Next Generation Earth System Prediction Strategies for Subseasonal to Seasonal Forecasts (2016) / Chapter Skim
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1 Introduction
1 Introduction
Pages 17-24
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From page 17... ...
. Enhancing the capability to forecast environmental conditions outside the well-developed weather timescale -- for example, extending predictions out to several weeks and months in advance -- could dramatically increase the societal value of environmental predictions, saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices.
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From page 18... ...
It identifies key strategies and proposes a research agenda with specific recommendations to guide progress toward that vision. There were four main motivations for initiating this study: • The need to develop a research agenda to close the "gap" between efforts to improve numerical weather prediction (NWP)
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From page 19... ...
Because of the short lead times involved with numerical weather prediction, efforts to improve weather forecasting have been focused on enhancing the accuracy of atmospheric and surface data for specifying initial conditions and on representing the short-term evolution of the atmosphere from this initial state. Earth system models that were first developed for making long-term climate projections have focused, in contrast, on representing Earth system processes that evolve more slowly (such as large-scale atmosphere and ocean circulation, the cryosphere, the state of land surface, and feedbacks between components)
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From page 20... ...
weather timescale models forward and climate models backward, in part through the development of improved and more highly coupled Earth system models. The continued development of coupled Earth system models also presents an opportunity to expand and improve S2S forecasts of environmental conditions well beyond the traditional weather variables, which represents a second major motivation for this report.
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From page 21... ...
ENSO and MJO are prime examples of modes of variability that provide predictability at S2S lead times. ENSO is a coupled atmosphereocean mode of variability that involves slow variations in the equatorial Pacific that impact sea surface temperatures in the central and eastern Pacific, and associated changes in surface pressure and wind in the atmosphere that extend over most of the tropical regions.
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From page 22... ...
asked the authoring committee to develop a strategy to accelerate progress on extending prediction skill for weather, ocean, and other Earth system forecasts from meso/synoptic scales to higher spatial resolutions and longer lead times, thereby increasing the nation's research capability and supporting decision-making at medium and extended lead times. In order to meet this request, the current study reviews present S2S forecasting capabilities and recommends a national research agenda to advance Earth system predictions at lead times of 2 weeks to 12 months.
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From page 23... ...
The chapter includes recommendations to further predictability research in the S2S context. Chapter 5 discusses in detail recent advances and activities needed to accelerate the improvement of S2S prediction systems, including discussions of gaps and research needs related to routine observations, data assimilation, and models, as well as calibration, combination, validation, and assessment of S2S forecast skill.
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