学术活动

【1.13】Recalibration of SSU observations for stratospheric temperature trend investigations

  题目:Recalibration of SSU observations for stratospheric temperature trend investigations

  报告人: Cheng-Zhi Zou ,NOAA/NESDIS/Center for Satellite Applications and Research

  时 间: 2014年01月13日  星期一, 10:00-11:00

  地 点:大气所40号楼912

    

  Abstract

  Warming of the troposphere and cooling of the stratosphere are two central features of global warming resulting from anthropogenic forcing. Although cooling of the lower stratosphere has been well documented with satellite observations from the Microwave Sounding Unit (MSU), changes in the middle and upper stratosphere were not well understood due to a lack of well documented satellite datasets as well as other in situ observations. NOAA/STAR has recently released a well documented stratospheric temperature time series derived from the Stratospheric Sounding Unit (SSU) observations onboard historical NOAA polar orbiting satellites. The stratospheric temperature trends derived from this dataset has been compared with an earlier version developed by UK Met Office and with climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Chemistry-Climate Model Validation phase 2 (CCMVal2) projects. Extensive differences were found in absolute magnitudes and temporal patterns, both within and between models and observations. The results have been published in Nature by a group of scientists, which further sparked significant debate on the causes of these differences.

  The NOAA STAR has been recalibrating the level-1c radiances in the SSU observations to understand the root causes for the differences. In this talk, key questions and issues in the stratospheric temperature trend differences between different datasets are presented. Recalibration results and a newer version of NOAA/STAR SSU dataset will be released soon. In this talk, I will discuss chains of reasoning how recalibration and decomposition of the SSU observations can provide high confidence on the resulting trend. Finally, a perspective of the application of the new dataset will be given.

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