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| Titel |
A trend-preserving bias correction – the ISI-MIP approach |
| VerfasserIn |
S. Hempel, K. Frieler, L. Warszawski, J. Schewe, F. Piontek |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2190-4979
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| Digitales Dokument |
URL |
| Erschienen |
In: Earth System Dynamics ; 4, no. 2 ; Nr. 4, no. 2 (2013-07-31), S.219-236 |
| Datensatznummer |
250017787
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| Publikation (Nr.) |
 copernicus.org/esd-4-219-2013.pdf |
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| Zusammenfassung |
Statistical bias correction is commonly applied within climate impact
modelling to correct climate model data for systematic deviations of the
simulated historical data from observations. Methods are based on transfer
functions generated to map the distribution of the simulated historical data
to that of the observations. Those are subsequently applied to correct the
future projections. Here, we present the bias correction method that was developed
within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project.
ISI-MIP is designed to synthesise impact projections in the agriculture,
water, biome, health, and infrastructure sectors at different levels of
global warming.
Bias-corrected climate data that are used as input for the impact simulations
could be only provided over land areas. To ensure consistency with the global
(land + ocean) temperature information the bias correction method has to preserve
the warming signal. Here we present the applied method that preserves the absolute
changes in monthly temperature, and relative changes in monthly values of precipitation
and the other variables needed for ISI-MIP. The proposed methodology represents a
modification of the transfer function approach applied in the Water Model Intercomparison
Project (Water-MIP). Correction of the monthly mean is followed by correction of the daily
variability about the monthly mean.
Besides the general idea and technical details of the ISI-MIP method, we show and discuss the
potential and limitations of the applied bias correction. In particular, while
the trend and the long-term mean are well represented, limitations with regards
to the adjustment of the variability persist which may affect, e.g. small scale
features or extremes. |
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