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A multiple model assessment of seasonal climate forecast skill for applications

Lavers, David and Luo, Lifeng and Wood, Eric F (2009) A multiple model assessment of seasonal climate forecast skill for applications. Geophysical Research Letters, 36. p. 209. ISSN 0094-8276

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URL of Published Version: http://www.agu.org/journals/ABS/2009/2009GL041365.shtml

Identification Number/DOI: 10.1029/2009GL041365

Skilful seasonal climate forecasts have potential to affect decision making in agriculture, health and water management. Organizations such as the National Oceanic and Atmospheric Administration (NOAA) are currently planning to move towards a climate services paradigm, which will rest heavily on skilful forecasts at seasonal (1 to 9 months) timescales from coupled atmosphere-land-ocean models. We present a careful analysis of the predictive skill of temperature and precipitation from eight seasonal climate forecast models with the joint distribution of observations and forecasts. Using the correlation coefficient, a shift in the conditional distribution of the observations given a forecast can be detected, which determines the usefulness of the forecast for applications. Results suggest there is a deficiency of skill in the forecasts beyond month-1, with precipitation having a more pronounced drop in skill than temperature. At long lead times only the equatorial Pacific Ocean exhibits significant skill. This could have an influence on the planned use of seasonal forecasts in climate services and these results may also be seen as a benchmark of current climate prediction capability using (dynamic) couple models.

Type of Work:Article
Date:15 December 2009 (Publication)
School/Faculty:Colleges (2008 onwards) > College of Life & Environmental Sciences
Department:School of Geography, Earth and Environmental Sciences
Subjects:G Geography (General)
GB Physical geography
Institution:University of Birmingham
Copyright Holders:American Geophysical Union
ID Code:612
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