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The State of Our Skill in Predicting Decadal-Scale Climate Change

Paper Reviewed
Kumar, A. and Wang, H. 2015. On the potential of extratropical SST anomalies for improving climate predictions. Climate Dynamics 44: 2557-2569.

Kumar and Wang (2015) begin their analysis of this intriguing but disturbing subject by reporting that "considerable efforts have been devoted to predicting the evolution of climate with a lead time of 1-30 years," while noting that these decadal predictions rely on our ability to predict "the low-frequency modes of coupled ocean-atmosphere variability," two examples of which are the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO), as discussed by Soloman et al. (2011) and Meehl et al. (2013).

In further exploring this situation, the two researchers report finding that (1) "of the analyses that do provide an assessment of skill over terrestrial regions -- Teng et al. (2011), van Oldenborgh et al. (2012), MacLeod et al. (2012), Kim et al. (2012), Muller et al. (2012), Goddard et al. (2013), Doblas-Reyes et al. (2013) -- the results have not been encouraging," that (2) "for ensemble mean prediction the observed associations between the sea surface temperature [SST] fingerprint of low-frequency modes of SST variability and the atmospheric and terrestrial variability are not replicated," citing Teng et al. (2011), van Oldenborgh et al. (2012) and Muller et al. (2012)," that (3) "a different set of observational studies analyzing the predictive value of SST associated with low-frequency modes such as the PDO done in forecast mode has not shown promising results," citing Davis (1976) and Guztler et al. (2002), and that (4) "inferences based on general circulation model simulations have also found little influence of extratropical SSTs in constraining atmospheric and terrestrial variability," citing Pierce (2002) and Kumar et al. (2013).

In concluding their analysis of the matter, Kumar and Wang thus state that their work provides explanations for (5) why "the skill of atmospheric and terrestrial quantities in initialized decadal predictions is not much better than their uninitialized counterpart," for (6) why "the observed teleconnection between SST and atmospheric and terrestrial quantities is not replicated in the ensemble of initialized decadal prediction runs," and (7) why "similar teleconnection relationships on a seasonal time scale have not translated to their application towards improving skill of seasonal predictions," all of which findings ultimately lead them to the conclusion that far into the future "the constraint of the coupled ocean-atmosphere variability will still be a basic limitation on prediction skill."

Davis, R.E. 1976. Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. Journal of Physical Oceanography 6: 249-266.

Guztler, D.S., Kann, D.M. and Thornbrugh, C. 2002. Modulation of ENSO-based long-lead outlooks of southwestern U.S. Winter Precipitation by the Pacific Decadal Oscillation. Journal of Climate 17: 1163-1172.

Kim, H.-M., Webster, P.J. and Curry, J.A. 2012. Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts. Geophysical Research Letters 39: 10.1029/2012GL051644.

Kumar, A., Wang, H., Wang, W., Xue, Y. and Hu, Z.-Z. 2013. Does knowing the oceanic PDO phase help predict the atmospheric anomalies in subsequent months? Journal of Climate 26: 1268-1285.

MacLeod, D.A., Caminade, C. and Morse, A.P. 2012. Useful decadal climate prediction at regional scales? A look at the ENSEMBLES stream 2 decadal hindcasts. Environmental Research Letters 7:10.1088/1748-9326/7/044012.

Meehl, G. A., Goddard, L., Boer, G., Burgman, R., Branstator, G., Cassou, C., Corti, S., Danabasoglu, G., Doblas-Reyes, F., Hawkins, E., Karspeck, A., Kimoto, M., Kumar, A., Matei, D., Mignot, J., Msadek, R., Pohlmann, H., Rienecker, M., Rosati, T., Schneider, E., Smith, D., Sutton, R., Teng, H., Van Oldenborgh, G. J., Vecchi, G. and Yeager, S. 2013. Decadal climate prediction: an update from the trenches. Bulletin of the American Meteorological Society: 10.1175/BAMS-D-12-00241.1.

Muller, W.A., Baehr, J., Haak, H., Jungclaus, J.H., Kröger, J., Matei, D., Notz, D., Pohlmann, H., von Storch, J.S. and Marotzke, J. 2012. Forecast skill of multi-year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology. Geophysical Research Letters 39: 10.1029/2012GL053326.

Pierce, D.W. 2002. The role of sea surface temperatures in interactions between ENSO and the North Pacific Oscillation. Journal of Climate 15: 1295-1308.

Soloman, A., Goddard, L., Kumar, A., Carton, J., Deser, C., Fukumari, I., Greene, A.M., Hegerl, G., Kirtman, B., Kushnir, Y., Newman, M., Smith, D., Vimont, D., Delworth, T., Meehl, G.A. and Stockdale, T. 2011. Distinguishing the roles of natural and anthropogenically forced decadal climate variability: implications for prediction. Bulletin of the American Meteorological Society 92: 141-155.

Teng, H., Branstator, G. and Meehl, G.A. 2011. Predictability of the Atlantic overturning circulation and associated surface patterns in two CCSM3 climate change ensemble experiments. Journal of Climate 24: 6054-6076.

Van Oldenborgh, G.J., Doblas-Reyes, F.J., Wouters, B. and Hazeleger, W. 2012. Decadal prediction skill in a multi-model ensemble. Climate Dynamics 38: 1263-1280.

Posted 1 June 2015