AGS-PRF: Reconciling Atmospheric Temperature Observations and Understanding Climate Feedbacks

监测地球气候需要对全球观测进行长期记录。卫星遥感技术提供了高时空分辨率的全球观测覆盖面。由于单个卫星的寿命相对较短，只有8-10年，因此需要将不同时期发射的多颗卫星的观测记录联合收割机结合起来，以监测和了解气候变化。利用卫星观测进行气候研究的挑战在于，除了需要对不同的传感器进行卫星间校准/验证之外，还需要消除/减少卫星使命内由于轨道漂移造成的不一致或偏差，以及使用相同传感器的连续卫星使命之间由于轨道参数变化造成的不一致或偏差。该资助项目的两个主要目标之一是利用其他观测记录系统地查明和纠正卫星在对流层和平流层微波亮温测量中的气候尺度偏差/漂移。PI在他的论文研究中开发并展示了这项资助研究的关键方法。该资助项目还将重新审视这个具有挑战性的问题：是什么导致了使用气候模型预测气候变化的巨大不确定性？ PI计划探索气候模型中气候反馈的强度是否以及如何与模型相关联？自己的气候国家。如果与模型有联系？气候反馈和模型？平均状态可以被识别，未来全球变暖预测的不确定性可以通过使用这样的观测值来约束模型平均状态来降低。 拟议的研究将促进我们对基于观测和气候模型偏差的来源的理解，这将有助于更好的决策。

Monitoring Earth's climate requires a lengthy record of global observations. Satellite remote sensing technologies provide global coverage of observations at a high spatio-temporal resolution. Due to the relatively short life span of between 8-10 years for the individual satellites, one needs to combine the observation records of multiple satellites that were launched at different eras for monitoring and understanding climate changes. The challenging of using satellite observations for climate studies is that one needs to remove/reduce inconsistencies or biases within a satellite mission due to orbital drifting and between continuous satellite missions with the same sensors due to changes of orbital parameters, besides the need of inter-satellite calibration/validation of different sensors. One of the two main objectives of this funded project is to systematically identify and correct climate scale biases/drifts in microwave brightness temperature measurements of satellites in both troposphere and stratosphere using other observation records. The PI has developed and demonstrated the key methodologies for this funded research in his dissertation research.The funded project will also take a fresh new look at this challenging question: what causes large uncertainties in projecting climate change using climate models? The PI plans to explore if and how the strength of climate feedbacks in climate models are linked to the models? own climate states. If a linkage to the models? climate feedbacks and the models? mean states can be identified, the future global warming projection uncertainties can be reduced by using such observations to constraint models mean states. The proposed research will advance our understanding of sources based on biases in both observations and climate models which will enable better decision-making.