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Sensitivity of global warming to the pattern of tropical ocean warming

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Abstract

The current generations of climate models are in substantial disagreement as to the projected patterns of sea surface temperatures (SSTs) in the Tropics over the next several decades. We show that the spatial patterns of tropical ocean temperature trends have a strong influence on global mean temperature and precipitation and on global mean radiative forcing. We identify the SST patterns with the greatest influence on the global mean climate and find very different, and often opposing, sensitivities to SST changes in the tropical Indian and West Pacific Oceans. Our work stresses the need to reduce climate model biases in these sensitive regions, as they not only affect the regional climates of the nearby densely populated continents, but also have a disproportionately large effect on the global climate.

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Notes

  1. The specific model version was ccm3.10.11.brnchT.366physics.7.

  2. The normalized optimal SST pattern (denoted “pattern I” in Table 1) is given by \(T_{\rm opt}=S\left(\frac{1}{A_P}\int\limits_P {S^2\;\hbox{d}A}\right)^{-1/2},\) where A P is the area spanned by the patches.

  3. The sensitivities shown in Fig. 5 are the result of the global response to a local SST change. For comparison, the baseline temperature sensitivity, obtained by taking the global average of only the local prescribed sea surface warming or cooling for a single patch, is 1/A e (about 2 × 10−9 km−2 in the units of Fig. 5a) where A e is the surface area of the Earth.

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Acknowledgments

This work was supported in part by a grant from NOAA’s Office of Global Programs, but does not reflect the official position of NOAA or of the US Government. We thank Jeffrey Yin for help with accessing the IPCC model output and Gil Compo for his assistance. We also appreciate the insightful comments of the two anonymous reviewers.

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Authors and Affiliations

  1. CIRES Climate Diagnostics Center and NOAA Earth System Research Laboratory, 325 Broadway, R/PSD1, Boulder, CO, 80305-3328, USA

    Joseph J. Barsugli, Sang-Ik Shin & Prashant D. Sardeshmukh

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  1. Joseph J. Barsugli
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  2. Sang-Ik Shin
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  3. Prashant D. Sardeshmukh
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Correspondence to Joseph J. Barsugli.

Appendix A: smoothing of the sensitivity maps

Appendix A: smoothing of the sensitivity maps

A thin-plate spatial smoothing spline based on the signal-to-noise ratio (Gu 1989) was applied to the climate sensitivites to ensure that only statistically robust features were retained in the sensitivity map. The smoothing parameter used in the spline calculation was based on an a-priori estimate of the expected variance of the ensemble mean responses. This procedure fits a smooth surface to the sampled data so that the variance of the residuals is consistent with the expected variance at the sampling locations. In this manner, variations among nearby data points that could have arisen merely by chance are smoothed out, and the effective sample size at these points is increased. For global sensitivity, the expected variance was taken to be the variance from the 100-year climatological SST control run divided by the sample size (the ensemble size times the number of seasons). Because the variance of precipitation over a patch will likely depend strongly on the total precipitation signal for that patch, the expected variance for the local sensitivities were calculated separately for each patch from the intraensemble variance. In practice the smoothing results in only small changes to the sensitivity maps shown in this paper. For example, Fig. 9 shows the actual values of the sensitivities used in constructing Fig. 4 before smoothing and contouring.

Fig. 9
figure 9

Unsmoothed values of the sensitivity of lower tropospheric temperature (850 hPa pressure level) and precipitation corresponding to the smoothed values contoured in Fig. 4. The numerical values of the sensitivity are shown in text at the center of each patch (red = positive, blue = negative). Units are as in Fig. 4

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Barsugli, J.J., Shin, SI. & Sardeshmukh, P.D. Sensitivity of global warming to the pattern of tropical ocean warming. Clim Dyn 27, 483–492 (2006). https://doi.org/10.1007/s00382-006-0143-7

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