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Improved monitoring and forecasting of El Niño, La Niña, and the Southern Oscillation (ENSO) with advanced maps, data, and prediction models

总结

El Niño, La Niña, and the Southern Oscillation (ENSO) is a widely studied climate phenomenon because of its impact on human welfare and the environment. Both developed and emerging countries value predictive information on ENSO for anticipating regional shifts in year-to-year climate that can effect crops, water, and public health. Many climate centers around the world make predictions of ENSO events, but differences in the models lead to differences and potential confusion about the current and evolving state of ENSO. The International Research Institute for Climate and Society (IRI) at Columbia University addresses this problem by collecting and synthesizing data from global partners and to produce analyses and probabilistic predictions of the ENSO state for the next 12 months. Through extensive collaboration with NOAA’s Climate Prediction Center and top modeling centers fro around the world, the IRI’s advanced forecasts and reporting services allow researchers and authorities to construct insightful expectations of ENSO evolution.

技术优势

Supplies monthly analysis and streams for passive monitoring and prediction of ENSO conditionsNumerous, disparate sources to produce the rich datasetImproves effectiveness of local and global forecastsAllows governments to better manage climate riskProvides access to peer institutions targeting similar problemsPatent Information:Tech Ventures Reference: CU17011

技术应用

Data, Models, & Forecasts for:* Climatology* Wildfire risk assessments* Agricultural production and food security* Ecosystem protection* Public health* Natural disaster mitigation* Water management* Financial instruments* Academic studies

详细技术说明

None

*Abstract

None

*Inquiry

Richard NguyenColumbia Technology VenturesTel: (212) 854-8444Email: TechTransfer@columbia.edu

*IR

cu17011

*Principal Investigation

*Publications

Barnston A, Tippett M. Predictions of Nino3.4 SST in CFSv1 and CFSv2: a diagnostic comparison. Climate Dynamics. 2013;41:1615-1633.Barnston A, Tippett M, L’Heureux M., et al. Skill of real-time seasonal ENSO model predictions during 2002-2011: Is our capability increasing? Bull. Amer. Meteor. Soc. 2012;93:631-651.Tippett M, Barnston A, Li S. Performance of recent multimodel ENSO forecasts. J. Appl. Meteor. Climatol. 2012;51:637-654.Barnston A, Tippett M, van den Dool H., et al. Toward and Improved Multimodel ENSO Prediction. J. Appl. Meteor. Climatol. 2015;51:1579-1595.Mason S, Goddard L. Probabilistic precipitation anomalies associated with ENSO. Bull. Amer. Meteor. Soc., 2001;82:619-638.

国家/地区

美国