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| Titel |
Assessment of bias correction under transient climate change |
| VerfasserIn |
Bert Van Schaeybroeck, Stéphane Vannitsem |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250109505
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| Publikation (Nr.) |
 EGU/EGU2015-9420.pdf |
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| Zusammenfassung |
| Calibration of climate simulations is necessary since large systematic discrepancies are
generally found between the model climate and the observed climate. Recent studies have
cast doubt upon the common assumption of the bias being stationary when the climate
changes. This led to the development of new methods, mostly based on linear sensitivity of
the biases as a function of time or forcing (Kharin et al. 2012). However, recent studies
uncovered more fundamental problems using both low-order systems (Vannitsem 2011)
and climate models, showing that the biases may display complicated non-linear
variations under climate change. This last analysis focused on biases derived from the
equilibrium climate sensitivity, thereby ignoring the effect of the transient climate
sensitivity.
Based on the linear response theory, a general method of bias correction is therefore
proposed that can be applied on any climate forcing scenario. The validity of the method is
addressed using twin experiments with a climate model of intermediate complexity
LOVECLIM (Goosse et al., 2010). We evaluate to what extent the bias change is sensitive to
the structure (frequency) of the applied forcing (here greenhouse gases) and whether the
linear response theory is valid for global and/or local variables.
To answer these question we perform large-ensemble simulations using different 300-year
scenarios of forced carbon-dioxide concentrations. Reality and simulations are assumed to
differ by a model error emulated as a parametric error in the wind drag or in the radiative
scheme.
References
[1]ÂÂÂH. Goosse et al., 2010: Description of the Earth system model of
intermediate complexity LOVECLIM version 1.2, Geosci. Model Dev., 3,
603–633.
[2]ÂÂÂS. Vannitsem, 2011: Bias correction and post-processing under climate
change, Nonlin. Processes Geophys., 18, 911–924.
[3]ÂÂÂV.V. Kharin, G. J. Boer, W. J. Merryfield, J. F. Scinocca, and W.-S. Lee,
2012: Statistical adjustment of decadal predictions in a changing climate,
Geophys. Res. Lett., 39, L19705. |
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