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Observed Regional Climate Change in Tibet over the Last Decadeslocked

  • Kun YangKun YangInstitute of Tibetan Plateau Research, Chinese Academy of Sciences


The Tibetan Plateau (TP) is subjected to strong interactions among the atmosphere, hydrosphere, cryosphere, and biosphere. The Plateau exerts huge thermal forcing on the mid-troposphere over the mid-latitude of the Northern Hemisphere during spring and summer. This region also contains the headwaters of major rivers in Asia and provides a large portion of the water resources used for economic activities in adjacent regions. Since the beginning of the 1980s, the TP has undergone evident climate changes, with overall surface air warming and moistening, solar dimming, and decrease in wind speed. Surface warming, which depends on elevation and its horizontal pattern (warming in most of the TP but cooling in the westernmost TP), was consistent with glacial changes. Accompanying the warming was air moistening, with a sudden increase in precipitable water in 1998. Both triggered more deep clouds, which resulted in solar dimming. Surface wind speed declined from the 1970s and started to recover in 2002, as a result of atmospheric circulation adjustment caused by the differential surface warming between Asian high latitudes and low latitudes.

The climate changes over the TP have changed energy and water cycles and has thus reshaped the local environment. Thermal forcing over the TP has weakened. The warming and decrease in wind speed lowered the Bowen ratio and has led to less surface sensible heating. Atmospheric radiative cooling has been enhanced, mainly through outgoing longwave emission from the warming planetary system and slightly enhanced solar radiation reflection. The trend in both energy terms has contributed to the weakening of thermal forcing over the Plateau. The water cycle has been significantly altered by the climate changes. The monsoon-impacted region (i.e., the southern and eastern regions of the TP) has received less precipitation, more evaporation, less soil moisture and less runoff, which has resulted in the general shrinkage of lakes and pools in this region, although glacier melt has increased. The region dominated by westerlies (i.e., central, northern and western regions of the TP) received more precipitation, more evaporation, more soil moisture and more runoff, which together with more glacier melt resulted in the general expansion of lakes in this region. The overall wetting in the TP is due to both the warmer and moister conditions at the surface, which increased convective available potential energy and may eventually depend on decadal variability of atmospheric circulations such as Atlantic Multi-decadal Oscillation and an intensified Siberian High. The drying process in the southern region is perhaps related to the expansion of Hadley circulation. All these processes have not been well understood.


The Tibetan Plateau (TP), a so-called “Third Pole,” is an elevated region in the central Asia that stretches approximately 1000 km along latitude and 2500 km along longitude. It is the highest plateau in the world, with an average elevation exceeding 4000 m ASL (above sea level) and an area of approximately 2.5×106 km2. The climate and environment in the TP are influenced by both westerlies and the Asian monsoon. In winter, westerlies prevail over the whole region, with two branches flowing around the southern and northern edges of the TP and one branch crossing the Plateau. In summer, the westerlies shift poleward, and the climate in southern and eastern regions of the Plateau is dominated by the Asian monsoon (Figure 1a).

Figure 1 (a) The terrain of the Tibetan Plateau and its adjacent regions. Major atmospheric circulation systems are given, with yellow arrows for summer and blue arrows for winter. (b) Major basins on the Tibetan Plateau.

The westerlies and Asian summer monsoon bring plentiful precipitation, which results in 49,900 km2 glacier area (Yao et al., 2007) and 47,400 km2 lake area (Chen et al., 2015) in the elevated region. The TP is also the headwater area of large Asian rivers (e.g., Yangtze River, Yellow River, Brahmaputra River, Ganges River, Indus River, etc.) and provides a large portion of water resources to maintain the oasis economic activities in central Asia (Yao et al., 2007) (Figure 1b).

In turn, the Plateau modifies the climate of adjacent regions and Northern Hemisphere through strong land–atmosphere interactions and topographic effects (Figure 2).

Figure 2 The impacts of land-ocean-atmosphere interactions on regional climate around the TP, i.e., enhancing Asia Summer Monsoon (ASM), intensifying rainstorm over the downstream, and accelerating the exchange of water vapor and aerosols between upper troposphere and lower stratosphere

(Courtesy of Dr. Toshio Koike, The University of Tokyo).

Because of its low airmass, aerosol and water vapor content, the TP surface receives strong solar radiation. The radiation is partitioned into sensible heat flux and latent heat flux. The land–atmosphere interactions affect the regional climate in three ways. First, the Plateau exerts huge thermal forcing on the mid-troposphere over the middle-latitude of the Northern Hemisphere during spring and summer (Flohn, 1957; Ye & Gao, 1979; Zhou et al., 2009). The sensible heating in the pre-monsoon season and latent heating due to water vapor condensation in the monsoon season over the TP plays a crucial role in the onset and maintenance of the Asian monsoon and modulates its variability (Yanai et al., 1992; Wu et al., 2012). Second, the eastward movement of vortices and mesoscale convective system over the TP intensifies the rainstorm activity downstream (Xu et al., 2002). Because of surface heating and wind shear, vortices and mesoscale convective systems frequently form and develop over the Plateau in summer (Yasunari & Miwa, 2006). In appropriate circumstances, they move out of the Plateau and further develop and even superimpose on the Meiyu system, causing substantial floods in the middle and lower reaches of Yangtze River (Xu et al., 2002). Third, the surface heating over the high elevations also triggers vigorous deep convections over the TP (Yang et al., 2004) that greatly enhance the troposphere–stratosphere exchanges of water vapor and air pollutants (Fu et al., 2006).

Over the past three decades, the Plateau has undergone evident climate changes that have changed atmospheric and hydrological cycles and thus reshaped the local environment (Kang et al., 2010; Chen et al., 2015). For instance, river discharge and lake levels have responded to climate changes (Cao et al., 2006; Ye et al., 2007). On the central Plateau, lakes have expanded rapidly since the middle of the 1990s, flooding surrounding grasslands and threatening the local economy and living conditions. Glacier retreat due to warming had been suggested to be the major cause of the expansion of glacier-fed lakes (Zhu et al., 2010), but non–glacier-fed lakes expanded, too (Lei et al., 2014). The warming shortened the surface soil frozen period by approximately half a month per decade during 1988–2007 (Li et al., 2012) and also caused permafrost degradation (Cheng & Wu, 2007; Ran et al., 2016). Along with climate changes, surface pressure over the Plateau increased significantly (Moore, 2012), and surface heating and atmospheric heating weakened (Zhu et al., 2007; Duan & Wu, 2008; Yang et al., 2011a). This warming and thermal weakening in spring and summer may have affected summer precipitation downstream (Wang et al., 2008; Ding et al., 2009; Liu et al., 2012; Duan et al., 2013). In addition, Immerzeel et al. (2010) projected that the warming may lead to fewer water resources for the downstream regions of Brahmaputra and Indus basins in the future. Because of the water and energy-cycle changes, the TP climate changes have become a major concern among local people and scientific communities, and the “Third Pole Environment” program was initiated to pool international efforts to understand climate and environment changes on the Plateau (Yao et al., 2012a).

To understand the processes of the regional water and energy cycles, several field campaigns have been conducted in the TP through Global Energy and Water Cycle Experiment (GEWEX) in 1998 (Koike et al., 1999) and has expanded in recent years (Ma et al., 2008; Xu et al., 2008; Su et al., 2011; Yang et al., 2013). These field activities have advanced our understanding of land–atmosphere interactions (e.g., Yamada & Uyeda, 2006; Ma et al., 2009; Zhang et al., 2012; Ueno et al., 2012) and have supported the development of land surface models (e.g., Yang et al., 2009a; van den Velde et al., 2009; Chen et al., 2010; Gerken et al., 2012) and satellite remote sensing (e.g., Ma et al., 2011; Chen et al., 2013). These model improvements are crucial steps to understanding the response of water and energy budgets to the climate changes.

In this article, first, recent findings in climate changes over the TP are reviewed. Then, the response of thermal forcing and the response of water cycle to the climate changes, with a focus on the change in lake water balance, are analyzed. Third, the response mechanism of surface energy and water budget to climate changes is presented. The possible connections between the climate changes and large-scale atmospheric circulations are discussed, and a conceptual model is proposed to synthesize these climate changes and their impacts in terms of the relationship between local warming and regional warming and in terms of the water and energy exchanges. Finally, relevant urgent issues to be clarified are recommended.

Observed Climatic Changes

The China Meteorological Administration (CMA) has provided long-term station data for climate change studies in the TP region. These stations are sparsely distributed on this region, and their operations started in different years; some stations were not regularly operated for some early years. In this review, the climate changes during 1984–2006 are mainly addressed because this period has not only accumulated more surface data for climate studies but has also undergone outstanding climate changes in the TP. In addition, radiation budget data from satellites, as a complement to station data have become available during this period. The decadal changes in wind speed and solar radiation are complex, so their investigations were extended to the 1960s and the results have been compared with the case in Mainland China.

An overview of the climate changes over the TP is shown in Figure 3, including the linear trends in annual mean air temperature, air specific humidity, wind speed, and solar radiation observed at the individual Plateau stations during 1984–2006. The air temperature and specific humidity increased significantly, with more warming over the northern TP and more moistening over the southern TP (Figures 3a and 3b).

Figure 3 Trends in air temperature, specific humidity, wind speed and solar radiation during 1984–2006 at the individual CMA stations on the TP. The solid triangle symbol indicates a trend passes the t-test (p<0.05) and its size indicates the magnitude of the trend.

The greater warming over the northern TP was attributed by Shen et al. (2015) to the modulation of less evapotranspiration. The overall rapid warming since the 1980s has been reported in many previous studies (e.g., Liu & Chen, 2000; Guo & Wang, 2012) and has also been qualitatively produced with satellite data (Salama et al., 2012). The warming over the TP has continued over the past two decades, regardless of a global warming hiatus since 1998 (Gao et al., 2015; Duan & Xiao, 2015). The wind speed significantly declined at almost all stations over this period (Figure 3c), which is consistent with the prevailing decrease in wind speed occurring over Mainland China since the beginning of the 1970s (Xu et al., 2006; Jiang et al., 2010). Meanwhile, solar radiation decreased at most of the stations (Figure 3d), as reported by Tang et al. (2011). Other observed climate changes over the TP include pan evaporation decreases (Zhang et al., 2007; Zhang et al., 2009) except in the northeastern TP, total cloud cover decreases but deep cloud cover (or low cloud cover) increases (Yang et al., 2012), and downward longwave radiation increases (Shi & Liang, 2013).

Surface Temperature Change

The environmental changes over the Plateau are mostly associated with the rapid surface warming. Understanding elevation dependence is crucial for the assessment of glacier/snow dynamics. Using station data, Liu and Chen (2000) showed that the warming increased with respect to elevation. Actually, the elevation-dependent warming was widespread (Mountain Research Initiative EDW Working Group, 2015). However, little was known about the warming status for regions above 5000 m ASL, where most glaciers and snow surfaces are located but no CMA station is available. Also, the vast western Plateau has very few stations. Therefore, Qin et al. (2009) utilized satellite data to investigate recent temperature changes over the Plateau.

Qin et al. (2009) evaluated several Moderate Resolution Imaging Spectroradiometer (MODIS) monthly products of nighttime land surface temperature (LST). They found that the version 4 of MODIS LST data was able to reproduce the station-observed trend. The observed warming at the individual CMA stations is not always comparable to the MODIS-based estimate at the collocated pixel, but the latter is close to the former if averaged over multiple stations (i.e., ~10 stations). This finding indicates that their differences in spatial representativeness may be effectively eliminated through the spatial average. Thus, the MODIS LST product is used to quantify the elevation-dependent warming over the Plateau.

The warming rate averaged over all MODIS pixels within each 200 m-elevation interval is shown in Figure 4a.

Figure 4 Nighttime surface temperature change during 2000–2006 on the Tibetan Plateau. (a) Elevation-dependence of temperature change estimated from MODIS LST (version 4). The number of pixels within each 200 m elevation interval is given above each bar (Reprinted from Qin et al., 2009); (b) The spatial variation of MODIS LST change at 0.5° grids (Reprinted from Yang et al., 2014a).

The warming rate is clearly elevation dependent; it increased from 3000 m to 4800 m ASL then became relatively stable between 5000 m and 6200 m ASL and finally decreased near the highest elevations. Simultaneously, Liu et al. (2009b) projected the warming over the Tibetan Plateau with a general circulation model and found a very similar elevation-dependent pattern. The increasing pattern below 4800 m has been reported according to station data (Liu & Chen, 2000). The decreasing trend above 6200 m ASL is a new finding, but it is understandable. The land surfaces at this elevation range protrude into the middle–high troposphere and occupy only a small fraction of the space (as illustrated by the pixel number in Figure 4a); thus, the surface warming over the very high elevations actually denotes the warming of the mid-troposphere, which decreases with the increase of elevation. The relative stable warming rate between 5000 m and 6200 m ASL may be related to the presence of snow/glacier that is typical above 5000 m ASL, which alleviates further warming through both reflecting solar radiation by the surface and exhausting energy by ice melting. In addition, this elevation range is a transitional zone between the increasing warming trend in the lower elevation and the decreasing warming trend in the higher elevation; thus, its warming rate becomes relatively stable.

The horizontal distribution of surface temperature change derived from the MODIS LST data is shown in Figure 4b. Clearly, warming is a dominant phenomenon on the Plateau. The areas of greatest warming are the southeastern TP and eastern Himalayas, a major glaciated area. Surprisingly, west of 80°E, another major glaciated area, exhibited strong cooling. Early studies found that the glaciers in the southeastern TP and eastern Himalayas retreat most rapidly (Yao et al., 2012b), whereas the glaciers in Karakorum and western Kunlun Mountains were fairly stable and were even advancing (Scherler et al., 2011; Yao et al., 2012b). Yao et al. (2012b) attributed this spatial pattern of glacier change to precipitation variability. Nevertheless, the glacier change pattern is also consistent with the warming pattern, implying that the latter also contributed to the spatial variability of the glaciers.

Water Vapor Change

The station data revealed the moistening trend in central and eastern TP, but little is known in the western region of the TP, where few stations are available to provide data. To understand the spatiotemporal pattern of water vapor over the TP, Lu et al. (2015) applied Bayesian inference theory to construct long-term and spatially continuous monthly precipitable water (PW) data for the TP based on the station observations and a MODIS PW product. The reconstruction method was developed by Qin et al. (2013) to maximize values of station data and satellite data. The prior information on the monthly mean PW from MODIS and 63 stations over the TP for 2000–2006 was used to establish the posterior probability knowledge for building a Bayesian estimation model. This model was then operated to estimate continuous monthly mean PW for 1970–2011, and its performance was validated using the monthly MODIS PW anomalies (2007–2011) and annual GPS PW anomalies (1995–2011), with root mean square errors below 0.65 mm. This demonstrates that the Bayesian estimation can reproduce the PW variability over the TP in both space and time.

The long-term trend in PW was derived from the Bayesian estimate and was compared with the trend in several reanalysis data sets. Figure 5a shows the comparison between the trend derived from the Bayesian estimate and the trend from ERA-Interim reanalysis for 1979–2011.

Figure 5 The precipitable water for the Tibetan Plateau for 1979–2011 with (a) annual anomaly series (thin dashed line) averaged over the whole TP, plotted together with a 15-year Gaussian low-pass filter (thick line), and the linear trends derived from (b) ERA-interim and (c) Bayesian estimation. The series are expressed as anomalies relative to 1979–2011. In panels (b−c), stippling indicates where the linear trends are insignificant at the 95% confidence level over the entire period (Reprinted from Lu et al., 2015).

Both indicate a significant increasing trend of PW for this period, but the Bayesian estimate produced a greater trend (0.21 mm decade−1) than the ERA-Interim (0.12 mm decade−1), as PW anomalies of ERA-Interim were above the Bayesian estimate before 1998 and below the Bayesian estimate afterward. Figures 5b and 5c show the linear trends derived from ERA-Interim and from the Bayesian estimate, respectively. They exhibited similar spatial variability, with increasing trends in most of the TP region, but the PW increasing trends were not significant in the westernmost TP. This pattern is consistent with the scant warming and even cooling trend in the westernmost TP (Figure 4b).

Wind Speed Change

Since the beginning of the 1970s, wind speed over China has decreased (e.g., Xu et al., 2006; Jiang et al., 2010), and has decreased even more over the TP (Yang et al., 2011c). Several hypotheses have been proposed to explain the decrease in wind speed over China, such as effects of urbanization and air pollution. However, these factors cannot play an important role in weakening wind speed over the Plateau, due to negligible urbanization and low-level aerosol load over there. Vautard et al. (2010) found that the decrease in wind speed between 1979 and 2008 was a prevailing phenomenon over the mid-latitudes of the Northern Hemisphere, and they concluded that the decrease in wind speed can be partly attributed to the increase in surface roughness. This may explain the contrasting trends between surface wind speed and upper-air wind speed occurring over Europe and North America, but it is not applicable over the Plateau, where bare soils and short grasses are the dominant land types. Furthermore, the major part of the Plateau is vegetation-free in wintertime, when wind speed decelerates. Thus, it is unlikely that the TP decrease in wind speed was caused by an increase in the surface roughness.

Instead, Zhang et al. (2009) showed that in NCEP reanalysis, the summertime pressure gradient force over the Plateau declined over the period that wind speed decreased, and they attributed the decrease in wind speed to the adjustment of atmospheric circulation. Duan and Wu (2009), based on numerical modeling, further suggested that more warming across the high latitudes of Asia may be responsible for this weakening. To further investigate the characteristics of the wind speed change over this region, Lin et al. (2013) analyzed the longer-term variations of wind speed using both CMA data and IGRA (Integrated Global Radiosonde Archive; Durre et al., 2006) data, in which they not only provided direct evidence to support the finding by Duan et al. (2009) but also revealed some new features in decadal variability and abrupt changes.

Since 1960, wind speed over China proceeded through three stages: a sudden increase in wind speed around 1970, followed by a long-term decline until 2002, and a stable and even recovery trend afterward (Lin et al., 2013). Figure 6a shows the case for the TP. The sudden increase in wind speed around 1970 is so outstanding that it appears spurious.

Figure 6 (a) Variations of annual mean surface wind speed (U) and ground-air temperature difference (TgTa) during 1961–2006 on the Plateau; (b) variations of 500-hPa geopotential height pressure difference (“HGT”) derived from NCEP reanalysis data and surface temperature difference (“TEMP”) derived from Climatic Research Unit (New et al., 2002) data between a low latitude belt (20°N to 25°N) and a high latitude belt (45°N to 50°N) during 1961–2006 in China (Reprinted from Lin et al., 2013).

Lin et al. (2013) presented a cross-check to explain the sudden change. Wind speed and ground-air temperature gradient (TgTa) together determine the sensible heat flux. If sensible heat flux dominates the surface energy budget, a high wind speed usually results in a low value of TgTa, and vice versa. This negative correlation between wind speed and TgTa may be used to judge the reasonability of the sudden increase in wind speed. As in Figure 6a, during the spring (March–April–May), a season with strong sensible heat flux, there is indeed a strong negative correlation between the wind speed and TgTa. Around 1970, the sudden increase in wind speed corresponds to a significant decrease in TgTa, after which the steady decrease in wind speed contrasts with the increase in TgTa. Because wind speed, air temperature, and ground temperature were measured independently, the opposite changes prove that the sudden increase in wind speed around 1970 and the following decrease in wind speed are very likely.

The sudden increase and following decline exhibit neither a diurnal cycle nor a seasonal cycle (not shown), indicating that the wind speed change was controlled by processes that have a scale beyond the atmospheric boundary layer. This can be further substantiated by the coherent changes between surface wind speed and upper-air wind speed (see details in Lin et al., 2013). In addition, another important finding is the elevation dependence of the wind speed variability; that is, the decadal variability of the surface wind speed over the Plateau is larger than the one over the rest of China. This finding implies that the signal of wind decline is transferred from the upper air into the boundary layer. The wind speed over highlands may respond earlier to atmospheric adjustment than wind speed over lowlands. Therefore, the variability of wind speed over the TP might be a precursor of the overall change in atmospheric circulation over East Asia.

Lin et al. (2013) presented observed evidence that the wind speed change over China (including the Plateau) is due to the latitudinal gradient of surface warming over Central and East Asia. As shown in Figure 6b, the difference in 500-hPa geopotential height between a low-latitude belt (20°N to 25°N) and a high-latitude belt (45°N to 50°N) in this region (70°E to 140°E) is highly correlated with the difference in surface temperature between the two belts (R=0.86). As the anomaly of the temperature difference reflects the differential warming between the two latitude belts, the high correlation in Figure 6b indicates that the latitudinal gradient of surface warming is an essential factor. It first changed the upper-air pressure gradient force by lifting the geopotential height, then changed the upper-air wind through geostrophic adjustment, and finally changed the surface wind speed through downward momentum transport into the boundary layer. This finding substantiates the modeling results in Duan and Wu (2009). Specifically, during the period that wind speed decreased, the Siberian region underwent more warming than South China. During the recent period of wind speed recovery, the Siberian region received more snowfall (Ghatak et al., 2012) that may have suppressed its rapid warming and made the wind speed recovering.

Solar Radiation Change

Solar radiation is an important indicator of global climatic changes. A number of studies (e.g., Stanhill & Cohen, 2001; Liepert, 2002; Wild et al., 2005) have suggested a transition from global dimming to brightening around the end of 1980s or beginning of the 1990s. Several studies also stated that China underwent a similar radiation transition (e.g., Che et al., 2005; Liang & Xia, 2005). However, trends in solar radiation are very small, and their estimation is sensitive to measurement errors, so quality control of station data is a prerequisite for the trend analysis (Shi et al., 2008). Tang et al. (2010) presented a method to control data quality of radiation measurement in China. After excluding spurious data and inaccurate measurements, only ten radiation stations with long-term reliable records remained for the years 1979–2006. Therefore, the high-quality radiation data over China are very limited.

Alternatively, Tang et al. (2011) revisited this issue based on a reconstructed long-term solar radiation dataset at all CMA routine stations. The reconstructed data were based on a widely validated physical model and sunshine duration records from CMA stations. The trend derived from the data was validated against the observation data from the ten quality-verified stations and against the trends simulated by an artificial neural network-based model at all CMA radiation stations. These reconstructed data sets provided the basis of the radiation trend analysis for Mainland China.

The changes in annual mean solar radiation averaged over all CMA stations in both TP and the Mainland China are shown in Figure 7.

Figure 7 Variations of estimated annual mean solar radiation averaged over China and over the Plateau (Reprinted from Tang et al., 2011). The star symbol (*) denotes a trend that passes the significance test (p<0.05). The unit of the trend is W m2 a1.

The solar radiation averaged over all CMA stations shows a decreasing trend before the 1990s, but it does not show an overall recovery trend since 1990. This result is different from previous studies stating that solar radiation in China has been recovering since 1990. In addition, the solar dimming rate before 1990 is approximately 2.5 W m−2 decade−1, which is approximately half of the values (4–5 W m−2 decade−1) reported in previous studies. On the Plateau, the averaged solar radiation showed an increasing trend until the end of the 1970s and decreased thereafter, indicating a transition from brightening to dimming around the end of the 1970s. Therefore, the solar radiation changes over the last 50 years are quite different between the TP and the Mainland China. Figure 3d shows the solar radiation changes at the individual Plateau stations for 1984–2006. It is evident that the dimming occurred at most of the stations over this period. Further analysis shows that the dimming mainly occurred in summer.

Conventionally, a solar radiation trend is explained by aerosol loads and/or cloud cover changes. The Plateau region is one of the cleanest regions in the world, and considerable aerosol loading is not expected. Meanwhile, the total cloud cover decreased over the region, which does not explain the solar dimming. To understand the solar dimming, Yang et al. (2012) analyzed the effects of aerosols, water vapor, and cloud cover. First, the inverse of visibility, a proxy of aerosol optical depth (Wang et al., 2009), observed at CMA stations has shown a downward trend since 1980. The major aerosol over the TP is dust (Xia et al., 2011), whose amount and frequency should have decreased along with the decrease in wind speed; thus, aerosol optical depth decreased, too. This hypothesis is consistent with the observed trend in Figure 8a.

Figure 8 Interannual variations of (a) the proxy of aerosol optical depth (i.e., inverse of visibility) and wind speed, (b) solar radiation and specific humidity, and (c) solar radiation and deep cloud cover (DCC) during 1984–2006. All the values are obtained by averaging over all CMA stations on the Plateau. Panel (b–c): modified from Figures 34 in Yang et al., 2012).

Therefore, it is unlikely that aerosol loading can explain solar dimming. Second, the increase in water vapor amount is highly correlated with the solar dimming (Figure 8b), but the estimated direct absorption of solar radiation by the vapor (~1 W m−2) is much lower than the observed dimming (~6 W m−2). Third, the amount of deep cloud cover is highly correlated with the solar radiation values at both annual and decadal scales. Deep cloud cover information can be approximately represented by the station-observed low cloud cover, and Figure 8c shows that the low cloud cover has an increasing trend, opposite to the decreasing change in solar radiation. Such an opposite correlation was also seen in satellite observations given by the International Satellite Cloud Climatology Project (ISCCP). The increase in deep cloud cover was caused by the increase in convective available potential energy, which is in turn due to the rapid warming and moistening over the TP. In summary, it is important for the radiation trend analysis to discriminate cloud types, in addition to the often-mentioned cloud coverage and aerosol changes.

Response of Thermal Forcing to Climate Change

Because of the importance of the Plateau thermal forcing in the context of the Asia monsoon, a number of studies since the end of 1970s have quantified the heat source over the TP and its effect on various weather and climate events. The total heat source comprises three components: surface sensible heat flux, latent heat release due to water vapor condensation, and radiative convergence. Their estimation was first presented in Ye and Gao (1979) and has been followed by many studies (e.g., Chen et al., 1985; Zhao & Chen, 2000).

The surface sensible heat flux is usually calculated according to the bulk heat transfer equation, which needs input of observed wind speed and ground-air temperature difference (TgTa) as well as the parameterized heat transfer coefficient CH. Conventionally, CH is either given a constant value (Chen et al., 1985) or is simply correlated with meteorological variables (Chen & Wong, 1984). These parameterizations are widely applied in the Plateau heating studies. However, near-surface atmospheric stability depends on wind speed and TgTa. With the decrease in wind speed and increase in TgTa, the atmosphere became more unstable; thus, CH should have increased. Therefore, a CH scheme that does not properly account for the stability at diurnal, seasonal, and decadal scales would cause vital errors in estimating the trend in the surface sensible heat flux.

Accordingly, Yang et al. (2009b) developed a new scheme to estimate the heat flux from CMA station data. The new scheme disaggregates CMA station data to hourly data to capture the diurnal variations of atmospheric stability, then uses the Monin-Obukhov similarity theory to account for the stability effect. Yang et al. (2011a) applied this parameterization to the Plateau, in comparison with a conventional scheme given by Chen and Wong (1984) and another produced by Chen et al. (1985). The estimated trend during 1984−2006 is sensitive to the selected scheme. The Chen and Wong scheme assumes a linear relation between CH and the inverse of wind speed; thus, CH increases too quickly with the decrease in wind speed. As a result, this scheme yields a positive trend in the sensible heat flux (0.3 W m−2 decade−1). By contrast, the Chen scheme assumes a constant value of CH and, thus, ignores the effects of the atmospheric stability trend on CH; therefore, it produces a strong negative trend (−2.5 W m−2 decade−1). The newer scheme designed by Yang et al. (2009b) considered both diurnal variations of heat transfer and the atmospheric stability, and it yielded a moderate decadal decreasing trend (−1.2 W m−2 decade−1) (Yang et al., 2011a). Guo et al. (2011) used 6-hourly observation data and showed a trend similar to that of Yang et al. (2011a) after considering the diurnal variations of the land−atmosphere coupling intensity.

The trend in the sensible heat flux has also been investigated with land-surface modeling driven by CMA data (Yang et al., 2011b). Again, the simulated sensible heat flux (SH) showed a negative trend, and the trend slope was comparable to the parameterized result produced by Yang et al. (2011a). Zhu et al. (2012) compared several data sets of sensible heat flux, including reanalysis products and land-surface model (LSM) simulations; they confirmed that the weakening trend in TP sensible heat flux occurred in most of the datasets. Shi and Liang (2014) developed a data set of surface sensible heat fluxes at 0.5° over the TP for 1984−2007 by synthesizing multiple data sources including ground measurements, reanalysis products, and remote-sensing products. The reconstructed dataset also produced a negative trend in sensible heat flux for 1984−2007 and the trend slope is comparable to the one supported by ground measurements (Figure 9).

Figure 9 Variations of anomaly in annual mean sensible heat flux averaged over the TP from multiple data sets (CFSR, MERRA, ERA-Interim, JRA-25, Zhang10, GLEAM-E), fused data (Fusion), and ground measurement-based estimate (Yang11) (unit: Wm−2) for 1984–2007 (Reprinted from Shi & Liang, 2014). The CFSR is the NCEP global reanalysis product (Saha et al., 2010), MERRA is a recent reanalysis data set supported by NASA’s Global Modeling and Assimilation Office (Rienecker et al., 2011), ERA-Interim is the reanalysis product provided by the European Centre for Medium-Range Weather Forecasts (Dee et al., 2011), JRA-25 is the reanalysis product provided by Japan Meteorological Agency (Onogi et al., 2007), Zhang10 and GLEM-LE are two global evapotranspiration products developed by Zhang et al. (2010) and Miralles et al. (2011), respectively, and Yang11 is a ground measurement-based estimate by Yang et al. (2011b).

Therefore, a consensus was reached among all of these studies regarding the weakening trend in the TP sensible heat flux over the warming period.

Radiative convergence (RC) is the difference between the net radiation at the top of atmosphere and that at the surface. Usually, this term has negative values, which thus represent a cooling effect. Based on Tibetan experimental data, Yang et al. (2011b) evaluated the surface net radiation from the LSM simulation and two satellite products (the Global Energy and Water Cycle Experiment-Surface Radiation Budget and the ISCCP-Flux Data), and they found smaller errors in the simulation. Thus, the surface net radiation from the LSM simulation and the top-of-atmosphere net radiation averaged from the two satellite products were used to estimate RC. Figure 10a shows the trends in the estimated RC.

Figure 10 (a) Land surface modeling-based trend estimate (W m2 decade1) in seasonal means (MAM, JJA, SON, DJF) and annual means (ANN) of the heat source terms averaged over the Plateau for 1984–2006 (Reprinted from Yang et al., 2011a). (b) Satellite-based estimate of the linear trend (slope) in the annual mean heat source over the Yellow River and the Yangtze River upper basins in the eastern TP and the Qiangtang Plateau close basin in western TP (Reprinted from Yang et al., 2014b).

The trends are negative for both seasonal mean and annual mean, indicating enhanced cooling. The trend slope is approximately 6~8 W m−2 decade−1 and much less than that presented by Duan and Wu (2008), who calculated this term according to ISCCP-Flux Data only. A further investigation shows that the cooling trend was mainly caused by the increase in outgoing longwave radiation (by 5−6 W m−2 decade−1), and the weakening of the air-absorbed solar radiation played a secondary role (by 0−2 W m−2 decade−1). The increase in outgoing longwave radiation is due to both the warming and the decrease in total cloud cover (particularly high cloud cover). The latent heat release due to water vapor condensation (LH) is proportional to surface precipitation and has large interannual variability. No significant temporal trend in the latent heat release was detected.

Alternatively, a satellite data–based method was developed by Yang et al. (2014b) to quantify the total heat source over land. The required inputs are net radiation at the top of atmosphere, terrestrial water storage change, river runoff, and ground heat flux. The former two can be directly observed by satellites; the runoff is measured for major rivers in the world, and the ground heat flux is a small term that can be estimated by satellite remote sensing or LSM. Thus, this method relies on direct observations and does not involve the complex procedures used in previous studies. It is simple and straightforward and has small uncertainties. Figure 10b shows the results when applying this method to the upper basins of Yellow River and Yangtze River in the eastern TP and Qiangtang Plateau close basin in the western TP. The basins both in the eastern and western TP have experienced a heat source weakening, and the averaged trend slope is consistent with the findings of Yang et al. (2011b).

In a word, the trend in LH is not significant, SH declined slightly, and RC declined much more. As a result, the total heat source shows a significant negative trend (−7 W m−2 decade−1) and is dominated by the RC trend. The slope of the trend is comparable to that derived using NCEP/NCAR reanalysis data by Ding et al. (2009).

Response of Water Cycle to Climate Change

Spatial Pattern of Water Cycle Change

This section introduces how climate changes influenced the surface hydrological cycle on the TP. because there are limited data of river discharge and evaporation, the surface water budget was investigated using land-surface hydrological modeling. Yang et al. (2011c) validated the capability of a LSM adopted to the Plateau environment by simulating the discharge trend for four large rivers sourced from the central and eastern Plateau. Zhou and Huang (2012) also showed that this model could reproduce the observed anomaly of the surface water budget in the upper basin of the Yellow River. The simulated surface water budget presented in Yang et al. (2011c) is introduced below.

The simulation results indicate that the surface water balance changed over the past several decades (Figure 11).

Figure 11 Trend slope in annual mean values of observed precipitation, simulated evaporation, runoff, and soil moisture at the Plateau CMA stations for 1984–2006 (modified from Figure 4 in Yang et al., 2011c). The solid triangle symbol indicates a trend passing the t-test (p<0.05) and its size indicates the magnitude of the trend.

In general, the trend in evaporation has been increasing overall, which is consistent with a recent independent modeling study (Yin et al., 2013). Other water budget components exhibit a clear spatial pattern, as indicated by two curved regions. On the central Plateau, increasing trends in both precipitation and evaporation have been observed. The trade-off of the two trends leads to an increasing trend in runoff, as observed by Liu et al. (2009a). The southern and eastern regions of the Plateau, however, we can see decreasing trends in precipitation and increasing trends in evaporation; both contribute to lowering the runoff. These findings imply a reduction of water resources from the Plateau if the enhancement of the glacier melts are excluded from the total discharge.

In the broad Qiangtang Plateau in the northwestern TP, there are few long-term weather stations. Li et al. (2014) reconstructed evapotranspiration data for several basins over 1984–2006 by combining land modeling results and gravity data derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. As shown in Figure 12, the evapotranspiration in four large basins, including the Qiangtang Plateau, shows increasing trends.

Figure 12 Temporal variations of annual mean evapotranspiration (ET) derived by the water balance method as a residual from precipitation, terrestrial water storage change, and discharge for four basins (upper basin of the Yellow River, upper basin of the Yangtze River, the Qiangtang Plateau close basin, and the Qaidam close basin). Trend slope is given for annual mean values of ET for 1984–2006 (Data are derived from Li et al., 2014).

Because the Qiangtang Plateau is located in an arid and semiarid region, its evaporation is mainly controlled by precipitation. The increasing trend in evapotranspiration indicates that precipitation increased and water cycle in this region accelerated during last 30 years.

For the terrain-complex Himalayan region, Salerno et al. (2015) investigated climate change for last two decades (1994−2013) using station data from the Koshi Basin, central Himalaya. As shown in Figure 13, the air temperature showed a general warming trend, and more important, there was a substantial liquid precipitation weakening (−9.3 mm a−1) during the monsoon season.

Figure 13 Spatial distribution of the Sen’s slopes in the Koshi Basin of central Himalaya for (a) air temperature and (b) annual precipitation during 1994–2013 (Reprinted from Salerno et al., 2015).

This loss cut nearly half of the annual precipitation amount in high elevations. In the north slope of central Himalaya, precipitation decreased, too (Lu et al., 2014).

Based on all the evidence above, it is concluded that the monsoon-impacted region (eastern and southern TP) became less wet, and the region dominated by westerlies (central, northern, and western TP) became less dry. This pattern is different from the projection of the global aridity pattern (i.e., dry land expansion would accelerate under global warming) (Huang et al., 2016), indicating that the TP climate change had some uniqueness.

Decadal Change of Lake and River Water Balance

The spatial pattern of surface water budget change has corresponded well to lake area changes on the TP since the middle of the 1990s. As shown in Figure 14, the lakes in the central, northern, and western regions of the Plateau have generally expanded, but lakes in the southern TP and pools in Himalaya have shrunk (Lei et al., 2014; Song et al., 2015; Salerno et al., 2016).

Figure 14 Changes in lake area on the interior TP and northern Himalayas, 1976–2010 (Reprinted from Lei et al., 2014). In general, lakes expanded in the interior TP while they shrank in the northern Himalayas.

This indicates that precipitation (or water cycle) change dominated the lake expansion and shrinkage in the TP, which was consistent with the observed increase of gravity (Zhang et al., 2013; Wang et al., 2016). For instance, Lei et al. (2013) found that the increasing runoff in Figure 11c could account for the major part of the lake expansion in the central Plateau, and Lei et al. (2014) found that the expansion and shrinkage of typical lakes well follow the cumulative anomaly of precipitation.

On the other hand, enhanced glacier melt could be another major contributor to the expansion of some lakes (Lei et al., 2014; Song et al., 2014). For instance, glacier melting acceleration may have contributed to 50% of the expansion of Lake Nam Co (Zhu et al., 2010). Lake Siling Co is one of lakes that most expanded, and approximately 20–25% of the water balance change was due to increased glacier melt (Zhou et al., 2015; Tong et al., 2016). In addition, Li et al. (2014) speculated the potentially important contribution of permafrost degradation to the lake expansion, although it is difficult to collect field data to confirm this.

Lake evaporation change is another factor that may affect the variability of lake water balance. Zhou et al. (2015) approximated evaporation from Lake Siling Co using the Penman-Monteith equation, and they concluded that lake evaporation decreased and thus advanced the expansion of Siling Co. Meanwhile, Lazhu et al. (2016) used a semiempirical model to simulate the actual evaporation from Lake Nam Co and found that the potential evaporation derived using the Penman-Monteith equation was higher than actual evaporation. They gave a detailed analysis to the lake evaporation response to climate change and concluded that the lake evaporation increased and thus suppressed the expansion of Lake Nam Co (Figure 15).

Figure 15 (a) Interannual variation of simulated annual evaporation of Lake Nam Co for 1980–2014 with a semi-empirical lake model, and (b) the cumulative anomaly of the simulated annual evaporation relative to the average for 1980–1997 (Reprinted from Lazhu et al., 2016).

To date, there are few studies on the role of lake evaporation on the TP. Thus, it is not yet clear whether the different conclusions between Lake Siling Co and Lake Nam Co are due to the different methods used in the two studies or to the different climate regimes of the two lakes. Further investigations on more lakes should be encouraged.

In addition to the change in precipitation amount, the change in precipitation spatial distribution may also significantly change the surface water budget, which have seldom been addressed. An example is the upper basin of Yellow River on the TP. This basin experienced wet conditions in the 1980s and dry conditions in the 1990s. In response, the river discharge showed similar changes during the two periods. Since 2002, the precipitation amount has increased drastically, but the discharge did not follow the increase immediately. This incoherent change is attributable to the change in the precipitation spatial distribution (Zhou & Huang, 2012). According to CMA observations, the increase in precipitation occurred in the arid area of this basin, whereas the humid area received less precipitation than normal. In the arid area where water availability controls evaporation, most of precipitation turned into evaporation and the runoff did not increase much when precipitation increased. In the humid area where energy availability determines the evaporation, evaporation significantly increased due to the warming; the overlap of the increase in evaporation and the decrease in precipitation led to significant reduction in runoff. The increase in basin-averaged evaporation almost cancelled out the increase in precipitation; thus, the discharge did not increase evidently. Therefore, the change in precipitation spatial distribution changed the relationship between precipitation, evaporation, and runoff.

Mechanism of Surface Energy and Water Budget Response

The cause of the decrease in the sensible heat flux and the increase in the land evaporation (i.e., lower Bowen ratio) under the warming environment relates to the mechanism of surface energy and water budget response. Yang et al. (2014a) presented a simple theory to explain the response of Bowen ratio to warming and decrease in wind speed. The Bowen ratio can be given as follows:

BSHlE=γα(θ)(TgTa)[es(Tg)rhes(Ta)] (1)

where SH is sensible heat flux, lE is latent heat flux, γ is the psychometric constant, Tg is the ground temperature, Ta is the air temperature, es is the vapor pressure at saturation, and rh is the air relative humidity. α(θ) is a function of soil moisture (θ) and it has a value of near zero for dry soils and approaches unity for wet soils.

As a first-order approximation, it is assumed that that α(θ) and rh are time invariant at a decadal scale. The relative change in Bowen ratio then depends on the change in the ground temperature and the air temperature, as follows:


where ΔTg is the increase in the ground temperature, ΔTa is the increase in the air temperature, and ΔB is the change of Bowen ratio after warming.

The climatology and trend of meteorological parameters over the Plateau are derived from CMA station data, which gives Tg=280.9 K, Ta=276.5 K, rh=56%, ΔTg=1.8°C, and ΔTa=1.4°C. Given these average conditions, it can be shown with Eq. (2) that the Bowen ratio decreases by 6% with respect to warming, which indicates that a warming can result in less sensible heat flux and more latent heat flux (or evaporation). This qualitatively explains the sensible heat flux weakening and evaporation enhancement in the TP.

The Bowen ratio response to the warming may be modulated by wind speed change. If wind speed slows down, the efficiency of the energy exchange decreases; accordingly, the ground becomes warmer (i.e., Tg increases), leading to a decline in the Bowen ratio. During 1970–2000, both the warming and decrease in wind speed weakened the Bowen ratio, leading to the decrease in sensible heat flux. Since 2000, however, the warming and wind speed increase have played contrast roles in determining the trend in the Bowen ratio, leading to a stable sensible heat flux, as shown in Shi and Liang (2014).

In addition, other changes on the TP surface may have contributed to the decadal change of the surface energy partition. The increase in vegetation density (Zhong et al., 2010) may have resulted in more evaporation and less sensible heat flux, thus lowering the Bowen ratio, as shown by Zuo et al. (2011). The increase in soil moisture in the Plateau excluding the monsoon-impacted southern and eastern regions of the TP (Figure 11d) could also have enhanced evaporation and weakened sensible heat flux (Zhang & Zuo, 2011).

Possible Connections with Atmospheric Circulation Variability

Many studies have investigated the climate change over the TP, and diverse causes for the climate change presented in recent studies were summarized by Kuang and Jiao (2016). Numerical simulations with doubled levels of carbon dioxide by Chen et al. (2003) suggested that the changes in cloud cover and snow albedo play important roles in determining the elevation dependency of surface warming. Duan et al. (2006) found that a greenhouse gas (GHG) effect in two general circulation models could cause a warming of 0.16 °C decade−1 and 0.12 °C decade−1, respectively, over the TP. This effect only accounts for approximately half of the warming over the TP (0.28 °C decade−1) during 1961–1999. The GHG effect cannot explain the spatial pattern of climate change. Therefore, the climate changes over the TP have been affected not only by the global warming that was driven by GHG but also by the variability of large-scale circulations.

To elucidate the overall wetting in the TP, Figure 16 shows the correlation of water vapor changes on the interior TP with large-scale atmospheric circulation indices, including Northern Hemisphere summer monsoon circulation (NHSM), hemispheric thermal contrast (HTC), Atlantic Multidecadal Oscillation (AMO), and mega-ENSO.

Figure 16 Comparison of water vapor change over the interior TP with large-scale atmospheric circulation indices, including NHSM, HTC, AMO, and mega-ENSO. NHSM circulation, HTC and Mega-ENSO are from Wang et al. (2013), and the AMO index was downloaded from the website (Reprinted from Lei et al., 2014).

Water vapor data for the summer (May–September) from meteorological stations were used here because water vapor has much higher values in summer than in winter and can represent larger regional signals than precipitation. Clearly, the NHSM, HTC, and AMO have significant correlations with water vapor, whereas mega-ENSO, defined in Wang et al. (2013), does not. In addition, the Siberian High demonstrates decadal oscillations that have increased since the mid-1980s (Ding et al., 2015), which might change water vapor convergence and affect the precipitation variability in the northern TP. So far, no detailed analysis has been produced regarding their possible linkage with water vapor and precipitation change over the TP, but the high degree of correlation shown in Figure 16 implies that the climate change over the TP may be modulated by the decadal variability of large-scale circulations. In the central and northern regions of the Plateau, the weakened westerlies brought less water vapor flux, but the surface warming and moistening caused more convective available potential energy; thus, convective precipitation may occur more frequently.

The decrease in precipitation in the southern TP might be connected with the impact of Hadley circulation. Hu and Fu (2007) identified poleward expansion of the Hadley circulation since 1979. The expansion has mainly been attributed to the global warming caused by a GHG effect (Hu et al., 2013). The expansion of the northern branch of Hadley circulation is particularly significant in summer and autumn (Figure 17).

Figure 17 Poleward shifts of the northern branch of Hadley circulation from three reanalyses, averaged from 400 to 600 hPa. Positive trends indicate poleward shifts. The ERA40 is for 1979–2002 while the NCEP/NCAR and NCEP/DOE are for 1979–2005. Statistical significance of the trends in the hemisphere summer and autumn is above the 95% confidence level (Reprinted from Hu & Fu, 2007).

Because the north edge of the Hadley circulation is close to the southern TP, its poleward expansion and/or intensification could result in an anomaly of subsidence flow, causing less precipitation in the southern TP.

A Conceptual Model for the Plateau Climatic Changes and Their Impacts

A number of studies on the climatic changes over the Plateau have been conducted, but a general and consistent framework has not been well established for the interpretation of all the changes in the Plateau climate system. Yang et al. (2014a) proposed a conceptual model to synthesize these changes and to link these changes with the global warming. Herein, the conceptual model is modified slightly to consider atmospheric circulation variability, as shown in Figure 18.

Figure 18 A conceptual model for interpreting climate change and associated water and energy cycle changes over the Tibetan Plateau.

Decrease in wind speed is a response of the regional circulation to the changes in latitudinal temperature gradient over the Central and East Asia. Under the background of global warming, more warming has been observed over the high-latitude belt than over the low-latitude belt during the past three decades. As a result of the adjustment of geopotential height to the change in thermal contrast, the mid-level pressure gradient force between the low latitudes and the high latitudes declined (Figure 6b). Therefore, the mid-troposphere wind speed weakened (Figure 3c). Due to less momentum transport from free atmosphere down to the boundary layer, the surface wind speed declined, too. This situation over the TP and East Asia is different from the case over Europe and North America, where the surface wind slowed down while upper-air wind speeded up and Vautard et al. (2010) attributed their contrast changes to the increase in surface roughness lengths.

The decrease in wind speed weakened the energy exchange between the Plateau and its surroundings. As the Plateau provides huge thermal forcing to the mid-troposphere during the summer half-year, a weaker energy exchange would leave more energy remaining to warm the local air over the Plateau. Indeed, a correlation coefficient of 0.6 between wind speed and air temperature during the summer has been found. Thus, the TP warming during the summer half-year is partially attributed to the decline in wind speed. Another warming cause was also proposed in the literature. Duan et al. (2006) suggested that a GHG effect contributed half of the TP warming. Rangwala et al. (2009) argued that the enrichment of surface water vapor has enhanced the downward longwave radiation and is thus responsible for the prominent winter warming trend over the TP. Meanwhile, Zhang and Zhou (2009) found that the total ozone amount over the TP declined more than that over other regions along the same latitudes and thus suggested that ozone depletion is a possible cause of the greater warming over the TP. Similarly, Guo and Wang (2012) suggested that the most significant warming, which has occurred over the northern TP, may be related to radiative and dynamical heating, which are results of pronounced stratospheric ozone depletion. Clearly, no consensus has been reached regarding the rapid warming of the TP and its seasonality; the energy budget responsible for a regional warming is small and is influenced by a lot of factors.

The warming and air moistening over the central Plateau increased the availability of convective potential energy, which is favorable to triggering stronger clouds, which in turn causes the solar dimming (Figures 7 and 8). Rapid planetary warming has led to enhanced radiative cooling through outgoing longwave emission. Meanwhile, the warming and decrease in wind speed have lowered the Bowen ratio, thus reducing surface sensible heat flux. Both processes have contributed to the weakening of thermal forcing over the Plateau, which in turn might have contributed to the weakening of the Asia monsoon.

Climate warming has enhanced land evaporation overall on the Plateau, but a consensus has not been reached regarding the trend in lake evaporation. The monsoon weakening may result in less rainfall over the monsoon regions (southern and eastern regions of the Plateau), and Hadley circulation expansion or intensification may have enhanced a subsidence flow anomaly on the southern TP, and thus suppressed precipitation, too. By contrast, the warmer and moister atmosphere on the central and northern Plateau may trigger more convective precipitation. Also, the decadal variability of atmospheric circulations, such as AMO, HTC and Siberian High, may also play important roles in precipitation variability over the central and northern regions of the TP. Accordingly, climate change may reduce runoff on the southern and eastern regions of the TP but enhance runoff on the central, northern, and western regions of the Plateau. The former caused lake and pool shrinkage in the southern TP and Himalaya, whereas the latter have contributed to lake expansion in the other parts of the TP.

Concluding Remarks and Perspectives

The Tibetan Plateau consists of multiple spheres of the climate system (i.e., atmosphere, hydrosphere, cryosphere, biosphere, lithosphere); therefore, it is recognized as a natural laboratory of multisphere interactions or a miniature model of Earth’s climate system. The Plateau is well known for its provision of powerful thermal forcing to drive the Asian monsoon system and abundant water resources to feed major Asian rivers.

The TP surfaces experienced an overall rapid warming and moistening while decrease in wind speed and solar dimming over the past three decades. These changes further influenced the water and energy cycle over the Plateau: weakened sensible heat flux, enhanced radiative cooling and land evaporation, and changed precipitation amount and spatial distribution. Some subtle factors and processes could be vital for understanding climatic changes. For instance, it is important to discriminate total cloud cover and deep cloud cover to understand solar dimming, and it is crucial to consider spatial pattern change of precipitation to clarify the incoherent changes between precipitation and runoff since 2002 in the upper basin of the Yellow River.

All climatic changes may be deemed different but interdependent aspects of a changing regional system. A conceptual model has been proposed to interpret major changes observed in the TP region. Meanwhile, some key processes in the conceptual model need to be strengthened in future studies because current reanalysis data sets are not able to capture some crucial aspects (Wang & Zeng, 2012). For example, the warming trend over the Plateau is not captured in NCEP reanalysis data (You et al., 2010), the decrease in wind speed is not captured in ERA-40 and ERA-interim data, and the negative trend in the heat source is not captured in the JRA-25. A conceptual model is helpful for a consistent and integrated interpretation of observed climatic changes and for the prediction of the Plateau water and energy cycles from precursor signals of global and regional changes. To reach this goal, we recommend the following outstanding issues be explored in future studies.

First, what determines the spatial pattern of the warming? The warming is the most striking phenomenon on the Plateau, and its spatial distribution and elevation dependence is important for interpreting the spatial pattern of the glacier changes, but an explanation for the warming rate over the Plateau is higher than over East China along the same latitude (Zhang & Zhou, 2009) has not been ascertained.

Second, is a decadal climatic change occurring over the Plateau and East Asia? Since the beginning of the 1970s, wind speed declined considerably over both the Tibetan Plateau and East Asia. However, Lin et al. (2013) found that the wind speed started to recover over the Plateau in 2002. Meanwhile, precipitation increased first in the central TP and then in the northern TP, and the sensible heat flux on the Plateau stopped decreasing. These signals are probably the precursor of a decadal climatic change over the Plateau and East Asia. Attention should be given to the impacts of atmospheric circulation variability (e.g., Hadley circulation expansion and Siberian High enhancement), in addition to global climate change.

Third, what is the trend in surface radiation budget? Solar dimming over the Plateau is evident, and both downward and upward longwave radiation have increased due to the warming and moistening. However, the trend in net radiation remains far from understood; for example, it is sensitive to the change in surface albedo and surface emissivity. Future studies need to find a reliable way to quantify net radiation and related trends to explain the warming. Considering the diurnal and seasonal asymmetry of the warming, the trend in net radiation may have diurnal and seasonal variation.

Last but not least, where is the source of water vapor over central Tibet? The lake expansion in this region indicates that the water vapor was transported from outside. To understand the hydrological cycle and its trend in this region, it is crucial to identify the source region and the transport processes of the water vapor.


This work was supported by National Natural Science Foundation of China (grant nos. 91537210, 41325019, 41190083). The author thanks Drs. Ning Lu, Yanbin Lei, and Sudeep Thakuri for providing high-quality figures from their publications.


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