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WIREs Clim Change
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Prospects for decadal climate prediction

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Abstract During the last decade, global surface temperatures did not increase as rapidly as in the preceding decades. Although relatively small compared to the observed centennial scale global warming, it has renewed interest in understanding and even predicting climate on time scales of decades, and sparked a community initiative on near‐term prediction that will feature in the fifth intergovernmental panel on climate change assessment report. Decadal prediction, however, is in its infancy, with only a few publications existing. This article has three aims. The first is to make the case for decadal prediction. Decadal fluctuations in global climate similar to that of recent decades were observed during the past century. Associated with large regional changes in precipitation and climate extremes, they are of socioeconomic importance. Climate models, which capture some aspects of observed decadal variability, indicate that such variations might be partly predictable. The second aim is to describe the major challenges to skilful decadal climate prediction. One is poor understanding of mechanisms of decadal climate variability, with climate models showing little agreement. Sparse observations in the past, particularly in the ocean, are also a limiting factor to developing and testing of initialization and prediction systems. The third aim is to stress that despite promising initial results, decadal prediction is in a highly experimental stage, and care is needed in interpreting results and utilizing data from such experiments. In the long‐term, decadal prediction has the potential to improve models, reduce uncertainties in climate change projections, and be of socioeconomic benefit. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Climate Models and Modeling > Earth System Models Climate Models and Modeling > Knowledge Generation with Models

Observed decadal variability indices and sea surface temperature (SST) patterns.26 (a) Atlantic multidecadal variability (AMV) index, defined as linearly detrended North Atlantic (0–60°N) average SST and (b) SST pattern, computed as the composite difference between positive and negative phases (shading in a). (c) Pacific decadal variability (PDV) index, first EOF of North Pacific (20–60°N) SST, and (d) SST pattern (computed as in b). Thick (thin) lines indicate 11‐eleven year running (annual) mean.

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Observed (blue) indices of climate variability and the ensemble mean of 21 climate‐model simulations (black dashed) that account only for external factors. Shown are global, North Atlantic (0–60°N) and Eastern Tropical Pacific (150–90°W and 20°S–20°N) average surface temperature1,4; December–February NAO index5; June–October precipitation averaged over the Sahel (http://jisao.washington.edu/); and June–November accumulated cyclone energy index of Atlantic hurricane activity (http://www.aoml.noaa.gov). Thick (thin) lines show 11‐year running (annual/seasonal) means, and gray shading the 90% confidence interval computed from model spread. Model data are from Coupled Model Intercomparison Project‐3 (CMIP3) database (http://www‐pcmdi.llnl.gov/ipcc/about_ipcc.php) used for the intergovernmental panel on climate change (IPCC) fourth assessment report.

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Retrospectively predicted and forecast 10‐year mean (a) global surface temperature and (b) Atlantic SST dipole indices from three decadal prediction systems.37–39 The dipole index, a proxy for MOC fluctuations, is the difference between north (60–10°W and 40–60°N) and south (30°W–10°E, 10–40°S) averaged SST. Corresponding 10‐year running mean values from (red) observations4,26 and (pink) the ensemble mean of 24 climate‐model simulations that account only for external factors1 (90% confidence interval pink shaded). Predictions for Smith et al.37 begin in 1982, with one per season and four ensemble members (spread gray shaded); Keenlyside et al.39 begin in 1955, with one every 5 years and three ensemble members (vertical bars); and Pohlmann et al.33 begin in 1953, with one per year, and one (seven) member per retrospective (future) predictions. The individual predictions of each system are centered on the corresponding prediction periods (i.e., 5 years after the initial date) and are joined by a continuous line. Separate vertical bars show future forecasts, centered on the forecast period. Predictions of Smith et al.37, Keenlyside et al.39, and Pohlmann et al.33 have been adjusted to have the observed means over the 1979–2001, 1955–2005, and 1953–2001 periods, respectively (Reprinted with permission from Ref 41. Copyright 2009 World Meteorological Organisation).

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Maximum Atlantic meridional overturning stream function at 30°N from five different data assimilations systems used for initializing decadal predictions. Data are from the EU‐ENSEMBLES project (http://www.ensembles‐eu.org/).

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Spectra of the maximum meridional overturning circulation (MOC) stream function (at 30°N) simulated by four state‐of‐the‐art climate models. The fitted theoretical red noise (i.e., order 1 auto‐regressive process) spectra are also shown, along with 5 and 95% confidence intervals. All simulations are 500 years long and assume preindustrial conditions. Data are from the CMIP3 data base (http://www‐pcmdi.llnl.gov/ipcc/about_ipcc.php) used for the IPCC fourth assessment report.1.

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Interdecadal and centennial surface temperature linear trend patterns, as observed4 and the ensemble mean of 21 climate‐model simulations that account only for external factors.1 Regions with insufficient observations (<70%) over the period considered are hashed. CMIP3 data are as in Figure 1. Note different units used for centennial trends.

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Climate Models and Modeling > Earth System Models
Climate Models and Modeling > Knowledge Generation with Models

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