Spatial Patterns of Climate Variability in Upper-Tropospheric Water Vapor Radiances from Satellite Data and Climate Model Simulations

A. J. Geer Space and Atmospheric Physics, Imperial College, London, United Kingdom

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J. E. Harries Space and Atmospheric Physics, Imperial College, London, United Kingdom

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H. E. Brindley Space and Atmospheric Physics, Imperial College, London, United Kingdom

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Abstract

The use of multivariate fingerprints and spatial pattern correlation in the detection and attribution of climate change has concentrated on radiosonde temperature fields. However, the large body of radiance data from satellite-borne instruments includes contiguous datasets of up to 17 yr in length and in future years will present the most well-calibrated and large-scale data archive available for climate change studies. Here the authors give an example of the spatial correlation technique used to analyze satellite radiance data. They examine yearly mean brightness temperatures from High Resolution Infrared Spectrometer (HIRS) channel 12, sensitive to upper-tropospheric water vapor and temperature. Atmospheric profiles from a climate change run of the Hadley Centre GCM (HADCM2) are used to simulate the pattern of brightness temperature change for comparison to the satellite data. Investigation shows that strong regional brightness temperature changes are predicted in the Tropics and are dominated by changes in relative humidity in the upper troposphere. At midlatitudes only small changes are predicted, partly due to a compensation between the effects of temperature and relative humidity. The observational data showed some significant regional changes, especially at 60°S, where there was a trend toward lower brightness temperatures. The pattern similarity statistics revealed a small trend between 1979 and 1995 toward the predicted climate change patterns but this was not significant. The detection of any trend is complicated by the high natural variability of HIRS-12 radiances, which is partly associated with the El Niño–Southern Oscillation.

Corresponding author address: Dr. A. J. Geer, Department of Physics, The Blackett Laboratory, Imperial College, London SW7 2BZ, United Kingdom.

Email: a.geer@ic.ac.uk

Abstract

The use of multivariate fingerprints and spatial pattern correlation in the detection and attribution of climate change has concentrated on radiosonde temperature fields. However, the large body of radiance data from satellite-borne instruments includes contiguous datasets of up to 17 yr in length and in future years will present the most well-calibrated and large-scale data archive available for climate change studies. Here the authors give an example of the spatial correlation technique used to analyze satellite radiance data. They examine yearly mean brightness temperatures from High Resolution Infrared Spectrometer (HIRS) channel 12, sensitive to upper-tropospheric water vapor and temperature. Atmospheric profiles from a climate change run of the Hadley Centre GCM (HADCM2) are used to simulate the pattern of brightness temperature change for comparison to the satellite data. Investigation shows that strong regional brightness temperature changes are predicted in the Tropics and are dominated by changes in relative humidity in the upper troposphere. At midlatitudes only small changes are predicted, partly due to a compensation between the effects of temperature and relative humidity. The observational data showed some significant regional changes, especially at 60°S, where there was a trend toward lower brightness temperatures. The pattern similarity statistics revealed a small trend between 1979 and 1995 toward the predicted climate change patterns but this was not significant. The detection of any trend is complicated by the high natural variability of HIRS-12 radiances, which is partly associated with the El Niño–Southern Oscillation.

Corresponding author address: Dr. A. J. Geer, Department of Physics, The Blackett Laboratory, Imperial College, London SW7 2BZ, United Kingdom.

Email: a.geer@ic.ac.uk

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