Parametric decadal climate forecast recalibration (DeFoReSt 1.0)
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Published:2018-01-25
Issue:1
Volume:11
Page:351-368
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Pasternack Alexander, Bhend JonasORCID, Liniger Mark A.ORCID, Rust Henning W., Müller Wolfgang A., Ulbrich UweORCID
Abstract
Abstract. Near-term climate predictions such as decadal climate forecasts are
increasingly being used to guide adaptation measures. For near-term
probabilistic predictions to be useful, systematic errors of the
forecasting systems have to be corrected. While methods for the
calibration of probabilistic forecasts are readily available, these
have to be adapted to the specifics of decadal climate forecasts
including the long time horizon of decadal climate forecasts,
lead-time-dependent systematic errors (drift) and the errors in the
representation of long-term changes and variability. These features
are compounded by small ensemble sizes to describe forecast
uncertainty and a relatively short period for which typically pairs
of reforecasts and observations are available to estimate
calibration parameters. We introduce the Decadal Climate Forecast
Recalibration Strategy (DeFoReSt), a parametric approach to
recalibrate decadal ensemble forecasts that takes the above
specifics into account. DeFoReSt optimizes forecast quality as
measured by the continuous ranked probability score (CRPS). Using a
toy model to generate synthetic forecast observation pairs, we
demonstrate the positive effect on forecast quality in situations
with pronounced and limited predictability. Finally, we apply
DeFoReSt to decadal surface temperature forecasts from the MiKlip
prototype system and find consistent, and sometimes considerable,
improvements in forecast quality compared with a simple calibration
of the lead-time-dependent systematic errors.
Publisher
Copernicus GmbH
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