Bias in the Global Mean Temperature Estimated from Sampling a Greenhouse Warming Pattern with the Current Surface Observing Network

Roland A. Madden National Center for Atmospheric Research,* Boulder, Colorado

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Gerald A. Meehl National Center for Atmospheric Research,* Boulder, Colorado

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Abstract

Theoretical and modeling studies suggest that increasing greenhouse gases will cause the global mean temperature to rise a few degrees centigrade during the next century. Current global coupled GCMs have shown a distinct pattern of warming associated with this global mean rise. It is important to know how well our observing network will be able to capture the global mean temperature rise associated with this pattern if it occurs. The authors consider if a sampling bias exist as a result of the spatial distribution of observations as they are now located (1950–1979) when detecting a pattern of temperature change that should be typical of a warming due to increasing atmospheric CO2. The observations prove adequate to estimate the globally averaged temperature change associated with the pattern of CO2 warming from a general circulation model with a bias whose absolute value is generally less than 2%.

Abstract

Theoretical and modeling studies suggest that increasing greenhouse gases will cause the global mean temperature to rise a few degrees centigrade during the next century. Current global coupled GCMs have shown a distinct pattern of warming associated with this global mean rise. It is important to know how well our observing network will be able to capture the global mean temperature rise associated with this pattern if it occurs. The authors consider if a sampling bias exist as a result of the spatial distribution of observations as they are now located (1950–1979) when detecting a pattern of temperature change that should be typical of a warming due to increasing atmospheric CO2. The observations prove adequate to estimate the globally averaged temperature change associated with the pattern of CO2 warming from a general circulation model with a bias whose absolute value is generally less than 2%.

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