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1.
LPJ模型对1981～1998年中国区域潜在植被分布和碳通量的模拟 总被引：4，自引：0，他引：4
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096%。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105% 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。
相似文献
2.
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。 相似文献
3.
Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander 总被引：2，自引：0，他引：2
A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations.The results show that the model was able to capture the essential features of these path variations.We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method.Because of their relatively large uncertainties,three model parameters were considered:the interfacial friction coefficient,the wind-stress amplitude,and the lateral friction coefficient.We determined the CNOP-Ps optimized for each of these three parameters independently,and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm.Similarly,the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method.Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days.But the prediction error caused by CNOP-I is greater than that caused by CNOP-P.The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored.Hence,to enhance the forecast skill of the KLM in this model,the initial conditions should first be improved,the model parameters should use the best possible estimates. 相似文献
4.
数值天气预报和气候预测可预报性研究的若干动力学方法 总被引：4，自引：2，他引：2
简要回顾了数值天气预报和气候预测可预报性研究的若干动力学方法,包括用于研究第一类可预报性问题的线性奇异向量（LSV）和条件非线性最优初始扰动（CNOP-I）方法,以及Lyapunov指数和非线性局部Lyapunov指数方法。前两种方法用于研究预报或预测的预报误差问题,可以用于估计天气预报和气候预测的最大预报误差,而且根据导致最大预报误差的初始误差结构的信息,这两种方法可以用于确定预报或预测的初值敏感区。应该指出的是,LSV是基于线性化模式,对于描述非线性大气和海洋的运动具有局限性。因而,对于非线性模式,应该选择使用CNOP-I估计最大预报误差。Lyapunov指数和非线性局部Lyapunov指数可以用于研究第一类可预报性问题中的预报时限问题,前者是基于线性模式,不能解释非线性对预报时限的影响,而非线性局部Lyapunov指数方法则考虑了非线性的影响,能够较好地估计实际天气和气候的预报时限。第二类可预报性问题的研究方法相对较少,本文仅介绍了由我国科学家提出的关于模式参数扰动的条件非线性最优参数扰动（CNOP-P）方法,该方法可以用于寻找到对预报有最大影响的参数扰动,并可以进一步确定哪些参数最应该利用观测资料进行校准。另一方面,通过对比CNOP-I和CNOP-P对预报误差的影响,可以判断导致预报不确定性的主要误差因子,进而指导人们着力改进模式或者初始场。 相似文献
5.
Sensitivity of the Terrestrial Ecosystem to Precipitation and Temperature Variability over China 
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下载免费PDF全文 SUN Guo-Dong 《大气和海洋科学快报》2014,(5):382-387
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. 相似文献
6.
The approach of conditional nonlinear optimal perturbation related to parameter (CNOP-P) is employed to provide a possible climate scenario and to study the impact of climate change on the simulated net primary production (NPP) in China within a state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model (LPJ DGVM). The CNOP-P, as a type of climate perturbation to bring variation in climatology and climate variability of the reference climate condition, causes the maximal impact on the simulated NPP in China. A linear climate perturbation that induces variation in climatology, as another possible climate scenario, is also applied to explore the role of variation in climate variability in the simulated NPP. It is shown that NPP decreases in northern China and increases in northeastern and southern China when the temperature changes as a result of a CNOP-P-type temperature change scenario. A similar magnitude of change in the spatial pattern variations of NPP is caused by the CNOP-P-type and the linear temperature change scenarios in northern and northeastern China, but not in southern China. The impact of the CNOP-P-type temperature change scenario on magnitude of change of NPP is more intense than that of the linear temperature change scenario. The numerical results also show that in southern China, the change in NPP caused by the CNOP-P-type temperature change scenario compared with the reference simulated NPP is sensitive. However, this sensitivity is not observed under the linear temperature change scenario. The seasonal simulations indicate that the differences between the variations in NPP due to the two types of temperature change scenarios principally stem from the variations in summer and autumn in southern China under the LPJ model. These numerical results imply that NPP is sensitive to the variation in temperature variability. The results influenced by the CNOP-P-type precipitation change scenario are similar to those under the linear precipitation change scenario, which cause the increasing NPP in arid and semi-arid regions of the northern China. The above findings indicate that the CNOP-P approach is a useful tool for exploring the nonlinear response of NPP to climate variability. 相似文献
7.
Responses of soil carbon variation to climate variability in China using the LPJ model 总被引：1，自引：1，他引：0
In this study, we explored the maximal response of soil carbon in a part of China to climate change, including variations in climatology and climate variability, under the condition of global warming. A conditional nonlinear optimal perturbation (CNOP) approach was employed to discuss the above issue using the Lund–Potsdam–Jena (LPJ) model. The variation in the soil carbon was compared with those caused by a linear temperature or precipitation perturbation. The key difference between the CNOP-type and the linear perturbations depended on whether the perturbations brought the variation in the temperature or the precipitation variability in comparison with the reference temperature or the precipitation variability. The model results demonstrated that the variations in the soil carbon resulted from the CNOP-type and linear temperature perturbations in south of the study region, which was corresponding to part of South China, had different variations. We examined three components of the soil carbon in the LPJ model: fast-decomposing soil carbon, slow-decomposing soil carbon, and litter below the ground. The variations of these components derived by the two types of temperature perturbations were different in the chosen region. The reduction in the litter below the ground may be the main cause of decreased soil carbon in arid and semi-arid regions as a result of the two types of temperature perturbations. The different impacts of the two types of temperature perturbations in the south of the study region may be mainly caused by the variations in the fast-decomposing soil carbon. The variations in the soil carbon caused by the two types of precipitation perturbations were similar. In the arid and semi-arid regions, the soil carbon increased due to the two types of precipitation perturbations. This research implies that the variation in temperature variability plays a crucial role in the variations of the soil carbon and its components in the study region. 相似文献
8.
George M. Woodwell 《Climatic change》1991,19(1-2):245-251
Forests contain more than twice as much carbon as the atmosphere and process through their metabolism about 1/7 of the atmospheric carbon annually. Deforestation currently is adding carbon to the atmosphere as carbon dioxide at an increasing rate and causing the impoverishment of soils over large areas in the tropics. But deforestation is also occurring in the temperate and boreal forests. In most cases deforestation is the result of national policies. It proceeds in the United States in response to economic pressures and political weakness, even corruption.The re-establishment of forests has the potential for contributing to the stabilization of the composition of the atmosphere by removing carbon as carbon dioxide from the atmosphere and storing it on land for an indefinite period. Such a transition in land use is difficult to imagine in a world in which the human population is expanding continuously and impoverished land is accumulating.Global interests in management of forests introduce a new element into international relations. Progress in effecting the shifts in controls on land use required to control deforestation in the interests of stabilizing climate and preserving biotic resources will depend on clear definition of the details of the problem by the scientific community and a further definition of how to proceed. 相似文献
9.
Evaluating the Dependence of Vegetation on Climate in an Improved Dynamic Global Vegetation Model 总被引：2，自引：0，他引：2
ZENG Xiaodong 《大气科学进展》2010,27(5):977-991
The capability of an improved Dynamic Global Vegetation Model (DGVM) in reproducing the impact of climate on the terrestrial ecosystem is evaluated. The new model incorporates the Community Land ModelDGVM (CLM3.0-DGVM) with a submodel for temperate and boreal shrubs, as well as other revisions such as the two-leaf scheme for photosynthesis and the definition of fractional coverage of plant functional types (PFTs). Results show that the revised model may correctly reproduce the global distribution of tempera... 相似文献
10.
ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model. 相似文献
11.
Responses of vegetation distribution to climate change in China 总被引：1，自引：1，他引：0
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. 相似文献
12.
The dynamic vegetation model (LPJ-GUESS) is used to project transient impacts of changes in climate on vegetation of the Barents
Region. We incorporate additional plant functional types, i.e. shrubs and defined different types of open ground vegetation,
to improve the representation of arctic vegetation in the global model. We use future climate projections as well as control
climate data for 1981–2000 from a regional climate model (REMO) that assumes a development of atmospheric CO2-concentration according to the B2-SRES scenario [IPCC, Climate Change 2001: The scientific basis. Contribution working group I to the Third assessment report of the IPCC. Cambridge University Press, Cambridge (2001)]. The model showed a generally good fit with observed data, both qualitatively when model outputs were compared to vegetation
maps and quantitatively when compared with observations of biomass, NPP and LAI. The main discrepancy between the model output
and observed vegetation is the overestimation of forest abundance for the northern parts of the Kola Peninsula that cannot
be explained by climatic factors alone. Over the next hundred years, the model predicted an increase in boreal needle leaved
evergreen forest, as extensions northwards and upwards in mountain areas, and as an increase in biomass, NPP and LAI. The
model also projected that shade-intolerant broadleaved summergreen trees will be found further north and higher up in the
mountain areas. Surprisingly, shrublands will decrease in extent as they are replaced by forest at their southern margins
and restricted to areas high up in the mountains and to areas in northern Russia. Open ground vegetation will largely disappear
in the Scandinavian mountains. Also counter-intuitively, tundra will increase in abundance due to the occupation of previously
unvegetated areas in the northern part of the Barents Region. Spring greening will occur earlier and LAI will increase. Consequently,
albedo will decrease both in summer and winter time, particularly in the Scandinavian mountains (by up to 18%). Although this
positive feedback to climate could be offset to some extent by increased CO2 drawdown from vegetation, increasing soil respiration results in NEE close to zero, so we cannot conclude to what extent
or whether the Barents Region will become a source or a sink of CO2. 相似文献
13.
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs. 相似文献
14.
A temperate and boreal deforestation experiment has been performed at Météo-France using the ARPEGE climate model. A first
simulation was performed as a control with a present-day vegetation map, and another one with all forests north of 45 °N replaced
by meadows. Prescribed monthly mean climatological SSTs were used in both integrations. The ARPEGE climate model includes
a physically based land surface scheme, which has been tested both on snowfree and snow-covered sites, and has a relatively
high horizontal resolution. Results of the 4-year integrations suggest that forests exert a strong influence on the surface
climate of the temperate and boreal regions. Deforestation induces a significant cooling which modifies the atmospheric circulation
simulated in the high latitudes, and also in the tropics. The most important impact is observed during the melting season
which is delayed by the forest removal. This result is consistent with preliminary stand-alone experiments showing that the
atmospheric boundary layer can be heated by the forest, even if the ground is covered by snow. The study confirms that vegetation
feedbacks should be included when performing future climate studies such as doubled CO2 experiments, eventhough many uncertainties still remain with regard to other physical aspects of the climate models.
Received: 5 September 1995 / Accepted: 12 August 1996 相似文献
15.
The response of a grassland ecosystem to climate change is discussed within the context of a theoretical model.An optimization approach,a conditional nonlinear optimal perturbation related to parameter(CNOP-P) approach,was employed in this study.The CNOP-P,a perturbation of moisture index in the theoretical model,represents a nonlinear climate perturbation.Two kinds of linear climate perturbations were also used to study the response of the grassland ecosystem to different types of climate changes.The results show that the extent of grassland ecosystem variation caused by the CNOP-P-type climate change is greater than that caused by the two linear types of climate change.In addition,the grassland ecosystem affected by the CNOP-P-type climate change evolved into a desert ecosystem,and the two linear types of climate changes failed within a specific amplitude range when the moisture index recovered to its reference state.Therefore,the grassland ecosystem response to climate change was nonlinear.This study yielded similar results for a desert ecosystem seeded with both living and wilted biomass litter.The quantitative analysis performed in this study also accounted for the role of soil moisture in the root zone and the shading effect of wilted biomass on the grassland ecosystem through nonlinear interactions between soil and vegetation.The results of this study imply that the CNOP-P approach is a potentially effective tool for assessing the impact of nonlinear climate change on grassland ecosystems. 相似文献
16.
Tropical deforestation: Important processes for climate models 总被引：4，自引：0，他引：4
17.
土地利用和土地覆盖变化对气候系统影响的研究进展 总被引：8，自引：4，他引：4
土地利用和土地覆盖变化（LUCC或LULCC）不仅对人类赖以生存的地球环境有重要影响,同时与人类福祉密切联系。人类活动对气候的强迫不仅包括温室气体排放导致的气候变暖,还通过直接改变地表物理性状以及间接改变其他生物地球物理过程和生物地球化学过程等对气候系统产生深刻影响。作者在此认识的基础上回顾了LUCC对气候系统影响的研究历史,结合新近的研究结果归纳了诸如森林砍伐、城市化、修坝等LUCC活动在区域和全球尺度的气候效应。LUCC具有高度的空间异质性,因此气候系统对它的反馈也具有明显的空间差异。由于全球平均后变化幅度相对区域上的小,LUCC对区域气候影响显著,而对全球气候影响不明显。它对区域气候的影响取决于反照率、蒸散发效率和地表粗糙率等变化的综合效应：在热带地区LUCC主要引起温度升高,在高纬度地区使温度下降。在全球尺度上LUCC导致气候的变暖主要通过减少蒸散发和潜热通量引起陆表水循环的改变,其次通过改变地表反照率导致辐射强迫改变。最后指出目前LUCC在气候变化学科中的研究所存在的问题。在此基础上提出了未来的研究首先需要评估的3个气候指标,并提倡多学科间的相互合作。 相似文献
18.
局地大气能量有效性中的表面扰动位能特征 总被引：2，自引：0，他引：2
在局地扰动位能理论工作基础上,进一步研究了与实际地形有关的表面扰动位能部分,理论推导了数学表达式,表明地形和表面热状况是其决定因素。利用再分析资料考察了其气候学和气候变率特征。研究表明,表面扰动位能具有独特的热动力学意义,它的量值与地球表面高大地形密切联系,而其季节变动特点则与表面热状况的季节变化息息相关。高纬度极地地形区域为明显的全年基本不变的负扰动位能分布区,低纬度热带区域则呈现季节变动非常小的正扰动位能分布。表面扰动位能的季节变动和年际变率的极值区都位于北半球中纬度的高大地形区域,夏季达到正的极大值,而冬季则转变成负值区。这一特征在青藏高原区最为典型,其表面扰动位能在时域和频域上均表现出明显的年代际特征,年际变率以2—4a周期为主。 相似文献
19.
Many studies have explored the importance and influence of planetary boundary layer processes on tropical cyclones (TCs). However, few studies have focused on the influence of land surface processes on the activity of TCs. To test the effect of initial perturbations of land surface processes on TCs, a land surface process perturbation module is built in a global ensemble prediction system. Ensemble experiments for the TCs that occurred from 12 UTC 22 August to 18 UTC 24 November, 2006 show that consideration of the uncertainties within the land surface process could increase the predictability of the global ensemble prediction system. Detailed analysis on TC Xangsane (2006) indicates that the perturbation of land surface processes may increase the variation of sensible heat flux and latent heat flux. Meanwhile, the effect from land surface perturbation can be transferred to the upper atmosphere, which leads to better TC forecasts. 相似文献
20.
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. 相似文献