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Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology. Uppsala Universitet, Sverige.ORCID iD: 0000-0002-3344-2468
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology. Uppsala Universitet, Sverige.
2012 (English)In: Hydrology and Earth System Sciences Discussions, ISSN 1812-2108, E-ISSN 1812-2116, Vol. 9, no 11, 12765-12795 p.Article in journal (Other academic) Published
Abstract [en]

In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied.  We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

Place, publisher, year, edition, pages
2012. Vol. 9, no 11, 12765-12795 p.
Keyword [en]
RCM, bias correction, downscaling, hydrology, differential split-sample test
National Category
Oceanography, Hydrology, Water Resources Climate Research
Research subject
Physical Geography
Identifiers
URN: urn:nbn:se:su:diva-84190DOI: 10.5194/hessd-9-12765-2012OAI: oai:DiVA.org:su-84190DiVA: diva2:578754
Funder
Formas, 2007-1433
Available from: 2012-12-18 Created: 2012-12-18 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Hydrological Modeling for Climate Change Impact Assessment: Transferring Large-Scale Information from Global Climate Models to the Catchment Scale
Open this publication in new window or tab >>Hydrological Modeling for Climate Change Impact Assessment: Transferring Large-Scale Information from Global Climate Models to the Catchment Scale
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A changing climate can severely perturb regional hydrology and thereby affect human societies and life in general. To assess and simulate such potential hydrological climate change impacts, hydrological models require reliable meteorological variables for current and future climate conditions. Global climate models (GCMs) provide such information, but their spatial scale is too coarse for regional impact studies. Thus, GCM output needs to be downscaled to a finer scale either through statistical downscaling or through dynamic regional climate models (RCMs). However, even downscaled meteorological variables are often considerably biased and therefore not directly suitable for hydrological impact modeling. This doctoral thesis discusses biases and other challenges related to incorporating climate model output into hydrological studies and evaluates possible strategies to address them. An analysis of possible sources of uncertainty stressed the need for full ensembles approaches, which should become standard practice to obtain robust and meaningful hydrological projections under changing climate conditions. Furthermore, it was shown that substantial biases in current RCM simulations exist and that correcting them is an essential prerequisite for any subsequent impact simulation. Bias correction algorithms considerably improved RCM output and subsequent streamflow simulations under current conditions. In addition, differential split-sample testing was highlighted as a powerful tool for evaluating the transferability of bias correction algorithms to changed conditions. Finally, meaningful projections of future streamflow regimes could be realized by combining a full ensemble approach with bias correction of RCM output: Current flow regimes in Sweden with a snowmelt-driven spring flood in April will likely change to rather damped flow regimes that are dominated by large winter streamflows.

Place, publisher, year, edition, pages
Stockholm: Department of Physical Geography and Quaternary Geology, Stockholm University, 2013. 44 p.
Series
Dissertations from the Department of Physical Geography and Quaternary Geology, ISSN 1653-7211 ; 34
Keyword
Bias Correction, Climate Change, Climate Models, Ensembles, GCM, HBV, Hydrological Modeling, Precipitation, RCM, Split Sample Test, Streamflow, Sweden, Temperature, Uncertainty
National Category
Oceanography, Hydrology, Water Resources Climate Research
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-84197 (URN)978-91-7447-622-4 (ISBN)
Public defence
2013-02-15, Nordenskiöldsalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Formas, 2007-1433
Available from: 2013-01-24 Created: 2012-12-18 Last updated: 2014-01-31Bibliographically approved

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