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  • View in gallery

    (a) The spatial distribution of 45 meteorological stations in the Inner Mongolian Plateau (IMP) and 35 lakes with an area of more than 10 km2 around the MP and (b) the linear trend (red circles indicate lake expansion, and blue circles show lake shrinkage) of the lake area obtained from the spline interpolation during the period 1976–2013. The diagram in the upper-left corner of (b) shows the temporal variation in the total lake area averaged over 10 contiguous retrieved lakes. The shading (units: m) indicates the altitude in the IMP.

  • View in gallery

    (a) The diagram of the proportion of the monthly accumulated precipitation climatology (units: mm) to the annual precipitation and (b) the linear trend in the monthly accumulated precipitation (units: mm) during the period 1976–2016. The five-point star indicates that the linear trend is significant at the 99% confidence level.

  • View in gallery

    The spatial distribution of summer (July and August) (a) accumulated precipitation (units: mm) and (b) mean surface air temperature (SAT; unit: °C). The time series of (c) the standard IMP summer precipitation index (PI) and (d) the standard SAT averaged by 45 meteorological stations. The blue curve indicates the result of the nine-point sliding average. (e),(f) Curves showing the decadal shift in IMP summer precipitation according to the methods of the running t test and Lepage test, respectively. The dashed line in (e) indicates the critical value at the 0.01 significance level. The two dashed lines in (f) represent the critical values at the 0.05 and 0.01 significance levels.

  • View in gallery

    The regression patterns of summer 500-hPa wave activity fluxes (vectors; m2 s−2) and geopotential heights (shading; units: gpm) anomalies against the PI during the period of (a) 1979–99 and (b) 1999–2016. The linear trends in the PI and atmospheric variables were removed before regression analysis. The black dots indicate that the regression is significant at the 90% confidence level with Student’s t test.

  • View in gallery

    The regression patterns of (a),(b) summer 850-hPa wind fields (vectors; units: m s−1) and (c),(d) vertically integrated water vapor flux (wvf; vectors; units: kg m−1 s−1) from the surface to 200 hPa against the PI during the periods (a),(c) 1979–1999 and (b),(d) 1999–2016. The linear trends in the PI and atmospheric variables were removed before regression analysis. The green shading indicates that the regression is significant at the 90% confidence level with Student’s t test.

  • View in gallery

    The regression maps of summer vertical wind anomalies (shading; units: m s−1) and meridional circulation (vector; units: m s−1) averaged over the IMP (100°–126°E) against the PI during the periods of (a) 1979–99 and (b) 1999–2016. The linear trends in the PI and atmospheric variables were removed before regression analysis. The black vectors indicate that the wind is significant at the 90% confidence level with Student’s t test.

  • View in gallery

    The time series of the standard (a) Pacific decadal oscillation (PDO) index and (b) Atlantic multidecadal oscillation (AMO) index during the period 1976–2016. The purple curves indicate the 11-yr running average. (c) The time series of the three standard indexes (PDO, AMO, and PI).

  • View in gallery

    The regression maps of summer (a) 850-hPa wind anomalies (vector; units: m s−1) and (b) 500-hPa wave activity flux (vectors; m2 s−2) and geopotential height (shading; units: gpm) anomalies against the summer AMO index during the period 1979–2016. The linear trends in the PI and atmospheric variables were removed before regression analysis. The green shading in (a) and black dots in (b) indicate that the regression is significant at the 90% confidence level with Student’s t test.

  • View in gallery

    The composite maps of summer (a) 850-hPa wind anomalies (vector; units: m s−1), (b) vertically integrated water vapor flux (vectors; units: kg m−1 s−1) from the surface to 200-hPa, and (c) vertical wind anomalies (shading; units: m s−1) and meridional circulation (vector; units: m s−1) averaged over the IMP between 1999 and 2016 and between 1979 and 1998. The green shading in (a),(b) and black vectors in (c) indicate that the regression is significant at the 90% confidence level with Student’s t test.

  • View in gallery

    Correlation maps between the sea ice concentration (SIC) in (a),(d) April–June (AMJ), (b),(e) May–July (MJJ), and (c),(f) June–August (JJA) and the PI index during the periods 1976–98 and 1999–2016. The linear trends in the PI and SIC index were removed before correlation analysis. The black dots indicate that the regression is significant at the 90% confidence level with Student’s t test.

  • View in gallery

    The time series of the standard Arctic (83°–87°N, 20°–80°E) SIC index in (a) AMJ, (b) MJJ, and (c) JJA during the period 1976 to 2016.

  • View in gallery

    The regression maps of (a),(b) sea surface temperature (SST) anomalies (units: °C) and (c),(d) 500-hPa divergent wind (vectors; units: m s−1), velocity potential (contour; m2 s−1), and Rossby wave source (shading; units: s−2) anomalies against the Arctic SIC (multiplied by −1) in (a),(c) AMJ and (b),(d) summer during the period 1999–2016. The linear trends in the Arctic SIC and atmospheric variables were removed before regression analysis. The black dots indicate that the regression is significant at the 90% confidence level with Student’s t test.

  • View in gallery

    The regression maps of summer (a) 850-hPa wind anomalies (vector; units: m s−1), (b) vertically integrated water vapor flux (vectors; units: kg m−1 s−1) from the surface to 200 hPa, and (c) 500-hPa wave activity flux (vectors; m2 s−2) and geopotential height (shading; units: gpm) anomalies against the Arctic SIC index (multiplied by −1) during the period 1999–2016. The linear trends in the Arctic SIC index and atmospheric variables were removed before regression analysis. The green shading in (a),(b) and black dots in (c) indicate that the regression is significant at the 90% confidence level with Student’s t test.

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Changes in Lake Area in the Inner Mongolian Plateau under Climate Change: The Role of the Atlantic Multidecadal Oscillation and Arctic Sea Ice

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  • 1 Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • | 2 Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University for Information Science and Technology, Nanjing, and Joint Laboratory of Climate and Environment Change, Chengdu University of Information Technology, Chengdu, China
  • | 3 CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China
  • | 4 Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University for Information Science and Technology, Nanjing, China
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Abstract

The lake area in the Inner Mongolian Plateau (IMP) has experienced a rapid reduction in recent decades. Previous studies have highlighted the important role of intensive human activities in IMP lake shrinkage. However, this study found that climate change–induced summer precipitation variations can exert great influences on the IMP lake area variations. The results suggest that the decadal shift in the IMP summer precipitation may be the predominant contributor to lake shrinkage. Further analysis reveals that the Atlantic multidecadal oscillation (AMO) and Arctic sea ice concentration (SIC) play important roles in the IMP summer precipitation variations. The AMO seems to provide beneficial large-scale circulation fields for the decadal variations in the IMP summer precipitation, and the Arctic SIC decline is favorable for weakening the IMP summer precipitation intensity after the late 1990s. Evidence indicates that the vorticity advection related to the Arctic SIC decline can result in the generation of Rossby wave resources in the midlatitudes. Then, the strengthened wave resources become favorable for enhancing the stationary wave propagation across Eurasia and inducing cyclonic circulation over the Mongolia–Baikal regions, which might bring more rainfall northward and weaken the IMP summer precipitation intensity. Consequently, due to the decreased rainfall and gradual warming after the late 1990s, the lake area in the IMP has experienced a downward trend in recent years.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chen Huopo, chenhuopo@mail.iap.ac.cn

Abstract

The lake area in the Inner Mongolian Plateau (IMP) has experienced a rapid reduction in recent decades. Previous studies have highlighted the important role of intensive human activities in IMP lake shrinkage. However, this study found that climate change–induced summer precipitation variations can exert great influences on the IMP lake area variations. The results suggest that the decadal shift in the IMP summer precipitation may be the predominant contributor to lake shrinkage. Further analysis reveals that the Atlantic multidecadal oscillation (AMO) and Arctic sea ice concentration (SIC) play important roles in the IMP summer precipitation variations. The AMO seems to provide beneficial large-scale circulation fields for the decadal variations in the IMP summer precipitation, and the Arctic SIC decline is favorable for weakening the IMP summer precipitation intensity after the late 1990s. Evidence indicates that the vorticity advection related to the Arctic SIC decline can result in the generation of Rossby wave resources in the midlatitudes. Then, the strengthened wave resources become favorable for enhancing the stationary wave propagation across Eurasia and inducing cyclonic circulation over the Mongolia–Baikal regions, which might bring more rainfall northward and weaken the IMP summer precipitation intensity. Consequently, due to the decreased rainfall and gradual warming after the late 1990s, the lake area in the IMP has experienced a downward trend in recent years.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chen Huopo, chenhuopo@mail.iap.ac.cn

1. Introduction

Mounting evidence has suggested that the Mongolian Plateau (MP) has experienced significant lake shrinkage and grassland degradation in recent decades (e.g., Huang et al. 2016; Tao et al. 2015; Yang and Lu 2014; Zhang et al. 2019; Zhang et al. 2017a). The abrupt shrinkage of lakes in the MP has aggravated the deterioration of the regional ecological environment and posed a great threat to the habitat of migratory waterfowl and the livelihood of local residents (e.g., Tao et al. 2015). Hence, an increasing number of studies have been devoted to investigating the phenomena and causes of rapid shrinkage of lake area in this region (Tao et al. 2015; Yang and Lu 2014; Zhang et al. 2019). Numerous studies have indicated that intensive human activities and climate change may be the most predominant contributor to the change in lake area in the MP (Tao et al. 2015; Yang and Lu 2014). For example, the rapid development of animal husbandry and overgrazing greatly impact the land runoff and lake water storage, and the vigorous exploitation of minerals, such as coal mining, can dramatically reduce the lake water volume by blocking rivers and destroying the underground water supply (Takehiro et al. 2008; Zhang et al. 2013). In addition, climate change also exerts great influences on the lake area variations and lake ice phenology (Liu et al. 2018; Liu et al. 2019a; Liu et al. 2020; Salerno et al. 2014; Zhao et al. 2017). Several studies have revealed that variations in precipitation have played a dominant role in the change in lake area over the Tibetan Plateau (TP) and Inner Mongolian Plateau (IMP) in recent decades (Liu et al. 2019b; Qiao et al. 2019; Zhang et al. 2017a; Zhang et al. 2017b). For example, Zhang et al. (2017a) found that the increased net precipitation can account for approximately 74% of the lake volume in the TP from the perspective of the water mass budget. However, it remains unclear how the lake area variations respond to climate change from the perspective of climate dynamic mechanisms.

It is evident that a better understanding of lake area variations in the IMP is favorable for lake management and protection. Notably, as one of the typical wetland ecosystems on Earth, lakes play an important role in regulating and storing floodwater, conserving water sources, supporting aquaculture, and keeping the regional ecological balance (Dudgeon et al. 2006). Hence, the monitoring of lake area changes and associated climate mechanism studies are of particular importance. Numerous studies have suggested that the atmospheric processes controlling regional precipitation and overland runoff are dominant in regulating the lake volume in terminal lakes (Song et al. 2014; Zhao et al. 2017). Therefore, the changes in regional precipitation may be seen as the direct proxy of lake area variations in the IMP.

Previous studies have documented that the IMP has experienced an interdecadal decrease in summer precipitation, with more precipitation occurring from 1980 to the mid-1990s followed by severe drought in the late 1990s (Iwao and Takahashi 2006, 2008). Simultaneously, some studies have indicated that the continuous severe droughts during the 2000s in the IMP mainly resulted from the joint effects of increasing temperature and decreasing precipitation (Huang et al. 2015; Liu et al. 2016; Wang et al. 2019). Furthermore, mounting evidence has also suggested that anomalous regional precipitation variations are mainly caused by changes in large-scale atmospheric circulation. Some studies have pointed out that the midlatitude westerly disturbance and the monsoon circulation could exert great influences on the summer precipitation in this region (Li et al. 2016; Zhang and Yang 1996). For example, the summer droughts in the IMP are usually related to the persistent maintenance of blocking highs over the Baikal region and northeast Asia (Zhang and Yang 1996), and the warming climate has also played an important role in this serious aridification over this region in recent decades (Liu et al. 2016). Evidently, the hot summer drought and reduced precipitation result in the lake water storage reduction, which is consistent with the lake area variations (e.g., Yang and Lu 2014; Zhao et al. 2017). However, it is still unclear why precipitation presents a significant decrease after the late 1990s, which substantially reduced the lake water supply.

Recent studies have indicated that Arctic sea ice loss plays an important role in the variation in summer precipitation in China (He et al. 2018; Li et al. 2018; Shen et al. 2019). The reduction in June sea ice over the Barents Sea can trigger a meridional overturning wave-like pattern extending toward the midlatitudes and then resulting in a tripole precipitation pattern over East Asia (He et al. 2018). Li et al. (2018) also suggested that the Barents Sea ice decline in spring, along with the subsequently reduced snow cover across the Eurasian continent, can enhance the hot summer drought by triggering the polar–Eurasian teleconnection pattern. Evidently, large-scale teleconnection patterns greatly impact summer precipitation in China (e.g., Zhang et al. 2019b). Xu et al. (2015) found that the summer precipitation over China was characterized by different decadal variations from north to south after the 1990s, and the large heterogeneity in the decadal shift of precipitation may be attributed to the tropical Atlantic sea surface temperature (SST) changes and the Pacific decadal oscillation (PDO). Zhang et al. (2018) also highlighted the important role of the joint influences of the PDO, the Atlantic multidecadal oscillation (AMO), and the Indian Ocean basin mode (IOBM) on the interdecadal variability in East Asian summer monsoon precipitation. In addition, some studies have suggested that the interannual variability in summer precipitation in northeast Asia is contrary to the precipitation mode in Siberia and associated with stationary wave propagation along the Asian jet and northern Eurasia in the upper troposphere (Iwao and Takahashi 2006).

Despite the substantial efforts devoted to investigating summer drought in the IMP (e.g., Huang et al. 2015; Wang et al. 2019), few studies have explored the physical mechanisms associated with precipitation variations in the IMP at the interannual and interdecadal time scales. Furthermore, few studies have been conducted to clarify the impact of interannual and interdecadal variations in precipitation given long-term changes in lake areas. This study aims to investigate the variations in the IMP lake area and clarify the impact of climate change on lake shrinkage. This paper is arranged as follows. Section 2 presents a brief description of the data and methods. In section 3, the main results are provided, including the spatial–temporal variations in the lake area and the associated impact from climate change. Finally, a discussion and conclusions are presented in section 4.

2. Data and methods

a. Dataset

The lake area datasets used here are primarily derived from 1240 Landsat multispectral scanner, thematic mapper (TM), and enhanced thematic mapper (ETM+) images between the 1970s and 2010 (Tao et al. 2015). Additionally, 41 Landsat Operational Land Imager (OLI) images were used to map lakes in 2013 (Zhang et al. 2017b). A total of 26 lakes (>10 km2) in the IMP, along with 8 lakes (>10 km2) near the IMP, are included in this study (Fig. 1). This results in the lake datasets spanning from 1976 to 2013 with a 3-yr interval.

Fig. 1.
Fig. 1.

(a) The spatial distribution of 45 meteorological stations in the Inner Mongolian Plateau (IMP) and 35 lakes with an area of more than 10 km2 around the MP and (b) the linear trend (red circles indicate lake expansion, and blue circles show lake shrinkage) of the lake area obtained from the spline interpolation during the period 1976–2013. The diagram in the upper-left corner of (b) shows the temporal variation in the total lake area averaged over 10 contiguous retrieved lakes. The shading (units: m) indicates the altitude in the IMP.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

In addition, the other datasets employed in this study are as follows: 1) the monthly SST and monthly Arctic sea ice concentration (SIC) dataset are derived from the Hadley Centre Sea Ice and Sea Surface Temperature dataset version 1 (HadISST1) on a 1° × 1° grid from 1871 (Rayner et al. 2003); 2) the monthly atmospheric circulation data, with a horizontal resolution of 0.75° × 0.75°, are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) dataset (Dee et al. 2011); 3) the AMO and PDO indexes are provided by the National Oceanic and Atmospheric Administration (https://www.esrl.noaa.gov/psd/data/climateindices/list/); and 4) the station-observed daily precipitation and temperature datasets from 1976 to 2016 over the IMP used in this study were obtained from the National Climate Center, China Meteorological Administration.

b. Methods

Considering the available satellite data and satellite data processing capabilities, the retrieved lake area datasets in the IMP span from 1976 to 2013 with a 3-yr interval. Hence, in order to investigate the temporal–spatial trend of lake area variation in the IMP, we used spline interpolation to reconstruct continuous lake area datasets, which are used to match with climatic datasets and analyze the long-term trends of lake area in the IMP. Furthermore, we employed the moving t test and Lepage test technique (Lepage 1971) to detect the interdecadal characteristics of the IMP climate. For the general analysis of climate mechanisms, this study mainly adopted composite and regression methods to investigate the impact of atmospheric circulation anomalies on precipitation variations over the IMP. Statistical significance was assessed using Student’s t test. To emphasize interannual variability, the linear trends of both the climate indexes and atmospheric circulation variables were removed before regression analysis. Additionally, we calculated the three-dimensional stationary wave activity flux to investigate the remote impact of atmospheric fluctuations on the precipitation variations over the IMP (Takaya and Nakamura 2001):
W=P2|U|{u¯(ψx2ψψxx)+υ¯(ψxψyψψxy)u¯(ψxψyψψxy)+υ¯(ψy2ψψyy)f2N2[u¯(ψxψzψψxz)+υ¯(ψyψzψψyz)]}.
Here, ψ denotes the streamfunction, f is the Coriolis parameter, U is the horizontal wind velocity, and N2 = (RPκ/H)(∂θ/∂z) is the buoyancy frequency squared, where R represents the gas constant, θ denotes the potential temperature, P equals pressure divided by 1000 hPa, and κ is defined as R normalized by the specific heat of air for constant pressure.

3. Results

a. The changing characteristics of lake area over the IMP

Previous studies have pointed out that a number of lakes over the IMP have been drying up in recent decades due to worsening droughts and intensive human activities (e.g., Huang et al. 2016; Liu et al. 2016; Tao et al. 2015; Zhang et al. 2017b). This is consistent with our reexamination based on the extended datasets. Figure 1 shows the spatial distribution of the linear trend in the lake area and the variation averaged over this region. It is clear that most of the lakes present a consistently shrinking trend in the past decades, although a few lakes show an opposite characteristic. In addition, the averaged lake area shows an interdecadal changing characteristic with an increasing trend before the late 1990s, which shifts toward a decreasing trend.

Some evidence has suggested that decreased net precipitation plays a dominant role in regulating lake area shrinkage and volume decrease, especially for inland lakes (Zhang et al. 2017a; Zhao et al. 2017). Hence, to investigate the impact of regional precipitation on the lake area variations, we further analyzed the long-term changing trends of IMP precipitation. Figure 2 shows the climatology and changing trends of monthly accumulated precipitation in the IMP for the period 1976–2016. The statistical results indicate that the precipitation in the IMP mainly occurs from June to September, which accounts for 78% of the total annual precipitation. In terms of monthly accumulated precipitation, the results show that the precipitation in July and August contributes the most to the annual accumulated precipitation, with contribution ratios of 28% and 22%, respectively. Simultaneously, it is clear that the precipitation reduction is also most evident in summer (July and August), which is significant at the 99% confidence level. Therefore, the decrease in IMP summer precipitation may be the main cause of lake shrinkage in recent decades.

Fig. 2.
Fig. 2.

(a) The diagram of the proportion of the monthly accumulated precipitation climatology (units: mm) to the annual precipitation and (b) the linear trend in the monthly accumulated precipitation (units: mm) during the period 1976–2016. The five-point star indicates that the linear trend is significant at the 99% confidence level.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

To quantify the relationship between the lake area and summer precipitation, there is a need to investigate the spatial–temporal distribution of meteorological elements in the IMP. Figure 3 shows the long-term trends of summer precipitation and surface air temperature (SAT) for the period 1976–2016. It is clear that the changes in precipitation and SAT are spatially homogeneous with the general decreasing trends in precipitation and increasing trends in SAT. The regional mean SAT presents a gradually increasing trend, which can result in lake water loss through accelerating evaporation (e.g., Zhu et al. 2010). However, the regional mean precipitation presents significant interdecadal changing characteristics, with more precipitation from the mid-1970s to the late 1990s and less precipitation after the late 1990s. Simultaneously, we also employed two methods to detect abrupt climate change, including the moving t test and Lepage test, to determine the jumping point of summer precipitation in the IMP. The results of both methods indicate that 1999 is the transition year of summer precipitation, and both results pass the significance tests at the 99% confidence level. The consistency with the decadal transition of the IMP lake area indicates that the shrinking of the lakes mainly results from the reduction in summer precipitation over this region in the past decade.

Fig. 3.
Fig. 3.

The spatial distribution of summer (July and August) (a) accumulated precipitation (units: mm) and (b) mean surface air temperature (SAT; unit: °C). The time series of (c) the standard IMP summer precipitation index (PI) and (d) the standard SAT averaged by 45 meteorological stations. The blue curve indicates the result of the nine-point sliding average. (e),(f) Curves showing the decadal shift in IMP summer precipitation according to the methods of the running t test and Lepage test, respectively. The dashed line in (e) indicates the critical value at the 0.01 significance level. The two dashed lines in (f) represent the critical values at the 0.05 and 0.01 significance levels.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

Evidently, the regional summer precipitation anomalies have exerted great influences on the decadal variability in the lake area in the IMP, mainly because net precipitation can control the lake water balance and then modulate the lake area (e.g., Zhao et al. 2017). Although numerous studies have explored the main causes for decadal change in summer precipitation around northern China (including the region of IMP; e.g., Qian et al. 2014), the reason for the abrupt change in summer precipitation in the IMP alone remains unclear. Hence, we investigated the atmospheric circulation anomalies associated with the IMP summer precipitation during the two periods (P1: 1979–98 and P2: 1999–2016) and explored the possible mechanism for the interdecadal changes in summer precipitation in the IMP.

b. The associated atmospheric circulation anomalies

Figure 4 shows the regression patterns of the geopotential height anomalies and the Rossby wave activity fluxes at 500 hPa with respect to the standardized IMP summer precipitation index (PI) during P1 and P2. Obviously, the significant Eurasian wave-like pattern is dominant in the atmospheric circulation field over the midtroposphere. Early studies have indicated that wave trains along the Asian jet and northern Eurasian would result in precipitation anomalies over northeast Asia and Siberia through triggering anomalous vertical flows (Iwao and Takahashi 2006; Zhang et al. 2019a). It is clear that there is a significant difference in the stationary wave propagation during P1 and P2. The wave propagation path in P1 prefers to move toward high latitudes, and the route is characterized by an arch-like shape, which means that the meridional component of the wave propagation is stronger. Moreover, due to the enhanced wave propagation in high latitudes, the Ural high becomes stronger and extends eastward to the north of Lake Baikal, which can weaken the westerlies over the Baikal region and benefit the accumulation of water vapor in the IMP. However, during P2, the stationary wave route along Eurasia features horizontal propagation. The structure of this wave propagation is favorable to disperse the energy from the upstream waves to the IMP and results in anomalous negative geopotential heights over the midtroposphere, which is beneficial to the moisture transfer northward and results in more precipitation over the Baikal and less precipitation over the IMP.

Fig. 4.
Fig. 4.

The regression patterns of summer 500-hPa wave activity fluxes (vectors; m2 s−2) and geopotential heights (shading; units: gpm) anomalies against the PI during the period of (a) 1979–99 and (b) 1999–2016. The linear trends in the PI and atmospheric variables were removed before regression analysis. The black dots indicate that the regression is significant at the 90% confidence level with Student’s t test.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

Figure 5 shows the regression patterns of vertically integrated water vapor flux and the low-troposphere circulation with regard to the PI during P1 and P2. Clearly, during P1, the anomalous easterlies hinder the northward water vapor transport and are favorable to the accumulation of water vapor over the IMP, which is in accordance with the aforementioned analysis. Furthermore, it is clear that the enhanced vertical circulation occurs around the IMP (Fig. 6), which is beneficial for the precipitation increase in the IMP. However, during P2, an intensive cyclonic circulation anomaly dominated over the MP–Baikal regions would strengthen the transportation of water vapor to the north. Along with the enhanced vertical circulation moving northward, the rainband would move northward, which means more rainfall over the Baikal regions and the reduction in precipitation intensity in the IMP. The remarkable differences in atmospheric circulation during P1 and P2 could explain the IMP precipitation change between these two periods and provide vigorous evidence of why the lakes shrank after the late 1990s. However, what factors are involved in this interdecadal transition of summer precipitation in the IMP? To address this issue, we will investigate the possible mechanism for the interdecadal change in IMP summer precipitation in the late 1990s in the following.

Fig. 5.
Fig. 5.

The regression patterns of (a),(b) summer 850-hPa wind fields (vectors; units: m s−1) and (c),(d) vertically integrated water vapor flux (wvf; vectors; units: kg m−1 s−1) from the surface to 200 hPa against the PI during the periods (a),(c) 1979–1999 and (b),(d) 1999–2016. The linear trends in the PI and atmospheric variables were removed before regression analysis. The green shading indicates that the regression is significant at the 90% confidence level with Student’s t test.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

Fig. 6.
Fig. 6.

The regression maps of summer vertical wind anomalies (shading; units: m s−1) and meridional circulation (vector; units: m s−1) averaged over the IMP (100°–126°E) against the PI during the periods of (a) 1979–99 and (b) 1999–2016. The linear trends in the PI and atmospheric variables were removed before regression analysis. The black vectors indicate that the wind is significant at the 90% confidence level with Student’s t test.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

c. Mechanisms for the interdecadal variations in IMP summer precipitation

1) Impact of the AMO

Previous studies have revealed that the AMO exerts great influences on summer drought in China (Qian et al. 2014; Zhang et al. 2018). For example, Qian et al. (2014) suggested that the AMO can induce a dipolar drought pattern in China by stimulating a Eurasian wave train. To address whether these major teleconnection patterns exert impacts on the IMP summer precipitation changes, a relationship analysis between the summer precipitation index and the AMO, as well as the PDO, is first conducted (Fig. 7). The results show that both the AMO and PDO experience a decadal shift around the late 1990s. Further correlation analysis suggests that there is an intimate correlation between the AMO and summer precipitation in the IMP at the 0.05 significance level, while the PDO has not exerted significant impact. Therefore, the AMO variation may exert substantial impacts on the decadal variation in IMP summer precipitation.

Fig. 7.
Fig. 7.

The time series of the standard (a) Pacific decadal oscillation (PDO) index and (b) Atlantic multidecadal oscillation (AMO) index during the period 1976–2016. The purple curves indicate the 11-yr running average. (c) The time series of the three standard indexes (PDO, AMO, and PI).

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

Figure 8 shows the regression patterns of low-troposphere and midtroposphere atmospheric circulation anomalies with regard to the AMO index for the period 1979–2016. There is a significant zonal stationary wave propagation emanating from the North Atlantic Ocean to the Eurasian continent, with positive geopotential height anomalies over the North Atlantic and Ural regions and negative geopotential height anomalies over western Europe. The low-troposphere circulation field remarkably resembles the wave-like pattern in the midtroposphere level, which implies an equivalent barotropic structure of this pattern. This wave-like pattern related to the AMO indicates that the stationary wave route prefers zonal propagation. To investigate the regional atmospheric circulation anomalies associated with the IMP summer precipitation under the different AMO phases, we also conducted composite analyses on the low-troposphere circulation and water vapor conditions (Fig. 9). Clearly, the wind anomalies in the low troposphere exhibit significant anticyclonic circulation over the Ural regions, which is in accordance with the analysis in Fig. 8. Furthermore, an anomalous anticyclonic is dominant over the MP, which might be related to the strengthened Ural high eastward. Therefore, the enhanced northeasterly anomalies over the IMP would hinder water vapor transport because of monsoon circulation. Moreover, the anomalous downdraft over the MP along with decreased water vapor would result in less precipitation over the IMP. The decadal variations in atmospheric circulation associated with the AMO could thus explain the decadal decrease in the IMP summer precipitation and then result in the reduction in the lake area in the IMP after the late 1990s.

Fig. 8.
Fig. 8.

The regression maps of summer (a) 850-hPa wind anomalies (vector; units: m s−1) and (b) 500-hPa wave activity flux (vectors; m2 s−2) and geopotential height (shading; units: gpm) anomalies against the summer AMO index during the period 1979–2016. The linear trends in the PI and atmospheric variables were removed before regression analysis. The green shading in (a) and black dots in (b) indicate that the regression is significant at the 90% confidence level with Student’s t test.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

Fig. 9.
Fig. 9.

The composite maps of summer (a) 850-hPa wind anomalies (vector; units: m s−1), (b) vertically integrated water vapor flux (vectors; units: kg m−1 s−1) from the surface to 200-hPa, and (c) vertical wind anomalies (shading; units: m s−1) and meridional circulation (vector; units: m s−1) averaged over the IMP between 1999 and 2016 and between 1979 and 1998. The green shading in (a),(b) and black vectors in (c) indicate that the regression is significant at the 90% confidence level with Student’s t test.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

2) Impact of Arctic sea ice loss

Recently, numerous studies have indicated that the rapid reduction in Arctic sea ice exerts great influences on the midlatitude atmospheric circulation and weather patterns by changing the heat exchange between the ocean and atmosphere (He et al. 2018; Li et al. 2015; Li et al. 2018; Lin and Li 2018; Liu et al. 2019a; Vihma 2014; Wang et al. 2015; Wang and Liu 2016). The anomalous precipitation and aggravated hot summer droughts in China could also be partly attributed to the rapid decline in Arctic sea ice cover (He et al. 2018; Li et al. 2018; Shen et al. 2019). Although there is a debate on whether Arctic sea ice has a significant impact on the midlatitude climates (Barnes and Screen 2015), increasing research is devoted to exploring the mechanisms for climate variations due to the Arctic sea ice decline (Kug et al. 2015; Liu et al. 2012; Mori et al. 2014). On the basis of the documented studies, we also investigated the impact of Arctic SIC variations on the IMP summer precipitation after the late 1990s and further propose a possible physical mechanism to explain their strong relationship.

Figure 10 shows the regression patterns of Arctic SIC with regard to the PI index during P1 and P2. It is clear that there is an intimate correlation between the Arctic SIC (83°–87°N, 20°–80°E) in late spring and early summer (April, May, and June) and the IMP summer precipitation during P2, and this significant relationship can last until the summer. However, there is no apparent correlation in the Arctic region during P1. In the following, we further analyze the temporal evolution of average Arctic SIC for the period 1976–2016 and conduct correlation analysis between these two indexes. The results indicate that Arctic SIC is characterized by strong interannual variability, which is different from the decadal feature of IMP summer precipitation. Furthermore, the relationship between Arctic SIC and IMP summer precipitation became much stronger after the late 1990s, especially in AMJ and MJJ (May–July), in which the correlation coefficient was up to −0.78 and −0.61 at the 0.01 significance level, respectively (Fig. 11). The results suggest that the Arctic sea ice change may play an important role in the interannual variation in IMP summer precipitation after the late 1990s.

Fig. 10.
Fig. 10.

Correlation maps between the sea ice concentration (SIC) in (a),(d) April–June (AMJ), (b),(e) May–July (MJJ), and (c),(f) June–August (JJA) and the PI index during the periods 1976–98 and 1999–2016. The linear trends in the PI and SIC index were removed before correlation analysis. The black dots indicate that the regression is significant at the 90% confidence level with Student’s t test.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

Fig. 11.
Fig. 11.

The time series of the standard Arctic (83°–87°N, 20°–80°E) SIC index in (a) AMJ, (b) MJJ, and (c) JJA during the period 1976 to 2016.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

Previous studies have revealed that Arctic sea ice decline can trigger a wave-like pattern by inducing upper-level vorticity advection and stimulating positive Rossby wave source anomalies (Shen et al. 2019). It is evident that the upper-stream wave propagation exerts great influences on the downstream climate (Takaya and Nakamura 2001; Tao et al. 2010). Hence, to investigate the possible impact of Arctic SIC anomalies on the midlatitude wave-like pattern, this study also analyzes the Rossby wave source [−∇ ⋅ Vχ(f + ζ)] defined by Sardeshmukh and Hoskins (1988). Figure 12 shows the regression maps of SST and divergent wind and the wave source with regard to the SIC index (multiplied by −1) in AMJ. It is clear that there are obvious positive SST anomalies when Arctic sea ice decreases and the warming trend extends southward to the low-latitude regions of the Arctic Ocean. Simultaneously, the divergent wind anomalies dominate over the Arctic, accompanied by negative velocity potential anomalies associated with Arctic SIC decline. Theoretically, divergent wind anomalies can stimulate rotational wind through vorticity stretching. The rotational wind further disperses the wave energy from the polar region southward to the midlatitudes through the divergent wind and thus results in the generation of vorticity in the midlatitudes. Accordingly, in the following summer, there are significant convergent wind anomalies and positive velocity potential over western Europe and west Siberia accompanied by positive Rossby wave source anomalies. It is evident that the strengthened wave sources can enhance the stationary Rossby wave propagation, which can be demonstrated in Fig. 13c.

Fig. 12.
Fig. 12.

The regression maps of (a),(b) sea surface temperature (SST) anomalies (units: °C) and (c),(d) 500-hPa divergent wind (vectors; units: m s−1), velocity potential (contour; m2 s−1), and Rossby wave source (shading; units: s−2) anomalies against the Arctic SIC (multiplied by −1) in (a),(c) AMJ and (b),(d) summer during the period 1999–2016. The linear trends in the Arctic SIC and atmospheric variables were removed before regression analysis. The black dots indicate that the regression is significant at the 90% confidence level with Student’s t test.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

Fig. 13.
Fig. 13.

The regression maps of summer (a) 850-hPa wind anomalies (vector; units: m s−1), (b) vertically integrated water vapor flux (vectors; units: kg m−1 s−1) from the surface to 200 hPa, and (c) 500-hPa wave activity flux (vectors; m2 s−2) and geopotential height (shading; units: gpm) anomalies against the Arctic SIC index (multiplied by −1) during the period 1999–2016. The linear trends in the Arctic SIC index and atmospheric variables were removed before regression analysis. The green shading in (a),(b) and black dots in (c) indicate that the regression is significant at the 90% confidence level with Student’s t test.

Citation: Journal of Climate 33, 4; 10.1175/JCLI-D-19-0388.1

The above analysis provides a detailed explanation of how Arctic SIC decline impacts upper-level atmospheric dynamic anomalies. In the following, we will investigate the atmospheric circulation anomalies associated with Arctic SIC (multiplied by −1) and clarify the related atmospheric dynamic process (Fig. 13). It is obvious that there is significant stationary Rossby wave propagation emanating from the North Atlantic to Eurasia, with positive geopotential height anomalies over western Europe and west Siberia and negative geopotential height anomalies over the North Atlantic, eastern Europe, and the Mongolia–Baikal region. In addition, it is clear that there is an enhanced wave activity propagation emanating from western Siberia to the Mongolia–Baikal region, which is in accordance with the positive Rossby wave source anomalies in Fig. 12d. The results indicate that Arctic SIC decline may enhance wave propagation along Eurasia by stimulating Rossby wave sources in the midlatitudes. Consequently, the enhanced cyclonic circulation over Mongolia can strengthen the water vapor transport northward and may bring more rainfall over the Mongolia–Baikal region. The anomalous low troposphere circulation and water vapor conditions remarkably resemble the atmospheric circulation anomalies in Figs. 5b and 5d. Hence, the atmospheric circulation anomalies associated with Arctic SIC may contribute to the weak IMP summer precipitation intensity and result in IMP lake shrinkage in recent years.

4. Conclusions and discussion

Mounting evidence has suggested that lakes play an important role in water storage capacity, ecosystem, biodiversity, and regional climate (e.g., Dudgeon et al. 2006). Some studies have also revealed that lakes are sensitive to climate change, and in turn, variations in the lakes can exert great influences on regional climate by altering the thermal contrast between the lakes and the atmosphere (Latifovic and Pouliot 2007). Lake variations can thus serve as an important indicator of climate change (Ma et al. 2010; Zhang et al. 2019). Hence, the abrupt reduction in lake area in the IMP has attracted a large amount of research due to the great scientific value and profound social influence (Tao et al. 2015; Yang and Lu 2014; Zhang et al. 2017b). Although some studies have highlighted the impact of increased human activities on lake shrinkage in the MP (Tao et al. 2015; Yang and Lu 2014), few studies have investigated the impact of climate change on the IMP lake area variations.

In this study, we first investigate the spatial–temporal evaluation of lake area in the IMP and analyze the impacts of precipitation variations on the decadal variations in the IMP lake area and the associated possible climate dynamic mechanisms. Statistical results suggest that the IMP lakes have experienced a shrinking trend, accompanied by a significant decadal change. The aggregated lake area presents an increasing trend from the 1970s to the late 1990s and then shows a downward trend after the late 1990s. Correspondingly, the summer precipitation in the IMP also exhibits significant interdecadal variations around 1999. Further analyses indicate that the AMO and the Arctic SIC decline exert great influences on the interdecadal variations in IMP summer precipitation. First, the anomalous anticyclonic circulation is dominant in the MP during the positive phase of the AMO, accompanied by the enhanced downward draft and less water vapor supply, which finally results in less precipitation in the IMP. Moreover, it is found that the wave-like pattern across Eurasia is characterized by zonal propagation when the AMO strengthens, and this wave route is favorable for providing large-scale circulation background fields for the occurrence of midlatitudes circulation anomalies during P2. Further analysis reveals that the relationship between the Arctic SIC and the IMP summer precipitation became much stronger after the late 1990s. The Arctic SIC decline generally accompanies anomalous positive SST, divergent wind and negative velocity potential anomalies over the Arctic region. The divergence over the polar region can excite the rotational wind through vorticity stretching, and the rotational wind disperses the energy southward toward midlatitudes, which can result in the generation of vorticity. Consequently, the anomalous converging wind and positive velocity potential anomalies are dominant in western Europe and west Siberia in summer and thus strengthen the Rossby wave sources over these regions. Then, the enhanced wave sources further strengthen the stationary wave propagation over Eurasia and induce significant cyclonic circulation anomalies over the Mongolia–Baikal region. The anomalous low-troposphere circulation and water vapor conditions are favorable for bringing more rainfall over the MP–Baikal regions and weakening the IMP summer precipitation intensity during P2.

The consistent variations in the IMP lake area with summer precipitation indicate the important role of climate change in lake variations over the IMP. This study provides a new perspective for understanding IMP lake shrinkage in recent decades and demonstrates that the IMP lakes are vulnerable to climate change–induced precipitation variations. However, the limited datasets have hindered further studies of how lakes respond to climate change at the interannual scale. Moreover, it is still unclear whether other teleconnection patterns impact the IMP precipitation variability at the seasonal time scale. Hence, more research should be devoted to investigating the impacts of climate change on lake variations and quantifying the water mass balance of lakes. In addition, mounting evidence has indicated that the TP lake area has experienced an increasing trend due to the warmer and wetter climate (Qiao et al. 2019; Zhang et al. 2017b), which presents a reverse change compared with the lake area in the IMP. These opposing changes in lake areas between the two large East Asian plateaus encourage more comparison studies in the future to better understand the lake change in response to climate change.

Acknowledgments

This research was jointly supported by the National Key Research and Development Program of China (Grant 2016YFA0600701), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA19070201), the National Natural Science Foundation of China (Grant 41421004), and the open program of Joint Laboratory of Climate and Environment Change, Chengdu University of Information Technology (Grant JLCEC201801).

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