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Climate Variability and Trends in SSU Radiances: A Comparison of Model Predictions and Satellite Observations in the Middle Stratosphere

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  • 1 Space and Atmospheric Physics Group, Imperial College, London, United Kingdom
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Abstract

Several recent studies have highlighted the potential of utilizing statistical techniques to pattern match observations and model simulations in order to establish a causal relationship between anthropogenic activity and climate change. Up to now these have tended to concentrate upon the spatial or vertical patterns of temperature change. Given the availability of contiguous, global-scale satellite observations over the past two decades, in this paper the authors seek to employ an analogous technique to spatially match model predictions to directly measured radiances. As part of the initial investigations, the technique to channel 1 of the Stratospheric Sounding Unit, sensitive to stratospheric temperature and carbon dioxide concentrations, is applied. Over the majority of the globe the observations show a negative trend in brightness temperature, with significant decreases occurring throughout the Tropics. The influence of the volcanic eruptions of El Chichón and Mount Pinatubo can also be clearly identified. Simulated brightness temperature fields, against which the satellite data are compared, are calculated using atmospheric temperature profiles from a transient climate change run of the Hadley Centre GCM. The modeled change pattern also indicates a global reduction in brightness temperature but with an altered spatial distribution relative to the observations. This tendency is reflected in the trends seen in the correlation statistics. One, dominated by the spatial mean change, shows a significant positive trend; while the other, influenced by patterns around this mean, exhibits a reducing correlation with time. Possible reasons for this behavior are discussed, and the importance of both improving model parameterizations and performing additional“unforced” simulations to assess the role of natural variability is stressed.

Corresponding author address: Dr. H. Brindley, SPAT, Imperial College, Blackett Laboratory, Prince Consort Road, London SW7 2BZ, United Kingdom.

Email: h.brindley@ic.ac.uk

Abstract

Several recent studies have highlighted the potential of utilizing statistical techniques to pattern match observations and model simulations in order to establish a causal relationship between anthropogenic activity and climate change. Up to now these have tended to concentrate upon the spatial or vertical patterns of temperature change. Given the availability of contiguous, global-scale satellite observations over the past two decades, in this paper the authors seek to employ an analogous technique to spatially match model predictions to directly measured radiances. As part of the initial investigations, the technique to channel 1 of the Stratospheric Sounding Unit, sensitive to stratospheric temperature and carbon dioxide concentrations, is applied. Over the majority of the globe the observations show a negative trend in brightness temperature, with significant decreases occurring throughout the Tropics. The influence of the volcanic eruptions of El Chichón and Mount Pinatubo can also be clearly identified. Simulated brightness temperature fields, against which the satellite data are compared, are calculated using atmospheric temperature profiles from a transient climate change run of the Hadley Centre GCM. The modeled change pattern also indicates a global reduction in brightness temperature but with an altered spatial distribution relative to the observations. This tendency is reflected in the trends seen in the correlation statistics. One, dominated by the spatial mean change, shows a significant positive trend; while the other, influenced by patterns around this mean, exhibits a reducing correlation with time. Possible reasons for this behavior are discussed, and the importance of both improving model parameterizations and performing additional“unforced” simulations to assess the role of natural variability is stressed.

Corresponding author address: Dr. H. Brindley, SPAT, Imperial College, Blackett Laboratory, Prince Consort Road, London SW7 2BZ, United Kingdom.

Email: h.brindley@ic.ac.uk

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