Regional Simulations of Greenhouse Warming Including Natural Variability

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  • 1 Climate System Research Program, Texas A&M University, College Station, Texas
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The perception of the hypothesized greenhouse effect will differ dramatically depending upon the location on the earth at which the effect is analyzed. This is due mainly to two causes: 1) the warming signal depends upon the position on the earth, and 2) the natural variability of the warming has a strong position dependence. To demonstrate these phenomena, simulations were conducted of the surface temperature field with a simple stochastic climate model that has enough geographical resolution to see the geographic dependence. The model was tuned to reproduce the geographical distribution of the present climate, including its natural variability in both the variance and the space–time correlation structure. While such effects have been discussed elsewhere with even more realistic climate models, it is instructive to actually see simulations of time series laid side by side in order to easily compare their differences and similarities. Because of the model's simplicity, the causes of the variations are easy to analyze. Not surprisingly, some realizations of the temperature for some local areas show countertrends for a period of several decades in the presence of the greenhouse warming.

Corresponding author address: Prof. Kwang-Y. Kim, Climate System Research Program, Texas A&M University, College of Geosciences and Maritime Studies, College Station, TX 77843-3150. E-mail: kykim@csrp.tamu.edu

The perception of the hypothesized greenhouse effect will differ dramatically depending upon the location on the earth at which the effect is analyzed. This is due mainly to two causes: 1) the warming signal depends upon the position on the earth, and 2) the natural variability of the warming has a strong position dependence. To demonstrate these phenomena, simulations were conducted of the surface temperature field with a simple stochastic climate model that has enough geographical resolution to see the geographic dependence. The model was tuned to reproduce the geographical distribution of the present climate, including its natural variability in both the variance and the space–time correlation structure. While such effects have been discussed elsewhere with even more realistic climate models, it is instructive to actually see simulations of time series laid side by side in order to easily compare their differences and similarities. Because of the model's simplicity, the causes of the variations are easy to analyze. Not surprisingly, some realizations of the temperature for some local areas show countertrends for a period of several decades in the presence of the greenhouse warming.

Corresponding author address: Prof. Kwang-Y. Kim, Climate System Research Program, Texas A&M University, College of Geosciences and Maritime Studies, College Station, TX 77843-3150. E-mail: kykim@csrp.tamu.edu
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