1. Introduction
The Tibetan Plateau (TP), known as the “third pole,” displays the highest elevation and most complex surface characteristics in the world. It is surrounded by the Hengduan Mountains in the east, the Karakoram Mountains in the west, and the Himalaya Range, which separates South Asia and the TP in the south and the Kunlun and Qilian Mountains in the north and northeast, respectively. The altitude of the majority of these mountains exceeds 6000 m. Many basins (e.g., Qaidam, Qiangtang Basins), valleys (e.g., Yalungtsangpo, Polungtsangpo Canyons), mountains (e.g., Tanggula, Nyenchen Tanglha, Gangdise Mountains) and lakes (e.g., Nam Co, Qinhai Lake) are located in the TP. As the “water tower of Asia,” the TP is also the cradle of the Yangtze, Yellow, Salween, Mekong, Brahmaputra, Indus, and Ganges Rivers. Mountains, glaciers, lakes, rivers, permafrost, and alpine meadow coexist in the sensitive cryospheric environment.
As the “sensor” for global climate change (Schwalb et al. 2008), the temperature of the TP increased rapidly (Kang et al. 2010) in recent decades. Warming could lead to changes of agriculture (Qin 2002), ecology (Wu et al. 2006; Klein et al. 2007), natural disasters (Yao 2010), hydrological processes, and water resources (Yao et al. 2007, 2004; Ye et al. 2008). Many studies about the climate change of the TP have been based on observed data (Qin et al. 2006; Liu et al. 2006; You et al. 2010a,b). However, the meteorological stations are scarce in the TP, especially in mountainous areas. This limits the research that has to be carried out.
Previous studies had analyzed climate change in the TP using the results of the general circulation models (GCMs) (Xu et al. 2003). However, the performance of GCMs was not good enough because of the coarse resolution (Gao et al. 2008) that makes it difficult to capture details of the surface characteristics in the TP. On the other hand, regional climate models (RCMs) can compensate for the shortage of lower grid space from GCMs. Thus, the downscaling results of RCMs show more realistic climatological distribution compared with the GCM outputs (Shi 2010). However, the errors, especially the cold bias between RCMs and observations, were still obvious in the TP (Zhang et al. 2005; Shi et al. 2011b).
Generally, the horizontal grid space of RCM is at 30–60 km, which is largely determined by the GCM’s resolution (the ratio of RCM and GCM resolutions should be in the range of 3–5) (Gao et al. 2011). However, that resolution does not perform well over the regions of complex terrain. Thus, much finer results can be obtained by the double-nested technique (Leung and Qian 2003). Im et al. (2006) used a one-way double-nested method to simulate the present climate over the Korea Peninsula at 20-km grid space. And Wu et al. (2012) investigated the climate effects of Three Gorges reservoir using two double-nested simulations. But relatively few results were conducted with small domains and short simulated periods. Until now, there are few results at the 10-km resolution over the TP.
In this paper, we used a double-nested dynamic downscaling method and conducted simulations at 10-km resolution over the TP. First, the model capability is evaluated by comparing with observations. Then, the projection of climatic change is displayed under two representative concentration pathway (RCP) scenarios. The RCP4.5 pathway is a stabilization of radiative forcing at 4.5 W m−2 in 2100 and it represents a low-emission scenario. The RCP8.5 pathway stands for a high level of greenhouse gas (GHG) emissions scenario and GHGs’ radiative forcing is near 8.5 W m−2 in the end of the twenty-first century (Moss et al. 2008). This work represents an early high-resolution regional climate simulation over the TP that may contribute to better understanding the impact of climate change and thus the adaptation strategies for the local society.
2. Model, data, and experimental design
The model employed is the Regional Climate Model, version 4, (RegCM4) developed by the group of Earth System Physics at Abdus Salam International Center for Theoretical Physics (Giorgi et al. 2012). RegCM4 is updated from the previous version of RegCM2 (Giorgi et al. 1993a,b) and RegCM3 (Pal et al. 2007). The series of RegCMs were widely used to address research about climate change (Gao et al. 2011, 2012; Shi et al. 2009, 2011a,b; Ji and Kang 2013), extreme-events assessment (Gao et al. 2002; Shi et al. 2010), hydrology-resources assessment (Wu et al. 2012), aerosols’ effects (Ji et al. 2010, 2011; Zhang et al. 2009), land use changes (Gao et al. 2007; Zhang et al. 2010), short-term climate prediction (Ju and Lang 2011), and paleoclimate simulations (Ju et al. 2007).
RegCM4 is based on the hydrostatic version of the dynamical core of the fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) (Grell et al. 1994). Radiation transfer is computed using the radiative package of the NCAR Community Climate Model 3 (CCM3; Kiehl et al. 1996), and the land surface processes are carried out with the Biosphere–Atmosphere Transfer Scheme (BATS1e; Dickinson et al. 1993). The nonlocal boundary scheme is represented by Holtslag et al. (1990) while the ocean flux parameterization follows Zeng et al. (1998). Convective precipitation is using the mass flux scheme of Grell (1993) with Arakawa and Schubert–type closure (Arakawa and Schubert 1974) assumption.
Initial and lateral boundary conditions were obtained from the global model Beijing Climate Center Climate System Model, version 1.1 (BCC_CSM1.1). BCC_CSM1.1 is one of the Chinese models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al. 2012). It is composed of the following parts: the BCC_AGCM2.1 atmospheric model (Wu et al. 2010; Wu 2011), which is developed from the NCAR Community Atmosphere Model version 3 (CAM3) (Collins et al. 2004); the BCC Atmosphere-Vegetation-Interaction Model, version 1 (BCC_AVIM1) land surface model (Ji 1995); the ocean and sea ice module of the Modular Ocean Model, version 4, with 40 vertical levels (MOM4-L40) (Griffes et al. 2004); and the Sea Ice Simulator (SIS) from the Geophysical Fluid Dynamics Laboratory (GFDL). Horizontal resolution of BCC_AGCM2.1 is T42 (~280 km) and the vertical layers are 26. Previous evaluation of the model performance shows good results in simulating the present temperature and precipitation (Wu et al. 2010; Zhang et al. 2011). Land use types are based on observed data within China (Liu et al. 2003) and satellite data of Global Land Cover Characterization (GLCC) (Loveland et al. 2000) developed by the U.S. Geological Survey (USGS) outside China.
The experiments are completed by two steps. First, we use a period of 150 yr (1950–2099; the first year is considered as model spinup time) simulation (EXP1) over East Asia. In EXP1, the horizontal grid spacing is 50 km and the vertical configuration is set at 18 sigma layers with the model top at 10 hPa. The center of model is fixed at 35°N, 105°E, with 160 grids in the west–east direction and 109 grids for the north–south. Figure 1a shows the model domain and topography of EXP1. It includes China and its neighboring countries. The box is the double-nested region that covers the TP.
Model domain and topography (m) of (a) EXP1 and (b) EXP2: 1—Shiquanhe, 2—Gerze, 3—Shigatse, 4—Lhasa, 5—Amdo, 6—Yushu, 7—Delingha, 8—Golmud, 9—Nyingchi, and 10—Ganzi.
Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0155.1
Second, the results of EXP1 drive the simulation at 10-km resolution (EXP2). The reference of EXP2 is simulated from 1995 to 2005. The period of 2089–99 is under RCP4.5 and RCP8.5 scenarios. In EXP2, the first year is used as the spinup time and not analyzed. The center of model is set at 33°N, 88°E, with 288 grids in the west–east direction and 192 grids in the north–south. Figure 1b shows the double-nested model domain of EXP2. It covers the TP. Comparison of the two domains shows more details of topographic distribution than are described in Fig. 1a.
The grid datasets of CN05 (a 0.5° × 0.5° daily temperature dataset over China) (Xu et al. 2009) and Xie–Arkin (Xie et al. 2007) are used also to validate the simulated temperature and precipitation, respectively. The surface air temperature at 10 meteorological stations (marked from 1 to 10 in Fig. 1b) in the TP is compared with simulations. Details of the 10 sites are shown in Table 1.
Information for 10 meteorological stations in the TP.
3. Model performance
The annual mean temperature of the TP is lower compared to the other regions in China owing to its high altitude. The spatial distributions of temperature are greatly affected by the topography and show a warm center in the Qaidam Basin and cold regions over the Qilian Mountains in the northeastern TP. Low temperatures (<−6°C) are also found in the Hoh Xil and Kunlun Mountains, which are located in the north (Fig. 2a). The results of EXP1 (Fig. 2b) and EXP2 (Fig. 2c) basically represent the spatial distributions of temperature, though cold bias still exists. The EXP2 10-km model reflects more detailed spatial characteristics than EXP1, such as the cold regions over the Gangdese Mountains in the southwest and the warm areas along the Yalungtsangpo valley in southern TP.
Distribution of temperature (°C) from observations, EXP1, and EXP2 in the TP: (a)–(c) annual mean, (d)–(f) DJF, and (g)–(i) JJA.
Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0155.1
In December–February (DJF), the surface air temperature is usually below 0°C (Fig. 2d) on the plateau. The coldest (<−18°C) areas are located in the Kunlun and Qilian Mountains in the northern TP. In the southeast, it is warm with values of 0°–6°C. Compared with observations, the cold region of EXP1 (Fig. 2e) is enlarged in the northern TP, and it shows a center that is below −21°C. Though EXP2 also displays cold bias (Fig. 2f) in the northern and in the southwestern TP, it captures the warm center over the Yalungtsangpo valley in the southern plateau.
In June–August (JJA) the results of EXP1 (Fig. 2h) and EXP2 (Fig. 2i) show a similar distribution as the observations (Fig. 2g). More details are represented in EXP2 owing to its high resolution—for example, the low temperature areas over the Gangdese Mountains and over the Himalayas, for which the 50-km model is not competent.
The grid datasets of CN05 are obtained by interpolation of the observed data from meteorological stations. However, there are only few observation sites in the TP, especially over the mountains and in the valleys. That largely affects the accuracy of grid datasets over these regions (Xu et al. 2009). We select 10 meteorological stations in the TP and compare the observed data (black curve) with EXP1 (blue curve) and EXP2 (red curve) outputs in Fig. 3.
Monthly mean temperature (°C) of 10 meteorological stations, EXP1 and EXP2 in the TP.
Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0155.1
Figure 3 shows the surface air temperature of 10 stations. Both models represent the annual cycle of temperature of the TP. However, cold bias, which appears in the spatial distribution, still exists in most stations. The results of EXP2 resemble the observations better than those of EXP1. For example, in Shiquanhe, the bias between EXP1 and observations nearly exceeds −6°C from January to December, whereas it is about −3°C in EXP2. The deviations in JJA are smaller than in DJF. Meanwhile, the simulated temperature of EXP2 shows improvement of 3°–6°C compared with that of EXP1 in Gerze, Lhasa, Amdo, Golmud, Delingha, and Ganzi.
The precipitation distribution of the TP is shown in Fig. 4. The annual mean of grid observations present a decreasing pattern from southeast to northwest (Fig. 4a). The most arid region, with precipitation rates lower than 100 mm, is located in the Qaidam Basin in the northeastern TP. EXP1 (Fig. 4b) and EXP2 (Fig. 4c) can largely simulate the spatial distribution. However, the differences between observations and simulations appear in the mountain ranges, such as the Himalayas and Hengduan Mountains. Both models show more subtle features compared to the grid data, especially in the western TP.
Distribution of precipitation (mm) from observations, EXP1, and EXP2 in the TP: (a)–(c) annual mean, (d)–(f) DJF, and (g)–(i) JJA.
Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0155.1
The averaged precipitation in DJF is usually less than 100 mm over most regions of the TP. Results of EXP1 (Fig. 4e) and EXP2 (Fig. 4f) likely overestimate the observed winter precipitation values (Fig. 4d). This may be largely due to the quality of the observation data over these isolated regions with few stations available (Xu et al. 2009; Wu and Gao 2013; Shi et al. 2012). The precipitation grid dataset in northern TP is interpolated from the stations over the northern areas surrounding the Tarim Basin and Taklimakan Desert, which may lead to the great underestimation in the northern TP (Wu et al. 2011).
In JJA, the precipitation is greater than 700 mm except in the Qaidam and western TP (Fig. 4g). EXP2 (Fig. 4i) with 10-km resolution displays more detail than EXP1 (Fig. 4h) because of complex terrain. The possible reason of missing greater precipitation over the southern part of the Himalayas in the observation grid dataset may be due to the fact that the stations are usually located in the valleys, which experience less precipitation than the mountainous locations (Shi et al. 2012). We also compare the precipitation of 10 stations with the outputs of EXP1 and EXP2. The models generally can capture the annual cycle of 10 sites in the TP (Fig. 5); however, there are still deviations between simulations and observations. It is considered that model performances for temperature are substantially better than those for precipitation.
Monthly mean precipitation (mm) of 10 meteorological stations, EXP1, and EXP2 in the TP.
Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0155.1
Overall, EXP2 improves the cold errors of EXP1. The double-nested method is more effective for simulated temperature than precipitation. Both models can basically depict the distribution of precipitation and topographic effects; moreover, EXP2 seems more feasible compared to the outputs of grid datasets. However, overestimations in the mountains, which were also found in previous studies (Gao et al. 2008), were retained in this work. It is suggested that the uncertainties of observations greatly affect the validation of model performances. Moreover, more appropriate model physical schemes of TP precipitation also need to be updated.
4. Climate change over the Tibetan Plateau under RCP scenarios
Under the RCP4.5 scenario, the annual mean temperature increases by 1.5°–2.4°C (Fig. 6a) in most regions. A warming center is located in the southwestern TP and the value is greater than 2.1°C. In the southeast, the increases are between 1.5°–1.8°C, similar to the results pointed out in the previous works (Gao et al. 2001, 2003, 2011; Xu et al. 2006). There is a strong warming in DJF over the plateau (Fig. 6b). Temperature increases by 2.4°C in the east, while it exceeds 1.8°C in the central and western TP. Heat spots where the warming is greater than 2.4°C are also identified in Karakoram, Gangdise, and Himalaya Mountains. In JJA (Fig. 6c), the increased value is lower compared with that in DJF. The spatial distribution of increased temperature is similar to that change of annual mean. In the western TP and Qaidam Basin, they warm by 1.8°–2.1°C while the values are at the range of 0.8°–1.2°C in the eastern plateau.
Changes of temperature (°C) in the TP under the (a)–(c) RCP4.5 and (d)–(f) RCP8.5 scenarios: (a),(d) annual, (b),(e) DJF, and (c),(f) JJA.
Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0155.1
Under the RCP8.5 scenario, the distributions of changes in temperature are largely consistent with those under RCP4.5. However, the warming is substantially strong. For example, the annual mean temperature is increased by 3.9°C (Fig. 6d) over the TP. Values greater than 4.5°C are found in the Qilian, Karakoram, and Gangdise Mountains. The southeastern TP displays less warming with temperature increases of 3.3°–3.9°C. Compared with RCP4.5, the increased temperature is much more significant in DJF (Fig. 6e). It rises about 4.5°C over most regions while the hot center (>5°C) is located in the Hengduan Mountains. Greater warming with increases exceeding 4°C is displayed in the northeastern TP during JJA (Fig. 6f). The influence of elevation on the temperature is significant in the mountain regions over the TP (You et al. 2010b).
Changes of regional mean temperature of EXP1 and EXP2 simulations under RCP4.5 and RCP8.5 scenarios for each month are illustrated in Table 2. It is considered that the results of EXP2 describe more spatial details than those of EXP1, while the regional mean increases between them show no difference in the annual cycle. The greatest (2.6°C) and least (0.8°C) increases are found in January and March in both simulations. It is suggested that warming in DJF is greater than that in JJA. The annual averaged increases are 1.7° and 3.9°C under RCP4.5 and RCP8.5 scenarios, respectively.
Changes of regional mean (26°–40°N, 75°–105°E) temperature (°C) under the RCP4.5 and RCP8.5 radiative forcing (RF) scenarios.
Figure 7 shows precipitation changes of EXP1 under two scenarios over the TP. Annual mean precipitation is increased by 10%–25% in most areas under RCP4.5 scenario (Fig. 7a), in particular in the northern and western TP. However, it is decreased or with little change in the central and eastern TP. In DJF, the percentage of increases is enhanced and the value exceeds 50% in northern and southern regions (Fig. 7b). In JJA, the area with greatest temperature rise is located in the Karakoram Mountains with increases higher than 75% (Fig. 7c), whereas changes in the other areas of TP are in the range of −25% to +25%.
Changes of precipitation (%) in the TP under the (a)–(c) RCP4.5 and (d)–(f) RCP8.5 scenarios: (a),(d) annual, (b),(e) DJF, and (c),(f) JJA.
Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0155.1
The distributions of precipitation changes under RCP8.5 are similar to those of RCP4.5. The annual mean increase (>25%) is also presented in northern and western TP (Fig. 7d). Compared with the results of RCP4.5, the regions where the precipitation increases are expanded significantly. In DJF, changes of precipitation are more than 75% in the north TP while it is greater than 25% in the other areas (Fig. 7e). In JJA, precipitation is increased in the western TP and decreased in the eastern TP. The largest center is in the Karakoram Mountains and the value is beyond 75% (Fig. 7f).
Changes of regional mean precipitation of EXP1 and EXP2 are increased throughout the year (Table 3). Under the RCP4.5 scenario, both the greatest percentages of EXP1 (59.5%) and EXP2 (55.6%) are simulated in February, while the lowest values are shown in April (1.4%) and July (0.0%), respectively. The annual averaged changes of EXP1 and EXP2 simulation are increased by 18.9% and 21.8%. Under the RCP8.5 scenario, precipitation increases are much greater than those of RCP4.5. They exceed 80% in January and May in both models. Annual averaged changes are increased by 36.7% and 43.9% for EXP1 and EXP2, respectively. It is suggested that the percentage of precipitation changes in DJF are larger than those in JJA.
Changes of regional mean (26°–40°N, 75°–105°E) precipitation (%) under the RCP4.5 and RCP8.5 RF scenarios.
5. Conclusions and discussion
A double-nested method is used to simulate climate change over the TP at the resolution of 10 km. First, the model performance is validated by comparing simulations with observations. Then, future climate changes over the TP are evaluated under RCP4.5 and RCP8.5 scenarios, respectively.
The results show that RegCM4 can capture the spatial distribution and annual cycle of the surface air temperature over the TP. EXP2 reflects more spatial details that are affected by the complex terrain. EXP2 effectively improves the common cold bias of EXP1. Though the model can reproduce the basic patterns of precipitation, the performance is not as good as that of temperature.
Temperature will increase in the future over the TP. In general, greater warming will occur in DJF. Increased values are much more significant in the Gangdese Mountains and the Himalayas compared to the central TP. Furthermore, the warming under the RCP8.5 scenario is more enhanced than that under RCP4.5. Precipitation is mainly projected to increase in DJF, whereas it partly shows decreases in JJA in the southern TP. Under the RCP8.5 scenario, the spatial distributions of precipitation changes are consistent with those under the RCP4.5 scenario; however, the amplitude is much greater.
It should be noted that changes of regional mean temperature between EXP1 and EXP2 are similar, whereas the precipitation rates show larger differences. Thus, it is suggested that the uncertainties of simulated precipitation are greater than the modeled temperatures. Projection of precipitation over the TP under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 (Gao et al. 2011) and A1B (Shi 2010) shows decreases in the winter, as opposed to our results. Uncertainties are mainly generated by the emission scenarios of GHGs and the capabilities of model performance. Multimodel ensembles and comparisons are helpful to understand these uncertainties (Giorgi et al. 2009; Gao et al. 2012).
In this study, the double-nested method corrects the errors of simulated temperature over the TP. However, cold bias is still retained in the double-nested results. It is suggested that the physical parameters and processes in the models need to be updated in the future, especially regarding the surface process module, which is very important to correctly simulate climate in the TP (Yang et al. 2009; Shi et al. 2011a,b).
Meanwhile, the grid observational data that are used to assess the model performance contains uncertainties and might display low applicability in some regions of the TP (Wu and Gao 2013). For example, the precipitation on the northern slope of the Kunlun Mountains is actually heavy. However, there are no long-term meteorological stations. Thus, the modeled scenario does not represent the reality in the grid datasets over these areas (Shen and Liang 2004). To acquire an accurate grid dataset, it is necessary to collect more observations over the TP. Moreover, the interpolation method needs to be improved.
Acknowledgments
This study was supported by the Globe Change Research Program of China (Grant 2010CB951401) and National Nature Science Foundation of China (Grants 41190081 and 40830743).
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