Responses of vegetation distribution to climate change in China

Transcription

1 Theor Appl Climatol DOI /s ORIGINAL PAPER Responses of vegetation distribution to climate change in China Dongsheng Zhao & Shaohong Wu Received: 21 September 2012 / Accepted: 8 July 2013 # Springer-Verlag Wien 2013 Abstract Climate plays a crucial role in controlling vegetation distribution and climate change may therefore cause extended changes. A coupled biogeography and biogeochemistry model called BIOME4 was modified by redefining the bioclimatic limits of key plant function types on the basis of the regional vegetation climate relationships in China. Compared to existing natural vegetation distribution, BIOME4 is proven more reliable in simulating the overall vegetation distribution in China. Possible changes in vegetation distribution were simulated under climate change scenarios by using the improved model. Simulation results suggest that regional climate change would result in dramatic changes in vegetation distribution. Climate change may increase the areas covered by tropical forests, warm-temperate forests, savannahs/dry woodlands and grasslands/dry shrublands, but decrease the areas occupied by temperate forests, boreal forests, deserts, dry tundra and tundra across China. Most vegetation in east China, specifically the boreal forests and the tropical forests, may shift their boundaries northwards. The tundra and dry tundra on the Tibetan Plateau may be progressively confined to higher elevation. 1 Introduction The increase in atmospheric greenhouse gases may result in significant changes in the climate (IPCC 2007). As a country located in a region dominated by East Asian monsoon, China is vulnerable to global climate change. The projections of numerous general circulation models (GCMs) indicate that D. Zhao : S. Wu (*) Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, No. 11 A, Datun Road, Anwai, Beijing , China wush@igsnrr.ac.cn China may experience an increase in surface air temperature, a rise in the frequency of extreme climate events, an enhancement of spatial and temporal heterogeneity in precipitation and an enlargement of arid regions in the future (TCNARCC 2011). These changes in climate can affect certain ecological processes, patterns and structures of natural ecosystems, consequently altering the goods and services provided by ecosystems to society. Therefore, an important aspect of the research on global climate change is to reveal the responses and adaptability of ecosystems to climate change by investigating the interrelationship between climate change and ecosystems at different scales (Cox et al. 2004; Hitz and Smith 2004; Wilson et al. 2005; Jiang et al. 2011). China is dominated by the monsoon climate and has a vast land area that covers approximately 10 % of the total world land area. Thus, the climate types in China vary from tropical in the south to cold temperate in the north and from humid in the east to dry in the west. These diverse climates together with the complex topography bring about the high biodiversity in China. Most of the main vegetation types in the world can be found in China. However, the species composition and the diversity of vegetation in China are sensitive to climate change, and large-scale vegetation distribution is vulnerable to climate warming (Ni 2011; Zhao et al. 2011). Ni (2011) reported that the climate warming and the general increase in precipitation that occurred in China during the past century lengthened the growing season of vegetation and modified the composition and geographical pattern of vegetation, particularly in ecotones and tree lines. A great number of paleobotanical studies (Davis and Botkin 1985; Prentice 1986; Huntley 1990; Prentice et al. 1991) have confirmed the climate-induced changes in ecosystems. Studies on the development and evolution of vegetation show that the vegetation on the Tibetan Plateau is dominated by mountain forests during warmer periods and by tundra during colder periods (Shi et al. 1998).

2 D. Zhao, S. Wu Changes in climate can also alter the distribution pattern of vegetation in China. Certain simulations were undertaken to assess the potential effect of climate change on vegetation. Zhang and Yang (1993) utilized Holdridge's life zone scheme to analyse the influence of climate change on potential vegetation distribution. By using the BIOME3 model, Ni et al. (2001) simulated the potential vegetation distribution and predicted the changes in vegetation distribution under climate change. Weng and Zhou (2006) developed a potential distribution model to analyse the response of potential vegetation in China to climate change. However, these studies primarily focused on the applicability of the models in China. Moreover, their projections for future effects were based on climate scenarios generated by GCMs at a resolution of 200 to 300 km, which is too coarse for regional studies. The initiative to study the potential effects of climate change is motivated by the increasing scientific and political interests in the interrelationship between climate change and ecosystems in regional levels. To date, few studies have investigated the influence of regional climate scenarios on potential vegetation. The biogeographical and biogeochemical aspects of ecological responses to environmental change are interdependent to each other. BIOME4 (Kaplan et al. 2003), which is a process-based terrestrial biosphere model, combines biogeographical and biogeochemical modelling approaches within single framework to simulate the distribution and the structure of global vegetation and its biogeochemical processes. In this study, BIOME4 was adopted to model vegetation dynamics. The model parameters were modified, and the model was calibrated for the study region on the basis of the distribution and the eco-physiological features of the vegetation types in China. The modified model was used to project the future effects of climate change on vegetation distribution according to regional climate scenarios. 2 Methods and data 2.1 The BIOME4 model BIOME4 (Kaplan et al. 2003) is a coupled biogeography and biogeochemistry model that simulates the distribution and structure of global vegetation. The model functions on the basis of 12 plant functional types (PFTs), which represent all major vegetation types on earth from the arctic tundra to the tropical rainforest. The computational core of BIOME4 is a coupled carbon and water flux scheme. This scheme determines the leaf area index (LAI) that maximizes the net primary productivity (NPP) for any given PFT on the basis of the daily time step simulation of soil water balance, canopy conductance, photosynthesis and respiration. BI- OME4 implicitly simulates competition between PFTs as a function of the relative value of NPP. On the basis of the identity of the most successful and second most successful PFT and their sustainable LAI, each model grid cell is assigned a biome according to a set of semi-empirical rules. The model is sensitive to changes in climate because of the NPP responses and the stomatal conductance to water and heat (Kaplan 2001). BIOME4 has a horizontal grid resolution of km. The model inputs include CO 2, soil texture, monthly surface temperature, precipitation and cloudiness. We provide only a short overview on BI- OME4 because Kaplan et al. (2003) have already described BIOME4 in detail. BIOME4 can predict the distributions of 26 biomes altogether. For simplicity of analysis and facilitative comparisons, the biome classification used in BIOME4 was reconstructed following the assignment scheme used by Harrison and Prentice (2003) to generate nine mega-biomes (tropical forest, warm-temperate forest, temperate forest, boreal forest, savanna and dry woodland, grassland and dry shrubland, desert, dry tundra, tundra) that would represent the vegetation types in China (Table 1). A set of widely accepted bioclimatic and eco-physiological parameters was used to define PFTs and to assign biomes (Prentice et al. 1992; Haxeltine and Prentice 1996; Kaplan et al. 2003). However, some of parameters were not suitable for PFTs in China. Thus, certain the bioclimate limiting factors were updated for key PFTs on the basis of the relationship between climate and vegetation distribution in China and published literature to capture the boundary of vegetation types in China more accurately (CVEC, CAS 1980; Ni et al. 2001; CVAEC, CAS 2001; Zhang et al. 2007; Weng and Zhou 2006). For example, the model tends to overestimate the elevation of alpine tree lines at lower latitudes. Thus, a minimum heat requirement was set for boreal forest at 500 growing degree days (5 C base). Given that tropical forest in China mainly consist of tropical monsoon forests, distinguishing evergreen forest from deciduous forest in the actual vegetation map is difficult. Therefore, tropical evergreen forest and tropical raingreen forest were unified into tropical monsoon forest (Table 2). 2.2 Model of biome mean centre The biome mean centre is the average x and y coordinates of a given biome in the study area. The biome mean centre is usually used to track changes in the distribution or to compare the distributions of different types of biomes. The biome mean centre is given as follows (Hart 1954; Yue et al ) XðÞ¼ t Xnt ðþ x i ðþ t i¼1 nt ðþ ; YðÞ¼ t Xnt ðþ y i ðþ t i¼1 nt ðþ ð1þ

3 Responses of vegetation in China Table 1 Allocation of original biome used in BIOME4 to mega-biome classification used in this study Original biome classification Tropical evergreen rainforest Tropical semi-deciduous forest Tropical deciduous forest Warm mixed forest Temperate deciduous forest Temperate conifer forest Cool mixed forest Cool conifer forest Cold mixed forest Evergreen taiga/montane forest Deciduous taiga/montane forest Tropical savanna Temperate sclerophyll woodland Temperate broadleaved savanna Open conifer woodland Tropical xerophytic shrubland Temperate xerophytic shrubland Tropical grassland Temperate grassland Desert Barren Graminoid forb tundra Shrub tundra Dwarf shrub tundra Prostrate shrub tundra Cushion forbs lichen moss tundra where t is the period of climate change; x i and y i are the longitude and latitude coordinates for the ith grid cell of a given biome type, respectively; and n is equal to the total number of a given biome type. ( XðÞ; t YðÞ t ) is the mean centre of a given biome type. The shift distance and direction of the biome type from t to t +1 are respectively calculated in ArcGIS 10.0 software (ESRI, Redlands, California, USA). 2.3 Kappa statistic Kappa statistic is widely used to assess the agreement between two maps with categorical data. The kappa statistic κ is formulated as follows. κ ¼ ðp o p e Þ Mega-biome classification used in this study Tropical forest Warm-temperate forest Temperate forest Boreal forest Savanna and dry woodland Grassland and dry shrubland Desert Dry tundra Tundra. ð1 p e Þ: ð2þ κ is approximately zero when agreement is no better than random and reaches unity when agreement is perfect. By letting p ij be the proportion of the total number of grid cells that belongs to category i in one map and belongs to the category j in another, the sum of these proportions is the overall proportion of observed agreement p o, as follows. m p o ¼ X p ii ð3þ i¼1 where m is the number of vegetation types and p ii is the proportions of grid cells on which both maps agree. The overall expected value of agreement p e due to chance alone is calculated as. m p e ¼ X p i :p: i ð4þ i¼1 An individual kappa statistic can also be calculated for each category i, as follows.. h. i κ i ¼ ðp ii p i :p: i Þ ðp i : þ p: i Þ 2 p i :p: i : ð5þ As suggested by Monserud and Leemans (1992), a kappa value <0.4 is considered poor or very poor. A kappa value ranging from 0.4 to 0.55 is considered fair, from 0.55 to 0.7 good, from 0.7 to 0.85 very good, and >0.85 excellent. 2.4 Climate data The climate data used in this study were provided by the climate change research group of the Institute of Environment and Sustainable Development in Agriculture of the Chinese Academy of Agricultural Sciences. The research group used the Providing Regional Climate for Impacts Studies (PRECIS) system (Jones et al. 2004) to create highresolution (50 50 km) climate data scenarios in China for the late twenty-first century on the basis of greenhouse gas emission scenarios of the IPCC Special Report on Emission Scenarios (SRES) (Xu 2004). The general circulation model UKMO_HadCM3 was used to obtain the boundary conditions for PRECIS simulations. UKMO_HadCM3 is considered more effective in simulating the spatial and temporal characteristics of climate change in China compared with the other global circulation models that contributed to the fourth assessment report of the IPCC (Miao et al. 2012). The effectiveness of PRECIS to simulate terrestrial climate change in China was validated by applying the reanalysis data derived from the European Centre for Medium-Range Weather Forecasts as lateral boundary conditions. The regional climate scenarios simulated by PRECES have been widely utilized to assess the effect of climate change on the ecosystems in China (Xiong et al. 2009, Wu et al. 2010, Zhao et al. 2013a). The projected climatic data that include the A2, B2 and A1B emission scenarios from 1961 to 2080 were used in this study. Among all the IPCC proposed scenarios, A1B is considered a high-emission scenario that is characterized by rapid economic growth, global population that

4 D. Zhao, S. Wu Table 2 Bioclimate limiting factors for each plant functional type PFTs T c min ( C) T c max ( C) T min min ( C) T min max ( C) GDD 5 min GDD 0 min T wm min ( C) T wm max ( C) Tropical monsoon forest [Tropical 3 [0] 12 [10] evergreen forest; Tropical raingreen forest] Temperate broadleaved forest 8 5 1,500 [1,200] 12 [10] Temperate deciduous forest 2 [ 15] 8 1,200 Temperature evergreen conifer forest Boreal evergreen forest 25 [ 32.5] [no limit] 21 Boreal deciduous forest [no limit] 21 Temperature grass Tropical grass 3 10 Desert woody plant Tundra shrub Cold herbaceous [15] Lichen/forb 10 [15] The original PFTs in square brackets in the first column were unified into a new PFT in this study. The original parameter values in square brackets given by Kaplan (2001) are replaced by the italics value in this study T c mean temperature of the coldest month, T min absolute minimal temperature, GDD 5 the growing degree days of over 5 C, GDD 0 the growing degree days of over 0 C, T wm mean temperature of the warmest month peaks in the mid-century and declines thereafter, and rapid introduction of new and more efficient technologies. A2 is the scenario with high emissions of greenhouse gases. In A2, self-reliance and local identities are emphasized, population increases continuously, and economic development is regionally oriented. B2 represents moderate emissions of the greenhouse gases. B2 is characterized by a continuous but moderate increase in population and a moderate economic growth that focuses on local solutions to economic, social and environmental stability. The periods were divided into the baseline term ( ), the near-term ( ), the mid-term ( ) and the long-term ( ). Each term was evaluated according to the average simulation of 30 years. The projection from PRECIS indicates that the annual average temperature in China during the long-term will increase from 3.80 C (A1B) to 2.63 C (B2) compared with the baseline term (Table 3), in which the highest temperature increase of approximately 5 C (A1B) is determined in northwest China. The lowest temperature increase may occur in southwest and southeast China, with an average increment of 2 C (B2) (Fig. 1). The total annual precipitation in the long-term scenario is projected to increase from % (A1B) to 9.3 % (B2) (Table 3).Thehighesttemperatureincreaseisprojectedto occur in northwest China and is 60 % greater than the baseline term. A slight decrease of approximately 5 % may exist in northeast China (Fig. 2). 2.5 Vegetation data The simulated output by BIOME4 was compared with the potential distribution of natural vegetation derived from the Vegetation Atlas of China at a scale of 1:1 million (CVAEC, CAS 2001). ArcGIS 10.0 (ESRI, Redlands, California, USA) was used to transform the digitalized vegetation map into a raster dataset in the ArcInfo Grid format with spatial resolution of km. A total of 573 actual vegetation types were reclassified into 26 potential biome types in the BIOME4 according to the scheme formulated by Ni et al. Table 3 Average changes in temperature and precipitation under SRES A1B, A2 and B2 scenarios in China from PRECIS simulation relative to baseline ( ) Periods Temperature ( C) Precipitation (%) A1B A2 B2 A1B A2 B2 Near-term ( ) Mid-term ( ) Long-term ( )

5 Responses of vegetation in China Fig. 1 Change of annual mean temperature in near-term ( ) (top), mid-term ( ) (middle) and long-term ( ) (bottom) relative to the baseline term ( ) projected by the PRECIS under SRES A1B (left), A2 (middle) and B2 (right) scenarios (2001, 2011), who produced a potential vegetation for China by using 1:4 million actual vegetation map on the basis of the floristic and bioclimate criteria. The 26 biomes were further reconstructed to the same nine major biomes in the same way as for the BIOME4 PFTs. 2.6 Soil data In this study, the soil texture data from the map of soil texture types (1:14,000,000) (Zhang et al. 2004) were adopted. The data contain the geographical distribution of different soil texture types and the proportions of mineral grains in the top soil. The soil textures were reclassified as clay, silt, sand, silty sand, sandy clay, silty clay and clay with silt and sand according to the FAO classification standard for soil texture (Ni et al. 2001) to meet the requirements of BIOME4. The soil data were then transformed into grid format and resampled to the spatial resolution of km. 3 Results 3.1 Comparison of simulated result The simulated vegetation distribution (Fig. 3a) was compared with the natural vegetation map of China (Fig. 3b). The result agrees well with an overall kappa value of The modified BIOME4 captured most vegetation distribution and boundaries accurately. All kappa values for the individual vegetation types are listed in Table 4. The kappa values indicate good simulation results for warmtemperate forest, temperate forest and desert; fair results

6 D. Zhao, S. Wu Fig. 2 Change of annual mean precipitation in near-term ( ) (top), mid-term ( ) (middle) and long-term ( ) (bottom) relative to the baseline term ( ) projected by the PRECIS under SRES A1B (left), A2 (middle) and B2 (right) scenarios for tropical forest, boreal forest, grassland and dry shrubland; but poor results for savanna and dry woodland, dry tundra and tundra. In the simulated vegetation distribution, dry woodland was mixed with other types instead of occupying clearly defined areas. Dry woodland and temperate forest shared the same PFT during simulation. However, they are differentiated spatially in biome assignment according to whether their PFT is a primary PFT or not. Sharing key PFT among biomes is a key reason for mismatches because the model cannot differentiate the biomes under similar bioclimatic conditions. Figure 1 shows that tundra and dry tundra types are generally not simulated accurately and the modified BIOME4 cannot predict their boundaries accurately. This problem may be a matter of PFT definition and biome assignment. Certain issues on vegetation classification made the assignment of alpine vegetation to tundra biomes difficult or incorrect in certain cases, consequently affecting the comparisons between the simulated result and the actual vegetation distribution. 3.2 Effect of climate change on simulated vegetation distribution The overall vegetation distribution was not changed significantly by climate change in the near-term compared with the baseline term. However, the grassland and dry shrubland biome and the savanna and dry woodland biome are sensitive to warming climate. These types of areas are expected to increase significantly from the baseline term (Table 5). Grassland and dry shrubland will begin to invade the Greater and the Lesser Higgnan Mountains (Figs. 4 and 5), replacing the temperate forest and the boreal forest. Meanwhile, grassland and dry shrubland will spread slightly to the interior of the Tarim Basin, particularly under the A1B scenario, in which the mean centre moved towards the west (Fig. 6). In

7 Responses of vegetation in China Fig. 3 Vegetation distribution simulated by the modified BIOME4 model (a) and derived from the Vegetation Atlas of China (b) north China, savanna and dry woodland will expand to temperate forests in relatively arid areas and in certain calcareous or salinized soils, especially under the B2 scenario, in which the mean centre will move 191 km southwestwards (Table 6). In addition, most forest biomes in China will begin to shift their northern boundary forward (Fig. 6). On the Tibetan Plateau, the dry tundra under A1B, A2 and B2 scenarios will slightly decline compared with that in the baseline term. No obvious changes were observed in the distribution of forest vegetation.

8 D. Zhao, S. Wu Table 4 Discrimination accuracy of simulated vegetation types in China by the modified BIOME4 Vegetation types Accuracy (%) Kappa value Tropical forest Warm-temperate forest Temperate forest Boreal forest Savanna/dry woodland Grassland/dry shrubland Desert Dry tundra Tundra Overall For the mid-term, the boreal forest in northeast China will experience a substantial northward shift in its distribution and will be partly replaced by expanding grasslands (Fig. 5). Thus, the mean centre of the boreal forest will experience maximum shift distance longer than 470 km towards the southwest under the A1B scenario (Table 7). The savanna and dry woodland biome and the grassland and dry shrubland biome will follow the trend of that in the near-term. In north China, the savanna and dry woodland biome is expected to expand, changing from the existing mixed distribution with temperate forest to a more continues coverage, particularly in A1B and A2 scenarios. The temperate forest will move its mean centre towards northeast (Table 7), and the distribution area of the temperate forest will continue to decrease. The mean centre of the warm-temperate forest is expected to experience a northeastward shift (Fig. 6), in which a significant area of the biome expands. The temperate forests around the Sichuan Basin (Fig. 4) will also be replaced by warm-temperate forests composed of continues coverage together with the eastern warmtemperate forest. The tropical forest will spread to cover an area that is twice as large as than that in the baseline (Table 5). However, this area will occupy approximately less than 3 % of the total land area of China. On the Tibetan Plateau, the temperate forest and the boreal forest will begin to invade the interior of the plateau and will mostly replace the original tundra and dry tundra, resulting in a decline in the area of these two vegetation types. For the long-term under the warmest climate, the boreal forest in the northern part of China is projected to shrink to a scattering of small fragments in the Great Higgnan Mountains. On the Tibetan Plateau, the distribution of the boreal forest will continue to expand into the interior of the plateau (Fig. 5). The mean centre of the boreal forest will experience a prominent 400 km shift towards the southwest (Table 8). The area of grassland and dry shrubland is projected to increase by 60 % relative to the baseline term under the A1B scenario, which is greater than those in the A2 and B2 scenarios. The mean centre of the grassland and dry shrubland will move southwest, occupying most of north China. The area of savanna and dry woodland will be approximately six times greater under A1B and A2 scenarios during the baseline period, replacing more temperate forests in north China. The warm-temperate forest biome will continue to shift northeast and will be the largest biome in China occupying >20 % of the area. The tropical forest is projected to increase in area by three times from the baseline term, mainly focusing on south China. The mean centre of the tropical forest will shift northeast. From the baseline term, the mean centre of the tropical forest will move over 200 km for A1B, A2 and B2 scenarios. Table 5 Simulated area of each vegetation type in the baseline, A1B, A2 and B2 scenarios (in percent) Vegetation type Baseline ( ) Near-term ( ) Mid-term ( ) Long-term ( ) A1B A2 B2 A1B A2 B2 A1B A2 B2 Tropical forest Warm-temperate forest Temperate forest Boreal forest Savanna/dry woodland Grassland/dry shrubland Desert Dry tundra Tundra

9 Responses of vegetation in China Fig. 4 Elevation map of China with major geographical region mentioned in this text 4 Discussions The modified BIOME4 was used to simulate the vegetation distribution in China under climate change characterized by rising temperature and increasing precipitation. The results indicate that climate change can result in shifts in vegetation distribution. However, the effects of such changes varied in different vegetation types. The results are consistent with previous studies except for certain discrepancies. The simulation suggests that forest vegetation is likely to shift northwards because of climate change, which is consistent with previous studies (Ni et al. 2001; Zhao et al. 2002; Weng and Zhou 2006). Forest biomes show considerable resilience to climate change. The areas occupied by all forest vegetation types except tropical forest varied by less than 30 % among all scenarios. Given the small percentage (0.88 %) of area occupied by tropical forests, the tropical forest biome may have a large proportional increase. This finding also explains why the warm-temperate forest does not shift its western boundary far, but mainly moves its boundary northwards in all climate scenarios. This phenomenon can be fundamentally associated with the steep climate gradient in the western boundary of warm-temperate forests because the mountains in this area increase in elevation abruptly. Topographical factors play an important role in the response of forest shift to climate change because the vegetation's moving velocity caused by temperature change is several orders of magnitude lower in mountainous regions compared with flat regions (Loarie et al. 2009). The temperate forest in the simulation decreased by 20 to 25 % in the long-term from the baseline, which is consistent with the results obtained by Wang et al. (2011), who suggested that temperate forests may decrease with a significant northward shift because of warming. A projection from Wu (2003) also showed that the temperate forest in northeast China will significantly shift its boundary northwards, with an areal coverage decrease of 20 to 35 %, which is similar to the projections in this study. The boreal forest, which is the most sensitive to climate warming because of low temperature environment and low productivity, may shrink to a scattering of small fragments on the Greater Higgnan Mountains, but may expand into the interior of the Tibetan Plateau. Previous studies (Ni et al. 2001, Wang et al. 2011) have obtained similar results on the boreal forest. Although temperature affects the distribution of the boreal vegetation, moisture availability is likely the more important factor. In the climate change scenarios, much of boreal forest region in northeast China is projected slight increase in precipitation, but increase in precipitation will likely be offset with increases in evapotranspiration. In addition, warming on permafrost condition can modify soil water availability and can further result in increase in drought stress during summer (Wilmking et al. 2004). These changes in climate can induce boreal forest northward shift beyond China. The Tibetan Plateau is regarded by many researchers as a region vulnerable to climate change because of its unique location, special climate and high altitude. In this study, a similar observation was suggested by the results. As a result

10 D. Zhao, S. Wu Fig. 5 Simulated biomes in near-term ( ) (top row), mid-term ( ) (middle row) in long-term ( ) (bottom row) and under A1B (left column), A2 (middle column) and B2 (right column) scenarios of warming, tundra and dry tundra may be converted into forest that invades into the interior of the plateau. On the basis of a simulation by BIOME3, Ni et al. (2001) also found that tundra would be largely reduced on the plateau and would be replaced by forests. In addition, the desert in Qaidam Basin may be replaced gradually by grassland or dry shrubland, which require increased precipitation. The conversion risk of vegetation types in China may further increase in the future because climate warming is faster on the plateau than on the rest of the country (Zhao et al. 2011). The area of grassland may enlarge in all three scenarios, particularly in northeast China. The temperate forest in north China may be largely reduced, whereas the woodland may expand significantly. The temperate forest only remained as a narrow belt from the northeast to the southwest of north China. Zhao et al. (2002) indicated that climate changes in the future would transform the vegetation in north China into dry woodland and grassland, which is consistent with the observation in this study. This transformation can be associated with the rising temperature. An increase in temperature may reduce water effectiveness through enhanced evapotranspiration, which limits temperate forest survival. By contrast, dry woodland and grassland can adapttosuchchanges.piaoetal.(2009) found that an increase in temperature alone does not benefit vegetation growth in temperate ecosystems because of water control. Studies on global terrestrial NPP suggest that water availability is the strongest limiting factor for vegetation growth in the mid-latitude Eurasian vegetation (Nemani et al. 2003). Three climate scenarios (A2, B2 and A1B) were adopted to predict the vegetation distribution in China. The simulation results from BIOME4 under possible future conditions, which are represented by three scenarios, exhibit general

11 Responses of vegetation in China Fig. 6 Mean centre for biome types with longer shift distances under A1B (a), A2 (b) and B2 (c) scenarios. In the figures, B refers to baseline term ( ), N refers to near-term ( ), M refers to mid-term ( ) and L refers to long-term ( ) consistency. If the ecosystem model can be considered realistic to a certain extent and assuming that the three scenarios can represent a proportion of the uncertainty, then the overall pattern of change in the vegetation in China in the coming century may quantitatively resemble the simulated results in this study. In addition, the vegetation distribution in three warming periods, namely near-term, mid-term and long-term, was examined to improve our understanding of the effects of the different warming levels. The response of ecosystems in China to climate change is characterized by substantial regional differences. The northern part of the north China Plain is a sensitive area. The warm forest in this area is projected to degrade into savanna and dry woodland because of warming and drying. The

12 D. Zhao, S. Wu Table 6 Shift distance (in kilometre) and direction of mean centre for each vegetation type from baseline ( ) to near-term ( ) under three climate scenarios Vegetation type A1B A2 B2 Distance Direction Distance Direction Distance Direction Tropical forest 63 Northwest 133 Northeast 148 Northeast Warm-temperate forest 42 Northeast 35 Northeast 48 Northeast Temperate forest 62 Northwest 83 Northwest 64 Northwest Boreal forest 365 Southwest 102 Southwest 395 Southwest Savanna/dry woodland 92 Northeast 81 Southwest 191 Southwest Grassland/dry shrubland 358 West 60 Northwest 66 West Desert 37 Northeast 11 Northeast 8 Northwest Dry tundra 43 Northeast 29 West 55 West Tundra 48 Northwest 28 Northwest 14 East biodiversity in this area would face higher risks of damage because of the high-level floods and increased human activities. The tundra vegetation may shrink into the interior of the Tibetan Plateau and may be partly replaced by forests because of the increase in temperature and precipitation. This phenomenon may significantly affect the livelihood of local herdsmen. Given that the tundra on the Tibetan Plateau is mainly dominated by alpine meadow (Ni and Herzschun 2011), the plateau is not only an ecosystem but also a key resource for the local people. According to the future climate trend from TCNARCC (2011), warming may be strongest in northwest China and precipitation will likely increase significantly. Consequently, the desert may retreat and may be replaced gradually by other vegetation types that require increased precipitation, resulting in the transformation of most areas in northwest China into oases. Carbon sequestration in terrestrial ecosystems is closely associated with vegetation distribution (Piao et al. 2009). Climate change influences carbon budget not only directly by affecting the flux but also indirectly by affecting vegetation distribution. Table 4 shows that climate change may increase the tropical forest distribution, which is beneficial to the carbon storage in terrestrial ecosystems because tropical forests are considered the greatest carbon sinks for their strong carbon sequestration capacity (Cox et al. 2004). The boreal forest is projected to decrease in the future and will be replaced by grassland and dry shrubland. This change can result in an adverse effect to carbon sequestration because boreal forests can sequestrate more carbon than grasslands and dry shrublands could (Lindroth et al. 1998). This vegetation change is beneficial to the carbon storage in the Tibetan Plateau because of the increase in forest distribution and the decrease in desert distribution. However, climate warming can accelerate permafrost thawing and can enhance soil microbial activities, thus inducing more carbon emission from the soil. This temperature change can partly offset the positive effects of vegetation change to the carbon sequestration in the Tibetan Plateau (Zhao et al. 2013b). In this study, three scenarios from PRECIS were used to investigate the potential response of Chinese vegetation to climate change. However, numerous scenarios are available, which would likely yield varied results. No model is perfect, and BIOME4 is no exception, it uses simplified ways in describing the ecological mechanisms, and time lags associated with succession and migration are not modelled explicitly, which bias the result. Such reasons can result in certain Table 7 Shift distance (in kilometre) and direction of mean centre for each vegetation type from near-term ( ) to mid-term ( ) under three climate scenarios Vegetation type A1B A2 B2 Distance Direction Distance Direction Distance Direction Tropical forest 174 Northeast 37 Northeast 30 Northwest Warm-temperate forest 102 Northeast 53 Northeast 40 Northeast Temperate forest 83 Northwest 55 Northwest 46 Northwest Boreal forest 386 Southwest 471 Southwest 255 Southwest Savanna/dry woodland 121 Southwest 31 Southwest 39 Southeast Grassland/dry shrubland 134 Southwest 113 Northwest 134 West Desert 42 Northeast 8 Northeast 31 West Dry tundra 64 East 21 West 33 West Tundra 48 Northeast 35 Northeast 38 West

13 Responses of vegetation in China Table 8 Shift distance (in kilometre) and direction of mean centre for each vegetation type from mid-term ( ) to long-term ( ) under three climate scenarios Vegetation type A1B A2 B2 Distance Direction Distance Direction Distance Direction Tropical forest 84 Northwest 44 Northeast 44 Northeast Warm-temperate forest 89 Northeast 85 Northeast 42 Northeast Temperate forest 79 Northeast 64 Northeast 43 Northwest Boreal forest 621 Southwest 444 Southwest 475 Southwest Savanna/dry woodland 46 Southwest 54 Northeast 44 Northeast Grassland/dry shrubland 371 Southwest 403 Southwest 213 West Desert 109 East 48 East 20 Northeast Dry tundra 26 West 31 Northwest 15 Northeast Tundra 50 East 30 Northwest 37 Northwest uncertainties in the simulation results. The results simulated by BIOME4 are also free from the disturbance of human activities. The great expansion in human activity can have major influences on vegetation dynamics. Therefore, this study could be further improved by integrating human disturbances in the simulation. 5 Conclusions In this study, a coupled biogeography and biogeochemistry model (BIOME4) was modified by redefining the bioclimatic limits of key PFTs to simulate the responses of biome distribution to future climate change in China. A comparison between the simulated biome and the vegetation map shows that the modified model can produce realistic predictions of biome distribution patterns in China. Three climate scenarios (A2, B2 and A1B) were used to drive the modified BIOME4 model to show the uncertainties in the simulations. Three periods, (near-term), (mid-term) and (long-term), were used for comparison with the baseline term to quantify the difference of the biome responses to climate change under different scenarios. In this study, the simulation suggests that regional climate change would result in dramatic changes in vegetation distribution in China. The areas with increased vegetation distribution under the A2, B2 and A1B scenarios include the tropical forest biome, warm-temperate forest biome, savannah and dry woodland biome, and grassland and dry shrubland biome. The areas with decreased vegetation include temperate forest, boreal forest, desert, dry tundra and tundra. In east China, a prominent movement northwards was observed for each vegetation distribution in response to future climate change. Under the three scenarios, boreal forest shifted the greatest distance northwards, followed by tropical forest and temperature forest. Savanna and dry woodland biome, and grassland and dry shrubland biome expanded the distribution. On the Tibetan Plateau, the area of forests increased by spreading into the interior of the plateau, whereas tundra and dry tundra retreated towards the northwest. The desert in northwest China may be reduced and replaced by grassland and dry shrubland because of increased precipitation. The responses of vegetation distribution to climate change at a regional scale will contribute to our understanding of the vulnerability of regional ecosystems to climate change. Acknowledgments This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA ), National Basic Research Program of China (2011CB403206), and National Natural Science Foundation of China ( ). We thank Prof. Yinlong Xu from Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agriculture Sciences, for providing climate scenario data. We appreciate two anonymous reviewers for their insightful comments on an earlier version of this manuscript. References Cox PM, Betts RA, Collis M, Harris PP, Huntingford C, Jones CD (2004) Amazonian forest dieback under climate-carbon cycle projections for the 21st century. Theor Appl Climatol 78: CVEC, CAS (The China Vegetation Editorial Committee, Chinese Academy of Sciences) (1980) Vegetation of China. Science, Beijing CVAEC, CAS (The China Vegetation Atlas Editorial Committee, Chinese Academy of Sciences) (2001) Atlas of China vegetation (1:1,000,000). Science, Beijing Davis MB, Botkin DB (1985) Sensitivity of cool-temperate forests and their fossil pollen record to rapid temperate change. J Quat Sci 23: Harrison SP, Prentice IC (2003) Climate and CO 2 controls on global vegetation distribution at the last maximum: analysis based on palaeovegetation data, biome modelling and palaeoclimate simulation. Glob Change Biol 9: Hart JF (1954) Central tendency in areal distribution. Econ Geogr 30:48 59 Haxeltine A, Prentice IC (1996) BIOME3: an equilibrium biosphere model based on ecophysiological constraints, resource availability

14 D. Zhao, S. Wu and competition among plant functional types. Global Biogeochem Cycles 10: Hitz S, Smith J (2004) Estimating global impacts from climate change. Global Environ Change 14: Huntley B (1990) European vegetation history: palaeovegetation maps from pollen data yr BP to present. J Quat Sci 5: IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge Jiang DB, Zhang Y, Lang XM (2011) Vegetation feedback under future global warming. Theor Appl Climatol 106: Jones R, Noguer M, Hassell D, Hudson D, Wilson S, Jenkins G, Mitchell J (2004) Generating high resolution climate change scenarios using PRECIS. Met Office Hadley Centre, Exeter Kaplan JO (2001) Geophysical applications of vegetation modeling. Ph.D. dissertation, Lund University, Lund, Sweden Kaplan JO, Bigelow NH, Prentice IC, Harrison SP, Bartlein PJ, Christensen TR, Cramer W, Matveyeva NV, McGuire AD, Murray DF, Razzhivin VY, Smith B, Walker DA, Anderson PM, Andreev AA, Brubaker LB, Edwards ME, Lozhkin AV (2003) Climate change and arctic ecosystems II: modeling, paleodata-model comparisons, and future projections. J Geophys Res 108:1 12 Lindroth A, Grelle A, Morén AS (1998) Long-term measurements of boreal forest carbon balance reveal large temperature sensitivity. Global Change Biol 4: Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, Ackerly DD (2009) The velocity of climate change. Nature 462: Miao C, Duan Q, Yang L, Borthwick AGL (2012) On the applicability of temperature and precipitation data from CMIP3 for China. PLoS One 7(9):e doi: /journal.pone Monserud RA, Leemans R (1992) Comparing global vegetation maps with the Kappa statistic. Ecol Model 62: Nemani RR, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ, Myneni RB, Running SW (2003) Climate-driven increase in global terrestrial net primary production from 1982 to Science 300: Ni J (2011) Impacts of climate change on Chinese ecosystems: key vulnerable regions and potential thresholds. Reg Environ Change 11(Suppl 1):S49 S64 Ni J, Herzschun U (2011) Simulating biome distribution on the Tibetan Plateau using a modified global vegetation model. Arct Antarct Alp Res 43(3): Ni J, Sykes MT, Prentice IC, Cramer W (2001) Modeling the vegetation of China using the process-based equilibrium terrestrial biosphere model BIOME3. Global Ecol Biogeogr 9: Prentice IC (1986) Vegetation response to past climatic variation. Vegetatio 67: Piao SL, Ciais P, Friedlingstein P, de Noblet-Duconudre N, Cadule P, Viovy N, Wang T (2009) Spatiotemporal patterns of terrestrial carbon cycle during the 20th century. Global Biogeochem Cy 23, GB4026. doi: /2008gb Prentice IC, Bartlein PJ, Webb T (1991) Vegetation and climate change in eastern North America since the last glacial maximum. Ecology 72: Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solomon AM (1992) A global biome model based on plant physiology and dominance, soil properties and climate. J Biogeogr 19: Shi YF, Li JJ, Li BY (1998) Uplift and environmental changes of the Tibetan Plateau in the late Cenozoic. Guangdong Science and Technology Press, Guangzhou (In Chinese) TCNARCC (Taskforce on China's National Assessment Report on Climate Change) (2011) China's national assessment report on climate change. Science, Beijing Wang H, Ni J, Prentice IC (2011) Sensitivity of potential natural vegetation in China to projected changes in temperature, precipitation and atmospheric CO 2. Reg Environ Change 11: Weng ES, Zhou GS (2006) Modeling distribution changes of vegetation in China under future climate change. Environ Model and Assess 11:45 48 Wilmking M, Juday GP, Barber VA, Zald HSJ (2004) Recent climate warming forces contrasting growth responses of white spruce at treeline in Alaska through temperature thresholds. Global Change Biol 10: Wilson K, Newton A, Echeverr C, Weston C, Burgman M (2005) A vulnerability analysis of the temperate forests of south central Chile. Biol Conserv 122:9 21 Wu SH, Yin YH, Zhao DS, Huang M, Shao XM, Dai EF (2010) Impact of future climate change on terrestrial ecosystems in China. Int J Climatol 30: Wu ZF (2003) Assessment of eco-climatic suitability and climate change impacts of broad-leaved Korean pine forest in northeast China. Chinese J Appl Ecol 14: (in Chinese) Xiong W, Conway D, Lin ED, Holman I (2009) Potential impacts of climate change and climate variability on China's rice yield and production. Clim Res 40:23 35 Xu YL (2004) High resolution climatic scenarios in China based on RCM from the Hadley Center. Clim Change Newslett 3:6 7 (in Chinese) Yue TX, Fan ZM, Chen CF, Sun XF, Li BL (2011) Surface modeling of global terrestrial ecosystems under three climate change scenario. Ecol Model 222: Zhang SH, Peng GB, Huang M (2004) The feature extraction and data fusion of regional soil textures based on GIS techniques. Clim Environ Res 6:65 79 (in Chinese) Zhang XS, Yang DA (1993) A study on climate vegetation interaction in China: the ecological model for global change. Coenoses 8: Zhang XS, Vegetation Map of China Editorial Committee, Chinese Academy of Science (2007) Vegetation map of China and its geographic pattern: illustration of the vegetation map of the People's Republic China (1:10,000,000). Geographical Publishing House, Beijing (in Chinese) Zhao DS, Wu SH, Yin YH, Yin Z-Y (2011) Vegetation distribution on Tibetan Plateau under climate change scenario. Reg Environ Change 11: Zhao DS, Wu SH, Yin YH (2013a) Responses of terrestrial ecosystems' net primary productivity to future regional climate change in China. PLoS One 8(4):e doi: /journal.pone Zhao DS, Wu SH, Yin YH (2013b) Dynamic responses of soil organic carbon to climate change in the Three-River Headwater region of the Tibetan Plateau. Clim Res 56:21 23 Zhao MS, Neilson PR, Yan XD, Dong WJ (2002) Modeling the vegetation of China under changing climate. J Geogr Sci 57:28 38 (in Chinese)