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  • View in gallery

    (top) Geographic location and (bottom) elevation of the NTP along with locations of the climate stations. In the upper panel, the boundaries of the Tibetan Autonomous Region (TAR) and Qing Hai Province (QHP) are outlined. TAR, QHP, and the surrounding areas with elevations above 2000 m constitute the Tibetan Plateau. Gray lines in the upper panel represent major rivers in East Asia. QHP is outlined in the lower panel and makes up a major portion of the northern Tibetan Plateau. Also labeled in the lower panel are major features of the terrain: QLM–Qi Lian Mountain range, AJM–Aerjin Mountain, EKL–East Kunlun Mountain range, TGL–Tanggula Mountain range, BYKL–Bayankela Mountain, ANMQ–Animaqin Mountain, and CDM–Chai Da Mu basin. Open circles, solid circles, and triangles denote climate stations, where the four solid circles represent the excluded stations and the four triangles represent the included stations used in the evaluation process.

  • View in gallery

    Comparison of the standard deviation normalized anomalies of annual precipitation between stations and grid cells covering the stations. Stations in the top four panels are not included in the SYMAP interpolation initially. Stations in the bottom four panels are included in the SYMAP interpolation. Comparisons are also made with the APHRODITE-MA cells.

  • View in gallery

    As in Fig. 2, but for annual mean temperature maxima.

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    As in Fig. 2, but for temperature minima.

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    As in Fig. 2, but for wind speed.

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    Mean annual precipitation, daily temperature maxima and minima, diurnal temperature range, and wind speed for the period 1957–2009.

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    Mann–Kendall trends of annual precipitation, temperature maxima and minima for 1957–2009, and wind speed for 1975–2009. Stippled cells in this and subsequent figures indicate trends that are statistically significant (p < 0.05).

  • View in gallery

    As in Fig. 7, but for the Mann–Kendall trends for variables related to precipitation.

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    As in Fig. 7, but for the Mann–Kendall trends for variables related to temperature.

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    As in Fig. 7, but for the Mann–Kendall trends of monthly snowfall. Numbers in the lower left corners of each panel refer to the month. Light blue background color represents zero trends.

  • View in gallery

    Mann–Kendall trends vs elevation for variables related to temperature and precipitation.

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    Correlation coefficients between weather system indices and precipitation in the winter and the summer for four subregions on the northern Tibetan Plateau. For EAM, SAM, NAO, AO, and WJ, winter months are December–February, and summer months are June–August. The bimonthly MEI values in November and December (representing December), December and January (representing January), January and February (representing February), May and June (representing June), June and July (representing July), and July and August (representing August) are used for the correlation analysis. Bars represent the magnitude of the correlation coefficients for weather systems listed on the x axis. Stars represent correlation coefficients that are statistically significant (p < 0.05). Notation is the same for the next figure.

  • View in gallery

    As in Fig. 12, but for wind speed.

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Climate Change on the Northern Tibetan Plateau during 1957–2009: Spatial Patterns and Possible Mechanisms

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  • 1 * Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
  • | 2 Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Climate Data Center, Qinghai Meteorological Bureau, Xining, Qinghai Province, China
  • | 4 Hehai Unversity, Nanjing, Jiangsu Province, China
  • | 5 Institute of Meteorology, Qinghai Meteorological Bureau, Xining, Qinghai Province, China
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Abstract

Gridded daily precipitation, temperature minima and maxima, and wind speed are generated for the northern Tibetan Plateau (NTP) for 1957–2009 using observations from 81 surface stations. Evaluation reveals reasonable quality and suitability of the gridded data for climate and hydrology analysis. The Mann–Kendall trends of various climate elements of the gridded data show that NTP has in general experienced annually increasing temperature and decreasing wind speed but spatially varied precipitation changes. The northwest (northeast) NTP became dryer (wetter), while there were insignificant changes in precipitation in the south. Snowfall has decreased along high mountain ranges during the wet and warm season. Averaged over the entire NTP, snowfall, temperature minima and maxima, and wind speed experienced statistically significant linear trends at rates of −0.52 mm yr−1 (water equivalent), +0.04°C yr−1, +0.03°C yr−1, and −0.01 m s−1 yr−1, respectively. Correlation between precipitation/wind speed and climate indices characterizing large-scale weather systems for four subregions in NTP reveals that changes in precipitation and wind speed in winter can be attributed to changes in the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the East Asian westerly jet (WJ), and the El Niño–Southern Oscillation (ENSO) (wind speed only). In summer, the changes in precipitation and wind are only weakly related to these indices. It is speculated that in addition to the NAO, AO, ENSO, WJ, and the East and South Asian summer monsoons, local weather systems also play important roles.

Corresponding author address: Lan Cuo, Institute of Tibetan Plateau Research 4A Datun Road, Chaoyang District, Beijing, 100101, China. E-mail: lancuo@itpcas.ac.cn

Abstract

Gridded daily precipitation, temperature minima and maxima, and wind speed are generated for the northern Tibetan Plateau (NTP) for 1957–2009 using observations from 81 surface stations. Evaluation reveals reasonable quality and suitability of the gridded data for climate and hydrology analysis. The Mann–Kendall trends of various climate elements of the gridded data show that NTP has in general experienced annually increasing temperature and decreasing wind speed but spatially varied precipitation changes. The northwest (northeast) NTP became dryer (wetter), while there were insignificant changes in precipitation in the south. Snowfall has decreased along high mountain ranges during the wet and warm season. Averaged over the entire NTP, snowfall, temperature minima and maxima, and wind speed experienced statistically significant linear trends at rates of −0.52 mm yr−1 (water equivalent), +0.04°C yr−1, +0.03°C yr−1, and −0.01 m s−1 yr−1, respectively. Correlation between precipitation/wind speed and climate indices characterizing large-scale weather systems for four subregions in NTP reveals that changes in precipitation and wind speed in winter can be attributed to changes in the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the East Asian westerly jet (WJ), and the El Niño–Southern Oscillation (ENSO) (wind speed only). In summer, the changes in precipitation and wind are only weakly related to these indices. It is speculated that in addition to the NAO, AO, ENSO, WJ, and the East and South Asian summer monsoons, local weather systems also play important roles.

Corresponding author address: Lan Cuo, Institute of Tibetan Plateau Research 4A Datun Road, Chaoyang District, Beijing, 100101, China. E-mail: lancuo@itpcas.ac.cn

1. Introduction

The Tibetan Plateau (TP) exerts significant influence on regional and global climate through thermal and mechanical forcing (Manabe and Broccoli 1990; Yanai et al. 1992; Liu et al. 2007; Nan et al. 2009; Sun and Ding 2011; Lin and Wu 2011). As a headwater region of the Yellow, Yangtze, and Mekong Rivers, the northern Tibetan Plateau (NTP) provides water for more than a billion people and numerous ecosystems in China and Southeast Asia. Any long-term changes in climatic conditions on the NTP under global warming could significantly affect the hydrologic cycle, and subsequently water resources and the environment both locally and far downstream. Therefore, understanding the spatial and temporal changes of climate conditions on the NTP is crucial for developing a sustainable water resource strategy for the region.

Near-surface air temperature increases in the recent decades have been documented over the TP based primarily on isolated station observations (Lin and Zhao 1996; Liu and Chen 2000; Li et al. 2004; Niu et al. 2004; Duan and Wu 2006; Wang et al. 2008; Kang et al. 2010). Changes in precipitation (referred to as both liquid and solid forms of precipitated water, and reported in water equivalent units regardless of solid or liquid forms) over the TP, however, do not show consistent and plateau-wide trends both temporally and spatially (Lin and Zhao 1996; Zhang et al. 2003; Zhao et al. 2004; Xu et al. 2008). The lack of consistent trends in station records of precipitation likely reflects the consequences of multiple drivers (such as terrain, mesoscale circulations, synoptic systems, atmospheric teleconnections, etc.) acting in numerous ways and differentially affecting climatic variability in various parts of the region. As a result, studies based on isolated station records may not fully reveal the climatic conditions over the vast TP.

Gridded data interpolated from station observations can be used to examine regional climate change in a more spatiotemporally complete manner than is generally possible with station records alone. Gridded climate data are also useful for performing hydrological analysis and modeling, given that the interpolation method is robust and appropriate. Over the TP, with a surface area larger than Greenland (~2 500 000 km2) and complex terrain, it is often challenging to generate gridded climate data of good quality. Global atmospheric reanalyses have been proven useful for climate change analysis in many regions of the world; however, they have limitations over the TP due to the coarse resolution and inadequate constraint of atmospheric conditions (You et al. 2010b). The only high-resolution gridded dataset that is available for the TP is the 0.25° × 0.25° Asian Precipitation–Highly Resolved Observational Data Integration toward the Evaluation of Water Resources–Monsoon Asia (APHRODITE-MA) products compiled by Yatagai et al. (2009) for 1951–2007. The APHRODITE-MA products are limited to precipitation only, synthesizing around 60 station records over the NTP. By comparison, we introduce an up-to-date (through 2009) gridded climatic dataset that employs an expanded network of station records for the NTP (81 in total; Fig. 1), which was made possible in part by additional records obtained from local contacts.

Fig. 1.
Fig. 1.

(top) Geographic location and (bottom) elevation of the NTP along with locations of the climate stations. In the upper panel, the boundaries of the Tibetan Autonomous Region (TAR) and Qing Hai Province (QHP) are outlined. TAR, QHP, and the surrounding areas with elevations above 2000 m constitute the Tibetan Plateau. Gray lines in the upper panel represent major rivers in East Asia. QHP is outlined in the lower panel and makes up a major portion of the northern Tibetan Plateau. Also labeled in the lower panel are major features of the terrain: QLM–Qi Lian Mountain range, AJM–Aerjin Mountain, EKL–East Kunlun Mountain range, TGL–Tanggula Mountain range, BYKL–Bayankela Mountain, ANMQ–Animaqin Mountain, and CDM–Chai Da Mu basin. Open circles, solid circles, and triangles denote climate stations, where the four solid circles represent the excluded stations and the four triangles represent the included stations used in the evaluation process.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

One objective of this study is to introduce and evaluate the new gridded daily climate dataset including precipitation, surface air temperature minima and maxima, and wind speed spanning 1957 to 2009 over the NTP. Another objective is to employ the new dataset to diagnose regional climate change signals over the NTP and investigate possible causal mechanisms. The causal mechanisms in climate variables are investigated in the context of the major large-scale weather systems that affect the region.

This study also examines changes in snowfall (defined as the solid form of precipitation) over the NTP during 1957–2009. Previous studies, using sporadic observations and short period satellite data, have not shown consistent spatial and temporal patterns in snow changes across the region. For example, using the Moderate Resolution Imaging Spectroradiometer (MODIS) data, Maskey et al. (2011) found that snow cover in the Himalayan region of Nepal decreased in January below 6000 m but increased in autumn above 4000 m. Shen et al. (2011) examined daily snowfall accumulation proxies obtained from ice cores over the NTP and found significant decadal variability superposed on an increasing snowfall accumulation trend during the last 200 years. Any changes in snowfall will certainly affect water resources over the TP. It has been shown in the U.S. Pacific Northwest that changes in snowfall occurrence and snow storage due to global warming have shifted the seasonal streamflow regime (Cuo et al. 2009, 2010). It is unclear whether or not similar shifts in streamflow exist over the NTP as there have been few studies on the long-term spatial and temporal changes of snow characteristics. As a first step, this study attempts to unveil the long-term changes in snowfall.

2. Study domain

The study area (30°–40°N, 90°–105°E; Fig. 1) encompasses the entire Qing Hai Province (QHP) and part of the neighboring Xin Jiang Uygur Autonomous Region and Gan Su and Si Chuan Provinces. The elevation of the area is greater than 1400 m and generally decreases from southwest to northeast (Fig. 1). Several large mountains are located in this area. The Qi Lian mountain range (QLM) in the north lies between the Qing Hai Plateau to the south and Gan Su Province to the north. The Aerjin Mountain (AJM) in the northwest separates the Qing Hai Plateau from the Xin Jiang Uygur Autonomous Region. In the south, the Tanggula Mountain range (TGL) divides QHP from the Tibetan Autonomous Region. And finally, the East Kunlun mountain range (EKL) cuts through the center of the domain and creates a two-tiered subdued topography to the west of 97°E (Fig. 1).

The study area is the headwater region of the Yellow, Yangtze, and Mekong Rivers, as well as the Qing Hai Lake, the largest salt lake in China. The Yellow River originates on Bayankela (BYKL) Mountain in the southeast of QHP. The Yangtze and Mekong Rivers originate between TGL and BYKL. The Chai Da Mu basin (CDM) is located in the north of EKL. The Huang Shui River basin (HSRB), one of the economic, cultural, and political centers on the TP, is situated in the northeast QHP (approximately 36°–37°N, 101°–102.5°E).

3. Data

Station data used in the interpolation were obtained from the China Meteorological Administration (CMA; http://cdc.cma.gov.cn/home.do) and the Qing Hai Institute of Meteorology. In total, 81 stations that have daily records of precipitation, 2-m temperature minima and maxima, and 10-m wind speed were used. Also, to determine the temperatures at which snowfall and rainfall (the liquid form of precipitation) occur, hourly temperature, snowfall, and rainfall observations at 44 stations across QHP after 2004 when automatic weather stations were installed were collected.

Station distribution is relatively dense in the east of the domain (Fig. 1). For instance, the distance between stations Xi Ning (station number 52866) and Hu Zhu (52863) is about 35 km. To the west of 97°E, station distribution becomes sparse, and the distance between the neighboring stations can be as large as 500 km (Fig. 1). Because of the subdued and relatively flat terrain to the west of 97°E, large distances between the neighboring stations do not appear to significantly affect the quality of the interpolated data. There are also a few stations with altitude greater than 4000 m. In interpolating the station observations, a resolution of 0.25° is chosen to enable an easy comparison with the other commonly used datasets that are available as well as for future large-scale medium-resolution hydrological simulations in the region.

The Shuttle Radar Topography Mission (SRTM) 90-m digital elevation map (DEM; http://nationalmap.gov/viewer.html) was interpolated using cubic convolution to generate the 0.25° DEM (ranging from 1400 to 6200 m), which was then used as the base map for station data interpolation (Fig. 1). Sharp gradients in the terrain elevation are mainly found in the east.

4. Interpolation and analysis methods

a. Raw data preparation

The quality of the observations at all 81 stations was thoroughly checked. All observations were screened first to remove abnormal values. Daily precipitation values less than 0 mm and greater than 150 mm, daily temperature maxima and minima greater than 50°C and lower than −50°C, and daily wind speed less than 0 m s−1 and greater than 50 m s−1 were discarded and treated as missing. Annual temperature maxima and minima, total precipitation, and wind speed were plotted for each station to visually check abnormal and abrupt changes (not shown). It was found that stations 52866 (Xi Ning in QHP) and 52737 (De Ling Ha in QHP) experienced abnormal changes in temperature. The relocation records revealed that station 52866 was relocated in 1996. The original time series at this station was thus divided at the time of the relocation to form two sets of data as if from two stations: the old (referred to as 52867) and new (52866) stations. Station 52737 has no detailed relocation record and its time series before 1975 when unexplained abrupt changes occurred was not used. This is also true for a couple of additional stations such as station 56065 (He Nan in QHP) that showed abrupt changes but without any relocation records. A few other stations were relocated during the study period but the time series did not show any abnormal or abrupt changes. In those cases, it was assumed that the relocated stations and the old stations were in the same climate zone and their time series was not treated further. In the case when only a short period showed abnormal changes but without any relocation records for a station, the short period was treated as missing.

Table 1 lists the station information after quality control. There are 51 stations located in QHP, 13 stations in Gan Su Province, and 17 stations in Si Chuan Province (Table 1). The elevation of the stations ranges from 1398 m (52895, Jin Yuan, Gan Su Province) to 4612 m (52908, Wu Dao Liang, QHP). Forty-one stations are located above 3000 m and only nine stations are situated below 2000 m. Most of the stations were established after the mid-1950s. Our interpolation was conducted for 1957–2009 during which all of the stations were in operation and have some records for precipitation, temperature maxima and minima, and wind speed (Table 1).

Table 1.

Information about the climate stations used in the interpolation process. WMO Sta. ID is the World Meteorological Organization station number. Coverage (cov., %) refers to the availability of data during the operational period at each station. Tmax is maximum daily temperature; Tmin, minimum daily temperature; and Prcp, precipitation.

Table 1.

Using records from the 81 stations, least squares fitting was performed on the mean daily temperature and station elevation and a best fit temperature lapse rate of −4.3°C km−1, larger than the standard lapse rate of −6.5°C km−1, was obtained for the domain with the coefficient of determination (R2) being 0.65. Mean daily precipitation shows a statistically insignificant increasing tendency with elevation (0.5 mm km−1; R2 = 0.1) during the wet and warm season (May–September). During the dry and cold season (October–April), the observed precipitation does not change with elevation.

Wind speed also shows a statistically insignificant increasing tendency with elevation in the domain with a slope of 0.4 m s−1 km−1 and R2 of 0.1. In this study, the temperature and wind speed lapse rates were used in the spatial interpolation throughout the entire period. For precipitation, the computed lapse rate was only applied during the wet and warm season. Although statistically insignificant, the increasing tendencies of precipitation and wind speed with elevation were used in the interpolation to reflect the slight changes of precipitation and wind speed with terrain.

b. Interpolation method and evaluation

Horizontal interpolation is performed for daily climate data using the well-established Synographic Mapping System (SYMAP) interpolation algorithm (Shepard 1984) that has been used in many hydrological modeling studies (e.g., Adam et al. 2009; Cuo et al. 2009; Elsner et al. 2010; Cuo et al. 2010). Essentially, the algorithm uses weights that are based on both direction and distance. It calculates distances and angles between a targeted cell and the neighboring stations. Stations that have closer distances but larger angles are assigned greater weights. The angle is used as a weight to reduce the impact of clustered stations around the targeted cell. The weights and the lapse rates are used by SYMAP to interpolate values for the targeted cell. The input variables for the SYMAP algorithm include station latitudes, longitudes, elevations, and the observed daily climate data. The algorithm uses a radius of up to 55 stations with a minimum radius of four stations.

To evaluate the performance of the SYMAP algorithm in our domain, four stations with various elevations and distances from the other stations were used to check the quality of the interpolated data. These four stations were initially not included in the interpolation procedure; they are Huang Zhong (52869), Wu Lan (52833), Wu Dao Liang (52908), and Gan De (56045) (denoted by large and solid circles in Fig. 1). In addition, four other stations—Mang Ai (51886), Tuo Tuo He (56004), Nuo Mu Hong (52825), and Tong Ren (52974) (denoted by solid triangles in Fig. 1)—that were included in the interpolation were also used to check the quality of the interpolated data. For the purpose of validation, means of annual time series at the eight grid cells containing one of the aforementioned stations were compared with those of the corresponding stations. Root-mean-square errors (RMSEs) and correlation coefficients of annual series between the grid cells and the stations were also calculated. To compare the variability of the interpolated and station data, time series of standard deviation normalized anomalies of annual precipitation, annual temperature maxima and minima, and annual wind speed were examined. For precipitation, comparisons were also made with the APHRODITE-MA data.

c. Climate change analysis using the interpolated data

The Mann–Kendall trends (Helsel and Hirsch 2002) and the significance level of the trends in each grid cell and for the entire domain were calculated for annual precipitation, temperature maxima and minima, wind speed, maximum daily precipitation, total precipitation days, wet season and dry season precipitation, extreme temperature minima and maxima, and diurnal range of temperature. Trend changes with elevation were also examined.

To investigate changes in snowfall, criteria were first developed to separate pure snowfall from pure rainfall using hourly temperature, snowfall, and rainfall records at 44 stations. All snowfall and rainfall events in any given day were selected and separated. The 99th percentile of snow-event temperature calculated from the Weibul plotting equation was selected as the upper limit of temperature at which snowfall could occur. Likewise, the first percentile of rainfall-event temperature was chosen as the lower limit of temperature at which rainfall could occur. This procedure generated 3.4°C as the upper limit for snowfall to occur, and 1.6°C as the lower range for rainfall to occur. Between 1.6° and 3.4°C, mixed rainfall and snowfall could happen. In this study, the recorded precipitation with temperature below 1.6°C was treated as pure snowfall.

5. Results and discussion

a. Evaluation of the gridded climate data

Comparisons between the gridded and station data are conducted for the period up to 2009 depending on the availability of station data (Tables 2a,b). Comparisons with the APHRODITE-MA precipitation are performed for the period up to 2007 (Table 2a). Statistics at the four excluded stations as well as at the four included stations are presented in Tables 2a,b. At the four excluded stations, Wu Dao Liang, Gan De, Huang Zhong, and Wu Lan, the station means and the corresponding grid cell means for precipitation (Table 2a), temperature maxima (Tmax) and minima (Tmin), and wind speed (Table 2b) are generally close to each other. Large differences in means and RMSEs between the stations and the corresponding grid cells are noted for precipitation and Tmax at Huang Zhong located in a valley over the northeast of QHP. It appears that the SYMAP method exhibits uncertainties over the complex topography of the northeastern domain. For precipitation, the correlation coefficients are reasonably high at Huang Zhong and Wu Lan, but low at Wu Dao Liang. For Tmax and Tmin, the correlation coefficients are higher than 0.8 except for Tmin at Huang Zhong (Tables 2a,b). The correlation coefficient for wind speed is negative at Gan De and Wu Lan, and above 0.54 at the other two stations. Note that wind speed is strongly affected not only by elevation, but also by the aspect of terrain and surface properties such as roughness that are not considered in the interpolation.

Table 2a.

Statistical comparisons between grid cells and stations for annual precipitation (units are mm).

Table 2a.
Table 2b.

Statistical comparisons between grid cells and stations for annual temperatures and wind speed. Temperature units are °C. Wind speed units are m s−1.

Table 2b.

At the four included stations, Mang Ai, Tuo Tuo He, Nuo Mu Hong, and Tong Ren, much closer means and smaller RMSEs with excellent correlation are evident when compared to those at the excluded stations (Tables 2a,b). This suggests that the SYMAP method is robust over complex terrain when constrained by station observations. Among the four included stations, the largest RMSEs of precipitation, Tmax, and Tmin are noted at Mang Ai, located in the northwestern corner of the domain. This suggests that sparse station distribution in the far west affects the quality of the interpolation.

Precipitation from SYMAP and APHRODITE-MA are compared with the observations for the overlapped periods, and the statistics are shown in Table 2a. Except at one included station (Mang Ai) and two excluded stations (Wu Dao Liang and Wu Lan) where APHRODITE-MA exhibits a better match to the observation means and lower RMSEs than SYMAP, SYMAP appears to be superior at all other stations when compared to APHRODITE-MA, possibly because fewer stations were used in APHRODITE-MA.

Figures 25 present the time series of the normalized anomalies of annual precipitation, Tmax, Tmin, and wind speed for the eight grid cells and the corresponding stations. For the four excluded stations, the time series of the corresponding grid cells show similar temporal variability, although for wind speed large differences are noted for three stations. For the four included stations, the time series of the stations and the corresponding grid cells are almost identical. APHRODITE-MA and SYMAP show similar variability at the excluded stations, whereas at the included stations SYMAP in general corresponds better to station variability when compared to APHRODITE-MA (Fig. 2).

Fig. 2.
Fig. 2.

Comparison of the standard deviation normalized anomalies of annual precipitation between stations and grid cells covering the stations. Stations in the top four panels are not included in the SYMAP interpolation initially. Stations in the bottom four panels are included in the SYMAP interpolation. Comparisons are also made with the APHRODITE-MA cells.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for annual mean temperature maxima.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

Fig. 4.
Fig. 4.

As in Fig. 2, but for temperature minima.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

Fig. 5.
Fig. 5.

As in Fig. 2, but for wind speed.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

The above analyses performed at stations with various elevations and various distances from the neighboring stations demonstrate that the SYMAP method reasonably reproduces the observed magnitude and variability of meteorological variables over the NTP, indicating that the interpolated data after all station observations included can be used for regional climate and climate change analysis, although caution should be exerted for wind speed.

b. Evaluation of the annual means

From this section on, the analysis will be based on the interpolated data. Figure 6 presents the spatial distributions of the annual means. For precipitation, amounts of more than 750 mm are noted over the southeast of the domain while values of less than 150 mm are evident over the northwest. Precipitation maxima occur along QLM (~600 mm), likely related to orographic lifting and moisture fed by westerlies and monsoons as noted by Y. Chen et al. (2006), Wang et al. (2007), and Xu et al. (2007). The Qing Hai Lake located to the south of QLM also supplies water vapor to the area. The spatial patterns of annual precipitation are consistent with findings by Ge et al. (2008), who examined rainfall and rainy days over the TP during 1971–2005 using station data.

Fig. 6.
Fig. 6.

Mean annual precipitation, daily temperature maxima and minima, diurnal temperature range, and wind speed for the period 1957–2009.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

Tmax and Tmin exhibit similar spatial patterns, in close correspondence with the elevation changes in the domain (Fig. 6). A warm corridor spans from the east slope of AJM to CDM, reaching downstream of HSRB. The vicinity of QHP and the south tip of the province are also comparatively warm. Colder areas are located in the high elevations along QLM and Kekexili in the south of EKL. The annual mean diurnal temperature range, calculated using the differences of daily Tmax and Tmin, is largest in the north and smallest in the east. The majority of the cells have an annual mean diurnal range of about 15°C.

The annual mean wind speed increases from east to west in line with the increases in elevation (Fig. 6). The largest (smallest) wind speed is located over the western (eastern) tip of the domain corresponding to the windward (leeward) side of the NTP with respect to the westerlies. Seasonally, wind speed is highest in February and March and lower in August through November (not shown). These spatial and temporal distributions match well with the long-term analysis of wind speed over QHP conducted at the Qing Hai Weather Forecasting Center (Q. Zhang 2011, personal communication).

c. Climate change analysis

1) Annual means

Figure 7 shows the Mann–Kendall trends for annual total precipitation, annual mean daily Tmax and Tmin, and annual mean wind speed. Strictly speaking, there are no uniform spatial patterns in changes of all variables across the domain, possibly indicating the strong influence of the complex terrain. For precipitation (Fig. 7), statistically significant decreasing trends are located in areas over and to the south of CDM and to the east of QHP. The northeastern part of the domain including the areas east of CDM, QLM, Qing Hai Lake basin, HSRB, and the middle reach of the Yellow River basin exhibits statistically significant increasing trends of 2–4 mm yr−1. In all, 69% of the domain (total area is roughly 1 200 000 km2) shows positive trends in annual precipitation (Table 3), with a domain averaged insignificant increasing trend of 0.39 mm yr−1. The spatial patterns of the precipitation trends in the eastern domain are consistent with the station trend derived by Li et al. (2010). However, this study arrives at larger trends than Li et al. (2010).

Fig. 7.
Fig. 7.

Mann–Kendall trends of annual precipitation, temperature maxima and minima for 1957–2009, and wind speed for 1975–2009. Stippled cells in this and subsequent figures indicate trends that are statistically significant (p < 0.05).

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

Table 3.

Changes in domain-wide variables over the northern Tibetan Plateau. There are 1871 cells in the domain. The numbers of cells experiencing positive or negative changes are counted, and their areal percentages are calculated and are reported in the table. Mann–Kendall temperature trend units are °C yr−1. Precipitation trend units are mm yr−1. Wind speed trend units are m s−1 yr−1.

Table 3.

Tmax and Tmin show generally similar spatial patterns in trends (Fig. 7), with significantly large increasing trends in the northwest and to the south of HSRB. Small decreasing trends are noted around HSRB to the east of QHP. The majority of the grid cells in the domain experience warming during the period: 97.4% (99.7%) of cells exhibit statistically significant increasing trends in Tmax (Tmin) with a domain-averaged significant change of 0.03°C yr−1 (0.04°C yr−1) (Table 3). It is clear that CDM and the area to its south experience both significant warming and significant drying, suggesting enhanced drought in the region.

Most stations in the domain exhibited abnormal changes in annual wind speed in 1968–70 (not shown) due to a massive equipment upgrade across China. Similar abnormalities were not found in precipitation, Tmax, and Tmin. Thus for wind speed, only the time series after 1975 were used for the trend analysis (Fig. 7). Annual wind speed has been decreasing over the majority of the domain, with the largest decreases observed over the western part where the annual wind speed is also the strongest (see Fig. 6). This is consistent with the findings by You et al. (2010a), who examined both station observations and reanalysis data. Isolated increasing trends are only noted over QLM and the eastern domain (Fig. 7). In all, 89.7% of the domain shows decreasing trends with a spatially averaged statistically significant change of −0.01 m s−1 yr−1 (Table 3).

2) Precipitation and temperature extremes

The trends of annual maximum daily precipitation, annual total precipitation days, and annual wet season and dry season total precipitation are presented in the top four panels of Fig. 8. For annual maximum daily precipitation, statistically significant increasing trends are found over the northeast and south of QLM while decreasing trends are noted over EKL and over small areas to the east-southeast of QHP. The significant decreasing trends along the southeast boundary of QHP appear to be dominated by the changes at stations Lang Mu Si (56075) and Ze Ku (52968). However, the changes in the other parts of the domain are not dominated by individual stations. The majority of the domain does not exhibit statistically significant changes, and therefore annual maximum daily precipitation has a trend close to zero for the entire domain (Table 3).

Fig. 8.
Fig. 8.

As in Fig. 7, but for the Mann–Kendall trends for variables related to precipitation.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

Annual precipitation days show statistically significant decreasing trends over most of the domain, and the domain-averaged trend is significant at −0.15 day yr−1 (Fig. 8; Table 3). The decrease of precipitation days and the increase of annual maximum daily precipitation along QLM suggest increasing precipitation intensity in the area.

Similar to annual precipitation and annual maximum daily precipitation, wet season precipitation shows significant increasing trends around QLM and decreasing trends to the south of CDM and in southeast (Fig. 8). This is largely consistent with You et al. (2008), who found increasing (decreasing) trends along QLM (CDM) in a majority of their precipitation indices. The similarity between annual precipitation (Fig. 7) and wet season precipitation indicates that changes in annual precipitation are primarily determined by changes in wet season precipitation over the NTP. About 60.4% of the domain experiences increasing trends in wet season precipitation, and the domain-averaged change is insignificant at 0.07 mm yr−1 (Table 3). The trends of dry season precipitation are positive and significant over the southeastern domain. The south of CDM becomes dry in both wet and dry seasons. A majority of the domain (76.7%) shows increasing trends for dry season precipitation with a domain-averaged significant increase of 0.20 mm yr−1. This domain-averaged trend is larger than that for wet season precipitation, and is likely due to the large increasing trends over the southeast.

The lowest (highest) daily temperature in a year was selected from the time series of daily minimum (maximum) temperature to create annual extreme temperature minima (maxima). Figure 9 presents their trends as well as the trend of annual mean diurnal temperature range. Both temperature extremes show statistically significant increasing trends at a majority of grid cells across the domain (see Table 3), which is largely similar to You et al. (2008), except in the eastern domain where they showed less well-defined significant trends, likely due to fewer stations used. You et al. (2008) pointed out that a majority of their stations have increasing extreme daily Tmax trends in the range of 0.2°–0.6°C decade−1. They showed that about a third of their stations have increasing extreme daily Tmin trends of less than 0.5°C decade−1, about a third between 0.5° and 0.7°C decade−1, and another third between 0.7° and 0.9°C decade−1. In this study, the domain-averaged extreme Tmax and Tmin increase significantly at rates of 0.02° and 0.06°C yr−1, respectively.

Fig. 9.
Fig. 9.

As in Fig. 7, but for the Mann–Kendall trends for variables related to temperature.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

Notable differences between extreme Tmin and Tmax changes are evident in the magnitude and spatial patterns, which may indicate that different mechanisms play roles during nighttime and daytime. The differences include the following: (a) the increasing trends of the annual extreme Tmin are generally larger than those of the annual extreme Tmax; (b) the largest increasing trends of extreme Tmin are located over the northwestern and southern parts of the domain as well as the south of HSRB, while for extreme Tmax the largest increasing trends are situated primarily to the south of CDM; and (c) the decreasing trends of extreme Tmin are scattered spatially and are found mainly along the eastern and southern parts of QHP, while decreasing trends for extreme Tmax occur in the north, east, and the southeastern parts of the domain. Similarities in spatial patterns are noted between the significant trends of annual mean Tmax (Tmin) and annual extreme Tmax (Tmin) (cf. Figs. 7 and 9).

Annual diurnal temperature range generally exhibits decreasing trends over the domain (Fig. 9), with a domain-averaged significant change of −0.01°C yr−1 and 89.5% of the domain having negative trends (Table 3). There are six areas in the domain that have statistically significant increasing trends of about 0.03°C yr−1. These areas correspond to where annual extreme temperature minima decrease or extreme Tmax increase is greater than extreme Tmin increase (Fig. 9). Further examination of the extreme Tmin and Tmax at individual stations that are located at and near the centers of the six spots (the “bull’s eyes”) do not reveal similar trends, suggesting that the extreme Tmin and Tmax trends of the gridded data are not dominated by those of individual stations, but are rather the result of terrain-based interpolation.

3) Snowfall

Compared to the trends of annual precipitation (Fig. 7), the trends of annual snowfall show more complex patterns with large spatial variations (bottom left panel of Fig. 8). Statistically significant increasing trends of annual snowfall are noted over eastern CDM and the lower reaches of HSRB. Statistically significant decreasing trends of annual snowfall are found mainly along high mountain ranges over southwestern QHP, southern CDM, southern QLM, eastern QHP, and the southernmost part of the domain (Fig. 8). About 83.3% of the domain showing decreasing snowfall trends, significant at −0.52 mm yr−1 when spatially averaged (Table 3). Annual snowfall days decrease at statistically significant rate of −0.43 days yr−1 in the domain, with the west domain exhibiting large trends (bottom right panel of Fig. 8; see also Table 3). With decreasing annual snowfall but increasing annual precipitation in the region on average (Table 3), the ratio of snowfall to rainfall is decreasing, which will affect hydrological regimes such as seasonal water balance partition that are important for water resources management.

To further elucidate seasonal changes in snowfall, the trends in monthly snowfall are examined in Fig. 10. Almost no changes are identified in January and December. In February, March, April, and November, snowfall exhibits small but statistically significant increasing trends in the range of 0.2–0.4 mm yr−1 in the southeastern part of the domain. From June to September, snowfall exhibits significant decreasing trends along high mountain ranges, with domain averaged trends ranging from −0.008 mm yr−1 in July to −0.01 mm yr−1 in August. It is clear from Fig. 10 that the overall decreasing snowfall trends on an annual basis (Fig. 8; Table 3) are driven primarily by decreasing snowfall trends during the summer months, which account for the majority of the annual precipitation. Snowfall changes in the south of QHP where the Yellow, Yangtze, and Mekong Rivers originate likely affect the hydrological processes in the headwaters.

Fig. 10.
Fig. 10.

As in Fig. 7, but for the Mann–Kendall trends of monthly snowfall. Numbers in the lower left corners of each panel refer to the month. Light blue background color represents zero trends.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

4) Trends and elevation relationship

There have been mixed reports about the relationship between temperature trends and elevation. For example, Liu and Chen (2000) found that warming trends increased with elevation, while You et al. (2010b) did not find such a relationship. The discrepancies might be related to different datasets and different study periods used. In this study, the relationships between the trends of various climate elements and elevation are examined with scatterplots (Fig. 11). Except for a weak relationship between snowfall trends and elevation in which snowfall shows a stronger decreasing tendency as elevation increases, there is no well-defined correspondence between temperature/precipitation trends and elevation over the NTP. Admittedly, more in situ observations in high elevations are needed to further evaluate the elevation dependence of trends in the region.

Fig. 11.
Fig. 11.

Mann–Kendall trends vs elevation for variables related to temperature and precipitation.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

d. Possible mechanisms

In this section, the possible mechanisms of climate change over the NTP are investigated. Obviously, prevailing temperature increases in the region are coincident with widespread warming globally (Solomon et al. 2007) although the complex terrain and heterogeneous land surfaces also play a role. Additionally, the reason that Tmin increases more than Tmax is likely due to the increasing low-level cloud amount at nighttime overwhelming the decrease in daytime low and total cloud amounts over the TP as noted by Duan and Wu (2006). Duan and Wu attribute the cloud cover changes to anthropogenic pollution such as aerosols. Changes in precipitation and wind speed over the NTP could be related to large-scale and local-scale systems that directly or indirectly affect the region, as will be examined below.

1) Precipitation

During the dry and cold season, the NTP is influenced directly by the East Asia westerly jet (WJ) and indirectly by the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) through teleconnections (Yeh and Gao 1979; Liu and Yin 2001; Wang et al. 2003; Tian et al. 2007; Han et al. 2008). In the wet and warm season when 85% of annual precipitation falls, a few large-scale systems such as the WJ, the East Asian summer monsoon (EAM), the South Asian summer monsoon (SAM), the west Pacific subtropical high (WPSH), the Tibetan high, the NAO–AO, and several local weather systems (e.g., the wind shear line and plateau vortices, which are shallow cyclonic vortices generated in situ over the Tibetan Plateau during the rainy season) with a horizontal scale of 400–500 km affect the region (Yeh and Gao 1979; Tao and Ding 1981; Wang 1987; Yanai et al. 1992; Liu and Yin 2001). The influence of the El Niño–Southern Oscillation (ENSO) on the TP climate has not been well defined, although strong correlations between ENSO and snow cover over the TP have been documented (e.g., Fasullo 2004; Wu et al. 2012).

The Pearson’s correlations between precipitation–wind speed and indices of large-scale weather systems including the WJ, NAO, AO, and ENSO were examined for four subregions in the NTP during 1957–2009. In addition, the EAM and SAM indices were examined for June, July, and August when 70% of wet season precipitation falls. The EAM and SAM indices were downloaded from the web http://ljp.lasg.ac.cn/dct/page/65544) and were derived using the approach detailed by Li and Zeng (2002). Indices for winter monsoons were not provided by Li and Zeng (2002). The NAO and AO indices were obtained from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) website (http://www.cpc.ncep.noaa.gov/products/precip/CWlink/). The Multivariate ENSO Index (MEI) was provided by the NOAA Earth System Research Laboratory (ESRL) (at http://www.esrl.noaa.gov/psd/enso/mei/table.html). The WJ index was calculated based on the method suggested by Duan and Wu (2009). The four subregions were divided based on the spatial distribution of mean annual precipitation (see Fig. 6): northeast (35°–40°N, 96°–105°E), northwest (35°–40°N, 90°–96°E), southeast (30°–35°N, 96°–105°E), and southwest (30°–35°N, 90°–96°E).

Figure 12 presents the correlation coefficients and statistical significance (p < 0.05) between the indices of large-scale weather systems and precipitation for winter months (December–February) and summer months (June–August) over the four subregions. Except in February for the NAO over the northwest subregion, the correlation coefficients between the NAO–AO indices and winter precipitation, negative in December but positive in February and January over the four subregions are all statistically significant. The same sign and relatively small differences in correlation coefficients between the subregions suggest that the NAO–AO exerts similar influence over the entire NTP and that the NAO and AO appear to be internally related. The impact of the NAO and AO on precipitation is opposite between December and January/February. It is possible that the associated teleconnection patterns are different between the months.

Fig. 12.
Fig. 12.

Correlation coefficients between weather system indices and precipitation in the winter and the summer for four subregions on the northern Tibetan Plateau. For EAM, SAM, NAO, AO, and WJ, winter months are December–February, and summer months are June–August. The bimonthly MEI values in November and December (representing December), December and January (representing January), January and February (representing February), May and June (representing June), June and July (representing July), and July and August (representing August) are used for the correlation analysis. Bars represent the magnitude of the correlation coefficients for weather systems listed on the x axis. Stars represent correlation coefficients that are statistically significant (p < 0.05). Notation is the same for the next figure.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

The NAO and AO influence within the domain diminishes significantly during the summer months (Fig. 12), consistent with the fact that the NAO and AO are most pronounced in winter despite their year-round presence (Hurrell et al. 2003). Generally insignificant positive (negative) correlations exist between the NAO–AO indices and precipitation for June (July and August) over the entire domain.

The WJ is a system affecting the NTP year-round. During January the northern domain exhibits larger positive and statistically significant correlations than the southern domain while during July the opposite is true (Fig. 12). The correlation is rather small during the other months over the four subregions except for the southeastern subregion, where a statistically significant negative (positive) correlation is identified in February (August). This out-of-phase influence of the WJ may be related to the intensity and latitudinal position of the WJ, which tend to vary greatly from month to month (Schiemann et al. 2009).

The influence of ENSO on precipitation is comparatively weak. The only statistically significant negative correlation is found for the western domain in August (Fig. 12). Our composite analysis of temperature and precipitation anomalies over the NTP during El Niño and La Niña events using the gridded climate data as well as the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis also revealed weak coherent patterns (not shown). In spite of the weak correlation between ENSO and precipitation, a consistent pattern emerges in Fig. 12 in which the correlation is positive during the winter months but negative during the summer months regardless of the subregions.

The influence of the EAM and SAM over the NTP exhibits a strong subregional preference in the summer months, with a generally larger impact over the southern domain than the northern domain (Fig. 12). The statistically significant correlation between the EAM index and precipitation in June and July is out of phase over the southeastern subregion. The correlation between the SAM index and precipitation is statistically significant only in July over the southern domain. Over the northern subregions, the correlation between the EAM and SAM indices and precipitation are small and insignificant.

In summary, the NAO and AO exert the strongest influence on precipitation during the winter months over the entire domain. The WJ, ENSO, EAM, and SAM, on the other hand, exhibit regional and monthly variations in their influence. Generally speaking, during summer the EAM has strong effects in the southeastern subregion, while the SAM appears to impact the southern domain the most. The WJ exerts strong influence over the northern domain in January and the southern domain in July. ENSO mainly affects the southwest and northwest subregions in August. Precipitation changes over the various parts of the domain are therefore determined by changes in the weather systems that affect the area. Complexities in summer are expected since local circulations and other large-scale systems such as WPSH also play important roles in affecting precipitation, while in winter most precipitation comes from large-scale systems. The impacts from local weather systems and WPSH on precipitation are difficult to quantify because of the lack of representative indices.

As a further analysis of the changes of large-scale weather systems and their possible influences on precipitation and wind speed, the Mann–Kendall trends of the EAM, SAM, AO, NAO, WJ, and ENSO monthly indices during 1957–2009 are examined and shown in Table 4. Statistically significant positive trends are noted for the NAO–AO in January and February (and March for the NAO) as well as in August and November for the AO, indicating the strengthening of the NAO–AO in these months and less intrusion of cold air toward Asia. October sees statistically significant negative trends for the NAO. For the WJ, statistically significant negative trends are evident from April through September. No significant trends are noted for the SAM during the summer months, while for the EAM significant negative trends are noted only in June. The MEI index exhibits rather weak although predominantly positive trends during the period. In the wet and warm season, the change of local weather systems should be examined further to account for precipitation changes.

Table 4.

Mann–Kendall trends (yr−1) of the EAM, SAM, AO, NAO, WJ, and ENSO indices during 1957–2009. Blank cells indicate missing data.

Table 4.

2) Wind speed

Figure 13 presents the correlation coefficients between the indices of large-scale weather systems and wind speed. For the NAO, a statistically significant negative correlation with wind speed is identified only over the northeastern subregion in January and February, while for the AO a statistically significant negative correlation occurs for all four subregions in January and for the northern domain in February (Fig. 13).

Fig. 13.
Fig. 13.

As in Fig. 12, but for wind speed.

Citation: Journal of Climate 26, 1; 10.1175/JCLI-D-11-00738.1

Except for January over the northeast, the correlation between the WJ indices and wind speed is positive and statistically significant during the winter months and over all four subregions. This is in contrast to precipitation for which a rather small correlation is noted for December and February (Fig. 12). This inconsistency between precipitation and wind speed in the context of the WJ as well as other major weather systems may be understandable considering that the generation of precipitation requires not only favorable large-scale conditions (e.g., instability) but also moisture and a triggering mechanism (e.g., lifting).

Negative trends of the WJ indices are noted for February through November during 1957–2009 (Table 4) possibly explaining the decreasing trends of wind speed over the NTP to some extent. December and January are the exceptions in that the WJ indices show increasing trends, though insignificant (Table 4), while wind speed over the NTP still exhibits decreasing trends. This appears to contradict the fact that a strong positive correlation exists between the WJ index and wind speed over the NTP in December and January (Fig. 13). It is possible that the strengthening of the NAO–AO indices that are negatively correlated with wind speed during winter months, together with a steady decline of the East Asia winter monsoon winds as reported by M. Xu et al. (2006), offset the impact by WJ and contribute to the decrease of wind speed over the NTP. ENSO surprisingly exhibits statistically significant negative correlations with wind speed over the entire domain in December (Fig. 13). Considering that ENSO exhibits positive trends in December (Table 4), it is possible that changes in ENSO also contribute to the decreasing wind speed on the NTP in December.

During the summer months, the correlation between the indices of major weather systems and wind speed is also generally small and shows strong subregional dependence (Fig. 13). For example, for the EAM in June, a comparatively small positive correlation is noted for the northern domain while a relatively larger negative correlation is seen for the southern domain. For the SAM, a positive correlation is noted for the northeast subregion in June and July while for the other three subregions, the correlation is opposite in sign between June and July. For the summer months, a statistically significant correlation is identified for NAO in July (August) over the northwest (southeast) subregion and for the WJ in August over the southern domain. ENSO is an exception in the sense that a statistically significant negative correlation is located for the entire domain in June and August and for the northeastern subregion in July, suggesting an important role by ENSO in contributing to the decreasing wind speed over the NTP. Figures 12 and 13 suggest that in summer large-scale forcing interacts with local circulations in complex ways over the NTP.

Decreasing near-surface wind speed over the TP has also been documented in several other studies (Niu et al. 2004; Wang et al. 2004; M. Xu et al. 2006; X. Xu et al. 2006; S. Chen et al. 2006; Duan and Wu 2008, 2009; You et al. 2010a; Guo et al. 2011; Zhong et al. 2011). M. Xu et al. (2006), Duan and Wu (2009), and Guo et al. (2011) have shown that the decrease of the pressure gradient in the troposphere resulting from the greater warming in the high latitudes than in low latitudes, especially in winter, is the major mechanism for the wind speed decrease. The decrease in the pressure gradient is also related to large-scale circulation changes as a result of global warming (Guo et al. 2011), and the relationship certainly exists in winter over the NTP based on our analysis. This work suggests that the NAO, AO, WJ, and ENSO partially contribute to decreasing wind speed over the NTP.

6. Conclusions

A 0.25° × 0.25° daily gridded precipitation, temperature minima and maxima, and wind speed dataset of reasonable quality is generated for the NTP for 1957–2009. Precipitation and its temporal trends exhibit large spatial variability over the NTP during 1957–2009. Warming trends prevail over the entire NTP, consistent with widespread warming globally. Spatially, the positive trends of annual temperature maxima and minima strengthen from the southeast to the northwest. Wind speed decreases over the majority of the domain, strengthening from east to west. Snowfall generally increases in the south of the domain during the dry and cold months and decreases along high mountain ranges during the wet and warm months.

Large-scale weather systems greatly affect precipitation and wind speed changes during winter. While the relationships between some large-scale indices and regional weather are consistently robust in winter, during summer large-scale and local drivers appear to interact in complex ways to impact weather on the NTP. As global warming continues, gradual changes in the NAO (Olsen and Buch 2004) and ENSO (van Oldenborgh et al. 2005; Müller and Roeckner 2008), the weakening in the Indian monsoon circulation, the South Asian monsoon and the East Asian westerly jet (Sabade et al. 2011; Ashfaq et al. 2009; Jiang and Wang 2005), and the westward extension of the western Pacific subtropical high (Zhou et al. 2009), which have already been observed, will likely continue to contribute to changes in local weather and climate over the Tibetan Plateau. It is important to understand how the large-scale and local climatic drivers evolve under global warming and what the consequences are for regional climate and hydrology, especially during the wet and warm season.

Acknowledgments

This study is supported by the “Hundred Talent” program granted to Lan Cuo by the Chinese Academy of Sciences in 2011. Part of the station data was obtained through the support by the Key Program of the National Natural Science Foundation of China, Grant 40830639. This study is also supported by National Science Foundation of China, Grants 41065007 and 41075066.

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