et al. 2011), the probability ratio (PR) and fraction of attributable risk (FAR) were calculated as follows:

 
formula
 
formula

Where P0 is the probability of an event occurring in the reference state (derived from simulations without anthropogenic influence) and P1 is the corresponding probability under the parallel state (calculated using simulations with all anthropogenic influence included). PR is the factor by which the probability of an event has changed under anthropogenic forcing, and FAR is the fractional contribution of human activity to a particular event. For example, if the event never occurred in the reference state but does occur in the new forced state, FAR equals unity, and this implies that the occurrence of this event can be entirely attributed to the forcing (Stone and Allen 2005).

The anomalies of all the climate variables are calculated relative to the climatology of 1961–90. The piControl anomalies are determined relative to the long-term mean. As pointed out by Hegerl (2015), structural deficiencies in a single models are overcome by using multiple state-of-the-art climate models. Thus, following previous studies (Christidis et al. 2015; King et al. 2015), the probability distributions of the anomalies for a climate variable in CMIP5 models are constructed with data from all CMIP5 models. Then the PR and FAR values are calculated by comparing the probability density functions (PDFs) in the various model simulations. The uncertainties of values in Eqs. (2) and (3) were estimated by a bootstrap procedure (Christidis et al. 2013).

3. Results

a. Observed characteristics of the 2009 autumn drought in the SEPTP

As autumn in the SEPTP is the transition from the rainy season (summer) to the dry season (winter), a significant intensification of autumn drought will inevitably cause a long dry period until the next rainy season comes. Drought events are not uncommon in the SEPTP. However, the spatial scale of the 2009 autumn drought was unprecedented (CMA 2010). Drought began in the late summer of 2009, reached its peak and greatest extent in autumn, and continued into the spring of 2010. Drought is predominately driven by precipitation deficits, while anomalously warm surface air temperature (SAT) amplifies drought through more evaporation of soil moisture and thus further facilitates dry conditions in the environment (IPCC 2012). Along with precipitation anomalies reaching less than 50% of the climatological mean (the driest on record) and temperature anomalies exceeding 1°C (the third warmest on record) in 2009 autumn over a wide area of the SEPTP, the SPEI, a climatic drought index (see section 2), was the lowest on record, with anomalies lower than −1 in most regions of the SEPTP (Fig. 1, left). For most parts south of 30°N over the SEPTP, conditions with this degree of combined scarcity of precipitation and higher temperature had not been experienced since at least 1961, resulting in the lowest SPEI for the area (Fig. 1, right). During this rare severe autumn drought over the SEPTP, the deficiency of precipitation and severity of drought over regions north of 30°N are not as obvious over regions north of 20°N where wetter than normal occurred. Thus, in this study, the core region we focus on is the area of 22°–30°N, 97°–110°E over the SEPTP.

Fig. 1.

Observed characteristics of the 2009 autumn drought over the southeastern periphery of the Tibetan Plateau. The autumn mean (a) SPEI, (c) precipitation (percentage), and (e) SAT (°C) anomalies relative to the mean of 1961–90. Also shown are the number of autumn periods during 1961–2008 in which (b) SPEI and (d) precipitation were less than the 2009 autumn value and (f) SAT was higher than the 2009 autumn value.

Fig. 1.

Observed characteristics of the 2009 autumn drought over the southeastern periphery of the Tibetan Plateau. The autumn mean (a) SPEI, (c) precipitation (percentage), and (e) SAT (°C) anomalies relative to the mean of 1961–90. Also shown are the number of autumn periods during 1961–2008 in which (b) SPEI and (d) precipitation were less than the 2009 autumn value and (f) SAT was higher than the 2009 autumn value.

It is worth noting that the autumn mean SPEI exhibited a significant decreasing trend (toward drier conditions) during 1961–2014, while there has been a substantial increase in frequency of below-normal autumn mean precipitation and above-normal surface air temperature in this period (Fig. 2). Since the 2000s in particular, the negative anomalies in SPEI and precipitation and positive anomalies in surface air temperature have been particularly strong. Thus, in the following analysis of severe drought events, such as that in autumn 2009, it is essential to consider the long-term context of climate variability and change.

Fig. 2.

Time series of autumn mean (a) SPEI, (b) precipitation (mm day−1), and (c) SAT (°C) anomalies (relative to the mean of 1961–90) during 1961–2014, averaged over the southeastern periphery of the Tibetan Plateau (SEPTP; 22°–30°N, 97°–110°E).

Fig. 2.

Time series of autumn mean (a) SPEI, (b) precipitation (mm day−1), and (c) SAT (°C) anomalies (relative to the mean of 1961–90) during 1961–2014, averaged over the southeastern periphery of the Tibetan Plateau (SEPTP; 22°–30°N, 97°–110°E).

b. The atmospheric circulation regime responsible for droughts in the SEPTP

Large-scale climate variability in SST can cause precipitation and temperature anomalies and hence influence the strength and duration of drought (Zhang et al. 2013; Feng et al. 2014; Wang et al. 2015). Therefore, we first identify the atmospheric circulation regime responsible for the severe droughts in the SEPTP and the associated thermodynamic aspects of climate change, and then understand how anthropogenic warming affects the intensity and probability of severe drought events in SWC though intensifying or weakening this unchanged originally atmospheric circulation mechanism. We examine the correlation coefficients between regional average precipitation in the SEPTP and SST anomalies in autumn (Fig. 3a). Significant correlations are evident in the Indian–Pacific warm pool (IPWP) domain: the absolute values of the negative correlation coefficients r are larger than 0.5, which is statistically significant at the 1% level, indicating that the autumn precipitation in the SEPTP can be affected by the remote forcing of SST anomalies from the IPWP. This relationship is further confirmed by regressing the autumn precipitation anomalies upon the standardized IPWP index, which is an average of SST anomalies over the region 10°S–10°N, 90°–180°E (Fig. 3b). Following a 1°C increase in IPWP SST in autumn, precipitation decreases significantly in most parts of southern China, especially over the eastern areas of the SEPTP, where the decrease in precipitation exceeds −1 mm day−1. After linear detrending, maps of r between the observed SST and SEPTP autumn precipitation and regression coefficient of autumn precipitation over China with the standardized IPWP index (not shown) are extremely similar to Fig. 3.

Fig. 3.

(a) Maps of the correlation coefficient between observed SST and precipitation averaged over the SEPTP (22°–30°N, 97°–110°E) in autumn during 1961–2014. (b) Maps of the regression coefficients (mm day−1 °C−1) of autumn precipitation in China with the Indian–Pacific warm pool (IPWP) SST averaged over the region 10°S–10°N, 90°–180°E. The stippling indicates the correlation in (a) and the regression coefficient in (b) are statistically significant at the 5% level.

Fig. 3.

(a) Maps of the correlation coefficient between observed SST and precipitation averaged over the SEPTP (22°–30°N, 97°–110°E) in autumn during 1961–2014. (b) Maps of the regression coefficients (mm day−1 °C−1) of autumn precipitation in China with the Indian–Pacific warm pool (IPWP) SST averaged over the region 10°S–10°N, 90°–180°E. The stippling indicates the correlation in (a) and the regression coefficient in (b) are statistically significant at the 5% level.

Why is the IPWP warm SST anomaly favorable to the autumn SEPTP drought? Severe SEPTP autumn drought events characterized by less precipitation, higher SAT, and lower SPEI than normal are observed (Fig. 2) when IPWP SST is extremely warm (Fig. 4). Meanwhile, the unprecedented SEPTP autumn drought of 2009 occurred in the background of a significant drying trend in the SEPTP and a warming trend in the IPWP (Fig. 4).

Fig. 4.

Time series of autumn SST anomalies (°C) averaged over the IPWP area (10°S–10°N, 90°–180°E) during 1961–2014. The black line is for observation. Red and green lines are for 17 CMIP5 climate model ensemble means of historical simulations with all forcings (hisAll) and only natural forcing (hisNat), respectively. Goldenrod and light blue shadings show the 5th–95th percentile ranges simulated by the 17 CMIP5 models.

Fig. 4.

Time series of autumn SST anomalies (°C) averaged over the IPWP area (10°S–10°N, 90°–180°E) during 1961–2014. The black line is for observation. Red and green lines are for 17 CMIP5 climate model ensemble means of historical simulations with all forcings (hisAll) and only natural forcing (hisNat), respectively. Goldenrod and light blue shadings show the 5th–95th percentile ranges simulated by the 17 CMIP5 models.

To understand how the IPWP SST anomalies drive the meteorological conditions of drought in the SEPTP, composite anomalies of atmospheric circulation and moisture transport are shown in Fig. 5, which was derived using warm IPWP years minus cool IPWP years. Only the years with warm (cool) SST anomaly in IPWP larger than one standard deviation were regarded as warm (cool) years. In response to a warmer IPWP, ascending motion is strengthened over most parts of the IPWP, especially over the Philippines (Fig. 5b). As a possible consequence of the enhancement of local Hadley circulation, an anomaly of downward motion occurs over the SEPTP, which is favorable to high surface air temperatures and suppresses precipitation, finally leading to the formation of drought. A warmer IPWP is associated with enhanced convection over the Philippines, as evidenced by stronger vertical velocity at 500 hPa (Fig. 5b) and enhanced convergence of moisture transport (Fig. 5b). The strengthened convection over the Philippines stimulates anomalously cyclonic motion over the South China Sea (Fig. 5b) through a Rossby wave response (Gill 1980). The anomalously northeasterly winds in the northwest of the anomalously cyclonic circulation go against the climatological southwesterly winds (Fig. 5a), so the moisture transport from the Bay of Bengal is reduced. The westerly anomalies in the south of the anomalously cyclonic region inhibit the northwestward transport of water vapor from the western tropical Pacific to the SEPTP. Consequently, the convergence of atmospheric column water vapor over the SEPTP is weakened, which results in decreased precipitation.

Fig. 5.

Autumn atmospheric column water vapor flux (vectors; kg m−1 s−1) and vertical motion at 500 hPa (shadings; 10−2 Pa s−1; positive value denotes ascending motion and negative value denotes descending motion). (a) The climatological mean of 1961–90. (b) The difference between the warm Indian–Pacific warm pool years and cool Indian–Pacific warm pool years.

Fig. 5.

Autumn atmospheric column water vapor flux (vectors; kg m−1 s−1) and vertical motion at 500 hPa (shadings; 10−2 Pa s−1; positive value denotes ascending motion and negative value denotes descending motion). (a) The climatological mean of 1961–90. (b) The difference between the warm Indian–Pacific warm pool years and cool Indian–Pacific warm pool years.

A scatter diagram also confirms the relationship between the autumn SEPTP drought events and a warmer than normal IPWP (Fig. 6a). In the SEPTP, all of the climate variables, including the autumn mean vertical velocity at 500 hPa, precipitation, SAT, and SPEI, have significant correlations with the IPWP SST, with r of −0.57, −0.43, 0.48, and −0.44, respectively. All r are statistically significant at the 0.01 level. In terms of linear regression coefficients, in response to a 1°C warming of the IPWP SST, the upward motion at 500 hPa averaged over the SEPTP decreases 1.80 × 10−2 Pa s−1, the precipitation decreases by −0.75 mm day−1, the surface air temperature warms by 0.98°C, and the SPEI reduces by −1.42. All of these regression coefficients are statistically significant from zero at the 1% level. Detrending the data reduces r to −0.45, −0.39, 0.40, and −0.38 for vertical velocity at 500 hPa, precipitation, SAT, and SPEI, respectively, but they are still statistically significant at the 5% level. During the severe 2009 SEPTP autumn drought event, IPWP SST was the fifth highest since 1961 and it was 0.45°C warmer than normal (Fig. 4), while the SEPTP area-averaged precipitation was 1.25 mm day−1 below normal and the SPEI was 2.05 below normal, both breaking the historical low record since at least 1961 (Fig. 2).

Fig. 6.

Relationships between the IPWP SST and observed variables related to the drought over the SEPTP. Scatterplots between the autumn IPWP SST (°C) and (a) SEPTP precipitation (mm day−1), (b) SEPTP SAT (°C), (c) SEPTP vertical velocity at 500 hPa (10−2 Pa s−1), and (d) SEPTP SPEI during 1961–2014. Red and green dots denote the first and last 27 years of the period, respectively. The least squares regression lines are drawn in black and the corresponding regression coefficients R are indicated on the top-right corner of plots. The p value is the significance of R.

Fig. 6.

Relationships between the IPWP SST and observed variables related to the drought over the SEPTP. Scatterplots between the autumn IPWP SST (°C) and (a) SEPTP precipitation (mm day−1), (b) SEPTP SAT (°C), (c) SEPTP vertical velocity at 500 hPa (10−2 Pa s−1), and (d) SEPTP SPEI during 1961–2014. Red and green dots denote the first and last 27 years of the period, respectively. The least squares regression lines are drawn in black and the corresponding regression coefficients R are indicated on the top-right corner of plots. The p value is the significance of R.

Under the assumption that the empirical distribution of the SEPTP autumn precipitation and surface air temperature shifts linearly with the IPWP SST, the observed warming IPWP SST (Fig. 4) implies an increase of the probability of low precipitation and high temperature in the SEPTP and thus an increase of drought events. From the scatterplot in Fig. 6, we can see that extremely low precipitation and high temperature events have mostly occurred in the last 27 years when the IPWP has been far warmer than during the first half of the period. Is the increasing frequency of the SEPTP drought events due to global warming? Given the intrinsic rarity of extremely low precipitation and high temperature, the two primary drivers of severe drought, it is reasonable to assume that the probability distribution functions of the SEPTP autumn precipitation and surface air temperature have not changed shape but have shifted to lower or higher values due to anthropogenic climate change (van Oldenborgh 2007). Following the significant warming trend of IPWP with a rate of 0.12°C decade−1 (Fig. 4), descending motion at 500 hPa, precipitation, and SAT over the SEPTP in autumn show a significant strengthening trend at a rate of −0.19 10−2 Pa s−1 decade−1, a significant decreasing trend at a rate of −2.34% decade−1, and a significant warming trend at rate of 0.16°C decade−1, respectively, while severe drought in the SEPTP also becomes more frequent with a significant decreasing trend of SPEI at a rate of −0.19 decade−1 (Fig. 2); all trends are statistically significant at the 1% level. It is indicated that the occurrence of autumn drought in the SEPTP is systematically conditioned by IPWP SST.

c. Anthropogenic influence on the severe droughts in the SEPTP

Because severe drought events can be triggered by considerable natural internal variability, such as ENSO and the IPO (Dai 2013; Trenberth et al. 2014), it is impossible to unambiguously attribute a single drought event to anthropogenic climate change. However, global warming may trigger drought events to occur more quickly and more intensely because of the strong interaction with internal variability (Trenberth et al. 2014). Meanwhile, as emphasized in the observational analysis above, the unprecedented severe STPEP drought in autumn 2009 occurred under the background of long-term drying and warming of the STPEP, and the warming in the IPWP may have an important role in driving the 2009 autumn drought in the STPEP. It is also obvious that the anthropogenic forcing is the dominant contributor to the observed IPWP warming, by comparing observations with CMIP5 simulations–only natural forcing included and all forcing included (Fig. 4). The IPWP is one of the regions sensitive to climate change; anthropogenic forcing has a detectable effect on the observed IPWP warming (Weller et al. 2016). To further elucidate the possible contribution of anthropogenic warming to drought frequency in the SEPTP, the atmospheric circulation regime identified in the analysis of observational data as being responsible for the SEPTP drought is first examined in CMIP5 historical simulations. Then, based on our inquiry detection and on attribution methods by Hegerl et al. (2009) and using the method of Harrington et al. (2014) and Zhou et al. (2014), the observed trends of climate variables associated with the SEPTP autumn drought are compared with CMIP5 model simulations of internal climate variability and model responses to both anthropogenic and natural forcing.

The CMIP5 historical simulations reasonably capture the IPWP SST–SEPTP vertical motion, IPWP SST–SEPTP precipitation, IPWP SST–SEPTP SAT, and IPWP SST–SEPTP SPEI relationships (Figs. 7a–d). The regression coefficient R of the SEPTP vertical velocity at 500 hPa, precipitation, SAT, and SPEI with IPWP SST is −0.43 × 10−2 Pa s−1 °C−1, −0.29 mm day−1 °C−1, 0.97°C °C−1, and −0.52°C−1, respectively. All R values are statistically significant from zero at the 1% level. The spread of R among historical simulations of 17 CMIP5 models is ±0.22 × 10−2 Pa s−1 °C−1 for vertical velocity, ±0.19 mm day−1 °C−1 for precipitation, ±0.15°C °C−1 for SAT, and ±0.25°C−1 for the SPEI. Although the magnitudes of R in simulations are smaller than those derived from observations, the signs of R in the simulations are basically the same as in observations. Compared with R derived original data, detrending the data reduces the magnitudes of R but does not change the signs of R. It is hence indicative of the reasonable performance of CMIP5 models in simulating the underlying mechanism behind anomalously drought years and thus forms a solid basis for further investigation of human influence on the SEPTP drought based on the ensemble simulations of the CMIP5 models.

Fig. 7.

(a)–(d) As in Figs. 6a–d, but for CMIP5 historical simulations. Also shown are the time series of autumn SEPTP (e) vertical velocity at 500 hPa (10−2 Pa s−1), (f) precipitation (mm day−1), (g) SAT (°C), and (h) SPEI during 1961–2014. Black lines in (e)–(h) are for observations. Red and green lines are for 17 CMIP5 climate model ensemble means of historical simulations with all forcings (hisAll) and only natural forcing (hisNat), respectively. Goldenrod and light blue shadings show the 5th–95th percentile ranges simulated by the 17 CMIP5 models.

Fig. 7.

(a)–(d) As in Figs. 6a–d, but for CMIP5 historical simulations. Also shown are the time series of autumn SEPTP (e) vertical velocity at 500 hPa (10−2 Pa s−1), (f) precipitation (mm day−1), (g) SAT (°C), and (h) SPEI during 1961–2014. Black lines in (e)–(h) are for observations. Red and green lines are for 17 CMIP5 climate model ensemble means of historical simulations with all forcings (hisAll) and only natural forcing (hisNat), respectively. Goldenrod and light blue shadings show the 5th–95th percentile ranges simulated by the 17 CMIP5 models.

Similar to the observations, in the simulations recent decades (1988–2014) with higher IPWP SST have also witnessed more extremely strong descending motion, extremely low autumn precipitation, extremely high temperature, and extremely low SPEI years compared to earlier decades (1961–87) (Figs. 7a–d), implying that the anthropogenic influence may modify the meteorological conditions of the droughts in the SEPTP, reflected as low precipitation and high temperature, through enhancing the IPWP SST and then strengthening the local Hadley circulation and promoting the anomalously cyclonic motion over the South China Sea.

To confirm the human-induced effect on the SEPTP droughts, we further examine the time series of SEPTP vertical velocity at 500 hPa, precipitation, SAT, and SPEI in CMIP5 historical simulations (Figs. 7e–h). It is notable that the rapid warming of IPWP SST (Fig. 4), strengthening of SEPTP descending, warming of SEPTP SAT, and decrease of SEPTP precipitation and SPEI (Figs. 7e–h) since the late 1980s in the simulation are strongly consistent with the observations. The multimodel ensemble (MME) mean of historical simulations shows a strengthening trend of −0.07 × 10−2 Pa s−1 decade−1 for SEPTP descending motion at 500 hPa, a drying trend of −1.38% decade−1 for SEPTP precipitation, a warming trend of 0.19°C decade−1 for SEPTP SAT, and a decreasing trend of −0.09 decade−1 for SEPTP SPEI during 1961–2014. All are statistically significant at the 1% level; the simulated MME trends of SEPTP SAT (0.19°C decade−1) match the observed trends (0.16°C decade−1); MME underestimates the trends of STPEP vertical velocity at 500 hPa, precipitation, and SPEI (Figs. 7e,f and 9). Further examination of historicalNat runs, where no anthropogenic forcing was prescribed, finds no corresponding changes. During 1961–2005, the trend of SEPTP vertical velocity at 500 hPa, precipitation, SAT, and SPEI in the MME of historicalNat simulations is 0.003 × 10−2 Pa s−1 decade−1, −0.144% decade−1, 0.027°C decade−1, and −0.003 decade−1, respectively; all trends are close to zero and are insignificant at the 0.1 level. Meanwhile, the observed trends of SEPTP climate variables fall in the simulated range of trends in CMIP5 historical simulations with all forcings and are outside the simulated range of trends in CMIP5 historicalNat simulations (Fig. 9). Furthermore, the distribution of trends in the historical simulations and historicalNat simulations is statistically distinguishable for SEPTP precipitation, SAT, and SPEI (all KS test p values < 0.001), but not the vertical velocity (KS test p value = 0.454). It is therefore suggested that the IPWP would have been cooler in recent decades without anthropogenic warming, thus leading to a cooler and wetter SEPTP.

To further confirm the substantial dynamic impacts of the IPWP warming on the severe drought events in the SEPTP, the evolution of the meteorological conditions of the SEPTP drought simulated by the two AGCMs, namely CAM5.1 and MIROC5, during 1961–2014 is examined (Fig. 8). Except for the vertical velocity in 2009 autumn, observed SEPTP vertical velocity, precipitation, SAT, and SPEI fall in the range of simulations. The All-Hist ensemble simulations of the two models capture the observed enhancement of descending motion, decease of precipitation, warming of SAT, and reduction of SPEI in the SEPTP especially since the late 1980s (Fig. 8). During 1961–2014, it is revealed that there is a significant strengthening trend of −0.10 (−0.15) × 10−2 Pa s−1 decade−1 for SEPTP vertical velocity at 500 hPa, a significant deceasing trend of −2.93% (−3.06%) decade−1 for SEPTP precipitation, a significant warming trend of 0.15 (0.08) °C decade−1 for STPEP SAT, and a significant reducing trend of −0.23 (0.14) decade−1 for SEPTP SPEI in the CAM5.1 (MIROC5) All-Hist ensemble mean simulation (Figs. 8 and 9). No similar changes are evident in the Nat-Hist simulations. The ensemble mean of the CAM5.1 Nat-Hist simulations exhibits insignificant trends at the 0.1 level for all SEPTP climate variables associated with drought during 1961–2014, with the extremely weak or even nearly zero trends of 0.0007 × 10−2 Pa s−1 decade−1 for vertical velocity, −0.14% decade−1 for precipitation, −0.001°C decade−1 for SAT, and −0.004 decade−1 for SPEI. The observed strengthening trend of descending motions, warming trend of SAT, and decreasing trends of precipitation and SPEI over the SEPTP lie within the simulated range of trends in CAM5.1 and MIROC5 All-Hist simulations, and fall outside the range of CAM5.1 Nat-Hist simulations (Fig. 9). The distributions of the trends for SEPTP vertical velocity, precipitation, SAT, and SPEI in CAM5.1 All-Hist and Nat-Hist simulations are statistically distinguishable (all corresponding KS test p values are <0.001). This implies that the warmer and drier conditions in the SEPTP under anthropogenic warming are favorable for an increase in the drought frequency of this region.

Fig. 8.

Time series of observed and simulated autumn (a) vertical velocity at 500 hPa (10−2 Pa s−1), (b) precipitation (mm day−1), (c) SAT (°C), and (d) SPEI averaged over the southeastern periphery of the Tibetan Plateau during 1961–2014. Black lines are for observations. Red and green lines are for CAM5.1 All-Hist (CAM5.1_his) and Nat-Hist (CAM5.1_hisNat) 50-member ensemble means, with the ensemble spreads shaded by goldenrod and pale turquoise colors, respectively. Blue lines and light blue shadings are for MIROC5 All-Hist (MIROC5_his) 10-member ensemble mean and ensemble spreads (i.e., minimum and maximum among ensembles).

Fig. 8.

Time series of observed and simulated autumn (a) vertical velocity at 500 hPa (10−2 Pa s−1), (b) precipitation (mm day−1), (c) SAT (°C), and (d) SPEI averaged over the southeastern periphery of the Tibetan Plateau during 1961–2014. Black lines are for observations. Red and green lines are for CAM5.1 All-Hist (CAM5.1_his) and Nat-Hist (CAM5.1_hisNat) 50-member ensemble means, with the ensemble spreads shaded by goldenrod and pale turquoise colors, respectively. Blue lines and light blue shadings are for MIROC5 All-Hist (MIROC5_his) 10-member ensemble mean and ensemble spreads (i.e., minimum and maximum among ensembles).

Fig. 9.

Box-and-whisker plots of the linear trends of the autumn (a) vertical velocity at 500 hPa, (b) precipitation, (c) SAT, and (d) SPEI averaged over the southeastern periphery of the Tibetan Plateau during 1961–2014. Black, blue, red, green, and orange boxes are for trends of CMIP5 historical all forcing simulations (CMIP5_hisAll) and only natural forcing simulations (CMIP5_hisNat), CAM5.1 All-Hist (CAM5.1_hisAll), and Nat-Hist (CAM5.1_hisNat), and MIROC5 All-Hist (MIROC5_hisAll), respectively. Box plots show the median, 5th, 25th, 75th, and 95th percentiles of trends. Black stars indicate the observed trends.

Fig. 9.

Box-and-whisker plots of the linear trends of the autumn (a) vertical velocity at 500 hPa, (b) precipitation, (c) SAT, and (d) SPEI averaged over the southeastern periphery of the Tibetan Plateau during 1961–2014. Black, blue, red, green, and orange boxes are for trends of CMIP5 historical all forcing simulations (CMIP5_hisAll) and only natural forcing simulations (CMIP5_hisNat), CAM5.1 All-Hist (CAM5.1_hisAll), and Nat-Hist (CAM5.1_hisNat), and MIROC5 All-Hist (MIROC5_hisAll), respectively. Box plots show the median, 5th, 25th, 75th, and 95th percentiles of trends. Black stars indicate the observed trends.

To quantify the anthropogenic warming effect on the chance of the SEPTP droughts such as that in autumn 2009, the probability density functions (PDFs) estimated by the kernel smoothing method for the SEPTP autumn precipitation, SAT, and SPEI anomalies are examined (Figs. 10 and 11a). The climatological PDFs of both precipitation and SAT in the SEPTP simulated by the two AGCMs and CMIP5 models (included SPEI) agree well with the observed distributions, with a two-sided Kolmogrov–Smirnov test suggesting that the simulated and observed PDFs are statistically indistinguishable at the 5% level. In both CAM5.1 and MIROC5, the PDF distributions of the SEPTP precipitation and temperature simulated by 2009 All-Hist realizations exhibit an obvious leftward shifting and a rightward shifting, respectively, relative to those in the 2009 Nat-Hist ensemble simulations (Figs. 10a–d). The probability of low autumn precipitation in the SEPTP in 2009 is estimated as 2.3% (6.3%) and with a standard deviation of ±1.07% (±2.36%) calculated through bootstrapping 1000 times based on subsamples of only 50% of available data in the realizations of CAM5.1 (MIROC5) All-Hist in 2009. The estimated probability is 0.91% ± 0.59% (3.1% ± 1.7%) in the Nat-Hist simulations, indicating that there is an approximately 2.5 (2.0) times increase in the occurrence of low precipitation in 2009 due to anthropogenic forcing, and the corresponding fraction of attributable risk is approximately 0.60 (0.51) (Fig. 11b). The probability of high temperature as in 2009 is reduced from the 11.1% ± 2.5% (5.4% ± 2.1%) in CAM5.1 (MIROC5) his-All 2009 realizations to 0.33 ± 0.33% (0.72 ± 0.57%) in the Nat-Hist 2009 realizations with anthropogenic emissions absent, indicating that anthropogenic warming increases the chance of high temperature in 2009 by approximately 33.4 (7.5) times, with the corresponding median FAR being 0.96 (0.87) (Fig. 11c).

Fig. 10.

Probability density functions (PDFs) estimated by the kernel smoothing method for autumn (left) precipitation and (right) SAT anomalies (relative to 1961–90) in the SEPTP, derived from CAM5.1, MIROC5, and CMIP5 models. Black curves are for observations over the period 1961–2014 (denoted as Obs). Blue curves are for ensemble mean of PDFs in (a),(b) 50-member realizations in CAM5.1 All-Hist and (c),(d) 10-member realizations in MIROC5 All-Hist (All-long) during 1961–2014. Red (green) curves are for CAM5.1 100-member All-Hist (100-member Nat-Hist) runs in (a) and (b) and MIROC5 60-member All-Hist (50-member Nat-Hist) runs in 2009, denoted as All-2009 (Nat-2009), in (c) and (d). In (e) and (f), red (green) curves are for multimodel ensemble mean of PDFs in individual CMIP5 historical simulations with all forcing (only natural forcing) during 1961–2014 (1961–2005); blue curves are for piControl simulations (relative to the long-term mean). The vertical purple lines in (a)–(f) are the observed 2009 autumn precipitation and SAT anomaly in STPEP.

Fig. 10.

Probability density functions (PDFs) estimated by the kernel smoothing method for autumn (left) precipitation and (right) SAT anomalies (relative to 1961–90) in the SEPTP, derived from CAM5.1, MIROC5, and CMIP5 models. Black curves are for observations over the period 1961–2014 (denoted as Obs). Blue curves are for ensemble mean of PDFs in (a),(b) 50-member realizations in CAM5.1 All-Hist and (c),(d) 10-member realizations in MIROC5 All-Hist (All-long) during 1961–2014. Red (green) curves are for CAM5.1 100-member All-Hist (100-member Nat-Hist) runs in (a) and (b) and MIROC5 60-member All-Hist (50-member Nat-Hist) runs in 2009, denoted as All-2009 (Nat-2009), in (c) and (d). In (e) and (f), red (green) curves are for multimodel ensemble mean of PDFs in individual CMIP5 historical simulations with all forcing (only natural forcing) during 1961–2014 (1961–2005); blue curves are for piControl simulations (relative to the long-term mean). The vertical purple lines in (a)–(f) are the observed 2009 autumn precipitation and SAT anomaly in STPEP.

Fig. 11.

(a) As in Figs. 10e,f, but for SPEI. Also shown are PDFs of the 1000 fraction of attributable risk (FAR) and probability ratio (PR) values for (b) autumn low precipitation, (c) high SAT, and (d) low SPEI in the SEPTP as in 2009. Values of FAR and PR are calculated from bootstrapping the model simulations under conditions in the absence of anthropogenic forcing and with anthropogenic forcing. The dashed vertical lines in (b)–(d) are the median FAR.

Fig. 11.

(a) As in Figs. 10e,f, but for SPEI. Also shown are PDFs of the 1000 fraction of attributable risk (FAR) and probability ratio (PR) values for (b) autumn low precipitation, (c) high SAT, and (d) low SPEI in the SEPTP as in 2009. Values of FAR and PR are calculated from bootstrapping the model simulations under conditions in the absence of anthropogenic forcing and with anthropogenic forcing. The dashed vertical lines in (b)–(d) are the median FAR.

In the CMIP5 ensemble simulations, the PDF distributions of the SEPTP precipitation, SAT, and SPEI in historical simulations under all external forcings also show shifts toward warmer and slightly drier conditions than those in the historical simulations without anthropogenic forcing, namely historical runs with natural forcing only (historicalNat) and piControl runs. This validates the assumption made by van Oldenborgh (2007). The extreme low precipitation events, high temperature events, and low SPEI events as in 2009 are 2.7 ± 0.7 (2.6 ± 0.4) times, 3.5 ± 0.5 (3.1 ± 0.3) times, and 2.8 ± 0.6 (2.4 ± 0.3) times more likely, respectively, in the historical simulations than in historicalNat (piControl) simulations. The corresponding median values of FAR for low precipitation, high temperature, and low SPEI anomalies are 0.61 (0.61) and 0.71 (0.68), and 0.64 (0.57) respectively, with the percentage uncertainty range of 0.35–0.83 (0.41–0.76), 0.62–0.84 (0.60–0.84), and 0.44–0.82 (0.42–0.73) for the historicalNat (piControl) runs, respectively.

The absolute values of FAR in CAM5.1, MIROC5, and CMIP5 models vary widely but with same signs (Fig. 11). Thus, there is a robust contribution of anthropogenic influence to an increased chance of drought conditions, as in 2009. It is also found that an attribution statement is very sensitive to the event attribution methods (Uhe et al. 2016). A robust attribution statement from different attribution methods can improve the reliability of the event attribution result.

Although the possible influence of internal variability, such as ENSO (Zhang et al. 2013) and the North Atlantic Oscillation (Feng et al. 2014), on the SEPTP severe autumn drought like in 2009 is unneglectable, based on the above attribution analysis, it is revealed that severe SEPTP droughts conditions of high temperature and low precipitation are favored by the significant SEPTP warming and drying trend in response to the anthropogenic IPWP warming, through modifying the local Hadley circulation and enhancing the descending motion over the SEPTP. It is beneficial to the increase in the occurrence probability of SEPTP severe drought, along with the increased occurrence probabilities of SEPTP autumn extremely low precipitation, high temperature, and low SPEI events that are attributable to anthropogenic climate change.

4. Summary

SEPTP experienced the severest autumn drought on record in 2009. This persistent drought, lasting from September 2009 to March 2010, was mainly represented by low precipitation and high temperature. However, the contribution of anthropogenic warming to severe droughts in the SEPTP like that in 2009 autumn is yet unclear. Thus, in this study, we use the CAM5.1 and MIROC5 models provided by the International CLIVAR C20C+ Detection and Attribution Project and simulations with CMIP5 models to analyze how anthropogenic emissions contributed to the severest drought in 2009 autumn in the SEPTP. Our results indicate that the severe 2009 autumn drought in the SEPTP occurred against the background of a substantial drying and warming tendency. Human activities strongly contributed to this type of severe drought event.

  1. The IPWP SST anomalies affect the meteorological conditions of drought in the SEPTP through modifying the local Hadley circulation and promoting anomalously cyclonic motion over the South China Sea. With the warm SST anomalies in the IPWP, ascending motions are strengthened in the IPWP, especially over the Philippines. As a consequence of the strengthened local Hadley circulation, descending motions controlled SEPTP, favoring the high temperature and precipitation deficits over there; combined with the below-normal convergence of atmospheric column water vapor flux resulted from the anomalously cyclonic motion in South China Sea, drought conditions are thus further intensified.

  2. Anthropogenic warming in the IPWP has strengthened the local Hadley circulation and reduced the moisture transport from the South China Sea, resulting in both the drying and warming trends in the SEPTP, both of which are favorable to an increase in severe drought events.

  3. The anthropogenic influence on climate has increased the probability of extreme high temperature events in the SEPTP, such as in autumn 2009, by approximately 33.4 (7.5) times, based on the detection and attribution simulations of CAM5.1 (MIROC5), and it has approximately threefold the chance of such high temperature events based on the CMIP5 simulations. The probability of the low SEPTP precipitation and SPEI in 2009 autumn also increased as the effect of human influences, although the fraction of attributable risk to anthropogenic warming is lower than that of high temperature events. Although it is much more difficult to quantify the absolute probability that severe drought conditions such as in 2009 were affected by human influences, probabilistic attributions of extreme drought, as in 2009, with CAM5.1, MIROC5 output, and output CMIP5 models, all suggest a positive attribution statement (i.e., increased odds of the drought event as a consequence of anthropogenic activity).

It is therefore indicated that, following the IPWP warming induced by anthropogenic forcing in the future, the SEPTP would experience drier and warmer conditions, and thus an increase in extreme drought events. Adaptation and mitigation policies for disaster prevention of such drought events call for attention.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grants 41330423, 41420104006) and the R&D Special Fund for Public Welfare Industry (meteorology) (GYHY201506012, GYHY201406020). We thank D. A. Stone for completing the CAM5.1 experiments and providing comments and suggestions to improve the manuscript.

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Footnotes

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