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.

监测地球的气候需要长期记录全球观察结果。卫星遥感技术以高时空分辨率提供了全球观察结果的覆盖范围。由于单个卫星的寿命相对较短，因此需要将在不同时代发射的多个卫星的观察记录结合在一起，以监视和理解气候变化。使用卫星观测来进行气候研究的挑战是，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的连续卫星任务，需要消除/减少卫星任务中的矛盾或偏见，此外还需要对不同传感器验证/验证不同传感器的验证。这个资助项目的两个主要目标之一是使用其他观测记录在对流层和平流层中卫星的微波亮度温度测量中系统地识别和纠正气候尺度偏差/漂移。 PI在他的论文研究中已经开发并证明了这项资助研究的关键方法。资助的项目还将对这个充满挑战的问题进行新的新观点：是什么导致了使用气候模型来预测气候变化的大型不确定性？ PI计划探索气候模型中气候反馈的强度与模型中的强度如何？自己的气候状态。如果与模型有联系？气候反馈和模型？可以确定平均状态，通过使用这些观察值对约束模型的含义状态，可以减少未来的全球变暖投影不确定性。 拟议的研究将根据观测和气候模型中的偏见来提高我们对来源的理解，这将使决策更好。