Amazon Integrated Carbon Analysis / AMAZONICA

亚马逊热带森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，物种极其丰富。由于伐木、林改牧和开采自然资源，它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。这些因子有可能显著影响全球大气温室气体负担（CO2、CH4）、化学和气候。陆地表面状态和变化的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贡献过程有关的通量估算值，以及未受干扰的雨林对不断变化的环境和年际大幅波动作出反应的证据，但不同的估算值差异很大。因此，很难确定这些独立估计的总体意义。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真实性变得困难，其中一些模型预测了热带雨林令人担忧的命运。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，解决各种碳通量估算差异的亚马逊河流域净平衡的一个主要大尺度限制因素是，对整个流域上方与对流层温室气体浓度场相关的三维碳循环进行准确表征。利用大气输送的逆模拟，时空浓度格局可以进一步转化为地表通量场。通过将来自LBA、RAINFOR网络和正在进行的TROBIT NERC项目的大量关于生态系统功能的现有地面数据和知识差距仍然存在的目标测量纳入耦合的陆地-地面-陆地生态系统模型，我们将开发一个适当的基于数据的系统模型表示。此外，将通过将该模式的预测结果与观测到的大气浓度模式进行比较来检验该模式。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联系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逆大气输送模式，自上而下获得温室气体的大尺度预算。2. 基于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，自下而上地估计与个别过程相关的通量。3. 利用现有的生态系统功能和气候响应的地面测量，并在缺少的情况下，利用新的、有针对性的地面测量，以限制土地生态系统和河流碳模型的代表性，然后将其结合到一个综合的土地碳循环模型中。4. 将一个完全整合的土地碳循环模型（从3开始）与一个区域气候模型耦合，并利用它(i)预测当前的浓度，以及（ii）在给定一系列具有代表性的情景下计算系统对气候和人口变化的响应。5. 在最后的综合步骤中，我们将分析和结合自上而下(1)和自下而上（2&3）的估计，以制定四年测量期间的多重约束和相互一致的碳通量。基于对潜在过程及其大规模影响的新理解，我们期望对全球碳循环中一个主要但目前约束不足的组成部分进行更好的量化。该项目还将为亚马逊对未来气候变化的反应提供更好的预测。

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.