Published in

American Meteorological Society, Journal of Climate, 6(27), p. 2271-2296, 2014

DOI: 10.1175/jcli-d-13-00451.1

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Projecting North American Climate over the Next 50 Years: Uncertainty due to Internal Variability*

Journal article published in 2014 by Clara Deser, Adam S. Phillips, Michael A. Alexander ORCID, Brian V. Smoliak
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Abstract This study highlights the relative importance of internally generated versus externally forced climate trends over the next 50 yr (2010–60) at local and regional scales over North America in two global coupled model ensembles. Both ensembles contain large numbers of integrations (17 and 40): each of which is subject to identical anthropogenic radiative forcing (e.g., greenhouse gas increase) but begins from a slightly different initial atmospheric state. Thus, the diversity of projected climate trends within each model ensemble is due solely to intrinsic, unpredictable variability of the climate system. Both model ensembles show that natural climate variability superimposed upon forced climate change will result in a range of possible future trends for surface air temperature and precipitation over the next 50 yr. Precipitation trends are particularly subject to uncertainty as a result of internal variability, with signal-to-noise ratios less than 2. Intrinsic atmospheric circulation variability is mainly responsible for the spread in future climate trends, imparting regional coherence to the internally driven air temperature and precipitation trends. The results underscore the importance of conducting a large number of climate change projections with a given model, as each realization will contain a different superposition of unforced and forced trends. Such initial-condition ensembles are also needed to determine the anthropogenic climate response at local and regional scales and provide a new perspective on how to usefully compare climate change projections across models.