Preparing for CO2 column datastreams from OCO and GOSAT satellite instrument

人类在许多方面影响了地球气候的演变，其中最引人注目的是化石燃料的燃烧以及随之而来的二氧化碳和其他温室气体的排放。更好地定量了解对生物圈大陆二氧化碳通量的控制，对于减少人类对气候的影响的不确定性至关重要。我们对大陆生物圈通量的了解，大部分是从实地数据推断出来的。这些数据在时间和空间上都是稀疏的，因此很难做出可靠的通量估计。相比之下，海洋二氧化碳通量通常在100公里范围内变化，因此更容易根据现场数据估计全球通量。两个专门建造的卫星仪器将从2008年起提供具有区域尺度代表性的二氧化碳观测数据。这些数据有可能彻底改变我们对碳循环的陆地成分的定量理解，但使用它们对碳循环界提出了重大挑战。这些数据并不容易解释，它代表了电磁波谱中近红外部分的二氧化碳吸收测量。每天处理成千上万的观测数据也是一项重大的技术挑战。数据同化提供了一种灵活而有效的方法来估算与观测到的二氧化碳浓度相一致的地表二氧化碳通量，并考虑到它们各自的不确定性。在此，我们建议开发有效的数据同化工具来解释这些即将到来的二氧化碳数据流，并评估这些数据在量化区域碳通量方面的优势和劣势。这些工具将使用两个新仪器的模拟观测结果进行严格测试，这将为研究人员提供一个极好的机会来评估仪器和模型误差对CO2表面通量估计的影响。当我们处理真实数据时，数据同化工具和随后的模拟结果将是无价的，目的是减少估计未来气候的不确定性。

Humans have influenced the evolution of Earth's climate in many ways, the most dramatic of which has been the burning of fossil fuels and the subsequent emission of carbon dioxide (CO2) and other greenhouse gases. A better quantitative understanding of the controls on biospheric continental CO2 fluxes is essential to reduce uncertainty of the human contribution to climate. Much of what we understand about continental biospheric fluxes has been inferred from in situ data. These data are sparse in both time and space, making it difficult to make reliable flux estimates. In contrast, the ocean CO2 fluxes typically vary over 100s km, making it easier to estimate global fluxes from in situ data. Two purpose-built satellite instruments will provide observations of CO2, representative of regional scales, from 2008. These data have the potential to revolutionize our quantitative understanding of the land-component of the carbon cycle, but using them presents significant challenges to the carbon cycle community. The data are not straightforward to interpret, representing a measurement of CO2 absorption in the near-infra red portion of the electromagnetic spectrum. Processing the hundreds of thousands of observations per day also represents a significant technical challenge. Data assimilation provides a flexible and efficient method of estimating surface CO2 fluxes that are consistent with the observed CO2 concentrations, accounting for their respective uncertainties. Here, we propose to develop efficient data assimilation tools to interpret these upcoming CO2 data streams and assess the strengths and weaknesses of these data for quantifying regional carbon fluxes. The tools will be tested rigorously using simulated observations from the two new instruments, providing the investigators an excellent opportunity to also assess the impact of instrument and model errors on surface flux estimates of CO2. The data assimilation tools, and the subsequent results from the simulations, will be invaluable when we process real data, with the purpose of reducing uncertainty in estimating future climate.