Details

Autor(en) / Beteiligte

• Mariotti, Annarita
• Baggett, Cory
• Barnes, Elizabeth A.
• Becker, Emily
• Butler, Amy
• Collins, Dan C.
• Dirmeyer, Paul A.
• Ferranti, Laura
• Johnson, Nathaniel C.
• Jones, Jeanine
• Kirtman, Ben P.
• Lang, Andrea L.
• Molod, Andrea
• Newman, Matthew
• Robertson, Andrew W.
• Schubert, Siegfried
• Waliser, Duane E.
• Albers, John

Titel

Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and Beyond

Ist Teil von

• Bulletin of the American Meteorological Society, 2020-05, Vol.101 (5), p.E608-E625

Ort / Verlag

Goddard Space Flight Center: American Meteorological Society

Erscheinungsjahr

2020

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.