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Change in Global Temperature: A Statistical Analysis

Gordon R. RichardsNational Association of Manufacturers, Washington, D.C.

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Abstract

This paper investigates several issues relating to global climatic change using statistical techniques that impose minimal restrictions on the data. The main findings are as follows: 1) The global temperature increase since the last century is a systematic development. 2) Short-term variations in temperature do not have long-lasting effects on the final realizations of the series; over time, stochastic perturbations dissipate and temperature reverts to trend. 3) Multivariate tests for causality demonstrate that atmospheric C02 is a significant forcing factor. The implied change in temperature with respect to a doubling of atmospheric C02 lies in a range of 2.17° to 2.57°C, with a mean value of 2.34°C. The contributions of solar irradiance and volcanic loading are much smaller. 4) In a multivariale system, shocks to forcing factors generate stochastic cycles in temperature comparable to the results from unforeed simulations of climatological models. 5) Extrapolation of regression equations predict changes in global temperature that are marginally lower than the results from climatological simulation models.

Abstract

This paper investigates several issues relating to global climatic change using statistical techniques that impose minimal restrictions on the data. The main findings are as follows: 1) The global temperature increase since the last century is a systematic development. 2) Short-term variations in temperature do not have long-lasting effects on the final realizations of the series; over time, stochastic perturbations dissipate and temperature reverts to trend. 3) Multivariate tests for causality demonstrate that atmospheric C02 is a significant forcing factor. The implied change in temperature with respect to a doubling of atmospheric C02 lies in a range of 2.17° to 2.57°C, with a mean value of 2.34°C. The contributions of solar irradiance and volcanic loading are much smaller. 4) In a multivariale system, shocks to forcing factors generate stochastic cycles in temperature comparable to the results from unforeed simulations of climatological models. 5) Extrapolation of regression equations predict changes in global temperature that are marginally lower than the results from climatological simulation models.

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