Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise.

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2015-04-21

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Abstract

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20(th) century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal.

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10.1038/srep09957

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◦Brown, P.T., W. Li, E.C. Cordero, S.A. Mauget (2015). Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise. Sci Rep, 5. p. 9957. 10.1038/srep09957 Retrieved from https://hdl.handle.net/10161/9593.

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Li

Wenhong Li

Associate Professor of Climate

Dr. Li's research interests focus primarily on climate dynamics, land-atmosphere interaction, hydroclimatology, and climate modeling. Her current research is to understand how the hydrological cycle changes in the current and future climate and their impacts on the ecosystems, subtropical high variability and change, unforced global temperature variability, and climate and health issues.


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