4.6 Article

Subseasonal to Seasonal Prediction of Weather to Climate with Application to Tropical Cyclones

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Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2018JD029375

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Funding

  1. Columbia University's Center for Climate and Life
  2. NOAA [NA16OAR4310079]

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Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between daily weather forecasts and seasonal climate outlooks. Recent scientific advances have identified sources of predictability on this time range, and modeling advances are leading to better forecasts. However, much remains to be done to further improve their skill and to develop new climate service forecast products to help countries and sectorial decision makers better manage weather risks and extremes and to adapt to climate change. This paper reviews the history and describes the main challenges and opportunities for the modeling and forecast-applications communities to improve subseasonal to seasonal (S2S) forecasts and products, along with current developments catalyzed by the World Weather Research Programme and World Climate Research Programme's joint Sub-Seasonal to Seasonal Prediction Project. The case of tropical cyclones is highlighted as an illustrative example of the points discussed. Plain Language Summary The forecast range between weather forecasts and seasonal outlooks was long thought to be a predictability desert with little forecast skill. However, many management decisions in agriculture and food security, water, disaster risk reduction, and health fall into this gap in prediction capabilities, so that developing forecast capabilities for this time range would be of considerable societal value. New research and better models have begun to close this gap through increased international collaboration between weather and climate forecasting centers, national research programs, and the academic and user communities. Better understanding of the coupled ocean-atmosphere-land-cryosphere system has identified multiple sources of S2S predictability that are starting to be exploited to fill the prediction gap spurred by creation of new forecast databases.

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