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Understanding uncertainty in future projections for the tropical Atlantic: relationships with the unforced climate

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Abstract

Through its control on the marine ITCZ, future changes in the tropical Atlantic meridional sea-surface temperature gradient (TAG) could have important impacts, on regional to global scales. We study the inter-model spread of projected TAG trends in response to increasing CO2, using results from 19 coupled GCMs which took part in the IPCC fourth assessment. Some models project substantial changes, with the smallest changes being in boreal autumn. There is substantial uncertainty, with no consistency even in the sign of change, and an ensemble mean close to zero. However, a strong statistical relationship is found between the simulated magnitudes of TAG trends and unforced TAG variability. Models with larger unforced variability in December–February show larger magnitude trends. We speculate that this relationship may be due to an underlying system of feedbacks whose strength varies considerably from model to model (the unforced variability ranges by a factor of 3 amongst these models, and the models exhibit large differences in mean state). We present evidence from further analysis and the literature to suggest which physical mechanisms may be involved. In particular, models projecting larger(smaller) magnitude TAG trends have larger(smaller) SST variability and cooler(warmer) mean SST in not just the Atlantic, but all three tropical/sub-tropical oceans, especially in the southern hemisphere near eastern coasts. These results could assist efforts to understand model errors in present and future tropical climate, and to develop observational constraints on future tropical projections.

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Acknowledgments

The authors are supported by the Joint Defra and MoD Programme (Defra) GA01101 (MoD) CBC/2B/0417_Annex C5. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. Two anonymous reviewers helped greatly in producing a clearer and more logical manuscript. We would also like to thank David Sexton, Ben Booth and Keith Williams who provided helpful comments.

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  1. Hadley Centre for Climate Prediction and Research, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK

    Peter Good, Jason A. Lowe & David P. Rowell

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  1. Peter Good
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Correspondence to Peter Good.

Appendix

Appendix

Models from the same institute might be expected to be more similar than pairs of models from different institutes. This is analogous to the issue of temporal autocorrelation and could bias our estimates of statistical significance and confidence limits. We avoid this by averaging pairs of models from the same institute before estimating significance or confidence limits, reducing the degrees of freedom (from 19 models to 15 institutes).

Confidence limits for the relationships between TAG variability and trend (Sect. 4.1) are estimated using a nonparametric bootstrap resampling procedure (with replacement, see, e.g., Wilks 1997). Confidence limits for the difference between two different correlation coefficients are evaluated in the same way, but by calculating the difference between the correlation coefficients for each bootstrap iteration.

The non-parametric Mann Kendall test (Mann 1945) is applied to test the significance of rank association between pairs of variables (used for masking in Figs. 2, 4 and 5).

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Good, P., Lowe, J.A. & Rowell, D.P. Understanding uncertainty in future projections for the tropical Atlantic: relationships with the unforced climate. Clim Dyn 32, 205–218 (2009). https://doi.org/10.1007/s00382-008-0466-7

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