TITLE

Medium-Range, Monthly, and Seasonal Prediction for Europe and the Use of Forecast Information

AUTHOR(S)
Rodwell, Mark J.; Doblas-Reyes, Francisco J.
PUB. DATE
December 2006
SOURCE
Journal of Climate;Dec2006, Vol. 19 Issue 23, p6025
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Operational probabilistic (ensemble) forecasts made at ECMWF during the European summer heat wave of 2003 indicate significant skill on medium (3–10 day) and monthly (10–30 day) time scales. A more general “unified” analysis of many medium-range, monthly, and seasonal forecasts confirms a high degree of probabilistic forecast skill for European temperatures over the first month. The unified analysis also identifies seasonal predictability for Europe, which is not yet realized in seasonal forecasts. Interestingly, the initial atmospheric state appears to be important even for month 2 of a coupled forecast. Seasonal coupled model forecasts capture the general level of observed European deterministic predictability associated with the persistence of anomalies. A review is made of the possibilities to improve seasonal forecasts. This includes multimodel and probabilistic techniques and the potential for “windows of opportunity” where better representation of the effects of boundary conditions (e.g., sea surface temperature and soil moisture) may improve forecasts. “Perfect coupled model” potential predictability estimates are sensitive to the coupled model used and so it is not yet possible to estimate ultimate levels of seasonal predictability. The impact of forecast information on different users with different mitigation strategies (i.e., ways of coping with a weather or climate event) is investigated. The importance of using forecast information to reduce volatility as well as reducing the expected expense is highlighted. The possibility that weather forecasts can affect the cost of mitigating actions is considered. The simplified analysis leads to different conclusions about the usefulness of forecasts that could guide decisions about the development of “end-to-end” (forecast-to-user decision) systems.
ACCESSION #
23467365

 

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