Development of the IAP Dynamic Global Vegetation Model

ABSTRACT The lAP Dynamic Global Vegetation Model （IAP-DGVM） has been developed to simulate the distribution and structure of global vegetation within the framework of Earth System Models. It incorporates our group＇s recent developments of major model components such as the shrub sub-model, establishment and competition parameterization schemes, and a process-based fire parameterization of intermediate complexity. The model has 12 plant functional types, including seven tree, two shrub, and three grass types, plus bare soil. Different PFTs are allowed to coexist within a grid cell, and their state variables are updated by various governing equations describing vegetation processes from fine-scale biogeophysics and biogeochemistry, to individual and population dynamics, to large-scale biogeography. Environmental disturbance due to fire not only affects regional vegetation competition, but also influences atmospheric chemistry and aerosol emissions. Simulations under observed atmospheric conditions showed that the model can correctly reproduce the global distribution of trees, shrubs, grasses, and bare soil. The simulated global dominant vegetation types reproduce the transition from forest to grassland （savanna） in the tropical region, and from forest to shrubland in the boreal region, but overestimate the region of temperate forest.     

Impact of Spin-up Forcing on Vegetation States Simulated by a Dynamic Global Vegetation Model Coupled with a Land Surface Model

A dynamic global vegetation model (DGVM) coupled with a land surface model (LSM) is generally initialized using a spin-up process to derive a physically-consistent initial condition. Spin-up forcing, which is the atmospheric forcing used to drive the coupled model to equilibrium solutions in the spin-up process, varies across earlier studies. In the present study, the impact of the spin-up forcing in the initialization stage on the fractional coverages (FCs) of plant functional type (PFT) in the subsequent simulation stage are assessed in seven classic climate regions by a modified Community Land Model’s Dynamic Global Vegetation Model (CLM-DGVM). Results show that the impact of spin-up forcing is considerable in all regions except the tropical rainforest climate region (TR) and the wet temperate climate region (WM). In the tropical monsoon climate region (TM), the TR and TM transition region (TR-TM), the dry temperate climate region (DM), the highland climate region (H), and the boreal forest climate region (BF), where FCs are affected by climate non-negligibly, the discrepancies in initial FCs, which represent long-term cumulative response of vegetation to different climate anomalies, are large. Moreover, the large discrepancies in initial FCs usually decay slowly because there are trees or shrubs in the five regions. The intrinsic growth timescales of FCs for tree PFTs and shrub PFTs are long, and the variation of FCs of tree PFTs or shrub PFTs can affect that of grass PFTs.     

基于卫星遥感的植被NDVI对气候变化响应的研究进展

回顾了以往植被对气候响应的有关研究,从此类研究常使用的数据、方法及获取的结论3个方面进行了分析,重点阐述了归一化植被指数（Normalized Difference Vegetation Index,NDVI）对降水、温度和辐射等气候因子的响应特征,并探讨了未来的发展趋势。结果表明,植被NDVI对降水的显著响应往往出现在干旱半干旱地区和干湿季气候差异明显地区,且具有一定的滞后特征,滞后的时间尺度与局地条件关系密切;温度成为植被NDVI 控制因子的情况常出现在温带或寒温带地区,与对降水的滞后响应相比,植被对于温度的滞后响应并不是特别明显;辐射对于植被的主导影响主要出现在低纬度的部分区域、高云量区域和高纬度地区的特定时间段内。认为量化人类在植被对气候变化响应过程中的作用,全球变暖情形下植被对气候响应特征的深入分析,以及植被受气候影响的多尺度特征可能是以后此类研究的发展方向。     

黄淮海地区植被活动对气候变化的响应特征

基于1982 -2003年GIMMSNDVI遥感数据和气象资料, 综合运用趋势分析、相关分析、奇异值分解等方法, 分析我国黄淮海地区植被活动对气候变化响应的时空特征。结果表明:黄淮海地区整体气候变暖趋势比较明显, 干旱化尚不显著, 年平均植被NDVI表现为略微增加的趋势。在年尺度上, 温度是敏感性最强的气候因子, 全年温度、降水、相对湿度对植被NDVI动态变化具有正效应, 而蒸发量具有负效应; 在季尺度上, 温度、降水的敏感性最强。自然植被对降水的敏感性最强, 其次是温度; 农业植被对温度的敏感性最强, 其次是降水。植被对气候变化响应的空间特征表现为, 植被主要生长季平均NDVI与温度距平场空间结构一致, 与蒸发量距平场反位相对应, 与降水量距平场呈北、南部正负相反分布, 与相对湿度距平场呈南、北向正负相反的空间分布。     

LPJ-WHyMe模型对1997～2010年中国东北地区潜在植被分布和碳循环的模拟研究

利用一套高分辨率的气候驱动场和全球动态植被模型LPJ-WHyMe(Lund-Potsdam-Jena-Wetland Hydrology and Methane),模拟了中国东北地区潜在植被分布,并对中国东北地区1997～2010年平均净初级生产力（Net Primary Production, NPP）、净生态系统生产力（Net Ecosystem Production, NEP）、燃烧面积、火灾碳排放、土壤温度和土壤湿度进行了估算。LPJ-WHyMe的特点在于能够描述冻融的物理过程以及土壤中多层的湿度和温度。数值结果表明,在LPJ-WHyMe模型提供的植被功能类型（Plant Function Type, PFT）划分的条件下,中国东北地区主要分布了5种植被功能类型,即温带夏绿阔叶林带、北方常绿针叶林带、北方夏绿针叶林带、北方夏绿阔叶林带和温带草本植物。在研究时间段内,中国东北地区NPP的年平均值为376 g(C) m-2,变化范围在324.15～424.86 g(C) m-2之间。火灾机制的引入使得LPJ-WHyMe模型对NEP的模拟能力进一步提高,即NEP年平均值为42.36 g(C) m-2,表明中国东北地区陆地生态系统总体表现为“碳汇”。中国东北地区年平均燃烧面积分数为0.84%,火灾碳排放量为42.41 g(C) m-2,整体上模型高估了燃烧面积值和火灾碳排放量,模型对东北地区火灾的模拟仍然存在一定的局限性。中国东北地区土壤温度与气温呈正相关关系,且各层土壤温度与气温的相关性随着深度的增加而减弱。中国东北地区土壤湿度与降水呈正相关关系,土壤湿度与气温呈反相关关系。上述结果表明LPJ-WHyMe模型模拟中国东北地区潜在植被分布和碳循环是有效的。     

LPJ模型对1981～1998年中国区域潜在植被分布和碳通量的模拟

利用一个基于过程的动态植被模型LPJ DGVM（Lund Potsdam Jena Dynamic Global Vegetation Model）,模拟了中国区域潜在植被分布,考察了1981～1998年中国区域净初级生产〖JP〗力（NPP）、异养呼吸（Rh）和净生态系统生产力（NEP）的年际变化。模拟结果表明,在LPJ模型提供的植被功能类型（PFT）划分的条件下,中国区域除了分布裸土外,主要分布了6种潜在植被功能类型,即热带常绿阔叶林带、温带常绿阔叶林带、温带夏绿阔叶林带、北方常绿针叶林带、北方夏绿针叶林带和温带草本植物。在所考察的时间段内,中国区域总NPP从2.91 Gt · a-1（C）（1982年）变化到3.37 Gt · a-1（C）（1990年）,平均每年增加0.025 Gt（C）,其平均增长率为096%。中国区域总Rh从2.59 Gt · a-1（C）（1986年）变化到3.19 Gt · a-1（C）（1998年）,具有105% 的平均年增长率,即平均每年增加0.025 Gt（C）,并且中国区域温带草本植物相比其他植被功能类型,其NPP和Rh线性增加的趋势最为显著。研究结果还表明,LPJ模型在引入火灾机制后,中国区域总NEP的变化范围更加合理,即每年总NEP在-0.06 Gt · a-1（C）（1998年）和0.34 Gt · a-1（C）（1992年）之间变化,其平均值为0.12 Gt · a-1（C）。该结果表明,在所考察的时间段内,中国区域的陆地生态系统是碳汇。上述结果与其他研究结果基本一致,因而此模型模拟中国区域潜在植被分布和碳循环是有效的。       

大规模植被恢复对东亚气候的可能影响

利用动态植被模型CLM4-CNDV、区域气候模式RegCM4.6-CLM3.5和全球气候模式CAM4探究了当前气候状态下东亚区域可能的自然植被分布以及自然植被恢复对东亚区域气候产生的可能影响。结果表明,当前气候条件下,农作物区可能分布的自然植被为:蒙古高原以北、东北、华北平原和四川盆地的部分地区为裸土;东亚东南部及蒙古高原以北地区主要为林地;四川盆地及山东半岛主要为灌木;东北地区、东南沿海和长江中下游地区主要为草地。将农作物区恢复为自然植被后将对区域气候产生显著影响。其中,东亚东部大部分地区由于植被叶面积指数增加引起的蒸散发增强,使得夏季降水增加且温度降低显著;华北、四川盆地和广东中部平原地区植被叶面积指数减小,伴随区域内夏季降水显著减少且温度升高。而蒙古高原地区的气候变化不仅受区域内植被覆盖变化影响,还可能与印度地区和我国东南部植被变化引起的大气环流调整有关,使得蒙古高原西部冬季温度降低,而其东部夏季温度升高,同时夏季降水减少显著。研究所采用的试验方案是在相对理想的情况下进行的,但其结果为进一步区分不同地区植被覆盖变化的影响提供一定的参考。     

Climate and Vegetation Drivers of Terrestrial Carbon Fluxes:A Global Data Synthesis

The terrestrial carbon(C) cycle plays an important role in global climate change, but the vegetation and environmental drivers of C fluxes are poorly understood. We established a global dataset with 1194 available data across site-years including gross primary productivity(GPP), ecosystem respiration(ER), net ecosystem productivity(NEP), and relevant environmental factors to investigate the variability in GPP, ER and NEP, as well as their covariability with climate and vegetation drivers.The results indicated that both GPP and ER increased exponentially with the increase in mean annual temperature(MAT)for all biomes. Besides MAT, annual precipitation(AP) had a strong correlation with GPP(or ER) for non-wetland biomes.Maximum leaf area index(LAI) was an important factor determining C fluxes for all biomes. The variations in both GPP and ER were also associated with variations in vegetation characteristics. The model including MAT, AP and LAI explained 53%of the annual GPP variations and 48% of the annual ER variations across all biomes. The model based on MAT and LAI explained 91% of the annual GPP variations and 92.9% of the annual ER variations for the wetland sites. The effects of LAI on GPP, ER or NEP highlighted that canopy-level measurement is critical for accurately estimating ecosystem–atmosphere exchange of carbon dioxide. The present study suggests a significance of the combined effects of climate and vegetation(e.g.,LAI) drivers on C fluxes and shows that climate and LAI might influence C flux components differently in different climate regions.     

Sensitivity of the Terrestrial Ecosystem to Precipitation and Temperature Variability over China

In this study, the sensitivities of net primary production （NPP）, soil carbon, and vegetation carbon to precipitation and temperature variability over China are discussed using the state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model （LPJ DGVM）. The im- pacts of the sensitivities to precipitation variability and temperature variability on NPP, soil carbon, and vegeta- tion carbon are discussed. It is shown that increasing pre- cipitation variability, representing the frequency of ex- treme precipitation events, leads to losses in NPP, soil carbon, and vegetation carbon over most of China, espe- cially in North and Northeast China where the dominant plant functional types （i.e., those with the largest simu- lated areal cover） are grass and boreal needle-leaved for- est. The responses of NPP, soil carbon, and vegetation carbon to decreasing precipitation variability are opposite to the responses to increasing precipitation variability. The variations in NPP, soil carbon, and vegetation carbon in response to increasing and decreasing precipitation variability show a nonlinear asymmetry. Increasing pre- cipitation variability results in notable interannual variation of NPP. The sensitivities of NPP, soil carbon, and vegetation carbon to temperature variability, whether negative or positive, meaning frequent hot and cold days, are slight. The present study suggests, based on the LPJ model, that precipitation variability has a more severe impact than temperature variability on NPP, soil carbon, and vegetation carbon.     

MM5 Simulations of the China Regional Climate During the LGM.II: In uence of Change of Land Area, Vegetation, and Large-scale Circulation Background

Using a regional climate model MM5 nested to an atmospheric global climate model CCM3, a series of simulations and sensitivity experiments have been performed to investigate the relative LGM climate response to changes of land-sea distribution, vegetation, and large-scale circulation background over China.Model results show that compared with the present climate, the fluctuations of sea-land distribution in eastern Asia during the LGM result in the temperature decrease in winter and increase in summer. It has significant impact on the temperature and precipitation in the east coastal region of China. The impact on precipitation in the east coastal region of China is the most significant one, with 25%-50% decrease in the total precipitation change during the LGM. On the other hand, the changes in sea-land distribution have less influence on the climate of inland and western part of China. During the LGM, significant changes in vegetation result in temperature alternating with winter increase and summer decrease, but differences in the annual mean temperature are minor. During the LGM, the global climate, i.e., the large-scale circulation background has changed signi cantly. These changes have signi cant influences on temperature and precipitation over China. They result in considerable temperature decreases in this area, and direct the primary patterns and characteristics of temperature changes. Results display that, northeastern China has the greatest temperature decrease, and the temperature decrease in the Tibetan Plateau is larger than in the eastern part of China located at the same latitude. Moreover, the change of large-scale circulation background also controls the pattern of precipitation change. Results also show that, most of the changes in precipitation over western and northeastern parts of China are the consequences of changing large-scale circulation background, of which 50%-75% of precipitation changes over northern and eastern China are the results of changes in large-scale circulation background. Over China, the LGM climate responses to di erent mechanisms in order of strength from strong to weak are, the large-scale circulation pattern, sealand distribution, vegetation, CO2 concentration, and earth orbital parameters.     

Improving the treatment of the vertical snow burial fraction over short vegetation in the NCAR CLM3

One deficiency of the NCAR Community Land Model (CLM3) is the disappearance of the simulated snow even in the middle of winter over a boreal grassland site due to unrealistically modeled high downward turbulent fluxes. This is caused by the inappropriate treatment of the vertical snow burial fraction for short vegetation. A new snow burial fraction formulation for short vegetation is then proposed and validated using in situ observations. This modification in the CLM3 largely removes the unrealistic surface turbulent fluxes, leading to a more reasonable snowmelt process, and improves the snow water equivalent (SWE) simulation. Moreover, global offline simulations show that the proposed formulation decreases sensible and latent heat fluxes as well as the ground temperature during the snowmelt season over short vegetation dominant regions. Correspondingly, the SWE is enhanced, leading to the increase in snowmelt-induced runoff during the same period. Furthermore, sensitivity tests indicate that these improvements are insensitive to the exact functional form or parameter values in the proposed formulation.     

全球植被分布对气候影响的数值试验

利用一个新的陆－气双向耦合模式R42_AVIM, 通过有无植被覆盖的对比试验分析, 探讨了全球植被分布对气候和大气环流产生的潜在影响。得出: 陆面植被覆盖使得地表特征参数发生行星尺度的明显改变, 在叶面积指数大的热带和中高纬度森林带尤其显著。在现实植被分布下, 陆地表面反照率减小, 地表净辐射收支和地表潜热通量增加, 而地表感热通量减小。植被叶面积指数比较大的区域地表温度降低, 并且这种温度的改变一直延伸到对流层中上层, 在热带表现为斜压结构, 而在中高纬表现为相当正压结构。植被的存在使热带和中高纬度森林带的蒸发和相应的高层凝结潜热加热增强, 从而增强了经圈环流的上升支, 使得冬季在热带和南半球中纬度降水增多, 夏季在热带和北半球中高纬地区降水明显增多; 而经圈环流下沉支的增强致使副热带降水减少且更干旱。同时, 植被的存在使大陆潜热释放增强, 气温下降, 减小了海陆温度对比, 亚洲夏季风也有所减弱。     

Responses of vegetation distribution to climate change in China

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.     

气候与植被覆盖变化对中国西南亚高山区流域碳水循环的影响模拟

正确认识气候变化对流域森林植被和水文的影响对于林业经营管理与流域生态修复具有重要意义。为了揭示气候与植被覆盖变化对西南亚高山区流域碳水循环过程的影响,用生物物理/动态植被模型SSiB4/TRIFFID（Simplified Simple Biosphere model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics model）与流域地形指数水文模型TOPMODEL（Topographic Index Model）的耦合模型（以下记为SSiB4T/TRIFFID）模拟了不同气候情景下西南亚高山区的梭磨河流域植被演替和碳水循环过程。结果表明,所有试验流域植被经历了从C3到苔原灌木最后到森林的变化;控制试验流域蒸散在流域植被主要为苔原灌木时达到最大而径流深最小;增温5 ℃并且增雨40%试验[记为T+5, (1+40%) P试验]流域蒸散在流域为森林覆盖时达到最大而径流深最小。随着温度增加,森林蒸腾、冠层截留蒸发和蒸散的增加幅度明显大于草和苔原灌木,导致森林从控制试验的增加径流量变为减小径流量。从控制试验到T+5, (1+40%) P试验,温度增加使森林净初级生产力有所增加,但对草和苔原灌木的净初级生产力影响很小;植被水分利用效率随温度增加明显减小。西南山区随着海拔高度降低（温度升高）,森林从增加径流量转变为减少径流量,植被水分利用效率也相应明显减小。西南山区气候的垂直地带性对森林—径流关系和水分利用效率的空间变化有着重要的影响。     

Vegetation Feedback at the Mid-Pliocene

A global atmospheric general circulation model and an asynchronously coupled global atmosphere-biome model are used to simulate vegetation feedback at the mid-Pliocene approximately 3.3 to 3.0 million years ago.For that period,the simulated vegetation differed from present conditions at 62%of the global ice-free land surface.Vegetation feedback had little overall impact on the global climate of the mid-Pliocene.At the regional scale,however,the interactive vegetation led to statistically significant increases in annual temperature over Greenland,the high latitudes of North America,the mid-high latitudes of eastern Eurasia,and western Tibet,and reductions in most of the land areas at low latitudes,owing to vegetation-induced changes in surface albedo.     

植被归一化植被指数对气候因子的空间敏感性

基于全球土地利用类型和覆盖度,利用生长季多年平均（1982～2015年）归一化植被指数（Normalized Difference Vegetation Index,NDVI）和气候平均态（气温、降水量）数据,讨论了全球植被格局与气候因子之间的关系,建立了两者之间的多元回归模型,并分析了植被对气温和降水气候态敏感性的特征。植被与气候因子在气候梯度上存在明显的对应关系,回归模型可较好拟合气候态NDVI的全球分布格局,拟合与观测NDVI的相关系数达0.90。其中,常绿阔叶林、混交林、常绿针叶林、落叶阔叶林、农田和木本稀树草原空间分布的拟合能力较好（r>0.8）。不同土地覆盖类型的NDVI对气温、降水气候态的空间敏感性特征不同。整体而言,植被对气温和降水的敏感性呈现反相关关系（r=-0.6）。不同土地覆盖类型对气温表现出正/负敏感性,寒带灌木对气温的敏感性最强,而农作物、草原、裸地对气温负敏感性较大;植被对降水的敏感性均表现出正敏感性,其中落叶针叶林、草原和稀树草原对降水的空间敏感性较强。     

Evaluating the influence of different vegetation biomes on the global climate

The participation of different vegetation types within the physical climate system is investigated using a coupled atmosphere-biosphere model, CCM3-IBIS. We analyze the effects that six different vegetation biomes (tropical, boreal, and temperate forests, savanna, grassland and steppe, and shrubland/tundra) have on the climate through their role in modulating the biophysical exchanges of energy, water, and momentum between the land-surface and the atmosphere. Using CCM3-IBIS we completely remove the vegetation cover of a particular biome and compare it to a control simulation where the biome is present, thereby isolating the climatic effects of each biome. Results from the tropical and boreal forest removal simulations are in agreement with previous studies while the other simulations provide new evidence as to their contribution in forcing the climate. Removal of the temperate forest vegetation exhibits behavior characteristic of both the tropical and boreal simulations with cooling during winter and spring due to an increase in the surface albedo and warming during the summer caused by a reduction in latent cooling. Removal of the savanna vegetation exhibits behavior much like the tropical forest simulation while removal of the grassland and steppe vegetation has the largest effect over the central United States with warming and drying of the atmosphere in summer. The largest climatic effect of shrubland and tundra vegetation removal occurs in DJF in Australia and central Siberia and is due to reduced latent cooling and enhanced cold air advection, respectively. Our results show that removal of the boreal forest yields the largest temperature signal globally when either including or excluding the areas of forest removal. Globally, precipitation is most affected by removal of the savanna vegetation when including the areas of vegetation removal, while removal of the tropical forest most influences the global precipitation excluding the areas of vegetation removal.     

Using the Lund-Potsdam-Jena Model to Understand the Different Responses of Three Woody Plants to Land Use in China

In this study, the approach of conditional nonlinear optimal perturbation related to initial perturbation (CNOP-I) was employed to investigate the maximum variations in plant amount for three main woody plants (a temperate broadleaved evergreen, a temperate broadleaved summergreen, and a boreal needleleaved evergreen) in China. The investigation was conducted within a certain range of land use intensity using a state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model (LPJ DGVM). CNOP-I represents a class of deforestation and can be considered a type of land use with respect to the initial perturbation. When deforestation denoted by the CNOP-I has the same intensity for all three plants, the variation in plant amount of the boreal needleleaved evergreen in northern China is greater than the variation in plant amount of both the temperate broadleaved evergreen and temperate broadleaved summergreen in southern China. As deforestation intensity increases, the plant amount variation in the three woody plant functional types carbon changes, in a nonlinear fashion. The impact of land use on plant functional types is minor because the interaction between climate condition and land use is not considered in the LPJ model. Finally, the different impacts of deforestation on net primary production of the three plant functional types were analyzed by modeling gross primary production and autotrophic respiration. Our results suggest that the CNOP-I approach is a useful tool for exploring the nonlinear and different responses of terrestrial ecosystems to land use.     

2000年以来中国区域植被变化及其对气候变化的响应

气候是植被变化的主要驱动因子,研究全球增暖背景下中国区域植被变化及其对气候的响应对于国家开展重大生态恢复评估和未来植被保护政策制定具有重要意义。利用2000-2016年MODIS植被指数(Normalized Difference Vegetation Index,NDVI)数据集,运用统计分析方法,从平均态、线性趋势、时间序列、相关性等方面系统分析了2000年以来中国区域植被变化及其对气候变化的响应。结果表明:中国区域NDVI在平均态上呈现从东南向西北递减的空间分布,受降水生长季的影响,东部地区植被指数明显较大;我国大部分地区NDVI呈现增加的趋势,其中湿润半湿润地区NDVI增长幅度为0.037·(10a)^-1,而在干旱半干旱地区变化较小[0.013·(10a)^-1]。NDVI的变化与气候驱动因素的相关性存在一定的区域差异,其中:NDVI与气温变化在东南沿海、东北东部以及青藏高原北部等地区呈现出显著正相关,而在青藏高原南部等地区呈现微弱的负相关;除青藏高原、塔里木盆地和东北北部等地区外,NDVI与降水量在全国大多数地区呈正相关。从全国平均来看,温度和降水变化对NDVI的贡献分别为7.5%和9.1%,其中温度对NDVI变化的贡献主要体现在湿润半湿润地区(9.3%),而降水的贡献则在干旱半干旱地区(12.2%)。植被变化对气候要素驱动的响应也呈现出明显的区域差异性,在我国东南沿海、云贵高原东部、四川盆地等南方地区以及黄河中下游、东北东部等部分地区,NDVI变化对气温的敏感性最强;而在中国北方干旱半干旱大部分地区,NDVI变化则是对降水驱动具有很显著的响应特征。总体而言,气温是驱动南方地区植被变化的主导因子,而降水则调控着北方地区植被生长变化。