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| Titel |
Correction of model errors of climate runs based on post-processing techniques |
| VerfasserIn |
Bert Van Schaeybroeck, Stéphane Vannitsem, Violette Zunz, Hugues Goosse |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250088891
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| Publikation (Nr.) |
 EGU/EGU2014-3068.pdf |
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| Zusammenfassung |
| It is a common practice to correct the bias of climate runs as a consequence of errors which
are systematic in nature. Three types of errors can be identified: initial condition errors,
forcing errors and model errors. Intense ongoing research focuses on improving initialization
and forcings that most prominently deteriorate the forecasts at seasonal and decadal
timescales, respectively (Meehl et al., 2013). Model errors, however, may be important at all
timescales and it remains unclear to what extent they impact the predictability of the
climate system and how much they can be corrected. We analyse the error growth and
post-processing corrections on seasonal up to decadal scale by means of twin experiments in
the presence of model errors. The climate model considered is the intermediate
complexity model LOVECLIM (Goosse et al., 2010). We explore how the forecast
can be improved using different post-processing techniques, including simple bias
correction and EVMOS correction (Vannitsem, 2009), an approach that provides
adequate corrections for both climatologic mean and variance. We disentangle the
bias correction from the variance correction and explore the spatial characteristics
of forecast improvement with an emphasis on the southern hemisphere and sea
ice. At seasonal scales we focus on atmospheric variables while for the decadal
scales the ocean temperature and salinity at the surface and at intermediate depth is
considered.
References
[1]H. Goosse et al., 2010: Description of the Earth system model of
intermediate complexity LOVECLIM version 1.2, Geosci. Model Dev., 3,
603–633.
[2]Vannitsem S., 2009: A unified linear Model Output Statistics scheme for
both deterministic and ensemble forecasts, Quart. J. Roy. Meteorol. Soc., 135,
1801-1815.
[3]Meehl et al., 2013: Decadal Climate Prediction: An Update from
the Trenches, Bulletin of the American Meteorological Society. doi:
http://dx.doi.org/10.1175/BAMS-D-12-00241.1. |
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