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。       

全球植被与大气之间碳通量的模式估计

用大气植被相互作用模式(AⅥM)模拟了全球陆地植被的净初级生产力(NPP)。AⅥM由相互耦合的两部分组成：物理过程，包括陆地表面水分和能量在土壤、植被与大气之间的传输；以及生理生态过程，如：光合、呼吸、干物质分配、凋落和物候等。全球的植被分为13类，土壤按质地分为6类。用EMDI提供的全球1637个包括不同植被类型的NPP观测点数据对模型进行了检验。NPP模拟的结果表明：全球陆地植被的平均NPP为405.13 g C m-2yr-1，不同植被类型的平均NPP变化范围在99.58 g C m-2yr-l(苔原)到996.2 g C m-2yr-l(热带雨林)之间。全球年总NPP为60.72 Gt C yr-l，其中最大的部分为热带雨林，15.84 Gt C yr-1，占全球的26.09％。最大的碳汇是在北半球的温带。模式模拟的NPP在全球的空间和季节分布是合理的。     

中国季风区植被净初级生产力对东亚夏季风的响应机理研究

东亚夏季风可显著影响中国季风区气候变化,但是季风区植被净初级生产力(NPP)对夏季风气候变化的响应机理尚不明确。利用大气—植被相互作用模型(AVIM2)模拟了中国季风区植被NPP,分析了其与夏季风指数的相关关系,探讨了其对夏季风变化的响应机理。研究发现,我国南、北方植被对夏季风强度变化的响应方式和机理并不相同。强夏季风年北方植被NPP增加,而南方植被NPP减少。东亚夏季风对中国华北平原植被生长季NPP的作用主要是通过影响该地降水量实现的;京、津、冀地区植被NPP受东亚夏季风带来的气温和降水量变化的叠加影响,因而成为北方对夏季风变化最敏感的区域。东亚夏季风对我国南方江苏、安徽、湖南、湖北、江西植被NPP的作用是通过影响太阳辐射实现的,强夏季风导致太阳辐射减弱,从而使各省植被NPP减少。南方沿海的浙江和福建,强季风年带来的弱太阳辐射和低温是该地植被NPP减少的原因。广东、台湾植被NPP则主要受强夏季风带来的低温影响。     

1981-2008年中国陆地植被NPP对气候变化响应的敏感性试验

在验证CENTURY模型对中国陆地植被净初级生产力(Net Primary Productivity,NPP)模拟能力的基础上,利用该模型探讨了1981-2008年中国陆地植被NPP的年际变异和变化趋势对CO₂浓度、温度和降水变化的响应。结果表明,中国陆地植被NPP对不同气候因子的响应程度存在明显不同。其中,CO₂浓度变化对植被NPP年际变异的影响不显著,但能够引起中国大部分地区植被NPP趋势系数增大;温度对中国中高纬度地区植被NPP的年际变化影响显著,但就全国范围而言,植被NPP年际变异对温度变化的响应程度总体低于对降水变化的响应程度;降水变化是对中国植被NPP变化趋势起主导作用的气候因子。此外,综合考虑温度和降水变化的影响发现,植被NPP变化趋势的响应特征类似于降水单独变化时植被NPP变化趋势的响应特征。     

青藏高原1981～2000年植被净初级生产力对气候变化的响应

基于分辨率为0.1°×0.1°的植被、土壤和气象数据,利用大气-植被相互作用模型（AVIM2）模拟研究了青藏高原1981～2000年植被净初级生产力（NPP）对气候变化的响应。结果表明：青藏高原近20年自然植被（森林、草地和灌木）受气温和降水量增加的影响,NPP总量呈现上升趋势。灌木和森林NPP总量分别以每年1.14％和0.88％的速度增加,均达到统计上的显著性水平。草地NPP上升趋势不如灌木和森林显著。降水量变化对森林和草地NPP的影响高于气温变化对它们的影响,而降水量变化对灌木的影响则小于气温变化影响。总的区域平均来看,尽管1981～2000年青藏高原年平均净辐射通量略有降低,但由于平均气温以0.058 ℃·a-1的速率增加,且降水量略有增长,降水量与气温的共同作用使得青藏高原植被NPP总量呈上升趋势。     

东亚地区陆地植被净初级生产力与夏季气候的模拟

利用中国科学院大气物理研究所(IAP)一个海洋-大气-动态植被耦合模式(GOALS-AVIM),进行了100年模拟积分.基于模拟结果,对东亚地区的植被净初级生产力(NPP)、降水、地面气温和短波辐射的季节变化进行了标准化对比,分析了NPP的时空格局与气候因子(气温、短波辐射和降水)的关系;利用奇异值分解(SVD)对东亚夏季降水场和NPP的关系进行分解.结果表明,夏季东亚地区植被NPP及相关气候因子的时空变化规律明显,耦合模式可以很好地模拟出观测存在的降水及NPP、LAI(叶面积指数)大值区随季节北移南退的形态;由于耦合模式中AVIM的双向特点,模式模拟的NPP与其他物理场的季节变化有很强的对应关系,而且在不同时间和地区,NPP与降水、地面气温、短波辐射表现出不同的对应关系,其中植被NPP时间变化与气温和降水的相关性都较高;从NPP场和降水场夏季逐月标准化距平奇异值分解的空间分布模态来看.NPP与降水在时空场上表现出很强的耦合性,NPP的空间格局与降水存在较好的相关性,不同地理位置的相关性强弱不同,分解出的降水场异常相关模态也再现了东亚夏季降水移动的时空特征,同时东亚雨带随季节变化与NPP的气候变率表现出不同的对应模态.     

近55a新疆植被净初级生产力的时空变化

利用大气植被相互作用模型AVIM2分析了时间长度为55 a、空间分辨率为0.05°×0.05°的新疆植被净初级生产力(NPP),分析了气候变化下NPP的时空演变特征,并研究了其与气温和降水量的关系。结果表明,(1)近55 a新疆NPP平均值为92.4 gC·m~(-2),其中1993年最高为107.1 gC·m~(-2);2014年最小为79.0 gC·m~(-2)。近55 a新疆NPP总量的时间动态变化呈缓慢增加趋势,每10 a的递增速率约为1.8 gC·m~(-2)。(2)夏季是NPP最大的季节,其次是秋季,春季列第三位。山区NPP值较平原高。(3)新疆NPP对降水量变化呈显著正相关,气温的变化对NPP的影响不显著,说明降水的增加相对气温的升高,对新疆植被净初级生产力的变化有着更加积极的影响。     

地球系统模式中植被净初级生产力百年尺度时空变化及其与气候的关系

利用6个地球系统模式模拟的植被净初级生产力（NPP）对1901～2005年NPP时空变化进行了研究,并结合气候因子分析了NPP的变化与气温和降水的关系。结果表明:（1）近百年来全球NPP呈现上升趋势,模式集合平均的趋势系数为0.88,通过了99.9%的信度检验;北半球的趋势比南半球明显。（3）近百年来800 g(C) m-2 a-1以上的NPP高值区主要分布在南美洲赤道地区、非洲赤道地区、中南半岛和印度尼西亚一带的热带雨林区;低值区主要分布在北半球高纬度地区、非洲北部地区、亚洲大陆干旱半干旱区以及青藏高原西北部地区。（3）全球NPP与气温百年演变在大部分地区主要为正相关关系,仅在赤道附近的南美洲、非洲以及印度地区为负相关关系,主要由于这些地区辐射是NPP的限制因子。全球NPP与降水的百年变化在大部分地区也主要是正相关关系,在非洲北部到西亚中亚的干旱半干旱地区为负相关关系。（4）6个地球系统模式在全球21个区域的大部分地区的NPP和气温降水的变化关系较为一致,西非地区不同模式变化不一致,NPP模拟的不确定性较大,其次是地中海地区。（5）东亚地区NPP与气候的百年演变同步并且相关性高,反映了强烈的植被大气相互作用过程。     

6kaBP中国陆地生态系统净初级生产力的模拟

利用植被与大气相互作用模式（AVIM）模拟了全新世中期（6 kaBP）及现代中国陆地植被净初级生产力（NPP）的大小与分布特征,计算了以上两个时期我国陆地植被NPP的碳总量。结果表明：全新世中期以来气候的变化是影响我国陆地植被NPP变化的主要原因,6 kaBP时期NPP平均值为409 g/（m2·a）, NPP碳总量为3.89 Pg/a,分别比现在高15%和19%。全新世中期至今,我国陆地植被NPP的变化特征与对应时期中国土壤碳储量的变化趋势具有很好的一致性,这表明了利用生态模式模拟长时间尺度下我国陆地植被NPP的变化特征是可行的。     

基于遥感沿海湿地植被生产力估算研究

植被净第一性生产力是评价地表植被状况的重要指标之一,对分析和评价全球以及区域生态环境、碳循环等变化具有重要作用.植被净第一生产力的研究方法很多,本文主要运用TM影像近红外和红光通道组成的标准化差植被指数（NDVI）,借助CASA模型机理以及气象学方法,建立盐城区域净第一性生产力（NPP）遥感估算模型,并以核心区-丹顶鹤自然保护区湿地为应用案例,分析了核心区2005年间8月份净第一性生产力的变化.分析结果表明,研究区8月份的湿地各种植被NPP分别为：人工芦苇和盐蒿为1184.863266 g/m2,芦苇为1083.435262g/m2,米草为822.766878g/m2.若以NPP为衡量标准,人工芦苇和盐蒿的净第一性生产力最高.     

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.     

Modeling the Impact of Black Spruce on the Fire Regime of Alaskan Boreal Forest

In the boreal biome, fire is the major disturbance agent affecting ecosystem change, and fire dynamics will likely change in response to climatic warming. We modified a spatially explicit model of Alaskan subarctic treeline dynamics (ALFRESCO) to simulate boreal vegetation dynamics in interior Alaska. The model is used to investigate the role of black spruce ecosystems in the fire regime of interior Alaska boreal forest. Model simulations revealed that vegetation shifts caused substantial changes to the fire regime. The number of fires and the total area burned increased as black spruce forest became an increasingly dominant component of the landscape. The most significant impact of adding black spruce to the model was an increase in the frequency and magnitude of large-scale burning events (i.e., time steps in which total area burned far exceeded the normal distribution of area burned). Early successional deciduous forest vegetation burned more frequently when black spruce was added to the model, considerably decreasing the fire return interval of deciduous vegetation. Ecosystem flammability accounted for the majority of the differences in the distribution of the average area burned. These simulated vegetation effects and fire regime dynamics have important implications for global models of vegetation dynamics and potential biotic feedbacks to regional climate.     

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.     

Earth System Model FGOALS-s2: Coupling a dynamic global vegetation and terrestrial carbon model with the physical climate system model

Earth System Models （ESMs） are fundamental tools for understanding climate-carbon feedback. An ESM version of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS） was recently developed within the IPCC AR5 Coupled Model Intercomparison Project Phase 5 （CMIP5） modeling framework, and we describe the development of this model through the coupling of a dynamic global vegetation and terrestrial carbon model with FGOALS-s2. The performance of the coupled model is evaluated as follows. The simulated global total terrestrial gross primary production （GPP） is 124.4 PgC yr-I and net pri- mary production （NPP） is 50.9 PgC yr-1. The entire terrestrial carbon pools contain about 2009.9 PgC, comprising 628.2 PgC and 1381.6 PgC in vegetation and soil pools, respectively. Spatially, in the tropics, the seasonal cycle of NPP and net ecosystem production （NEP） exhibits a dipole mode across the equator due to migration of the monsoon rainbelt, while the seasonal cycle is not so significant in Leaf Area Index （LAI）. In the subtropics, especially in the East Asian monsoon region, the seasonal cycle is obvious due to changes in temperature and precipitation from boreal winter to summer. Vegetation productivity in the northern mid-high latitudes is too low, possibly due to low soil moisture there. On the interannual timescale, the terrestrial ecosystem shows a strong response to ENSO. The model- simulated Nifio3.4 index and total terrestrial NEP are both characterized by a broad spectral peak in the range of 2-7 years. Further analysis indicates their correlation coefficient reaches -0.7 when NEP lags the Nifio3.4 index for about 1-2 months.     

四套土壤湿度再分析数据在中国西北东部—华北—江淮地区的适用性研究

基于1992~2010年全国778个农业气象站土壤湿度观测资料、ERA-Interim、JRA55、NCEP-DOE R2和20CR土壤湿度再分析资料,通过平均差值、相关系数、差值标准差、标准差比四个参数,利用Brunke排名方法和EOF(Empirical Orthogonal Function)分析,对四套土壤湿度再分析资料在中国西北东部—华北—江淮区域的适用性进行了分析。主要结论如下:不同季节的平均偏差空间分布上,JRA55资料同观测数据的平均偏差在±0.08m~3 m~(-3)之间,春、夏季西北东部JRA55土壤湿度偏小,ERA-Interim、NCEP-DOE R2、20CR资料较观测数据偏湿,华北南部、江淮地区平均偏差小于西北东部、华北北部。在年际变化上,各个季节ERA-Interim资料同观测资料最为接近,能稳定地再现西北东部、华北、江淮地区土壤湿度干湿变化趋势,反映出重要的旱涝年。整体而言,四套再分析资料中ERA-Interim资料同观测资料接近,JRA55、NCEP-DOE R2资料次之,20CR资料最差。     

东北雨养玉米田碳交换年际变化及影响因素北大核心CSCD

在气候变化背景下,农田净生态系统生产力变化趋势和影响因素不确定性大,为有效评估农田生态系统的固碳潜力,利用2005-2020年东北雨养春玉米田涡动相关数据分析该区域碳通量年际变化趋势及其气象、土壤和生物影响因素。结果表明:东北雨养春玉米田净生态系统生产力为272±109g·m^(-2)·a^(-1),且无显著变化趋势;与生态系统呼吸相比,净生态系统生产力年际变化主要受总生态系统生产力影响。气象因素的降水量和生物因素的作物水分利用效率是净生态系统生产力年际变化的主要影响因素,影响权重分别为28.4%和31.4%;气象、土壤和生物因素对总生态系统生产力年际变化的影响权重分别为61.0%,43.8%和62.8%;土壤因素和生物因素是生态系统呼吸年际变化的主要影响因素,且土壤因素对生态系统呼吸年际变化的影响权重(39.3%)大于生物因素(29.2%)。在气候变暖背景下,东北雨养春玉米田对水分更为敏感,同时日照和温度通过影响饱和水汽压差和土壤湿度间接影响净生态系统生产力的年际变化。     

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.     

Remotely sensed soil moisture integration in an ecosystem carbon flux model. The spatial implication

While remote sensing is able to provide spatially explicit datasets at regional to global scales, extensive application to date has been found only in the reporting and verification of ecosystem carbon fluxes under the Kyoto Protocol. One of the problems is that new remote sensing datasets can be used only with models or data assimilation schemes adapted to include a data input interface dedicated to the type and format of these remote sensing datasets. In this study, soil water index data (SWI), derived from the ERS scatterometer (10-daily time period with a spatial resolution of 50 km), are integrated into the ecosystem carbon balance model C-Fix to assess 10-daily Net Ecosystem Productivity (NEP) patterns of Europe from the remote sensing perspective on an approximate 1-by-1 km² pixel scale using NDVI-AVHRR data. The modeling performance of NEP obtained with and without the assimilation of remotely sensed soil moisture data in the carbon flux model C-Fix is evaluated with EUROFLUX data. Results show a general decrease of the RRMSE of up to 11 with an average of 3.46. C-Fix is applied at the European scale to demonstrate the potential of this ecosystem carbon flux model, based on remote sensing inputs. More specifically, the strong impact of soil moisture on the European carbon balance in the context of the Kyoto Protocol (anthropogenic carbon emissions) is indicated at the country level. Results suggest that several European countries shift from being a carbon sink (i.e., NEP > 1) to being a carbon source (i.e., NEP < 0) whether or not short-term water availability (i.e., soil moisture) is considered in C-Fix NEP estimations.     

Determining Effects of Area Burned and Fire Severity on Carbon Cycling and Emissions in Siberia

The Russian boreal forest contains about 25% of the global terrestrial biomass, and even a higher percentage of the carbon stored in litter and soils. Fire burns large areas annually, much of it in low-severity surface fires – but data on fire area and impacts or extent of varying fire severity are poor. Changes in land use, cover, and disturbance patterns such as those predicted by global climate change models, have the potential to greatly alter current fire regimes in boreal forests and to significantly impact global carbon budgets. The extent and global importance of fires in the boreal zone have often been greatly underestimated. For the 1998 fire season we estimate from remote sensing data that about 13.3 million ha burned in Siberia. This is about 5 times higher than estimates from the Russian Aerial Forest Protection Service (Avialesookhrana) for the same period. We estimate that fires in the Russian boreal forest in 1998 constituted some 14–20% of average annual global carbon emissions from forest fires. Average annual emissions from boreal zone forests may be equivalent to 23–39% of regional fossil fuel emissions in Canada and Russia, respectively. But the lack of accurate data and models introduces large potential errors into these estimates. Improved monitoring and understanding of the landscape extent and severity of fires and effects of fire on carbon storage, air chemistry, vegetation dynamics and structure, and forest health and productivity are essential to provide inputs into global and regional models of carbon cycling and atmospheric chemistry.