Effect of Observational Sampling Error on the Detection of Anthropogenic Climate Change

Gabriele C. Hegerl Department of Oceanography, Texas A&M University, College Station, Texas

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Philip D. Jones Climate Research Unit, University of East Anglia, Norwich, United Kingdom

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Tim P. Barnett Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California

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Abstract

The effect of sampling error in surface air temperature observations is assessed for detection and attribution of an anthropogenic signal. This error arises because grid-box values are based on varying densities of station and marine data. An estimate of sampling error is included in the application of an optimal detection and attribution method based on June–August trends over 50 yr. The detection and attribution method is applied using both the full spatial pattern of observed trends and spatial patterns from which the global mean warming has been subtracted.

Including the effect of sampling error is found to increase the uncertainty in estimates of the greenhouse gas–plus–sulfate aerosol signal from observations by less than 2%–6% for recent trend patterns (1949–98), and 3%–8% for signal estimates from observations in the first half of the twentieth century. Random instrumental error shows even smaller effects. However, the effects of systematic instrumental errors, such as changes in measurement practices or urbanization, cannot be estimated at present. The detection and attribution results for recent 50-yr summer trends are very similar between the case including and the case disregarding the global mean. However, results based on observations from the first half of the twentieth century yield high signal amplitudes with global mean and low ones without, suggesting little pattern agreement for that warming with the anthropogenic climate change signal.

Corresponding author address: Gabriele Hegerl, Dept. of Oceanography, Texas A&M University, College Station, TX 77843-3146.

Email: hegerl@ocean.tamu.edu

Abstract

The effect of sampling error in surface air temperature observations is assessed for detection and attribution of an anthropogenic signal. This error arises because grid-box values are based on varying densities of station and marine data. An estimate of sampling error is included in the application of an optimal detection and attribution method based on June–August trends over 50 yr. The detection and attribution method is applied using both the full spatial pattern of observed trends and spatial patterns from which the global mean warming has been subtracted.

Including the effect of sampling error is found to increase the uncertainty in estimates of the greenhouse gas–plus–sulfate aerosol signal from observations by less than 2%–6% for recent trend patterns (1949–98), and 3%–8% for signal estimates from observations in the first half of the twentieth century. Random instrumental error shows even smaller effects. However, the effects of systematic instrumental errors, such as changes in measurement practices or urbanization, cannot be estimated at present. The detection and attribution results for recent 50-yr summer trends are very similar between the case including and the case disregarding the global mean. However, results based on observations from the first half of the twentieth century yield high signal amplitudes with global mean and low ones without, suggesting little pattern agreement for that warming with the anthropogenic climate change signal.

Corresponding author address: Gabriele Hegerl, Dept. of Oceanography, Texas A&M University, College Station, TX 77843-3146.

Email: hegerl@ocean.tamu.edu

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