Amazon Integrated Carbon Analysis / AMAZONICA

亚马逊热带雨林覆盖了全球最大的森林面积，构成了最大的地上有机碳储存库，物种特别丰富。通过伐木、森林还草和开发自然资源，他们承受着强大的人类压力。他们面临着气候变暖和大气环境的变化。这些因素有可能对全球大气温室气体负担(二氧化碳、甲烷)、化学和气候产生重大影响。陆地表面状态和变化的一个中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。尽管存在与森林砍伐等已知促成过程有关的通量估计，以及未受干扰的热带雨林对不断变化的环境和巨大的年际波动做出反应的证据，但不同的估计值差别很大。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得评估亚马逊未来模型模拟预测的现实性变得困难，其中一些预测预测了热带雨林令人担忧的命运。归根结底，对化合物表面通量最严格的限制是它在上层空气中的积累/消耗。因此，对亚马逊净平衡的一个主要大范围限制将解决各种碳通量估计中的差异，即准确描述整个盆地上方与三维碳循环有关的对流层温室气体浓度场。利用大气输送的逆模型，可以进一步将时空浓度模式转化为地表通量场。通过将LBA、RAINFOR网络和正在进行的TROBIT NERC项目/以及仍然存在知识差距的有针对性的测量--的大量现有的关于生态系统功能的实地数据纳入到一个耦合的陆地-地表-陆地-生态系统模型中，我们将开发一个恰当的以数据为基础的系统模型表示。此外，该模型将通过将其预测与观测到的大气浓度模式进行比较来进行测试。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、气候相互作用和陆河连接也将首次包括在这些模拟中。因此，我们提出了一个为期5年的项目，基于以下五个支柱：1.基于大气浓度数据和大气逆传输模型，自上而下地获取大规模的温室气体预算。2.根据现有和新的遥感信息(毁林和火灾)、未受干扰森林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.使用现有的、在缺少的情况下有针对性的新的生态系统功能和气候反应实地测量，以限制陆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陆地碳循环模型中。4.将一个完全集成的陆地碳循环模型(从3)耦合到区域气候模型中，并使用它(I)预测当前的浓度，以及(Ii)计算系统对气候变化和人口变化的反应，给定一系列具有代表性的情景。5.在最后的综合步骤中，我们将分析和合并自上而下(1)和自下而上(2和3)的估计，以在四年的测量期内开发出多个约束和相互一致的碳通量。我们希望基于对基本过程及其大规模影响的新理解，更好地量化全球碳循环的一个主要组成部分，但目前制约程度较低。该项目还将大大改进对亚马逊地区对未来气候变化反应的预测。

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.