Amazon Integrated Carbon Analysis / AMAZONICA

亚马逊综合碳分析/AMAZONICA

• 批准号: NE/F005040/1
• 负责人: John Grace
• 金额: $ 59万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FF005040%2F1
• 关键词: Amazon; Integrated; Carbon; Analysis; AMAZONICA

Amazonian tropical forests cover the largest forested area globally, constitute the largest reservoir of above-ground organic carbon and are exceptionally species rich. They are under strong human pressure through logging, forest to pasture conversion and exploitation of natural resources. They face a warming climate and a changing atmospheric environment. These factors have the potential to affect significantly the global atmospheric greenhouse gas burden (CO2, CH4), chemistry and climate. A central diagnostic of the state and changes of the land surface is its net carbon balance but currently we do not even know the sign of this balance. Although estimates of fluxes associated with known contributing processes such as deforestation exist, along with evidence for responses of undisturbed rainforests to a changing environment and substantial inter-annual fluctuations, different estimates vary widely. Thus it is very difficult to determine the overall significance of these independent estimates. The uncertainty of the greenhouse gas balances have also made it difficult to assess the realism of future model simulation predictions of the Amazon, some of them predicting alarming fates for the rainforests. Ultimately, the most stringent constraint on surface fluxes of a compound is its accumulation / depletion in overlying air. A major large-scale constraint on the net balance of the Amazon that would resolve the discrepancy in the various carbon flux estimates is therefore an accurate characterization of the 3D carbon cycle related tropospheric greenhouse gas concentration fields above the entire basin. Spatio-temporal concentration patterns can further be translated into surface flux fields using inverse modelling of atmospheric transport. By incorporating the large amount of existing on-ground data on ecosystem functioning from LBA, the RAINFOR network, and the ongoing TROBIT NERC project / and targeted measurements where knowledge gaps remain - into a coupled land-surface land-ecosystem model, we will develop a properly data-grounded model representation of the system. Further, the model will be tested by comparing its predictions with observed atmospheric concentration patterns. In turn this will permit defensible projections of the future of Amazonian vegetation. Human activity climate interactions and the land river link will also for the first time be included in these simulations. Therefore, we propose a project of 5 year duration based on the following five pillars: 1. To obtain large-scale budgets of greenhouse gases top-down, based on atmospheric concentration data and inverse atmospheric transport modelling. 2. To estimate fluxes associated with individual processes bottom-up, based on existing and new remote sensing information (deforestation and fires), tree-by-tree censuses in undisturbed forests, and river carbon measurements. 3. To use existing, and, where missing, targeted new, on-ground measurements of ecosystem functioning and climate response, in order to constrain land ecosystem and river carbon model representation, which will then be combined in an integrated land carbon cycle model. 4. To couple a fully integrated land carbon cycle model (from 3) into a regional climate model and use it (i) to predict current concentrations, and (ii) to calculate the systems response to a changing climate and human population, given a representative range of scenarios. 5. In a final synthesis step we will analyse and combine top-down (1) and bottom-up estimates (2&3) to develop multiple constraint and mutually consistent carbon fluxes over the four-year measurement period. We expect to obtain much better quantification of a major but currently poorly constrained component of the global carbon cycle, based on a new understanding of the underlying processes and their large-scale effect. The project will also provide much improved predictions of the response of the Amazon to future climate change.

亚马逊热带森林覆盖全球最大的森林面积，是地上有机碳的最大储存库，物种异常丰富。由于伐木、将森林转变为牧场和开发自然资源，他们承受着巨大的人类压力。他们面临着气候变暖和大气环境变化。这些因素有可能对全球大气温室气体（CO2、CH 4）负担、化学和气候产生重大影响。陆地表面的状态和变化的一个核心诊断是其净碳平衡，但目前我们甚至不知道这种平衡的迹象。虽然存在与毁林等已知促成过程相关的通量估计数，沿着有证据表明未受干扰的雨林对不断变化的环境和重大的年际波动作出反应，但不同的估计数差异很大。因此，很难确定这些独立估计数的总体意义。温室气体平衡的不确定性也使人们难以评估亚马逊未来模型模拟预测的现实性，其中一些预测对热带雨林的命运令人担忧。最终，对化合物表面通量的最严格限制是其在上覆空气中的积累/消耗。因此，解决各种碳通量估计值之间差异的亚马逊净平衡的一个主要大规模约束是整个流域上方与对流层温室气体浓度场相关的3D碳循环的准确表征。利用大气迁移的逆模型，时空浓度模式可进一步转化为地面通量场。通过将大量现有的地面生态系统功能数据从LBA，RAINFOR网络，正在进行的TROBIT NERC项目/和有针对性的测量知识差距仍然存在-到耦合的陆地表面陆地生态系统模型，我们将开发一个适当的数据接地模型表示的系统。此外，将通过将该模型的预测与观测到的大气浓度模式进行比较，对该模型进行测试。反过来，这将允许对亚马逊植被的未来进行合理的预测。人类活动、气候相互作用和陆地河流连接也将首次纳入这些模拟。因此，我们提出了一个基于以下五大支柱的五年期项目：1。根据大气浓度数据和逆大气迁移模型，自上而下获得温室气体的大规模预算。2.根据现有的和新的遥感信息（森林砍伐和火灾）、在未受干扰的森林中进行的逐树普查和河流碳测量，自下而上估计与个别过程有关的通量。3.利用现有的、以及在缺少的情况下有针对性的新的生态系统功能和气候响应实地测量结果，以限制土地生态系统和河流碳模型的代表性，然后将其合并到一个综合的土地碳循环模型中。4.将一个完全集成的陆地碳循环模型（从3）耦合到一个区域气候模型中，并使用它（i）预测当前浓度，（ii）计算系统对气候变化和人口变化的响应，给定一系列有代表性的情景。5.在最后的综合步骤中，我们将分析并结合联合收割机自上而下（1）和自下而上（2和3）的估计，以开发多个约束和相互一致的碳通量在四年的测量期。我们期望获得更好的量化的一个主要的，但目前制约不佳的组成部分，全球碳循环的基础上的基本过程及其大规模的影响的新的理解。该项目还将大大改进对亚马逊应对未来气候变化的预测。

项目成果

Fluvial carbon export from a lowland Amazonian rainforest in relation to atmospheric fluxes

• DOI: 10.1002/2016jg003464
• 发表时间: 2016-12
• 期刊: Journal of Geophysical Research: Biogeosciences
• 作者: L. Vihermaa;S. Waldron;T. Domingues;J. Grace;E. Cosio;Fabián Limonchi;C. Hopkinson;H. Rocha;E. Gloor

Perturbations in the carbon budget of the tropics.

• DOI: 10.1111/gcb.12600
• 发表时间: 2014-10
• 期刊: Global change biology
• 影响因子: 11.6
• 作者: Grace J;Mitchard E;Gloor E
• 通讯作者: Gloor E

Methane emissions from sheep pasture, measured with an open-path eddy covariance system

• DOI: 10.1111/j.1365-2486.2011.02466.x
• 发表时间: 2011-12-01
• 期刊: GLOBAL CHANGE BIOLOGY
• 影响因子: 11.6
• 作者: Dengel, Sigrid;Levy, Peter E.;Skiba, Ute M.
• 通讯作者: Skiba, Ute M.