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燃烧化石燃料以及随后排放的二氧化碳（CO2）和其他温室气体。更好地定量了解生物圈大陆CO2通量的控制是必不可少的，以减少人类对气候的贡献的不确定性。我们对大陆生物圈通量的了解大部分是从现场数据中推断出来的。这些数据在时间和空间上都很稀少，因此很难进行可靠的通量估计。相比之下，海洋CO2通量的变化通常超过100公里，使其更容易估计全球通量的原位数据。从2008年起，两个专门建造的卫星仪器将提供具有区域代表性的二氧化碳观测。这些数据有可能彻底改变我们对碳循环土地组成部分的定量理解，但使用它们对碳循环社区提出了重大挑战。这些数据并不容易解释，代表了电磁波谱近红外部分的CO2吸收测量。每天处理数十万次观测也是一项重大的技术挑战。数据同化提供了一个灵活和有效的方法，估计地面CO2通量是一致的观测CO2浓度，占各自的不确定性。在这里，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CO2数据流，并评估这些数据的优点和缺点，以量化区域碳通量。这些工具将使用两台新仪器的模拟观测进行严格测试，为调查人员提供一个极好的机会，评估仪器和模型误差对CO2表面通量估计的影响。当我们处理真实的数据时，数据同化工具和模拟的后续结果将是非常宝贵的，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.