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Climatic Change

, Volume 21, Issue 1, pp 1–16 | Cite as

Trends in global temperature

  • Peter Bloomfield
Article

Abstract

Statistical models consisting of a trend plus serially correlated noise may be fitted to observed climate data such as global surface temperature, the trend and noise representing systematic change and other variations, respectively. When such a model is fitted, the estimated character of the noise determines the precision of the estimated trend, and hence the precision of the estimate of the magnitude of the systematic change in the variable considered. The results of fitting such models to global temperature imply that there is uncertainty in the amount of temperature change over the past century of up to ± 0.2 °C, but that the change of around one half of a degree Celsius is significantly different from zero.

The statistical models for climate variability also imply that the observed temperature data provide only imprecise information about the climate sensitivity. This is defined here as the equilibrium response of global temperature to a doubling of the atmospheric concentration of carbon dioxide. The temperature changes observed to date are compatible with a wide range of climate sensitivities, from 0.7 °C to 2.2 °C. When data uncertainties are taken into account, the interval widens even further.

Keywords

Dioxide Carbon Dioxide Surface Temperature Statistical Model Temperature Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1992

Authors and Affiliations

  • Peter Bloomfield
    • 1
  1. 1.Department of StatisticsNorth Carolina State UniversityRaleighU.S.A.

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