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1278 JOURNAL OF THE ATMOSPHERIC SCIENCES VOLUME 70

Double-Nested Dynamical Downscaling Experiments over the Tibetan Plateau and Their Projection of Climate Change under Two RCP Scenarios

ZHENMING JI Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China

SHICHANG KANG Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, and State Key Laboratory of Cryospheric Science, Chinese Academy of Sciences, Beijing, China

(Manuscript received 30 May 2012, in ﬁnal form 7 November 2012)

ABSTRACT

A high-resolution regional climate model is used to simulate climate change over the Tibetan Plateau (TP). The model is driven at the grid spacing of 10 km by nesting the outputs of 50-km-resolution simulations. The results show that the models can capture the spatial and temporal distributions of the surface air temperature over the TP. The so-called double-nested method has a higher horizontal resolution and represents more spatial details. For example, the temperature simulations from the double-nested method reﬂect the observations better compared to the 50-km-resolution models. This is mainly due to the fact that topographical effects of complex terrains are detected better at higher resolution. Although both models can represent the basic patterns of precipitation, the simulated results are not as good as those of temperature. In the future, signiﬁcant warming seems to develop over the TP under two representative concentration pathway (RCP) scenarios. Greater increases occur in December–February (DJF) compared with June–August (JJA). The increasing temperature trend is more pronounced over the Gangdese Mountains and over the Himalayas than in the central TP. The projection of precipitation shows the main increases in DJF. In JJA, it predicts decreases or slight changes in the southern TP. The comparison between RCP8.5 and RCP4.5 scenarios shows a similar spatial distributions of temperature and precipitation, whereas the respective values of RCP8.5 are enhanced compared with those under RCP4.5.

1. Introduction Gangdise Mountains) and lakes (e.g., Nam Co, Qinhai Lake) are located in the TP. As the ‘‘water tower of Asia,’’ The Tibetan Plateau (TP), known as the ‘‘third pole,’’ the TP is also the cradle of the Yangtze, Yellow, Salween, displays the highest elevation and most complex surface Mekong, Brahmaputra, Indus, and Ganges Rivers. characteristics in the world. It is surrounded by the Mountains, glaciers, lakes, rivers, permafrost, and alpine Hengduan Mountains in the east, the Karakoram Moun- meadow coexist in the sensitive cryospheric environment. tains in the west, and the Himalaya Range, which sepa- As the ‘‘sensor’’ for global climate change (Schwalb rates South Asia and the TP in the south and the Kunlun et al. 2008), the temperature of the TP increased rapidly and Qilian Mountains in the north and northeast, re- (Kang et al. 2010) in recent decades. Warming could spectively. The altitude of the majority of these mountains lead to changes of agriculture (Qin 2002), ecology (Wu exceeds 6000 m. Many basins (e.g., Qaidam, Qiangtang et al. 2006; Klein et al. 2007), natural disasters (Yao Basins), valleys (e.g., Yalungtsangpo, Polungtsangpo 2010), hydrological processes, and water resources (Yao Canyons), mountains (e.g., Tanggula, Nyenchen Tanglha, 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). Corresponding author address: Zhenming Ji, Institute of Tibetan Plateau Research, Building 3, Courtyard 16, Lin Cui Road, However, the meteorological stations are scarce in the Chaoyang District, Beijing 100101, China. TP, especially in mountainous areas. This limits the re- E-mail: [email protected] search that has to be carried out.

DOI: 10.1175/JAS-D-12-0155.1

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Previous studies had analyzed climate change in the et al. 1993a,b) and RegCM3 (Pal et al. 2007). The series TP using the results of the general circulation models of RegCMs were widely used to address research about (GCMs) (Xu et al. 2003). However, the performance of climate change (Gao et al. 2011, 2012; Shi et al. 2009, GCMs was not good enough because of the coarse res- 2011a,b; Ji and Kang 2013), extreme-events assessment olution (Gao et al. 2008) that makes it difficult to cap- (Gao et al. 2002; Shi et al. 2010), hydrology-resources ture details of the surface characteristics in the TP. On assessment (Wu et al. 2012), aerosols’ effects (Ji et al. the other hand, regional climate models (RCMs) can 2010, 2011; Zhang et al. 2009), land use changes (Gao compensate for the shortage of lower grid space from et al. 2007; Zhang et al. 2010), short-term climate pre- GCMs. Thus, the downscaling results of RCMs show diction (Ju and Lang 2011), and paleoclimate simula- more realistic climatological distribution compared with tions (Ju et al. 2007). the GCM outputs (Shi 2010). However, the errors, es- RegCM4 is based on the hydrostatic version of the pecially the cold bias between RCMs and observations, dynamical core of the fifth-generation Pennsylvania were still obvious in the TP (Zhang et al. 2005; Shi et al. State University (PSU)–National Center for Atmo- 2011b). spheric Research (NCAR) Mesoscale Model (MM5) Generally, the horizontal grid space of RCM is at 30– (Grell et al. 1994). Radiation transfer is computed using 60 km, which is largely determined by the GCM’s res- the radiative package of the NCAR Community Cli- olution (the ratio of RCM and GCM resolutions should mate Model 3 (CCM3; Kiehl et al. 1996), and the land be in the range of 3–5) (Gao et al. 2011). However, that surface processes are carried out with the Biosphere– resolution does not perform well over the regions of Atmosphere Transfer Scheme (BATS1e; Dickinson complex terrain. Thus, much finer results can be ob- et al. 1993). The nonlocal boundary scheme is repre- tained by the double-nested technique (Leung and Qian sented by Holtslag et al. (1990) while the ocean flux 2003). Im et al. (2006) used a one-way double-nested parameterization follows Zeng et al. (1998). Convective method to simulate the present climate over the Korea precipitation is using the mass flux scheme of Grell Peninsula at 20-km grid space. And Wu et al. (2012) (1993) with Arakawa and Schubert–type closure (Arakawa investigated the climate effects of Three Gorges reser- and Schubert 1974) assumption. voir using two double-nested simulations. But relatively Initial and lateral boundary conditions were obtained few results were conducted with small domains and from the global model Beijing Climate Center Climate short simulated periods. Until now, there are few results System Model, version 1.1 (BCC_CSM1.1). BCC_ at the 10-km resolution over the TP. CSM1.1 is one of the Chinese models in phase 5 of the In this paper, we used a double-nested dynamic down- Coupled Model Intercomparison Project (CMIP5) scaling method and conducted simulations at 10-km (Taylor et al. 2012). It is composed of the following resolution over the TP. First, the model capability is parts: the BCC_AGCM2.1 atmospheric model (Wu evaluated by comparing with observations. Then, the et al. 2010; Wu 2011), which is developed from the NCAR projection of climatic change is displayed under two Community Atmosphere Model version 3 (CAM3) representative concentration pathway (RCP) scenarios. (Collins et al. 2004); the BCC Atmosphere-Vegetation- The RCP4.5 pathway is a stabilization of radiative Interaction Model, version 1 (BCC_AVIM1) land sur- 2 forcing at 4.5 W m 2 in 2100 and it represents a low- face model (Ji 1995); the ocean and sea ice module of emission scenario. The RCP8.5 pathway stands for a high the Modular Ocean Model, version 4, with 40 vertical level of greenhouse gas (GHG) emissions scenario and levels (MOM4-L40) (Griffes et al. 2004); and the Sea 2 GHGs’ radiative forcing is near 8.5 W m 2 in the end of Ice Simulator (SIS) from the Geophysical Fluid Dy- the twenty-first century (Moss et al. 2008). This work namics Laboratory (GFDL). Horizontal resolution of represents an early high-resolution regional climate sim- BCC_AGCM2.1 is T42 (;280 km) and the vertical layers ulation over the TP that may contribute to better un- are 26. Previous evaluation of the model performance derstanding the impact of climate change and thus the shows good results in simulating the present temperature adaptation strategies for the local society. 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 2. Model, data, and experimental design Characterization (GLCC) (Loveland et al. 2000) devel- The model employed is the Regional Climate Model, oped by the U.S. Geological Survey (USGS) outside China. version 4, (RegCM4) developed by the group of Earth The experiments are completed by two steps. First, we System Physics at Abdus Salam International Center for use a period of 150 yr (1950–2099; the first year is con- Theoretical Physics (Giorgi et al. 2012). RegCM4 is sidered as model spinup time) simulation (EXP1) over updated from the previous version of RegCM2 (Giorgi East Asia. In EXP1, the horizontal grid spacing is 50 km

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TABLE 1. Information for 10 meteorological stations in the TP.

No. Name Province Latitude Longitude Elevation (m) 1 Shiquanhe Tibet 328300N808050E 4278.6 2 Gerze Tibet 328090N848250E 4414.9 3 Shigatse Tibet 298150N888530E 3836.0 4 Lhasa Tibet 298400N918080E 3648.9 5 Amdo Tibet 328210N918060E 4800.0 6 Yushu Qinghai 338010N978010E 3681.2 7 Delingha Qinghai 378220N978220E 2981.5 8 Golmud Qinghai 368250N948540E 2807.6 9 Nyingchi Tibet 298400N948200E 2991.8 10 Ganzi Sichuan 318370N 1008000E 3393.5

(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.

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 FIG. 1. Model domain and topography (m) of (a) EXP1 and Qilian Mountains in the northeastern TP. Low tempera- (b) EXP2: 1—Shiquanhe, 2—Gerze, 3—Shigatse, 4—Lhasa, 5— tures (,268C) are also found in the Hoh Xil and Kunlun Amdo, 6—Yushu, 7—Delingha, 8—Golmud, 9—Nyingchi, and Mountains,whicharelocatedinthenorth(Fig.2a).The 10—Ganzi. results of EXP1 (Fig. 2b) and EXP2 (Fig. 2c) basically represent the spatial distributions of temperature, though and the vertical configuration is set at 18 sigma layers cold bias still exists. The EXP2 10-km model reflects with the model top at 10 hPa. The center of model is more detailed spatial characteristics than EXP1, such as fixed at 358N, 1058E, with 160 grids in the west–east di- the cold regions over the Gangdese Mountains in the rection and 109 grids for the north–south. Figure 1a southwest and the warm areas along the Yalungtsangpo shows the model domain and topography of EXP1. It valley in southern TP. includes China and its neighboring countries. The box is In December–February (DJF), the surface air tem- the double-nested region that covers the TP. perature is usually below 08C (Fig. 2d) on the plateau. Second, the results of EXP1 drive the simulation at The coldest (,2188C) areas are located in the Kunlun 10-km resolution (EXP2). The reference of EXP2 is and Qilian Mountains in the northern TP. In the simulated from 1995 to 2005. The period of 2089–99 is southeast, it is warm with values of 08–68C. Compared under RCP4.5 and RCP8.5 scenarios. In EXP2, the first with observations, the cold region of EXP1 (Fig. 2e) is year is used as the spinup time and not analyzed. The enlarged in the northern TP, and it shows a center that center of model is set at 338N, 888E, with 288 grids in the is below 2218C. Though EXP2 also displays cold bias west–east direction and 192 grids in the north–south. (Fig. 2f) in the northern and in the southwestern TP, it Figure 1b shows the double-nested model domain of captures the warm center over the Yalungtsangpo valley EXP2. It covers the TP. Comparison of the two domains in the southern plateau. shows more details of topographic distribution than are In June–August (JJA) the results of EXP1 (Fig. 2h) described in Fig. 1a. and EXP2 (Fig. 2i) show a similar distribution as the ThegriddatasetsofCN05(a0.5830.58 daily temper- observations (Fig. 2g). More details are represented in ature dataset over China) (Xu et al. 2009) and Xie–Arkin EXP2 owing to its high resolution—for example, the low

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FIG. 2. Distribution of temperature (8C) from observations, EXP1, and EXP2 in the TP: (a)–(c) annual mean, (d)–(f) DJF, and (g)–(i) JJA. temperature areas over the Gangdese Mountains and between EXP1 and observations nearly exceeds 268C over the Himalayas, for which the 50-km model is not from January to December, whereas it is about 238Cin competent. EXP2. The deviations in JJA are smaller than in DJF. The grid datasets of CN05 are obtained by in- Meanwhile, the simulated temperature of EXP2 shows terpolation of the observed data from meteorological improvement of 38–68C compared with that of EXP1 in stations. However, there are only few observation sites Gerze, Lhasa, Amdo, Golmud, Delingha, and Ganzi. in the TP, especially over the mountains and in the The precipitation distribution of the TP is shown in valleys. That largely affects the accuracy of grid datasets Fig. 4. The annual mean of grid observations present over these regions (Xu et al. 2009). We select 10 mete- a decreasing pattern from southeast to northwest (Fig. orological stations in the TP and compare the observed 4a). The most arid region, with precipitation rates lower data (black curve) with EXP1 (blue curve) and EXP2 than 100 mm, is located in the Qaidam Basin in the (red curve) outputs in Fig. 3. northeastern TP. EXP1 (Fig. 4b) and EXP2 (Fig. 4c) can Figure 3 shows the surface air temperature of 10 sta- largely simulate the spatial distribution. However, the tions. Both models represent the annual cycle of tem- differences between observations and simulations ap- perature of the TP. However, cold bias, which appears in pear in the mountain ranges, such as the Himalayas and the spatial distribution, still exists in most stations. The Hengduan Mountains. Both models show more subtle results of EXP2 resemble the observations better than features compared to the grid data, especially in the those of EXP1. For example, in Shiquanhe, the bias western TP.

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FIG. 3. Monthly mean temperature (8C) of 10 meteorological stations, EXP1 and EXP2 in the TP.

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FIG. 4. Distribution of precipitation (mm) from observations, EXP1, and EXP2 in the TP: (a)–(c) annual mean, (d)–(f) DJF, and (g)–(i) JJA.

The averaged precipitation in DJF is usually less than may be due to the fact that the stations are usually lo- 100 mm over most regions of the TP. Results of EXP1 cated in the valleys, which experience less precipitation (Fig. 4e) and EXP2 (Fig. 4f) likely overestimate the than the mountainous locations (Shi et al. 2012). We also observed winter precipitation values (Fig. 4d). This may compare the precipitation of 10 stations with the outputs be largely due to the quality of the observation data over of EXP1 and EXP2. The models generally can capture these isolated regions with few stations available (Xu the annual cycle of 10 sites in the TP (Fig. 5); however, et al. 2009; Wu and Gao 2013; Shi et al. 2012). The there are still deviations between simulations and ob- precipitation grid dataset in northern TP is interpolated servations. It is considered that model performances from the stations over the northern areas surrounding for temperature are substantially better than those for the Tarim Basin and Taklimakan Desert, which may precipitation. lead to the great underestimation in the northern TP Overall, EXP2 improves the cold errors of EXP1. The (Wu et al. 2011). double-nested method is more effective for simulated In JJA, the precipitation is greater than 700 mm temperature than precipitation. Both models can basi- except in the Qaidam and western TP (Fig. 4g). EXP2 cally depict the distribution of precipitation and topo- (Fig. 4i) with 10-km resolution displays more detail than graphic effects; moreover, EXP2 seems more feasible EXP1 (Fig. 4h) because of complex terrain. The possible compared to the outputs of grid datasets. However, over- reason of missing greater precipitation over the south- estimations in the mountains, which were also found in ern part of the Himalayas in the observation grid dataset previous studies (Gao et al. 2008), were retained in this

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FIG. 5. Monthly mean precipitation (mm) of 10 meteorological stations, EXP1, and EXP2 in the TP.

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FIG. 6. Changes of temperature (8C) 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. work. It is suggested that the uncertainties of obser- TP and the value is greater than 2.18C. In the southeast, vations greatly affect the validation of model perfor- the increases are between 1.58–1.88C, similar to the re- mances. Moreover, more appropriate model physical sults pointed out in the previous works (Gao et al. 2001, schemes of TP precipitation also need to be updated. 2003, 2011; Xu et al. 2006). There is a strong warming in DJF over the plateau (Fig. 6b). Temperature increases by 2.48C in the east, while it exceeds 1.88C in the central 4. Climate change over the Tibetan Plateau under and western TP. Heat spots where the warming is RCP scenarios greater than 2.48C are also identiﬁed in Karakoram, Under the RCP4.5 scenario, the annual mean tem- Gangdise, and Himalaya Mountains. In JJA (Fig. 6c), perature increases by 1.58–2.48C (Fig. 6a) in most re- the increased value is lower compared with that in DJF. gions. A warming center is located in the southwestern The spatial distribution of increased temperature is

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TABLE 2. Changes of regional mean (268–408N, 758–1058E) western TP. However, it is decreased or with little temperature (8C) under the RCP4.5 and RCP8.5 radiative forcing change in the central and eastern TP. In DJF, the per- (RF) scenarios. centage of increases is enhanced and the value exceeds RCP4.5-RF RCP8.5-RF 50% in northern and southern regions (Fig. 7b). In JJA, Month EXP1 EXP2 EXP1 EXP2 the area with greatest temperature rise is located in the Karakoram Mountains with increases higher than 75% 1 2.6 2.6 5.1 4.9 2 1.1 1.1 3.8 3.9 (Fig. 7c), whereas changes in the other areas of TP are in 3 0.8 0.8 3.2 3.3 the range of 225% to 125%. 4 1.7 1.7 4.4 4.4 The distributions of precipitation changes under 5 2.2 2.2 3.9 3.8 RCP8.5 are similar to those of RCP4.5. The annual 6 1.6 1.5 3.5 3.4 mean increase (.25%) is also presented in northern and 7 1.6 1.5 3.6 3.5 8 1.3 1.4 3.4 3.4 western TP (Fig. 7d). Compared with the results of 9 1.6 1.7 4.0 4.0 RCP4.5, the regions where the precipitation increases 10 1.8 1.8 3.8 3.8 are expanded significantly. In DJF, changes of pre- 11 2.4 2.4 4.2 4.3 cipitation are more than 75% in the north TP while it is 12 2.2 2.1 4.2 4.3 greater than 25% in the other areas (Fig. 7e). In JJA, Average 1.7 1.7 3.9 3.9 precipitation is increased in the western TP and decreased in the eastern TP. The largest center is in the Karakoram similar to that change of annual mean. In the western Mountains and the value is beyond 75% (Fig. 7f). TP and Qaidam Basin, they warm by 1.88–2.18C while Changes of regional mean precipitation of EXP1 and the values are at the range of 0.88–1.28C in the eastern EXP2 are increased throughout the year (Table 3). plateau. Under the RCP4.5 scenario, both the greatest percent- Under the RCP8.5 scenario, the distributions of ages of EXP1 (59.5%) and EXP2 (55.6%) are simulated changes in temperature are largely consistent with those in February, while the lowest values are shown in April under RCP4.5. However, the warming is substantially (1.4%) and July (0.0%), respectively. The annual aver- strong. For example, the annual mean temperature is aged changes of EXP1 and EXP2 simulation are in- increased by 3.98C (Fig. 6d) over the TP. Values greater creased by 18.9% and 21.8%. Under the RCP8.5 scenario, than 4.58C are found in the Qilian, Karakoram, and precipitation increases are much greater than those of Gangdise Mountains. The southeastern TP displays less RCP4.5. They exceed 80% in January and May in both warming with temperature increases of 3.38–3.98C. models. Annual averaged changes are increased by 36.7% Compared with RCP4.5, the increased temperature is and 43.9% for EXP1 and EXP2, respectively. It is sug- much more significant in DJF (Fig. 6e). It rises about gested that the percentage of precipitation changes in 4.58C over most regions while the hot center (.58C) is DJF are larger than those in JJA. located in the Hengduan Mountains. Greater warming with increases exceeding 48C is displayed in the north- 5. Conclusions and discussion eastern TP during JJA (Fig. 6f). The influence of elevation on the temperature is significant in the mountain A double-nested method is used to simulate climate regions over the TP (You et al. 2010b). change over the TP at the resolution of 10 km. First, the Changes of regional mean temperature of EXP1 and model performance is validated by comparing simula- EXP2 simulations under RCP4.5 and RCP8.5 scenarios tions with observations. Then, future climate changes for each month are illustrated in Table 2. It is considered over the TP are evaluated under RCP4.5 and RCP8.5 that the results of EXP2 describe more spatial details scenarios, respectively. than those of EXP1, while the regional mean increases The results show that RegCM4 can capture the spatial between them show no difference in the annual cycle. distribution and annual cycle of the surface air tempera- The greatest (2.68C) and least (0.88C) increases are found ture over the TP. EXP2 reflects more spatial details that in January and March in both simulations. It is suggested are affected by the complex terrain. EXP2 effectively that warming in DJF is greater than that in JJA. The improves the common cold bias of EXP1. Though the annual averaged increases are 1.78 and 3.98C under model can reproduce the basic patterns of precipitation, RCP4.5 and RCP8.5 scenarios, respectively. the performance is not as good as that of temperature. Figure 7 shows precipitation changes of EXP1 under Temperature will increase in the future over the TP. two scenarios over the TP. Annual mean precipitation is In general, greater warming will occur in DJF. Increased increased by 10%–25% in most areas under RCP4.5 values are much more significant in the Gangdese Moun- scenario (Fig. 7a), in particular in the northern and tains and the Himalayas compared to the central TP.

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FIG. 7. 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.

Furthermore, the warming under the RCP8.5 scenario is precipitation are greater than the modeled tempera- more enhanced than that under RCP4.5. Precipitation is tures. Projection of precipitation over the TP under the mainly projected to increase in DJF, whereas it partly Intergovernmental Panel on Climate Change (IPCC) shows decreases in JJA in the southern TP. Under the Special Report on Emissions Scenarios (SRES) A2 RCP8.5 scenario, the spatial distributions of precipitation (Gao et al. 2011) and A1B (Shi 2010) shows decreases changes are consistent with those under the RCP4.5 sce- in the winter, as opposed to our results. Uncertainties nario; however, the amplitude is much greater. are mainly generated by the emission scenarios of GHGs It should be noted that changes of regional mean and the capabilities of model performance. Multi- temperature between EXP1 and EXP2 are similar, model ensembles and comparisons are helpful to under- whereas the precipitation rates show larger differences. stand these uncertainties (Giorgi et al. 2009; Gao et al. Thus, it is suggested that the uncertainties of simulated 2012).

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TABLE 3. Changes of regional mean (268–408N, 758–1058E) as coupled to the NCAR community climate model. National precipitation (%) under the RCP4.5 and RCP8.5 RF scenarios. Center for Atmospheric Research Tech. Note NCAR/TN- 3871STR, 72 pp. RCP4.5-RF RCP8.5-RF Gao, X. J., Z. C. Zhao, Y. H. Ding, R. H. Huang, and G. Filippo, Month EXP1 EXP2 EXP1 EXP2 2001: Climate change due to greenhouse effects in China as simulated by a regional climate model. Adv. Atmos. Sci., 18, 1 7.5 8.4 84.4 90.3 2 59.5 55.6 55.7 65.5 1224–1230. 3 25.7 29.1 26.6 29.2 ——, ——, and G. Filippo, 2002: Changes of extreme events in 4 1.4 15.9 38.6 66.2 regional climate simulations over East Asia. Adv. Atmos. Sci., 5 37.1 35.3 80.7 88.3 19, 927–942. 6 7.6 15.3 20.9 19.6 ——, ——, Y. H. Ding, R. H. Huang, and F. Giorgi, 2003: Climate 7 3.0 0.0 9.3 15.9 change due to greenhouse effects in China as simulated by a 8 26.0 18.0 26.6 24.2 regional climate model. Part II: Climate change. Acta Meteor. 9 20.1 34.0 41.8 56.5 Sin., 17, 417–428. 10 13.4 15.5 17.0 20.5 ——, D. F. Zhang, Z. X. Chen, J. S. Pal, and F. Giorgi, 2007: Land 11 7.5 12.8 8.7 14.3 use effects on climate in China as simulated by a regional 12 18.1 21.9 30.3 35.9 climate model. Sci. China, Ser. D: Earth Sci., 50, 620–628. Average 18.9 21.8 36.7 43.9 ——, Y. Shi, R. Song, F. Giorgi, Y. Wang, and D. Zhang, 2008: Reduction of future monsoon precipitation over China: Comparison between a high resolution RCM simulation and In this study, the double-nested method corrects the the driving GCM. Meteor. Atmos. Phys., 100, 73–86. ——, ——, and F. Giorgi, 2011: A high resolution simulation of errors of simulated temperature over the TP. However, climate change over China. Sci. China Earth Sci., 54, 462–472. cold bias is still retained in the double-nested results. It is ——, ——, D. Zhang, J. Wu, F. Giorgi, Z. Ji, and Y. 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