江苏地区二氧化碳浓度时空分布特征分析

利用瓦里关和上甸子大气本底站观测的月平均CO₂浓度数据对GOSAT卫星反演的CO₂浓度数据进行验证,结果表明GOSAT产品与台站观测数据有较好的一致性.利用2009年6月—2011年5月GOSAT反演的CO₂浓度数据,分析了江苏地区CO₂浓度的时空变化特征,结果表明:1)975 hPa高度层CO₂浓度高于850 hPa高度层,CO₂浓度的水平变化要小于垂直变化;2)在季节变化上,CO₂浓度冬季最高,夏季最低,这可能与植被光合作用的强弱变化有关;比较前后两年的CO₂浓度数据,夏季和秋季的增速较快,冬季和春季的增速较慢;3)在日变化上,发现徐州和南京站02时CO₂浓度最高,14时CO₂浓度最低,这可能也与植被光合作用的强弱有关.     

锡林浩特草原CO2通量特征及其影响因素分析

利用锡林浩特国家气候观象台开路涡度相关系统、辐射土壤观测系统,测得的长期连续通量观测数据,对锡林浩特草原2009—2011年期间的CO₂通量观测特征进行了分析。结果表明:CO₂通量存在明显的年际、季节和日变化特征。3 a中NEE年际变率达到200 g·m^-2,季节变率最大达到460 g·m^-2,日变化幅度生长季最大达到0.25 mg·m^-2·s^-1。通过不同时间尺度碳通量与温度、水分、辐射等环境因子的分析,认为CO₂通量日变化主要受温度和光合有效辐射影响,而季节变化和年变化主要受降水和土壤含水量的影响。降水强度及时间分布是制约牧草CO₂吸收的关键因素,大于15%的土壤含水量有利于促进牧草生长。     

内蒙古地区植被对气候变化的响应

植被作为表征陆地生态环境变化的指示器,其对气候变化的响应研究已成为当前全球变化领域的主要内容之一。基于1982—2012年归一化植被指数(NDVI)和同期观测温度和降水资料,分析了内蒙古地区植被对气候变化的响应。研究表明,1982—2012年,植被指数总体呈波动变化,且区域差异明显。从年际变化来看,植被受降水影响明显;年内变化上,气温对植被的作用明显。内蒙古不同区域植被对气候的响应方式不同,其中内蒙古东北部地区对气温和降水的响应无滞后时间,其余地区对气温和降水的滞后时间为1个月。     

我国南水北调东线地区陆地植被NPP变化特征

基于EOS/MODIS（TERRA）卫星遥感资料,讨论中国南水北调东线地区陆地植被年均净初级生产力（NPP）的变化特征。结果表明,2000-2004年该地区的陆地植被年均NPP的变化范围为0～1494 g/(m2·a),5 a平均值为395.06 g/(m2·a)。对不同植被的年均NPP分析表明,常绿阔叶林的NPP最大,草地最小。气温是影响该地区陆地植被NPP变化的主要因素,未来南水北调东线地区地表水资源的减少不会对陆地植被的生长产生明显影响。     

东亚季风区夏季陆地生态系统碳循环对东亚夏季风的响应

东亚地区陆地生态系统的时空变率表现出明显的对季风气候的响应特征。使用EOF（经验正交分解）方法分析了AVIM2动态植被陆面模式离线模拟试验模拟的1953～2004年东亚季风区夏季陆地生态系统总初级生产力（GPP）、生态系统净初级生产力（NPP）、净生态系统初级生产力（NEP）、植被呼吸以及土壤呼吸的时空分布特点,探讨了东亚夏季风对陆地生态系统碳循环影响机制。研究发现,在强季风年,江淮地区高温少雨的特点限制了光合作用,造成GPP偏低;而华南地区在强季风年气候温暖湿润,利于植被生长,GPP偏高。季风对于植被呼吸和土壤呼吸影响不明显,使得GPP和植被呼吸之差NPP的变化及NPP和土壤呼吸之差NEP的变化与GPP的变化保持一致。在强季风年江淮流域地区干热的气候条件使得NPP和NEP降低;但是在华南地区温度升高的同时降水增多使得在NPP偏高的基础上NEP也偏高。     

利用改进的CASA模型分析三江源区净植被生产力

利用改进CASA模型计算了三江源地区植被净初级生产力(NPP).NPP值在区域上呈现由东南向西北递减的趋势,黄河源区东南部地区的植被NPP值较高,而长江源西北部的植被生长稀疏;2004—2008年三江源区NPP值呈略下降趋势,2006年该区植被的NPP年总量最大为62.93 Tg·a^-1,2005年NPP总量最小为60.9 Tg·a^-1;从季节分布来看,NPP值从5月开始增加,到7月达到最大,随后又逐渐降低.三江源地区草甸植被NPP值最大为188.95 g·m^-2·a^-1;高寒草原为129.41 g·m^-2·a^-1.其中,草原植被受气候年际变化影响相对较大,高寒草原年际变化表现为2004—2006持续上升.NPP的波动主要是由于该地区的温度、年降水量以及年太阳总辐射量等因素的变化造成的.在海拔较高的地区,温度与NPP的呈极显著相关,相关系数为0.8,而降水量与NPP的相关系数为0.7.     

东亚地区二氧化碳体积分数变化特征

利用2004年以来东亚地区10个本底观测站大气φ(CO₂)观测资料,分析了各站大气φ(CO₂)的变化特征及其各站之间的差异,讨论了下垫面特征、源汇作用等对φ(CO₂)变化的影响.结果表明:10个本底站大气月均φ(CO₂)有明显的季节变化,高值多出现在冬春等寒冷季节,而低值则多出现在6—9月,属于北半球的夏季;大气φ(CO₂)日变化趋势较为一致,15时(当地时间)前后达到全天最低,随后φ(CO₂)升高,并在日落后继续积累,至清晨7时(当地时间)前后达到全天最高,之后φ(CO₂)随着太阳辐射的增强而逐渐降低,且平均φ(CO₂)水平与下垫面植被量成反比,φ(CO₂)日变化的幅度与下垫面植被量成反比.作为全球基准站之一的瓦里关山站,2004—2008年φ(CO₂)年均值逐年增加,年增长率为2.28×10^-6/a.     

6 kaBP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。     

重庆地区年气温与降水量变化特征及对NPP的影响

选取重庆34个测站1959—2001年共43年逐月平均气温和降水量资料, 利用Thornth-waite Memoriae模型, 即根据植物生物产量与年平均气温、年降水量之间的关系用实际蒸散量估算NPP (净第一性生产力), 采用EOF及MHF小波等方法分析重庆地区年平均气温、降水量及NPP的时空变化特征及相互关系, 最后采用Thornth-waite Memoriae模型分析气温、降水变化对NPP的影响, 并结合未来气候预测结果对NPP的变化进行了预估。结果表明:重庆区域的年平均气温、年总降水量及NPP空间变化均比较有规律, 在整个时间域内, 气温呈下降趋势, 而降水变化趋势不明显, NPP略有下降, 但它们都具有明显的阶段性变化特征, NPP与降水的变化趋势比较一致; 在不同时间尺度上, NPP的变化趋势与降水接近, 在10年时间尺度以下时, 它与气温变化关系不明显, NPP与降水的年际振动特征明显, 而气温的年代际振动特征较显著; 重庆地区“暖湿型”气候对NPP增加最有利, 而“冷干型”气候对NPP增加最不利, 未来50年内重庆地区气温及降水变化趋势将有利于NPP的增加, 2030年前后可能达到最大值。     

大气CO2浓度升高及气候变化对作物冠层光合影响的数值模拟

利用美国Licor-6200光合作用测定仪,对黄淮海地区代表性冬小麦品种鲁麦23号叶片光合作用速率进行了较为全面的测定,分别确定了冬小麦叶片光—光合作用响应曲线和CO₂—光合作用响应曲线,在此基础上,建立了叶片光合作用模式,并进而建立了一个具有瞬时时间尺度,空间积分为叶片尺度的冬小麦冠层模式,利用模式分别分析了大气中CO₂浓度升高和温度变化对冠层光合作用的不同影响,并在此基础上进一步进行了综合数值分析。单因子分析表明:晴天状况下,冠层光合速率随CO₂浓度升高而上升,当CO₂浓度由330×10^-6上升至660×10^-6时,冠层光合日总量可增加19.7%;冠层光合速率随辐射增加而增大,辐射量增加10.0%,冠层光合日总量可增加6.7%;冠层光合速率随温度升高而下降,温度升高1℃,冠层光合日总量减少2.9%。多因子综合数值分析表明:在辐射量较大的气候背景下,冠层光合日总量对温度和CO₂变化响应更加敏感。本文的实测数据为研究气候变化对中国农业影响提供了最基本的可靠模型参数,冠层光合模型为未来改进作物模型提供了理论基础。     

Modeling responses of the meadow steppe dominated by Leymus chinensis to climate change

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in regional climate and global carbon cycling. Understanding the sensitivity of Chinese grassland ecosystems to climate change and elevated atmospheric CO₂ and the effect of these changes on the grassland ecosystems is a key issue in global carbon cycling. China encompasses vast grassland areas of 354 million ha of 17 major grassland types, according to a national grassland survey. In this study, a process-based terrestrial model the CENTURY model was used to simulate potential changes in net primary productivity (NPP) and soil organic carbon (SOC) of the Leymus chinensis meadow steppe (LCMS) under different scenarios of climatic change and elevated atmospheric CO₂. The LCMS sensitivities, its potential responses to climate change, and the change in capacity of carbon stock and sequestration in the future are evaluated. The results showed that the LCMS NPP and SOC are sensitive to climatic change and elevated CO₂. In the next 100 years, with doubled CO₂ concentration, if temperature increases from 2.7-3.9˚C and precipitation increases by 10% NPP and SOC will increase by 7-21% and 5-6% respectively. However, if temperature increases by 7.5-7.8˚C and precipitation increases by only 10% NPP and SOC would decrease by 24% and 8% respectively. Therefore, changes in the NPP and SOC of the meadow steppe are attributed mainly to the amount of temperature and precipitation change and the atmospheric CO₂ concentration in the future.     

A Modeling Study of Climatic Change and Its Implication for Agriculture in C

The trends and features of China’s climatic change in the past and future are analysed by applying station obser-vations and GCM simulation results. Nationally, the country has warmed by 0.3oC in annual mean air temperature and decreased by 5% in annual precipitation over 1951-1990. Regionally, temperature change has varied from a cooling of 0.3oC in Southwest China to a warming of 1.0oC in Northeast China. With the exception of South China, all regions of China have shown a declination in precipitation. Climatic change has the features of increasing remark-ably in winter temperature and decreasing obviously in summer precipitation. Under doubled CO2 concentration, climatic change in China will tend to be warmer and moister, with increases of 4.5oC in annual mean air temperature and 11% in annual precipitation on the national scale. Future climatic change will reduce the temporal and spatial differences of climatic factors.     

Simulation and Evaluation of Terrestrial Ecosystem NPP with M-SDGVM over Continental China

Using the regional terrestrial Net Primary Production (NPP) from different observations and models over China, we validated the NPP simulations and explored the relationship between NPP and climate variation at interannual and decadal scales in the Modified Sheffield Dynamic Global Vegetation Model (M-SDGVM) during 1981–2000. M-SDGVM shows agreement with the NPP data from 743 sites under the Global Primary Production Data Initiative (GPPDI). The spatial and the zonal averaged NPP of M-SDGVM agree well with ...     

The Potential Response of Terrestrial Carbon Storage to Changes in Climate and Atmospheric CO2

We use a georeferenced model of ecosystem carbon dynamics to explore the sensitivity of global terrestrial carbon storage to changes in atmospheric CO₂ and climate. We model changes in ecosystem carbon density, but we do not model shifts in vegetation type. A model of annual NPP is coupled with a model of carbon allocation in vegetation and a model of decomposition and soil carbon dynamics. NPP is a function of climate and atmospheric CO₂ concentration. The CO₂ response is derived from a biochemical model of photosynthesis. With no change in climate, a doubling of atmospheric CO₂ from 280 ppm to 560 ppm enhances equilibrium global NPP by 16.9%; equilibrium global terrestrial ecosystem carbon (TEC) increases by 14.9%. Simulations with no change in atmospheric CO₂ concentration but changes in climate from five atmospheric general circulation models yield increases in global NPP of 10.0–14.8%. The changes in NPP are very nearly balanced by changes in decomposition, and the resulting changes in TEC range from an increase of 1.1% to a decrease of 1.1%. These results are similar to those from analyses using bioclimatic biome models that simulate shifts in ecosystem distribution but do not model changes in carbon density within vegetation types. With changes in both climate and a doubling of atmospheric CO₂, our model generates increases in NPP of 30.2–36.5%. The increases in NPP and litter inputs to the soil more than compensate for any climate stimulation of decomposition and lead to increases in global TEC of 15.4–18.2%.     

Sensitivity of carbon budget to historical climate variability and atmospheric CO2 concentration in temperate grassland ecosystems in China

Chinese temperate grasslands play an important role in the terrestrial carbon cycle. Based on the parameterization and validation of Terrestrial Ecosystem Model (TEM, Version 5.0), we analyzed the carbon budgets of Chinese temperate grasslands and their responses to historical atmospheric CO₂ concentration and climate variability during 1951–2007. The results indicated that Chinese temperate grassland acted as a slight carbon sink with annual mean value of 7.3 T?g C, ranging from -80.5 to 79.6 T?g C yr^-1. Our sensitivity experiments further revealed that precipitation variability was the primary factor for decreasing carbon storage. CO₂ fertilization may increase the carbon storage (1.4 %) but cannot offset the proportion caused by climate variability (-15.3 %). Impacts of CO₂ concentration, temperature and precipitation variability on Chinese temperate grassland cannot be simply explained by the sum of the individual effects. Interactions among them increased total carbon storage of 56.6 T?g C which 14.2 T?g C was stored in vegetation and 42.4 T?g C was stored in soil. Besides, different grassland types had different responses to climate change and CO₂ concentration. NPP and R_H of the desert and forest steppes were more sensitive to precipitation variability than temperature variability while the typical steppe responded to temperature variability more sensitively than the desert and forest steppes.     

Effects of climate change and elevated atmospheric CO2 on soil organic carbon: a response equation

Climate change, such as warming and precipitation change, as well as elevated CO₂ can affect soil organic carbon (SOC) dynamics and cause changes in soil carbon sequestration. In this study, we introduced a response equation, relating the relative change of SOC to the relative changes of annual average temperature, annual precipitation, and atmospheric CO₂ concentration, as well as their inter-products. Using Nelson Farm as a case study, based on simulations of CENTURY model and multiple regressions, we examined the response equation for three vegetation covers (i.e., soybean, corn, and grass) and scenarios with different soil erosion rates and initial SOC contents. The response equation fit the simulation results very well with high adjusted coefficients of determination (R ²) (0.982 to 0.990). The results showed that the SOC was negatively related to the annual average temperature, positively related to the annual precipitation, and positively related to the elevated CO₂ for all the vegetation covers (p?<?0.001). The SOC was also significantly impacted by the interaction effects between elevated CO₂ and warming or precipitation change (p?<?0.001). The general form of the response equations for the different vegetation covers, soil erosion rates, and initial SOC contents was the same although the parameters varied with the different conditions. Based on the response equation, ??cutoff surfaces?? were defined to clearly quantify the synthesis effects of any possible combination of climate change and elevated CO₂ on the SOC, and the SOC sequestration potential was assessed under climate change and elevated CO₂ for different vegetations. Compared with the empirical models in the literature, this response equation provides a simple yet but robust method to represent the relationship between the SOC relative change vs. the relative changes of atmospheric temperature, precipitation, and atmospheric CO₂ concentration.     

Influence of dynamic vegetation on climate change arising from increasing CO<Subscript>2</Subscript>

A dynamic global vegetation model (DGVM) is coupled to an atmospheric general circulation model (AGCM) to investigate the influence of vegetation dynamics on climate change under conditions of global warming. The model results are largely in agreement with observations and the results of previous studies in terms of the present climate, present potential vegetation, present net primary productivity (NPP), and pre-industrial carbon budgets. The equilibrium state of climate properties are compared among pre-industrial, doubled, and quadrupled atmospheric CO₂ values using DGVM–AGCM and current AGCM with fixed vegetation to evaluate the influence of dynamic vegetation change. We also separated the contributions of temperature, precipitation and CO₂ fertilization on vegetation change. The results reveal an amplification of global warming climate sensitivity by 10% due to the inclusion of dynamic vegetation. The total effects of elevated CO₂ and climate change also lead to an increase in NPP and vegetation coverage globally. The reduction of albedo associated with this greening results in enhanced global warming. Our separation analysis indicates that temperature alters vegetation at high latitudes such as Siberia or Alaska, where there is a switch from tundra to forest. On the other hand, CO₂ fertilization provides the largest contribution to greening in arid/semi-arid region. Precipitation change did not cause any drastic vegetation shift.     

CLIMATE CHANGE DUE TO GREENHOUSE EFFECTS IN CHINA AS SIMULATED BY A REGIONAL CLIMATE MODEL-PART I:EVALUATION OF THE MODEL SIMULATIONS*

Impacts of greenhouse effects(2×CO₂) on climate change over China as simulated by a regional climate model have been investigated.The model was based on RegCM2 and is nested in one-way mode within a global coupled atmosphere-ocean model(CSIRO R₂₁L₉ AOGCM).Two multi-year simulations,the control run with normal CO₂ concentration and the sensitivity run with doubled CO₂ concentration are conducted. As Part I of the publications,results of control run of the CSIRO,i.e.its simulation of present climate in China,are analyzed briefly.It shows that the model can basically reproduce the surface air temperature and precipitation pattern over China.Therefore,its outputs can be used to drive the regional model. Analysis of control run of RegCM shows that with a high resolution,the model improves the simulations of surface air temperature and precipitation in China as compared to the CSIRO model, especially for the precipitation.The spatial correlation coefficient between simulated and observed annual temperature increased from 0.83 in the CSIRO to 0.92 in the RegCM and for annual precipitation from 0.48 in the CSIRO to 0.65 in the RegCM.A similar improvement in the RegCM compared to the CSIRO was found in all simulated months.The main improvement for surface temperature is that RegCM can simulate the fine scale structure of temperature caused by topography.RegCM greatly improved the spatial distribution of precipitation by eliminating the virtual precipitation center in central China,which was simulated by many other GCMs.The precipitation simulated by RegCM in North and Northwest China is smaller than that by CSIRO, which makes it closer to the observation.     

The interactive climate and vegetation along the Pole-Equator belts simulated by a global coupled model

The interaction between climate and vegetation along four Pole-Equator-Pole （PEP） belts were explored using a global two-way coupled model, AVIM-GOALS, which links the ecophysiological processes at the land surface with the general circulation model （GCM）. The PEP belts are important in linking the climate change with the variation of sea and land, including terrestrial ecosystems. Previous PEP belts studies have mainly focused on the paleoclimate variation and its reconstruction. This study analyzes and discusses the interaction between modern climate and vegetation represented by leaf area index （LAI） and net primary production （NPP）. The results show that the simulated LAI variation, corresponding to the observed LAI variation, agrees with the peak-valley variation of precipitation in these belts. The annual mean NPP simulated by the coupled model is also consistent with PIK NPP data in its overall variation trend along the four belts, which is a good example to promote global ecological studies by coupling the climate and vegetation models. A large discrepancy between the simulated and estimated LAI emerges to the south of 15°N along PEP 3 and to the south of 18°S in PEP 1S, and the discrepancy for the simulated NPP and PIK data in the two regions is relatively smaller in contrast to the LAI difference. Precipitation is a key factor affecting vegetation variation, and the overall trend of LAI and NPP corresponds more obviously to precipitation variation than temperature change along most parts of these PEP belts.