Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
stxb201112292007 Varying responses in mean surface air temperature from land use/cover change in different seasons over northern China Siyan Donga,b,c; Xiaodong Yana,d,*; Zhe Xionga a Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China b Graduate University of the Chinese Academy of Sciences, Beijing 100049, China c National Climate Center, Beijing 100081, China d Beijing Normal University, Beijing 100875, China Abstract: Research on the impacts of land use change on climate change has become a foremost topic in the field of global climate change research. Although many researchers have studied the impacts of LUCC, data related to these impacts on the Chinese climate system remain sparse because of the diversity of China's regional changes in land use, especially related to agricultural changes. Therefore, additional studies are needed that address regional LUCC in combination with climate modeling. Two simulations with current land use/cover patterns and potential natural vegetation cover were used to investigate the impact of LUCC on surface air temperature in northern China. Simulations of 11 years of climate in northern China (1 January 1990 to 31 December 2000) were carried out using Regional Environment Integrated Modeling System 2.0 (RIEMS2.0). The results showed that: (1) When potential natural vegetation cover types were changed to current vegetation cover types, mean summer surface air temperature decreased in the central northeastern area, eastern Gansu Province and Ningxia Hui Autonomous Region, but increased in Shanxi, Henan and Anhui provinces. Also, surface air temperature changed significantly on a local scale in the central northeastern area, central Henan Province and eastern Gansu Province (P<0.05). In winter, major portions of the study area exhibited non-significant decreases in mean surface air temperature. (2) In summer, a temperate forests removal simulation in northern China behaved more like a tropical forests removal simulation. In winter, removal of the temperate forests in northern China behaved more like a boreal forests removal simulation. In model grids where forest were converted to cropland, the net radiation absorbed has less influence on surface air temperature at lower vs. higher latitudes. Further, latent heat flux has a stronger influence on surface air temperature at lower latitudes. 1 Introduction Land use refers to the human activities of development and utilization of land resources. Agricultural, forestry, traffic, and residential lands represent different land use types. Land use change is directly related to land cover change. Land cover refers to the natural surface formation or human-induced land cover situation. Land cover includes the earth's land surface and near-natural state of the ground surface, mainly referring to the land's natural attributes, including the result of human activity [1]. Therefore, land use/cover change (LUCC) is the result of the *Corresponding author E-mail address:[email protected](X.D. Yan) impacts of two aspects about natural variation of the earth system and human activity. However, land development and utilization of the earth surface associated with several thousand years of human activity is the main reason for changes in land cover. Human activities result in deforestation, expansion of cropland, grassland degradation, urbanization and other large-scale LUCC, among which deforestation and cropland expansion are two of the most important processes. These processes alter the physical properties of the land surface, such as albedo and roughness of the surface, thereby affecting land-atmosphere material and energy exchange. Many researchers have found through numerical simulation that deforestation increases surface albedo at high latitudes, producing cooling [2,3], and it decreases evapotranspiration in low-latitude (tropical) regions, increasing surface air temperature [4,5]. However, much uncertainty remains regarding deforestation impacts on land-atmosphere exchange and regional climate in temperate regions [6,7,8]. Northern China is a typical temperate forests area. There are significant expansion of cropland and human activities in the region, which strongly influence climate and the ecological environment. In general, climate models have used more realistic vegetation to evaluate the effect of LUCC on regional climate, and the impact of anthropogenic LUCC on climate was recently examined using current and natural vegetation datasets. These studies analyzed specific seasons (winter or summer) [9,10]. Although some studies focused on surface air temperature change from LUCC in northern China during different seasons [11,12], there was no in-depth analysis of the causes of these seasonal surface air temperature changes. Therefore, this study would address the above scientific questions based on potential vegetation cover and current land use/cover data. We used a high-resolution, long-term integral simulation of a regional climate model (RIEMS2.0) to reveal impact of anthropogenic LUCC on seasonal mean surface air temperature in northern China. 2 Methods The Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA) of the Institute of Atmospheric Physics, Chinese Academy of Sciences, developed the Regional Integrated Environmental Modeling System (RIEMS2.0), which was built on RIEMS1.0. The RIEMS2.0 model had better simulation performance for mean surface air temperature, precipitation and circulation characteristics of the Asian monsoon area over many years [13,14]. It also showed good performance in the Regional Climate Model Inter-comparison Project [15]. A number of physical parameterizations were incorporated in the model. These include a state-of-the-art surface physics package, namely the Biosphere-Atmosphere Transfer Scheme 1e (BATS1e), a Holtslag explicit planetary boundary layer formulation, the Grell cumulus convective parameterization scheme, and a modified radiation package (National Center for Atmospheric Research Community Climate Model 3). The Medium Range Forecast planetary boundary layer scheme was also used. Initial and lateral boundary conditions for winds, temperature, water vapor, and surface pressure were extracted from National Centers for Environmental Prediction – Reanalysis II. The simulation domain encompasses all of northern China and uses a Lambert projection centered on 102°E, 40°N, with horizontal resolution 30 km. *Corresponding author E-mail address:[email protected](X.D. Yan) Fig. 1 Potential natural vegetation cover (PVC) Fig. 2 Current land use/cover (CLC) Both experiments with current land use/cover (CLC) and potential natural vegetation cover (PVC) are of 11-year lengths, with boundary conditions from RIEMS2.0 simulation. In the CLC experiment, vegetation cover in the original RIEMS2.0 is used, which is representative of CLC. In the PVC experiment, all conditions are the same as in the CLC experiment. North of the Qinling Mountains and the Huaihe River except the Tibet Plateau is our study region, the potential vegetation data of Ramankutty and Foley were used [16] (R&F). These data have high-resolution (5') with repeated sampling, generating the required horizontal resolution of 30 km. Two land cover data types were used in the BATS classification method, which include 18 vegetation/land cover types: 1) crop/mixed farming; 2) short grass; 3) evergreen needleleaf trees; 4) deciduous needleleaf trees; 5) deciduous broadleaf trees; 6) evergreen broadleaf trees; 7) tall grass; 8) desert; 9) tundra; 10) irrigated crops; 11) semi-desert; 12) ice cap/glacier; 13) bog or marsh; 14) inland water; 15) ocean; 16) evergreen shrubs; 17) deciduous shrubs; 18) mixed woodland. The model was continuously integrated from 1 January 1990 to 31 December 2000 (11 years). The first year was used to spin up the model, and only results for the subsequent 10 years were analyzed. We analyzed the 10-year average differences between the two experiments (CLC- PVC) to characterize LUCC in northern China. At the same time, to test statistical significance of mean surface air temperature changes, we used a point-by-point, two-tailed Student’s t-test. Changes in the results passing a 95% confidence level in the t-test are significant (P < 0.05). Analyzing changes in different seasons and areas in northern China would help reveal small-scale phenomena and characteristics of these areas. Summer includes June, July and August, and winter includes December, January and February. According to characteristics of northern China climate change caused by LUCC. Analyzing focused on three districts (Figure 3): (1) Northeast (NE; east of 120°E) (2) Center of North (CN; 110°E–120°E, 35°N–52°N) (3) South (S; 105°E–122°E, 30°N–35°N) . Fig. 3 Modeled domain and focused regions 3 Results 3.1 Human activities of LUCC leading to characteristics in northern China Figures 1 and 2 showed LUCC and spatial characteristics in northern China caused by human activities. These include changes from predominantly deciduous broadleaf forest to cropland in the southeast, evergreen broadleaf to cropland in the part of the northeast, and grassland conversion to cropland in most of the central NE. By lattice calculations of LUCC, the statistics showed that 2495 of 5281 grid points were changed in northern China. Of these, 1820 *Corresponding author E-mail address:[email protected](X.D. Yan) grid points were changed from natural vegetation (including forest, short grass et al.) to cropland types, and forest (including mixed forest, deciduous broadleaf forest et al.) was converted to cropland at 1321 grids. These statistics demonstrated that human activities caused LUCC, which were accompanied by increased cropland and reduced natural vegetation (forest-based). These changes produce a significant temperature effect in the region. 3.2 Spatial characteristics of mean surface air temperature change in different seasons Many studies have shown that the impact on mean surface air temperature of LUCC has obvious seasonal characteristics [17,18]. Such impact was also found in northern China (Figure 4). When potential natural vegetation cover types were converted to current vegetation cover types, summer mean surface air temperature decreased in the central NE, eastern Gansu Province and the Ningxia Hui Autonomous Region, but increased in Shanxi, Henan and Anhui provinces. Surface air temperature changed significantly on a local scale in the central NE, central Henan Province and eastern Gansu Province (P < 0.05). In winter, large areas exhibited non-significant decreases of mean surface air temperature, similar to the simulation of Zheng [19] using the RegCM3 model. Fig. 4 Mean surface air temperature change pattern in summer (a) and winter (b). Unit: °C. Red dashed lines indicate changes significant at the 95% confidence level; dark dashed lines indicate changes significant at the 90% confidence level. 3.3 Mean surface air temperature change in different areas and its mechanism Region-wide mean surface air temperature in northern China decreased by 0.02°C in summer, and by 0.31°C in winter (Table 1). LUCC in different regions may have varying biogeophysical effects. Thus, surface air temperature change differed by region, as did its mechanism. In summer, this change was a decrease of 0.3°C in the NE, and a (non-significant) increase of 0.13°C in the S. Decreasing the net absorption solar radiation flux (by −1.43 W/m2) led to cooling in the NE. However, increasing surface albedo, which leaded to decreasing net absorption solar radiation flux (−3.86 W/m2) (Figure 5a), produced an increase of 0.13°C in the S, where the evapotranspiration effect is dominant, substantially reducing latent heat flux (−2.63 W/m2) (Figure 5b). The effect of decreasing evapotranspiration on (increasing) surface air temperature was greater than that of changing surface albedo on surface air temperature. Therefore, latent heat flux reduction was the main cause of increasing mean surface air temperature in the S. Table 1 Climate change in different seasons over various regions of northern China Northeast (NE) North South All northern China (CN) (S) China JJA DJF JJA DJF JJA DJF JJA DJF −0.3 −0.44 0 −0.39 0.13 −0.59 −0.02 −0.31 −1.43 −0.96 −1.64 −0.74 −3.86 −0.84 −1.42 −0.49 Latent heat flux /(W/m2) 1.48 −0.06 −0.74 −0.40 −2.63 0.45 −0.29 −0.06 Sensible heat −2.11 −0.36 −0.56 0.47 −0.15 0.30 −0.46 0.04 surface air temperature/°C Net solar absorbed radiation /(W/m2) *Corresponding author E-mail address:[email protected](X.D. Yan) flux/(W/m2) In winter, mean surface air temperature showed a cooling of 0.44°C in the NE, cooling of 0.39°C in CN, and cooling of 0.59°C in the S. Mean surface air temperature in the S is lower than the NE and CN (Table 1). In the S, net absorbed radiation flux decreased by 0.84 W/m2 (Figure 5c), and latent heat flux increased by 0.45 W/m2. It seems that during winter in the S, where forest was converted to cropland, surface roughness was reduced and wind speed increased. This increased turbulent heat exchange, and contemporaneously the latent heat flux and sensible heat fluxes increased evaporative cooling. This caused the greater surface air temperature decrease in the S relative to the other areas (Figure 5d). Fig. 5 Net absorbed solar flux (a) and latent flux (b) change in summer; net absorbed solar flux (c) and latent flux (d) change in winter. Unit: W/m2 3.4 Effect on mean surface air temperature at grids with forest conversion to cropland at different latitudes After land use/cover was converted from potential vegetation to current cover in northern China, summer mean surface air temperature showed a warming of 0.14°C at grids with forest conversion to cropland (Table 1). The winter change at these grids was a cooling of 0.64°C (Table 2). Previous studies of temperate forests change came to a similar conclusion. Snyder [17] showed that snow in winter and spring makes for high seasonal variability of the water cycle and energy fluxes after temperate deforestation. Bonan [20,21] suggested that a land use/cover shift from natural vegetation to modern vegetation, including temperate forest to cropland, caused summer warming in the western United States. We found with correlation analysis that net absorbed radiation has a stronger influence on surface air temperature in winter, whereas latent heat flux has a stronger influence in summer (Table 2). Conversion of boreal forest to cropland has a significant effect on surface air temperature. The reason for this is that in summer, deforestation decreases latent heat flux, which reduces evaporative cooling and increases surface air temperature. In winter, deforestation with cropland expansion increases surface albedo in conjunction with the positive feedback effect of snow. The effect of surface albedo change on surface air temperature is greater than that of decreasing evapotranspiration, increasing cooling [22]. After land use/cover type was converted from forest to cropland, the surface biogeophysical effects were very different, because of the latitude difference of the NE and S areas. This causes regional water and energy variations, and different mean surface air temperature changes with season. At the converted grids, whether in summer or winter (Table 2), latent heat flux effects on surface air temperature at low latitudes (S area) are stronger. This is associated with the relationship between low-latitude high temperature and saturated vapor pressure. Regional mean surface air temperature increased, and so did evapotranspiration. Comparing the high-latitude (NE) and low-latitude (S area) regions, we see that at lower latitudes, surface air temperature is less influenced by net absorbed radiation and more strongly affected by latent heat flux. Table 2 Mean surface air temperature change at grids converted from forest to cropland, and its spatial correlation with flux change (values to right of slashes) *Corresponding author E-mail address:[email protected](X.D. Yan) surface air temperature/°C Net solar absorbed radiation /(W/m2) Latent heat flux /(W/m2) JJA DJF JJA DJF JJA DJF CN 0.14 −0.64 −3.28/0.36 −1.52/0.49 −2.81/−0.32 −0.25/0.21 NE −0.07 −0.94 −3.94/0.53 −4.04/0.71 −1.91/-0.01 −0.92/0.62 S 0.17 −0.60 −3.62/0.38 −0.85/0.42 −3.38/-0.19 0.46/0.30 4 Discussions (1) After land use/cover was converted from potential vegetation to the current cover, differences between the two experiments (CLC- PVC) was found to have great impact on the spatial distribution of surface air temperature change in summer. There was a decrease of mean surface air temperature in the NE, which is the result of reducing net radiation flux and increasing latent heat flux. There was an increase of mean surface air temperature in the S, mainly associated with decreased latent heat flux. The decrease in roughness increased winter wind speed, leading to increased turbulent heat exchange and resulting enhancement of evaporative cooling. This caused mean surface air temperature in the S to be lower than other areas in winter. LUCC not only impacted the near-surface climate, but also modified atmospheric circulation via energy exchange. This in turn affected simulation of the East Asian monsoon. Therefore, monsoon circulation changes caused by land use/cover require further study. (2) In summer, a temperate forests removal simulation of mean surface air temperature in northern China behaved more like a tropical forest removal simulation. In winter, temperate forests removal simulation of mean surface air temperature behaves more like a boreal forest removal simulation. LUCC at varying latitudes not only has different impact on mean surface air temperature, but also on local surface air temperature under climate change. Stone et al. [23] pointed out that a reasonable adjustment of land use can mitigate climate change, which may be a more effective way than a reduction of greenhouse gas emissions. Therefore, land use/cover management in northern China should focus on its temperature effects. (3) We need more realistic land use/cover information and to more accurately describe physical parameters. Regional climate models do not directly use land use/cover types to drive climate change, but instead use roughness, surface albedo and other physical surface parameters that represent each type. Land cover classification at small scales is very important, but it is not directly used by models. Therefore, regional models need to better represent the relationship between land use types and physical parameters [24,25]. (4) Chinese LUCC is complex and diverse, which has been a problem for accurately reflecting land use/cover characteristics in research. In our study the use of remote sensing imagery, revised survey data, and a more rational classification of these data has increased the accuracy of land use /cover information, therefore, the new current land use/cover data were particularly important when input into our model. Acknowledgment This project was financially supported by the National Key Basic Research and Development Program (2010CB950903, 2009CB431100). References *Corresponding author E-mail address:[email protected](X.D. Yan) [1] Turner II B L, Skole D, Sanderson S, Fischer G, Fresco L, Leemans R. Land-Use and land-Cover Change, Science/Research Plan. IGBP Report No. 35, HDP Report No. 7. Stockholm and Geneva: IGBP and HDP, 1995. [2] Mahmood R, Foster S A, and Logan D. The GeoProfile metadata, exposure of instruments, and measurement bias in climatic record revisited. International Journal of Climatology, 2006, 26(8):1091-1124. [3] Govindasamy B, Duffy P B, Caldeira K. Land use changes and northern hemisphere cooling. Geophysical Research Letters, 2001, 28(2): 291-294. [4] Osborne T M, Lawrence D M, Slingo J M, Challinor A J, Wheeler T R. Influence of vegetation on the local climate and hydrology in the tropics: sensitivity to soil parameters. Climate Dynamics, 2004, 23(1): 45-61. [5] Gibbard S, Caldeira K, Bala G, Phillips T J, Wickett M. Climate effects of global land cover change. Geophysical Research Letters, 2005, 32(23): L23705, doi: 10.1029/2005GL024550. [6] Betts R A, Falloon P D, Goldewijk K K, Ramankutty N. Biogeophysical effects of land use on climate: model simulations of radiative forcing and large-scale temperature change. Agricultural and Forest Meteorology, 2007, 142(2-4):216–233. [7] Jackson R B, Randerson J T, Canadell J G, Anderson R G, Avissar R, Baldocchi D D, Bonan G B, Caldeira K, Diffenbaugh N S, Field C B, Hungate B A, Jobbágy E G, Kueppers L M, Nosetto M D, Pataki D E. Protecting climate with forests. Research Letters, 2008, 3(4): 044006, doi: 10.1088/1748-9326/3/4/044006. [8] Bonan G B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 2008, 320(5882): 1444-1449. [9] Chase T N, Pielke R A Sr, Kittel T G F, Nemani R R, Running S W. Simulated impacts of historical land cover changes on global climate in northern winter. Climate Dynamics, 2000, 16(2-3):93-105. [10] Fu C B. Potential impacts of human-induced land cover change on East Asia monsoon. Global and Planetary Change, 2003, 37(3-4): 219-229. [11] Zhang D F. Gao X J, Shi Y, Giorgi F, Dong W J. Agricultural land use effects on climate over China as Simulated by a regional climate model. Acta Meteorologica Sinica, 2010, 24(2):215-224. [12] Gao X J,Zhang D F, Chen Z X, Pal J S, Giorgi F. Land use effects on climate in China as simulated by a regional climate model. Science in China Series D: Earth Sciences, 2007, 50(4):620-628. [13] Xiong Z, Fu C B,Yan X D.Regional integrated environmental model system and its simulation of East Asia summer monsoon. Chinese Science Bulletin, 2009, 54(22): 4253-4261 [14] Zhao D M, Fu Z B, Yan X D. Testing the ability of RIEMS2.0 (Regional Integrated Environment Modeling System) to simulate multi-year precipitation and air temperature in China. Chinese Science Bulletin, 2009, 54(17): 3101-3111. [15] Fu C B, Wang S Y, Xiong Z. Regional climate model inter-comparison project for Asia, Bulletin of the American Meteorological Society, 2005, 86(2):257-266. [16] Ramankutty N, Foley J A. Estimating historical changes in global land cover: croplands from 1700 to 1992.Global Biogeochemical Cycles, 1999, 13(4): 997-1027. *Corresponding author E-mail address:[email protected](X.D. Yan) [17] Snyder P K, Delire C L, Foley J A. Evaluating the influence of different vegetation biomes on the global climate. Climate Dynamics, 2004, 23(3-4): 279-302. [18] Diffenbaugh N S. Influence of modern land cover on the climate of the United States. Climate Dynamics, 2009, 33(7-8):945-958. [19] Zheng J Y, Lin S S, and He F N. Recent progress in studies on land covers change and its regional climatic effects over China during historical times. Advances in Atmospheric Sciences, 2009, 26(4):793-802. [20] Bonan G B. Frost followed the plow: impacts of deforestation on the climate of the United States. Ecological Applications, 1999, 9(4): 1305-1315. [21] Bonan G B. Observational evidence for reduction of daily maximum temperature by croplands in the Midwest United States. Journal of Climate, 2001, 14(11): 2430–2442. [22] Claussen M, Brovkin V, Ganopolski A. Biogeophysical versus biogeochemical feedbacks of large-scale land cover change. Geophysical Research Letters, 2001, 28(6): 1011-1014. [23] Stone Brian, Jr. Mitigating climate change could be better achieved by regulating land use change than emissions reductions alone. Environmental Science Technology, 2009, 43 (24): 9052-9056 [24] Steyaert L T, Knox R G. Reconstructed historical land cover and biophysical parameters for studies of land-atmosphere interactions within the eastern United States. Journal of Geophysical Research-Atmospheres, 2008, 113: D02101, doi: 10.1029/2006JD008277. [25] Strack J E, Pielke R A Sr, Steyaert L T, Knox R G. Sensitivity of summer near-surface temperatures and precipitation in the eastern United States to historical land cover changes since European settlement. Water Resources Research, 2008, 44: W11401, doi: 10.1029/ 2007WR006546. *Corresponding author E-mail address:[email protected](X.D. Yan)