Precipitation over Southwest China (SWC) significantly decreased during
1979–2013. The months from July to September (JAS) contributed the most to
the decrease in precipitation. By tracing moisture sources of JAS
precipitation over the SWC region, it is found that most moisture originates
in regions from the northern Indian Ocean to SWC and from South China Sea to
SWC. The major moisture contributing area is divided into an extended west
region, SWC, and an extended east region. The extended west region is mainly
influenced by the South Asian summer monsoon (SASM) and the westerlies, while
the extended east region is mainly influenced by the East Asian summer
monsoon (EASM). The extended west, SWC, and extended east regions contribute
48.2, 15.5, and 24.5 % of the moisture for the SWC precipitation,
respectively. Moisture supply from the extended west region decreased at a
rate of
Frequent and severe droughts have hit Southwest China (SWC) over the last decades, with record-breaking events in the summer of 2006 and 2011, causing great losses to society. The intensified drought is characterized by the persistent deficit of precipitation (Wang et al., 2015b), and has attracted much attention (e.g., Barriopedro et al., 2012; Feng et al., 2014; Wang et al., 2015b; Tan et al., 2016; He et al., 2016; X. Zhang et al., 2017).
Many studies have analyzed the meteorological conditions that caused the extremely low precipitation for individual drought cases (e.g., Li et al., 2011; Lu et al., 2011; Yang et al., 2012). Taking the drought of summer 2006 as an example, a stronger western Pacific subtropical high (WPSH) was found to lie anomalously northward and westward (Li et al., 2011). Under the direct control of WPSH, descending motion prevailed over SWC and the moisture transport from the Bay of Bengal (BOB) and South China Sea (SCS) was suppressed (Liu et al., 2009; Li et al., 2011). Further analysis revealed that the active convection over the Philippines and the weaker-than-normal heat source of the Tibetan Plateau drove the strengthened WPSH to shift northward and westward (Li et al., 2011). Meanwhile, a weak blocking high in the Ural Mountains and a shallow East Asian trough facilitated a stronger-than-normal zonal circulation in the mid-latitudes, which hindered the intrusion of cold air into SWC (Zou and Gao, 2007). In summary, the configuration of the large-scale subtropical and mid-latitude circulations was unfavorable for the warm, moist air from the south and cold, dry air from the north to converge over SWC and thus produced the severe drought.
Some recent studies have endeavored to investigate the mechanisms causing the SWC drying from a long climatological perspective. Using stalagmite record as a proxy, Tan et al. (2016) found the period of 2009–2012 was the driest ever since AD 1760 in SWC. They further attributed the drying trend to the warming of tropical ocean, which had reduced the land–sea thermal gradient and the amount of moisture transported from the BOB. In another study, the possible influence of sea surface temperature (SST) in tropical northwest Pacific (NWP) on the autumn precipitation in SWC was investigated (Wang et al., 2015a). It was found that the warm SST in NWP had likely contributed to the dry conditions in SWC in recent decades.
Although previous studies have deepened our understanding of the SWC drying through attributing individual/general drought events or long-term precipitation trend to some probable causes, few of them have analyzed the changes in the precipitation moisture sources of this region. Tracing moisture sources not only can reveal the origin of moisture for precipitation (Gustafsson et al., 2010; C. Zhang et al., 2017; James et al., 2004; Sodemann and Zubler, 2010) but also provide insight into long-term change in moisture as well as how atmospheric circulations affect precipitation in SWC. This study intends to identify changes in moisture sources of the SWC precipitation and study the relative changes in moisture transport during the last several decades to investigate the possible mechanism of the SWC drying.
The reanalysis of the European Centre for Medium-Range Weather Forecasts
(ECMWF) Interim (ERA-I hereafter) was used to calculate precipitable water
and moisture flux (Dee et al., 2011). The ERA-I data adopted in this study
are on a grid of 1.5
Due to the existing limitation with precipitation estimates in reanalysis
products (Trenberth et al., 2011; Tong et al., 2014), the ground-based
0.5
The study area of SWC mainly encompasses the three provinces of Sichuan, Yunnan, and Guizhou, as well as the municipality of Chongqing (Fig. 1a). It sits in the southeast foot of the Tibetan Plateau. In north SWC, eastern Sichuan and Chongqing form the Sichuan Basin, while in south SWC, Yunnan and Guizhou form the Yungui Plateau with an average altitude of around 2 km. The topographic height data are provided by the Global Land One-km Base Elevation Project (GLOBE).
The Water Accounting Model (WAM) is an Eulerian model on moisture recycling, which can quantify the moisture source–sink relations between evaporation and precipitation by tracking moisture forward or backward in time (van der Ent et al., 2010; van der Ent and Savenije, 2011; Keys et al., 2012). It is quite different from those Lagrangian models such as FLEXPART and HYSPLIT which track moisture based on the particle trajectories (Stohl and James, 2004, 2005; Sodemann et al., 2008; Draxler and Hess, 1998). In this study, moisture backtracking of WAM was applied to track the moisture origins of and their changes with the SWC precipitation. The algorithm is briefly described as follows.
The input of WAM includes precipitation, evaporation, and atmospheric data
(precipitable water and the vertically integrated moisture transport). The
fallen precipitation in the target area was assumed to return to the air as
“tagged water” in the model. The tagged water was mixed into the
precipitable water with a ratio of
As seen from the algorithm, WAM is a 2-D model with the “well-mixed” assumption, where the tagged water mixes into the precipitable water sufficiently and the mixed ratio is independent of height. Though the well-mixed moisture conditions are not always met, a relatively low degree of vertical mixing suffices to maintain close to well-mixed conditions for the case of moisture flux with vertically uniform wind directions (Goessling and Reick, 2013). For the case of strong directional shear of the horizontal moisture flux, a two-vertical-layer version of WAM was introduced by van der Ent et al. (2013) that solved the vertical inhomogeneities satisfactorily. The two-layer WAM is also implemented in the sensitivity analysis section as a validation to the one-layer WAM results.
The time step of WAM was set to 0.5 h for the 1.5
When WAM is applied at a monthly scale, a large amount of tagged water may be
left in the air after 1 month of tracking rather than allocated to the
surface sources. Many studies have shown that the average residence time of
water vapor in the atmosphere is about 10 days (Trenberth, 1998; Numaguti,
1999; Trenberth, 1999). The ratio of tagged water to residence time is
To further understand the change of moisture transport in association with
the change of moisture origin, the monthly vertically integrated moisture
flux was decomposed into a stationary component and a transient component
(Eq. 1; L. Li et al., 2013).
The fluctuation of the stationary component in expression of divergence can
be further decomposed into thermodynamic and dynamic terms (Eq. 2; Seager et
al., 2010; L. Li et al., 2013).
The time series of
The monthly precipitation trends (mm month
Figure 1b shows the annual precipitation trends from 1979 to 2013 calculated
from the CMA gridded precipitation over the SWC region as marked out by the
red box. The SWC precipitation shows a declining trend in recent decades. The
area-averaged annual SWC precipitation has decreased significantly with a
rate of
The climatological moisture contributions from the source grids in JAS and
their trends during 1979–2013 are shown in Fig. 3. The major moisture
contributing region, i.e., grids with contribution over
0.27 mm month
As the moisture contribution trends show an opposite pattern in the west and east (Fig. 3b), the major moisture contributing region is divided into three regions, namely the extended west, SWC, and the extended east regions. The extended west region covers an area west and southwest to SWC, and the extended east region covers an area east to SWC and a part of the Indian Ocean. Figure S1 in the Supplement shows the climatological moisture transport from July to September. It indicates moisture from the extended west region largely enters the western and southern borders of SWC, whereas moisture from the extended east region enters the eastern border via a route through SCS. Moisture from the extended west region is likely affected by the South Asian summer monsoon (SASM) and the westerlies, while that from the extended east region is likely affected by the East Asian summer monsoon (EASM). When summed over regions, the extended west, SWC, and the extended east regions contribute 48.2, 15.5, and 24.5 % of the total precipitation moisture, respectively. As SWC situates eastward and downwind of the Tibetan Plateau (Fig. S1), moisture from regions to the west of the plateau is mainly blocked by the plateau, while the plateau itself serves as a more important moisture source. According to statistics, the Tibetan Plateau contributes around 11.5 % of the SWC precipitation, less than that from the SASM and EASM regions. Huang and Cui (2015) also notified the important role of Tibetan Plateau as a major source to provide moisture for precipitation in the Sichuan Basin. As the basin situates in north SWC, south SWC is, however, more accessible to the monsoons (Drumond et al., 2011). Thus, it is reasonable that the monsoons, which bring abundant moisture, contribute primary moisture to the JAS precipitation in SWC, while the westerlies contribute secondarily.
Moisture supply (i.e., moisture contributed to the SWC precipitation) from
most of the extended west region experienced a decreasing trend of
The difference of mean moisture contribution (unit: mm month
Figure 4 shows the changes in moisture contribution and moisture transport in July, August, and September between the first and last 10 years of the period of 1979–2013. Overall, there is an apparent decline of moisture supply from the west and southwest regions to SWC in all the 3 months. The area with the largest decline of moisture contribution includes the Indian subcontinent and Indochina over the land and the BOB over the sea. Compared with the moisture transport in the first 10 years, more moisture from the Indian Ocean has been routed to the northern Indian subcontinent or the Tibetan Plateau, rather than into SWC in the last 10 years. Consequently, moisture contribution influenced by the SASM is weakened. In contrast, moisture contribution has increased in many parts of the extended east region. In July, the area with increased moisture contribution includes the northern central Indian Ocean, SCS, and a northeastern area of SWC. It looks like that more moisture from the northern central Indian Ocean has been routed to SWC via SCS in the last decade of 1979–2013. In August and September, the main area with increased moisture contribution is located to the east and south of SWC, while a part of the northern central Indian Ocean also contributed more moisture to SWC compared to the first 10 years. The prevailing easterly moisture transport in recent decades in South China supports an enhanced contribution of the SWC precipitation moisture from the EASM region. The southern part of SWC that is largely affected by the SASM and westerlies experienced a decrease in moisture contribution.
The role of moisture transport is further investigated by analyzing the
relations between moisture divergence and precipitation over SWC during
1979–2013. Correlation coefficients between moisture divergence and
precipitation over SWC are calculated, which are as
Figure 5 shows the 35-year climatology and time series of moisture divergence over SWC and their stationary and transient parts. The moisture divergences over SWC are negative, as expected (see also Fig. S1). The stationary component of moisture divergence is also negative. The transient component is, however, positive. It indicates the transient eddies counteract the mean flow in moisture flux convergence/divergence in SWC. The magnitude of the transient component is about 30 % of that of the stationary component, further suggesting that the change in the stationary component plays a major role in changing the moisture divergence over SWC (Fig. 5a). During 1979–2013, the stationary component increased and the transient component decreased, resulting in an increasing trend of the moisture divergence (Fig. 5b). This suggests that the change in the mean flow rather than the transient eddies has led to the decrease in JAS precipitation in SWC.
The anomalies of moisture divergence over JAS SWC (unit:
mm month
The monthly difference flux (vectors) of the dynamic component of
the stationary moisture transport between the last and first 10 years
(last–first) and its divergence (unit: 10
Figure 6 shows the changes in thermodynamic and dynamic components of the stationary moisture transport in JAS over SWC during 1979–2013. The variation in the thermodynamic component is small compared with that of the dynamic component, suggesting that the dynamic processes, i.e., changes in atmospheric circulation (wind), exerted a dominant influence on the variation in moisture transport. The dynamic component shows an increasing trend significant at 5 % level during 1979–2013, while the thermodynamic component shows a small negative trend. The increase in moisture divergence, i.e., decrease in moisture convergence, by the dynamic component is in line with the decreasing precipitation (Table 1). The correlation coefficients between the dynamic component of moisture transport and precipitation over SWC are calculated. According to the calculated coefficients of determination, the dynamic component explains 80, 81, and 58 % of the precipitation variances for July, August, and September, respectively. It confirms the dominant role of the dynamic processes in regulating the precipitation change in SWC. Indeed, the interannual variation in the SASM net precipitation (within the Arabian Sea–Indian Subcontinent–BOB) is also dominated by the dynamic processes (Walker et al., 2015). This suggests that the dominant role played by the dynamic processes in regulating moisture transport and regional precipitation not only validates in SWC but prevails over a quite large area.
Figure 7 compares the dynamic component in JAS between the first and last 10 years of the period of 1979–2013. There is an overall positive anomaly of moisture divergence over SWC with an easterly anomaly of moisture transport. Though there is a southwesterly anomaly of moisture transport from the Indian Ocean to the SWC direction in July and September, it does not contribute moisture transport to SWC because the anomaly ends on the south of the Tibetan Plateau. There is an easterly anomaly along the southern edge of the Tibetan Plateau, routing the moisture transport to the northern Indian subcontinent instead of the SWC region. The anomaly of moisture divergence, dynamically caused by the changes in circulation, is generally negative in the Indian subcontinent but positive in SWC (Tan et al., 2016). The prevailing easterly anomaly of moisture transport and pronounced regional anomalies of moisture divergence over SWC are likely to result from the change in the Asian summer monsoon system (Wei et al., 2014), which might be related to recent Pacific cooling and Indian Ocean warming (Ueda et al., 2015).
Horizontal moisture flux shear factor in JAS averaged over
1979–2013 with ERA-I.
In the study, the one vertical layer version of WAM (WAM1) was applied. WAM1
uses the vertically integrated fluxes with the moisture being well-mixed
within the atmospheric column. In reality, “well-mixed” conditions of
tagged atmospheric moisture are usually not met (Bosilovich, 2002; Goessling
and Reick, 2013). At the same time, if the horizontal winds are sheared
vertically in direction, vertical inhomogeneities will generate, which may
lead to substantial errors with 2-D moisture tracking models (Goessling and
Reick, 2013). Van der Ent et al. (2013) advanced WAM1 to the two vertical
layer WAM (WAM2) that satisfyingly solved this problem and gave a simple
metric to assess wind shear on when to use which model. The equations on the
horizontal moisture flux shear following van der Ent et al. (2013) are
Moisture contribution of the SWC precipitation in JAS
1986 with WAM1
To further verify the applicability of WAM1, the year 1986, with the strongest moisture flux shear (the averaged zonal and meridional shear factor in JAS 1986 is 0.71), was selected to perform an inter-model comparison between WAM1 and WAM2. As the atmospheric input data for WAM2 are model-level based, additional suite of ERA-I model-level atmospheric data in 1986 was prepared. The moisture contribution for JAS precipitation in 1986 SWC with WAM1 and WAM2 is shown in Fig. 9. It demonstrates that the spatial patterns of moisture contribution between WAM1 and WAM2 match quite well with each other.
ERA-I, as a modern reanalysis, has significantly improved in comparison to its prior version, ERA-40 (Dee et al., 2011; Trenberth et al., 2011). The ERA-I variables differ according to whether they are produced by the analysis or the forecast. The analysis fields are constrained by the observations while the forecast are produced by the model (Berrisford et al., 2011; Dee et al., 2011). Thus, observation-constrained fields such as humidity and wind tend to be more reliable than those from model forecast as precipitation, evaporation, etc. (Berrisford et al., 2011), as does the moisture transport derived from humidity and wind directly. In a comparison among several reanalyses (Trenberth et al., 2011), the long-term variation in moisture transport with ERA-I is rather stable, which gives us more confidence in its application.
As in the study, observation-based precipitation (from CMA) and evaporation
(GLDAS, forced with precipitation gauge observations) instead of their ERA-I
forecast counterparts were used. On the one hand, the input data for WAM
becomes more accurate which facilitates more accurate results. On the other
hand, changes in the ERA-I water cycle may induce changes in moisture origin
and may further affect the trend results. In that consideration, moisture
tracking for the SWC precipitation with the original ERA-I evaporation and
precipitation is also performed. The basic results are shown in Fig. 10. The
basic patterns of moisture contribution with different
JAS precipitation over SWC has decreased significantly during 1979–2013. By
tracing the origins of moisture for JAS precipitation and by analyzing the
variations in moisture transport to SWC, we came to the following
conclusions.
Most moisture for the JAS precipitation in SWC originates in regions
from the northern Indian Ocean to SWC and from South China Sea to SWC. The
westerlies play a secondary role in supplying moisture. The extended west
region, SWC, and the extended east region contributes 48.2, 15.5, and
24.5 % of moisture to the JAS precipitation in SWC, respectively. The
Tibetan Plateau region contributes 11.5 % of the moisture for
precipitation. The decrease in the JAS precipitation is mainly attributed to the
reduced moisture supply from the extended west region. Moisture supply from
the extended west region has decreased at a high rate
( The change in the stationary component has reduced moisture transport
into SWC in JAS, whereas the change in transient component has increased
moisture transport during 1979–2013. The dynamic processes (i.e., changes in
wind) are more important than the thermodynamic processes (i.e., changes in
specific humidity) in affecting the precipitation. A prevailing easterly
anomaly that weakened moisture transport from the Indian Ocean is mainly
responsible for the decrease in the SWC precipitation. The change in
circulation may be related to the recent sea surface temperature change and
need further investigation.
The ERA-I data are supplied by ECMWF and are accessible at
The authors declare that they have no conflict of interest.
This work was supported through the National Natural Science Foundation of China (41425002), the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2016-6-4), and the National Youth Top-notch Talent Support Program in China. Support from Swedish VR, STINT, BECC, MERGE and SNIC through S-CMIP is also acknowledged. Edited by: Andreas Stohl Reviewed by: two anonymous referees