Collaborative Research: Testing Climate Models by Feedback Analysis Using Atmospheric Infrared Sounder (AIRS) and Global Positioning System (GPS) Radio Occultation Data

将对大气红外探测仪获得的向外长波频谱和CHAMP（跟踪小型卫星有效载荷）和COSMIC（气象、电离层和气候星座观测系统）飞行任务获得的全球定位系统无线电掩星数据进行分析，以提供对气候系统长波反馈和辐射强迫的观测约束。AIRS的高光谱分辨率辐射测量包含各种大气热力学变量和组分浓度的辐射强迫指纹，这些指纹直接导致长波温室效应强迫和大气反馈。由于云层和地表温度贡献了相似的光谱特征，无线电掩星将提供区分两者的信息。研究人员发现，在他们以前的NSF支持下，无线电掩星趋势限制了气候模型的地表气温响应，而红外光谱辐射趋势明确地限制了气候的辐射强迫和气候模型中的长波反馈。现在，他们将把这些发现应用于上述卫星数据，以获得全球混合温室气体辐射强迫的估计，至少有10%的不确定性，通过异常分析估计热带水汽和云长波反馈的不确定性约为7%，并开始通过趋势分析估计全球长波反馈，这项工作的更广泛影响在于使用多种数据类型的最佳指纹法对照卫星数据检验气候模型，并在气候预测领域。高度准确和可靠的数据将用于对气候模型提供强有力的观测约束。这项研究计划的最终目标是提高我们在十年尺度气候预测方面的技能。此外，哈佛和密歇根大学联合努力的毕业生将得到支持。

Outgoing longwave spectra obtained by the Atmospheric Infrared Sounder (AIRS) and GPS radio occultation data obtained by the CHAMP (Challenging Mini-satellite Payload) and COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) missions will be analyzed to provide observational constraints on longwave feedbacks and radiative forcing in the climate system. The high-spectral-resolution radiance measurements of AIRS contain the fingerprints of radiative forcing by a variety of atmospheric thermodynamic variables and constituent concentrations, and these fingerprints lead directly to longwave greenhouse forcing and atmospheric feedbacks. Because clouds and surface temperature contribute similar spectral signatures, radio occultation will provide the information to distinguish between the two. The investigators found, under their previous NSF support, that radio occultation trends constrain the surface air temperature response of climate models while infrared spectral radiance trends unambiguously constrain radiative forcing of the climate and the longwave feedbacks in climate models. Now they will apply these findings to the aforementioned satellite data to obtain estimates of radiative forcing by well-mixed greenhouse gases globally to at least 10% uncertainty, to estimate tropical water vapor and cloud longwave feedbacks to ~7% uncertainty by anomaly analysis, and to begin the estimation of global longwave feedbacks by trend analysis, albeit with large uncertainties due to natural variability.Broader impacts of this work lie in its use of optimal fingerprinting using multiple data types to test climate models against satellite data, and in the field of climate prediction. Highly accurate and reliable data will be used to provide strong observational constraints on climate models. The ultimate goal of this research program is to improve our skill at decadal scale climate projection. In addition, graduated students from the joint Harvard and University of Michigan efforts will be supported.