山体效应及其对林线分布的影响(英文)

The concept of mass elevation effect(massenerhebungseffect,MEE) was intro-duced by A.de Quervain about 100 years ago to account for the observed tendency for temperature-related parameters such as tree line and snowline to occur at higher elevations in the central Alps than on their outer margins.It also has been widely observed in other ar-eas of the world,but there have not been significant,let alone quantitative,researches on this phenomenon.Especially,it has been usually completely neglected in developing fitting mod-els of timberline elevation,with only longitude or latitude considered as impacting factors.This paper tries to quantify the contribution of MEE to timberline elevation.Considering that the more extensive the land mass and especially the higher the mountain base in the interior of land mass,the greater the mass elevation effect,this paper takes mountain base elevation(MBE) as the magnitude of MEE.We collect 157 data points of timberline elevation,and use their latitude,longitude and MBE as independent variables to build a multiple linear regres-sion equation for timberline elevation in the southeastern Eurasian continent.The results turn out that the contribution of latitude,longitude and MBE to timberline altitude reach 25.11%,29.43%,and 45.46%,respectively.North of northern latitude 32°,the three factors’ contribu-tion amount to 48.50%,24.04%,and 27.46%,respectively;to the south,their contribution is 13.01%,48.33%,and 38.66%,respectively.This means that MBE,serving as a proxy indi-cator of MEE,is a significant factor determining the elevation of alpine timberline.Compared with other factors,it is more stable and independent in affecting timberline elevation.Of course,the magnitude of the actual MEE is certainly determined by other factors,including mountain area and height,the distance to the edge of a land mass,the structures of the mountains nearby.These factors need to be included in the study of MEE quantification in the future.This paper could help build up a high-accuracy and multi-scale elevation model for alpine timberline and even other altitudinal belts.     

山体效应对生态垂直格局的影响（英文）

The varied altitudinal gradient of climate and vegetation is further complicated by mass elevation effect(MEE), especially in high and extensive mountain regions. However, this effect and its implications for mountain altitudinal belts have not been well studied until recently. This paper provides an overview of the research carried out in the past 5 years. MEE is virtually the heating effect of mountain massifs and can be defined as the temperature difference on a given elevation between inside and outside of a mountain mass. It can be digitally modelled with three factors of intra-mountain base elevation(MBE), latitude and hygrometric continentality; MBE usually acts as the primary factor for the magnitude of MEE and, to a great extent, could represent MEE. MEE leads to higher treelines in the interior than in the outside of mountain masses. It makes montane forests to grow at 4800–4900 m and snowlines to develop at about 6000 m in the southern Tibetan Plateau and the central Andes, and large areas of forests to live above 3500 m in a lot of high mountains of the world. The altitudinal distribution of global treelines can be modelled with high precision when taking into account MEE and the result shows that MEE contributes the most to treeline distribution pattern. Without MEE, forests could only develop upmost to about 3500 m above sea level and the world ecological pattern would be much simpler. The quantification of MEE should be further improved with higher resolution data and its global implications are to be further revealed.     

Spatio-temporal pattern and attribution analysis of the mass elevation effect in the Tianshan Mountains in China

The mass elevation effect(MEE) is a thermal effect, in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift, which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas. Based on multi-source remote sensing data and field observations, a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted, and the MEE was estimated and a spatio-temporal analysis was conduct...     

Snow water equivalent over Eurasia in the next 50 years projected by aggregated CMIP3 models

Based on remote sensing snow water equivalent (SWE) data, the simulated SWE in 20C3M experiments from 14 models attending the third phase of the Coupled Models for Inter-comparison Project (CMIP3) was first evaluated by computing the different percentage, spatial correlation coefficient, and standard deviation of biases during 1979–2000. Then, the diagnosed ten models that performed better simulation in Eurasian SWE were aggregated by arithmetic mean to project the changes of Eurasian SWE in 2002–2060. Results show that SWE will decrease significantly for Eurasia as a whole in the next 50 years. Spatially, significant decreasing trends dominate Eurasia except for significant increase in the northeastern part. Seasonally, decreasing proportion will be greatest in summer indicating that snow cover in warmer seasons is more sensitive to climate warming. However, absolute decreasing trends are not the greatest in winter, but in spring. This is caused by the greater magnitude of negative trends, but smaller positive trends in spring than in winter. The changing characteristics of increasing in eastern Eurasia and decreasing in western Eurasia and over the Qinghai-Tibetan Plateau favor the viewpoint that there will be more rainfall in North China and less in the middle and lower reaches of the Yangtze River in summer. Additionally, the decreasing rate and extent with significant decreasing trends under SRES A2 are greater than those under SRES B1, indicating that the emission of greenhouse gases (GHG) will speed up the decreasing rate of snow cover both temporally and spatially. It is crucial to control the discharge of GHG emissions for mitigating the disappearance of snow cover over Eurasia.     

Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982简

Based on the GIMMS AVHRR NDVI data （8 km spatial resolution） for 1982-2000, the SPOT VEGETATION NDVI data （1 km spatial resolution） for 1998-2009, and observa- tional plant biomass data, the CASA model was used to model changes in alpine grassland net primary production （NPP） on the Tibetan Plateau （TP）. This study will help to evaluate the health conditions of the alpine grassland ecosystem, and is of great importance to the pro- motion of sustainable development of plateau pasture and to the understanding of the func- tion of the national ecological security shelter on the TP. The spatio-temporal characteristics of NPP change were investigated using spatial statistical analysis, separately on the basis of physico-geographical factors （natural zone, altitude, latitude and longitude）, river basin, and county-level administrative area. Data processing was carried out using an ENVI 4.8 platform, while an ArcGIS 9.3 and ANUSPLIN platform was used to conduct the spatial analysis and mapping. The primary results are as follows： （1） The NPP of alpine grassland on the TP gradually decreases from the southeast to the northwest, which corresponds to gradients in precipitation and temperature. From 1982 to 2009, the average annual total NPP in the TP alpine grassland was 177.2x1012 gC yrl（yr represents year）, while the average annual NPP was 120.8 gC m^-2 yr^-1. （2） The annual NPP in alpine grassland on the TP fluctuates from year to year but shows an overall positive trend ranging from 114.7 gC m^-2 yr^-1 in 1982 to 129.9 gC m^-2 yr^-1 in 2009, with an overall increase of 13.3%; 32.56% of the total alpine grassland on the TP showed a significant increase in NPP, while only 5.55% showed a significant decrease over this 28-year period. （3） Spatio-temporal characteristics are an important control on an- nual NPP in alpine grassland： a） NPP increased in most of the natural zones on the TP, only showing a slight decrease in the Ngari montane desert-steppe and desert zone. The positive trend in NPP in the high-cold shrub-meadow zone, high-cold meadow steppe zone and high-cold steppe zone is more significant than that of the high-cold desert zone; b） with in- creasing altitude, the percentage area with a positive trend in annual NPP follows a trend of ＂increasing-stable-decreasing＂, while the percentage area with a negative trend in annual NPP follows a trend of ＂decreasing-stable-increasing＂, with increasing altitude; c） the varia- tion in annual NPP with latitude and longitude co-varies with the vegetation distribution; d） the variation in annual NPP within the major river basins has a generally positive trend, of which the growth in NPP in the Yellow River Basin is most significant. Results show that, based on changes in NPP trends, vegetation coverage and phonological phenomenon with time, NPP has been declining in certain places successively, while the overall health of the alpine grassland on the TP is improving.     

Combining LPJ-GUESS and HASM to simulate the spatial distribution of forest vegetation carbon stock in China简

It is very important in accurately estimating the forests＇ carbon stock and spatial distribution in the regional scale because they possess a great rate in the carbon stock of the terrestrial ecosystem. Yet the current estimation of forest carbon stock in the regional scale mainly depends on the forest inventory data, and the whole process consumes too much labor, money and time. And meanwhile it has many negative influences on the forest carbon storage updating. In order to figure out these problems, this paper, based on High Accuracy Surface Modeling （HASM）, proposes a forest vegetation carbon storage simulation method. This new method employs the output of LPJ-GUESS model as initial values of HASM and uses the inventory data as sample points of HASM to simulate the distribution of forest carbon storage in China. This study also adopts the seventh forest resources statistics of China as the data source to generate sample points, and it also works as the simulation accuracy test. The HASM simulation shows that the total forest carbon storage of China is 9.2405 Pg, while the calculated value based on forest resources statistics are 7.8115 Pg. The forest resources statistics is taken based on a forest canopy closure, and the result of HASM is much more suitable to the real forest carbon storage. The simulation result also indicates that the southwestern mountain region and the northeastern forests are the important forest carbon reservoirs in China, and they account for 39.82% and 20.46% of the country＇s total forest vegetation carbon stock respectively. Compared with the former value （1975-1995）, it mani- fests that the carbon storage of the two regions do increase clearly. The results of this re- search show that the large-scale reforestation in the last decades in China attains a signifi- cant carbon sink.     

MODIS-based estimation of air temperature of the Tibetan Plateau

The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.     

SURFACE MASS BALANCE AND ITS VARIATION ON THE MIZUHO PLATEAU, ANTARCTICA IN 1987-1988

From the surface mass accumulation data in year of 1987/88, the distribution and variation of annual mass balance on Mizuho Plateau are discussed. The authors also analyze the effects of shortterm climatic and topographical variations on the mass balance. It is found that there are some differences in spatial distribution and annual average state in the year of 1987/88 and other years. Ia the area at elevation lower than 550 m near the coast, the mass balance appears to be negative. The annual mass balance over 80 km distance from S_(16) to inland is 0.84m snow depth. A low mass balance zone from 80 km site to Mizuho Station, is considered to be only 0.14 m snow depth. It is found from the comparison of mass balances that the mass-balance level on the glaciers in West China is 9 times higher than that on Mizuho Plateau, where the massbalance level appears to be low accumulative and low expensive, but inverse in middle and low latitude regions, such as on glaciers in West China. The effects of short-term     

Analysis of the characteristics of snow drop size distribution in the Qilian Mountains

Under the Watershed Allied Telemetry Experimental Research (WATER) project, a significant amount of snow size data was collected from March to April 2008. However, because of limited observation data for the Qinghai-Tibet Plateau, the modeling behavior was not satisfactory. This paper demonstrates characteristics of the snow drop size distribution (SSD) in this region. The experimental area is located in the northeastern part of the Qinghai-Tibet Plateau. The Heihe River Basin, which is the second largest interior river basin in China and is located on the northern slopes of the Qilian Mountains, was selected as the simulation region. This basin ranges from approximately 5,000 m to 1,000 m in elevation. A new generation Parsivel disdrometer, the OTT Parsivel, was used for measurements. Four data sets were compiled to determine the average distributions for four different snowfall rates. The characteristics of the snow particle size distribution in the mountainous area were analyzed. Similar to the raindrop distribution, there was a multi-peak structure. Most peaks appear in the D 2 mm region (D: diameter of the snow drop size). An M-P distribution and a Г distribution were developed based on the precipitation data observed in Qilian mountainous area. We found that the Г distribution has a better fit than the M-P distribution for the actual distribution. In addition, we observed that the intercept parameter (N0) and the slope parameter (Λ) correlate well with the shape parameter (μ). The disdrometer data can also be used to model the reflectivity factor (ZH) and differential reflectivity factor (ZDR). The radar reflectivity (ZHH, ZVV) and differential reflectivity (ZDR) were modeled in order to facilitate understanding of the connections between radar and ground measurements, and were used to support work for the improvement of rainfall estimates by polarimetric radar. Rain rate estimation using radar measurements was based on empirical models, such as the Z-R relationship and R(ZH, ZDR) in the Qilian mountainous areas. The relationship of R=0.017×100.079×ZH-0.022×ZDR is better than R=0.019×100.078×ZH for estimating R (melted snow). The normalized errors (NE) of R(ZH) and R(ZH, ZDR) are 13.22% and 5.20%, respectively.     

Response to climate change of different tree species and NDVI variation since 1923 in the middle arid region of Ningxia, China

The normalized difference vegetation index （NDVI） is used extensively to describe vegetation cover and ecological environ- ment change. The purpose of this study was to contrast the response of different tree species growing in the same habitat to climate change and retrieve past NDVI using tree-ring width data from tree cores collected from the transitional zone of Pinus tabulaeformis and Picea crassifolia in the Luoshan Mountains in the middle arid region of Ningxia. Correlation analysis indi- cated that radial growth ofP tabulaeJbrmis is more sensitive to precipitation and temperature change than that ofP crassifolia. Natural factors such as water availability and heat at this elevation are more suited to the growth ofP crassifolia, and are more advantageous to its renewal and succession. P. crassifolia is probably the better of the two species for protecting the forest ecosystem and conserving water in the Luoshan desertification area. Ring width of P. crassifolia correlates significantly with average NDVI for April-May （r =0.641, p 〈0.01）, and both of them are influenced positively by precipitation in April-May. The reconstructed NDVI for 1923-2007 shows the relatively low vegetation cover occurred in the 1920s-1930s, the 1960s-1970s, and the early 21 st century. The reconstructed NDVI better reflected the drought climate in the study area.     

山体效应对北半球林线分布的影响(英文)

Alpine timberline, as the "ecological transition zone," has long attracted the attention of scientists in many fields, especially in recent years. Many unitary and dibasic fitting models have been developed to explore the relationship between timberline elevation and latitude or temperature. However, these models are usually on regional scale and could not be applied to other regions; on the other hand, hemispherical-scale and continental-scale models are usually based on about 100 timberline data and are necessarily low in precision. The present article collects 516 data sites of timberline, and takes latitude, continentality and mass elevation effect(MEE) as independent variables and timberline elevation as dependent variable to develop a ternary linear regression model. Continentality is calculated using the meteorological data released by WorldClim and mountain base elevation(as a proxy of mass elevation effect) is extracted on the basis of SRTM 90-meter resolution elevation data. The results show that the coefficient of determination(R2) of the linear model is as high as 0.904, and that the contribution rate of latitude, continentality and MEE to timberline elevation is 45.02%(p=0.000), 6.04%(p=0.000) and 48.94%(p=0.000), respectively. This means that MEE is simply the primary factor contributing to the elevation distribution of timberline on the continental and hemispherical scales. The contribution rate of MEE to timberline altitude differs in different regions, e.g., 50.49%(p=0.000) in North America, 48.73%(p=0.000) in the eastern Eurasia, and 43.6%(p=0.000) in the western Eurasia, but it is usually very high.     

山体效应对北半球林线分布的影响分析

通过搜集整理了北半球516 个林线数据, 结合WorldClim 气象数据计算了林线数据点上的大陆度, 并依据SRTM高程数据提取了林线处的山体基面高度(作为山体效应的代用因子), 然后以纬度、大陆度和山体基面高度为解释变量, 建立三元回归模型。结果表明:线性回归模型的判定系数R²为0.904, 二次回归模型的R²高达0.912。相比先前不考虑基面高度的林线分布模型(R² = 0.79), 纳入了山体基面高度的林线分布模型能够更加有效的拟合半球尺度的林线分布; 结果还表明, 山体基面高度对北半球林线高度分布的贡献率达到了48.94% (p =0.000), 而纬度和大陆度分别为45.02% (p = 0.000) 和6.04% (p = 0.000)。这揭示了山体效应对半球尺度林线分布具有重要的影响。基面高度在北美洲地区对林线高度的贡献率最大(50.49%, p=0.000), 在欧亚大陆东部地区为48.73% (p = 0.000), 在欧亚大陆西部地区为43.6% (p=0.000)。这一结果说明山体效应对林线分布高度的影响虽有区域差异, 但都有较高的贡献率。     

Implications of mass elevation effect for the altitudinal patterns of global ecology

The varied altitudinal gradient of climate and vegetation is further complicated by mass elevation effect (MEE), especially in high and extensive mountain regions. However, this effect and its implications for mountain altitudinal belts have not been well studied until recently. This paper provides an overview of the research carried out in the past 5 years. MEE is virtually the heating effect of mountain massifs and can be defined as the temperature difference on a given elevation between inside and outside of a mountain mass. It can be digitally modelled with three factors of intra-mountain base elevation (MBE), latitude and hygrometric continentality; MBE usually acts as the primary factor for the magnitude of MEE and, to a great extent, could represent MEE. MEE leads to higher treelines in the interior than in the outside of mountain masses. It makes montane forests to grow at 4800–4900 m and snowlines to develop at about 6000 m in the southern Tibetan Plateau and the central Andes, and large areas of forests to live above 3500 m in a lot of high mountains of the world. The altitudinal distribution of global treelines can be modelled with high precision when taking into account MEE and the result shows that MEE contributes the most to treeline distribution pattern. Without MEE, forests could only develop upmost to about 3500 m above sea level and the world ecological pattern would be much simpler. The quantification of MEE should be further improved with higher resolution data and its global implications are to be further revealed.     

山体基面高度对青藏高原及其周边地区雪线空间分布的影响

山体效应是地理地带性之外,在大尺度上影响垂直带分布的主要因素,山体基面高度则是山体效应的第一影响因子。青藏高原及其周边地区,雪线呈现出中心高、周围低,与山体基面高度相一致的环状分布模式。为分析山体基面高度对雪线分布的影响,本文共收集青藏高原及周边地区雪线数据142个,采用纬度、经度和基面高度为自变量的三元一次方程拟合研究区雪线分布,计算各自的标准回归系数和相对贡献率,再将基面高度划分成5个子集（0~1000 m、1001~2000 m、2001~3000 m、3001~4000 m和4001~5000 m）,分析基面高度不同的山地对雪线的影响差异。结果表明：① 在青藏高原,纬度、经度和基面高度对雪线高度分布的相对贡献率分别为51.49%、16.31%和32.20%;② 随着基面高度的增高,各子集模型的决定系数虽有逐渐降低的趋势,但仍保持在较高的值域（R²=0.895~0.668）,说明模型的有效性;③ 随基面高度的抬升,纬度和山体基面高度对雪线分布高度的相对贡献率分别表现出降低（92.6%~48.99%,R²=0.855）和增大（3.33%~31.76%,R²=0.582）的趋势,表明基面高度越高,其对雪线分布高度的影响越大。     

阿尔卑斯山山体效应及其对林线的影响分析

阿尔卑斯山是欧亚大陆上著名的山地,对欧洲的地理生态格局具有重要的影响。山体效应产生的原因在于隆起的高原或山地吸收了更多的太阳辐射。因此,论文以阿尔卑斯山为研究对象,利用收集到的气象台站观测数据、林线、数字高程数据,以及基于半球视域算法计算得到的太阳辐射数据等,分析阿尔卑斯山气温的空间分布格局以及最热月、最冷月、全年的太阳辐射量,同时以太阳辐射作为山体效应的代用因子,采用逐步回归分析方法构建了阿尔卑斯山林线分布模型,探究该山地的山体效应及其对林线的影响。研究结果表明：① 阿尔卑斯山具有明显的山体效应,山体内部的太阳辐射量远高于山体边缘地区,这也是山体内部气温和林线高度都高于山体边缘地区的主要原因。最热月、最冷月和全年总太阳辐射量在山体内部比边缘地区分别高10~20、20~40和200~400 kWh/m²。② 太阳辐射能更好地定量化山体效应,以太阳辐射为山体效应代用因子建立的林线分布模型具有更高的精度。与基于气温、降水构建的林线分布模型(R²= 0.522)相比,该模型具有更高的模拟精度(R² = 0.736),同时太阳辐射对林线分布的贡献率最大(1月、7月太阳辐射的贡献率分别为34.75%、27.82%),超过了气温和降水的贡献率(分别为26.24%和11.17%)。     

青藏高原和阿尔卑斯山山体效应的对比研究

山体效应不仅对气候产生重大影响,也对区域地理生态格局有深远影响,尤其是它对山地垂直带分布和结构类型等的影响已经为地理学家和地植物学家所认识。目前相关研究主要集中在山体效应定量化方面,缺少不同山地山体效应的对比研究,因此对山体效应的区域差异性了解不足。本文选择欧亚大陆上具有明显山体效应的两个山地青藏高原和阿尔卑斯山为研究对象,利用收集到的气象台站观测数据、林线和DEM数据以及基于MODIS地表温度估算的青藏高原和阿尔卑斯山气温数据等,通过对比分析青藏高原与阿尔卑斯山相同海拔高度上的气温以及林线分布高度等来探讨两个山地的山体效应差异性。分析结果表明青藏高原的山体效应比阿尔卑斯山更为强烈,表现为：① 由于山体效应影响,在相同海拔高度上（4500 m）,青藏高原内部气温远高于阿尔卑斯山的气温,尤其是在最热月高原内部气温比阿尔卑斯山内部气温高10~15℃,在最冷月高原内部气温比阿尔卑斯山内部气温高5~10℃。② 由于山体效应影响,青藏高原内部林线也远高于阿尔卑斯山内部林线,约高2000~3000 m。本研究将为山体效应的影响因素分析奠定基础,同时对于揭示欧亚大陆山地生态系统格局具有一定的科学意义。     

山体基面高度对青藏高原及其周边地区雪线空间分布的影响简

山体效应是地理地带性之外,在大尺度上影响垂直带分布的主要因素,山体基面高度则是山体效应的第一影响因子。青藏高原及其周边地区,雪线呈现出中心高、周围低,与山体基面高度相一致的环状分布模式。为分析山体基面高度对雪线分布的影响,本文共收集青藏高原及周边地区雪线数据142个,采用纬度、经度和基面高度为自变量的三元一次方程拟合研究区雪线分布,计算各自的标准回归系数和相对贡献率,再将基面高度划分成5个子集（0~1000 m、1001~2000 m、2001~3000 m、3001~4000 m和4001~5000 m）,分析基面高度不同的山地对雪线的影响差异。结果表明：① 在青藏高原,纬度、经度和基面高度对雪线高度分布的相对贡献率分别为51.49%、16.31%和32.20%;② 随着基面高度的增高,各子集模型的决定系数虽有逐渐降低的趋势,但仍保持在较高的值域（R²=0.895~0.668）,说明模型的有效性;③ 随基面高度的抬升,纬度和山体基面高度对雪线分布高度的相对贡献率分别表现出降低（92.6%~48.99%,R²=0.855）和增大（3.33%~31.76%,R²=0.582）的趋势,表明基面高度越高,其对雪线分布高度的影响越大。     

基于MODIS的秦巴山地气温估算与山体效应分析

秦巴山地作为横亘在中国南北过渡带的巨大山脉,其山体效应对中国中部植被和气候的非地带性分布产生了重要的影响,山体内外同海拔的温差是表征山体效应大小较为理想的指标。本研究结合MODIS地表温度（LST）数据、STRM-1 DEM数据和秦巴山地的118个气象站点的观测数据,分别采用普通线性回归（OLS）和地理加权回归（GWR）两种分析方法对秦巴山地的气温进行估算,在此基础上将秦巴山地各月气温转换为同海拔（1500 m,秦巴山地平均海拔）气温,对比分析秦巴山地的山体效应。结果表明：① 相比OLS分析,GWR分析方法的精度更高,各月回归模型的R ²均在0.89以上,均方根误差（RMSE）在0.68~0.98 ℃之间。② 利用GWR估算得到的同海拔气温,从东向西随海拔升高呈现了明显的升高的趋势,秦岭西部山地比东段升高约6 ℃和4.5 ℃;大巴山西部山地年均和7月份同海拔的气温较东段升高约8 ℃和5 ℃。③ 从南向北,以汉江为分界,秦岭与大巴山的同海拔的气温均呈现出由山体边缘向内部升高的趋势。④ 秦巴山地西部大起伏高山,秦岭大起伏高中山和大巴山大起伏中山,相比豫西汉中中山谷地,各月均同海拔气温分别升高了约3.85~9.28 ℃、1.49~3.34 ℃和0.43~3.05 ℃,平均温差约为3.50 ℃,说明秦巴山地大起伏中高山的山体效应十分明显。     

中国高山林线的分布高度与气候的关系

通过研究我国高山林线的分布高度沿纬度、经度的变化格局,和对高山林线处的温度和基带降水等气候指标的分析,对我国高山林线分布高度与气候因子的关系进行探讨。结果表明：(1) 我国高山林线高度表现出明显的纬向和经向变化,总体趋势是：在北纬30^o以北,高山林线高度随纬度升高而下降,下降速率为112 m/度左右;在30^oN以南,则表现出较大的东西部差异：在东部,高山林线高度变化不明显,西部则随纬度增加呈上升趋势。在相似的纬度上,高山林线高度呈现出从东向西升高的趋势。高山林线在藏东南的洛隆、丁青、工布江达一带 (约29^o~32^oN,94^o~96^oE) 达到4 600 m,为世界最高林线高度,并以此为中心向四周降低。(2) 影响高山林线高度的主导气候因子为生长季温度条件。我国高山林线高度的温度指标为年生物温度3.5 ^oC,温暖指数14.2 ^oC·月,生长季平均温度8.2 ^oC。该指标相应海拔高度的地理差异,导致了我国高山林线高度的纬向、经向变化,和从沿海到内陆林线高度的差异。(3) 降水对高山林线高度有显著影响。在中高纬度地区,相同纬度上干旱区域的高山林线高于较湿润区域,降水量是通过温度间接作用于林线高度的。