BOX 1.1

STATEMENT OF TASK

This study will review the current state of knowledge about estimates of predictability of the climate system on intraseasonal to interannual timescales, assess in what ways current estimates are deficient, and recommend ways to improve upon the current predictability estimates. The study will also recommend research and model development foci and efforts that will be most beneficial in narrowing the gap between the current skill of predictions and estimated predictability limits. The review of predictability estimates to be addressed will include oceanic and atmospheric variables such as sea surface temperature, sub-surface heat content, surface temperature, precipitation, and soil moisture, as well as indices like Nino3.4 sea surface temperatures or the phases of the Madden-Julian Oscillation.


Specifically, the study committee will:

  1. Review current understanding of climate predictability on intraseasonal to interannual time scales, including sources of predictability, the methodologies used to estimate predictability, current estimates of predictability, and how these estimates have evolved over time;

  2. Describe how improvements in modeling, observational capabilities, and other technological improvements (e.g., analysis, development of ensemble prediction systems, data assimilation systems, computing capabilities) have led to changes in our understanding and estimates of predictability;

  3. Identify any key deficiencies and gaps remaining in our understanding of climate predictability on intraseasonal to interannual timescales, and recommend research priorities to address these gaps;

  4. Assess the performance of current prediction systems in relation to the estimated predictability of the climate system on intraseasonal to interannual timescales, and recommend strategies (e.g.,observations, model improvements, and research priorities) to narrow gaps that exist between current predictive capabilities and estimated limits of predictability; and

  5. Recommend strategies and best practices that could be used to quantitatively assess improvements in both predictability estimates and prediction skill over time.



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