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

AGS-PRF：协调大气温度观测并了解气候反馈

• 批准号: 1624881
• 负责人: Stephen Po-Chedley
• 金额: $ 8.6万
• 依托单位: Po-Chedley Stephen
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1624881&HistoricalAwards=false
• 关键词: AGS; PRF; Reconciling; Atmospheric; Temperature

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.

监测地球气候需要长期的全球观测记录。卫星遥感技术以高时空分辨率提供全球覆盖的观测数据。由于单颗卫星的寿命相对较短，只有8-10年，需要结合不同时代发射的多颗卫星的观测记录来监测和了解气候变化。使用卫星观测进行气候研究的挑战在于，除了需要相互了解之外，还需要消除/减少由于轨道漂移而导致的卫星任务内以及由于轨道参数的变化而导致具有相同传感器的连续卫星任务之间的不一致性或偏差。不同传感器的卫星校准/验证。该资助项目的两个主要目标之一是使用其他观测记录系统地识别和纠正对流层和平流层卫星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：是什么导致使用气候模型预测气候变化存在巨大的不确定性？ 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？自己的气候状态。如果有链接到模型？气候反馈和模型？可以识别平均状态，通过使用此类观测来约束模型平均状态，可以减少未来全球变暖预测的不确定性。 拟议的研究将增进我们对基于观测和气候模型偏差的来源的理解，这将有助于做出更好的决策。