Abstract
Abstract. This study discusses the effect of empirical-statistical bias correction methods like quantile mapping (QM) on the temperature change signals of climate simulations. We show that QM regionally alters the mean temperature climate change signal (CCS) derived from the ENSEMBLES multi-model data set by up to 15 %. Such modification is currently strongly discussed and is often regarded as deficiency of bias correction methods. However, an analytical analysis reveals that this modification corresponds to the effect of intensity-dependent model errors on the CCS. Such errors cause, if uncorrected, biases in the CCS. QM removes these intensity-dependent errors and can therefore potentially lead to an improved CCS. A similar analysis as for the multi-model mean CCS has been conducted for the variance of CCSs in the multi-model ensemble. It shows that this indicator for model uncertainty is artificially inflated by intensity-dependent model errors. Therefore, QM also has the potential to serve as an empirical constraint on model uncertainty in climate projections. However, any improvement of simulated CCSs by empirical-statistical bias correction methods can only be realized if the model error characteristics are sufficiently time-invariant.
Highlights
Society is increasingly demanding reliable projections of future climate change to analyze adaptation options and costs, to explore climate change mitigation benefits, and to support political decisions
This framework allows the impact of such errors to be investigated, on the multi-model mean climate change signal (CCS) in an ensemble of climate simulations, and on the inter-model variability, which is often used as a measure of uncertainty in climate projections (e.g., Hawkins and Sutton, 2009, 2011; Prein et al, 2011)
For the ENSEMBLES multimodel data set it has been demonstrated that quantile mapping (QM) dampens projected summer warming in southeastern Europe and France by about 0.5 K and enhances projected warming in Scandinavia by about the same amount
Summary
Society is increasingly demanding reliable projections of future climate change to analyze adaptation options and costs, to explore climate change mitigation benefits, and to support political decisions. In this paper we go a step further and argue that, under the assumption of time-invariant model error characteristics, the modification of the CCS by QM can be interpreted as improvement, rather than as deterioration, since it is capable of mitigating intensity-dependent model errors To support this hypothesis, we develop a linearized analytical description of the effect of intensity-dependent model errors on the CCS. We develop a linearized analytical description of the effect of intensity-dependent model errors on the CCS This framework allows the impact of such errors to be investigated, on the multi-model mean CCSs in an ensemble of climate simulations, and on the inter-model variability, which is often used as a measure of uncertainty in climate projections (e.g., Hawkins and Sutton, 2009, 2011; Prein et al, 2011).
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