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PHYSICAL SCIENCES / APPLIED PHYSICAL SCIENCES
Forced and unforced ocean temperature changes in Atlantic and Pacific tropical cyclogenesis regions

B. D. Santer^a,b, T. M. L. Wigley^c, P. J. Gleckler^a, C. Bonfils^d, M. F. Wehner^e, K. AchutaRao^a, T. P. Barnett^f, J. S. Boyle^a, W. Brüggemann^g, M. Fiorino^a, N. Gillett^h, J. E. Hansenⁱ, P. D. Jones^h, S. A. Klein^a, G. A. Meehl^c, S. C. B. Raper^j, R. W. Reynolds^k, K. E. Taylor^a, and W. M. Washington^c

^aProgram for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550; ^cNational Center for Atmospheric Research, Boulder, CO 80307; ^dUniversity of California, Merced, CA 95344; ^eLawrence Berkeley National Laboratory, Berkeley, CA 94720; ^fScripps Institution of Oceanography, La Jolla, CA 92037; ^gInstitut für Unternehmensforschung, Universität Hamburg, 22765 Hamburg, Germany; ^hClimatic Research Unit, University of East Anglia, Norwich NR4 7TJ, United Kingdom; ⁱNational Aeronautics and Space Administration/Goddard Institute for Space Studies, New York, NY 10025; ^jCentre for Air Transport and the Environment, Manchester Metropolitan University, Manchester M1 5GD, United Kingdom; and ^kNational Oceanic and Atmospheric Administration/National Climatic Data Center, Asheville, NC 28801

Edited by Isaac M. Held, National Oceanic and Atmospheric Administration, Princeton, NJ, and approved July 24, 2006 (received for review April 7, 2006)

Previous research has identified links between changes in sea surface temperature (SST) and hurricane intensity. We use climate models to study the possible causes of SST changes in Atlantic and Pacific tropical cyclogenesis regions. The observed SST increases in these regions range from 0.32°C to 0.67°C over the 20th century. The 22 climate models examined here suggest that century-timescale SST changes of this magnitude cannot be explained solely by unforced variability of the climate system. We employ model simulations of natural internal variability to make probabilistic estimates of the contribution of external forcing to observed SST changes. For the period 1906–2005, we find an 84% chance that external forcing explains at least 67% of observed SST increases in the two tropical cyclogenesis regions. Model "20th-century" simulations, with external forcing by combined anthropogenic and natural factors, are generally capable of replicating observed SST increases. In experiments in which forcing factors are varied individually rather than jointly, human-caused changes in greenhouse gases are the main driver of the 20th-century SST increases in both tropical cyclogenesis regions.

Conflict of interest statement: No conflicts declared.

This paper was submitted directly (Track II) to the PNAS office.

^l The ACR and PCR used here are identical to those defined in ref. 6. Gridded, monthly mean model and observational SST data were spatially averaged over 6°N–18°N, 60°W–20°W (ACR) and over 5°N–15°N, 180°E–130°E (PCR).

^m For visual display, the modeled and observed SST data in Figs. 1 and 6 were smoothed by using a digital filter (19) with a window width W of 21 months, corresponding to a half-power point of 25 months. This smoothing damps variability on interannual and El Niño/Southern Oscillation timescales, while information on the SST response to volcanic forcing is largely preserved. The overall linear trend was subtracted before filtering and was reinserted after filtering. Data loss was avoided by "reflecting" (W – 1)/2 points at the beginning and end of the time series. To estimate modeled and observed variability on decadal and longer timescales (Fig. 4C), we applied the same digital filter to the detrended SST anomaly data and set W = 145 months, yielding a half-power point at 119 months. The response functions for both choices of W are shown in Fig. 7.

n Ensembles of the 20CEN simulations were performed with 13 of the 22 models analyzed here (see Supporting Text). Each ensemble contains multiple realizations of the same experiment, differing only in their initial conditions, but with identical changes in external forcings. This procedure yields many different realizations of the noise that is superimposed on the climate "signal" (the response to the imposed forcing changes). Averaging over multiple realizations reduces noise and facilitates signal estimation. Here, we calculated averages over V and No-V 20CEN runs. In each case, is the arithmetic mean of the ensemble means (for the models for which ensembles are available) and of individual realizations, i.e., = (1/Nm)j=1Nm j, where Nm is the total number of V or No-V models (11 here), and j is the ensemble mean signal (or individual realization) of the jth model. This weighting avoids undue emphasis on results from a single model with a large number of realizations.

^o F1 is calculated with observed trends over 1906–2005, 1956–2005, etc., whereas F2 is based on bOBS and – trends over 1900–1999 only. This is because most of the 20CEN experiments end in 1999, thus hampering direct comparisons with the full observational record.

^p Missing or incorrectly specified forcings also influence the model-versus-observed variability differences shown in Fig. 4C. For example, the observed decadal variability in ACR and PCR SSTs receives a contribution from volcanic forcing (see Figs. 1 and 6), which is neglected in the No-V group of models. This missing forcing must contribute to the No-V models' underestimate of observed SST variability in the ACR.

^q The temporal standard deviation of the observed low-pass filtered ACR SST data, s_filt(OBS), is 0.18°C for both the HadISST and ERSST data (see Fig. 4C). Model-average values of this quantity, s_filt(MOD), are 0.12°C and 0.13°C for the V and No-V 20CEN runs.

^bTo whom correspondence should be addressed. E-mail: santer1{at}llnl.gov

© 2006 by The National Academy of Sciences of the USA

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