The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal

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

Towards an integrated probabilistic nowcasting system (En-INCA)

Ensemble prediction systems are becoming of more and more interest for various applications. Especially ensemble nowcasting systems are increasingly requested by different end users. In this study we introduce such an integrated probabilistic nowcasting system, En-INCA. In a case study we show the added value and increased skill of the new system and demonstrate the improved performance in comparison with a state-of-the-art LAM-EPS

Error characteristics of high resolution regional climate models over the Alpine area

This study describes typical error ranges of high resolution regional climate models operated over complex orography and investigates the scale-dependence of these error ranges. The results are valid primarily for the European Alpine region, but to some extent they can also be transferred to other orographically complex regions of the world. We investigate the model errors by evaluating a set of 62 one-year hindcast experiments for the year 1999 with four different regional climate models. The analysis is conducted for the parameters mean sea level pressure, air temperature (mean, minimum and maximum) and precipitation (mean, frequency and intensity), both as an area average over the whole modeled domain (the "Greater Alpine Region", GAR) and in six subregions. The subregional seasonal error ranges, defined as the interval between the 2.5th percentile and the 97.5th percentile, lie between -3.2 and +2.0 K for temperature and between -2.0 and +3.1 mm/day (-45.7 and +94.7%) for precipitation, respectively. While the temperature error ranges are hardly broadened at smaller scales, the precipitation error ranges increase by 28%. These results demonstrate that high resolution RCMs are applicable in relatively small scale climate impact studies with a comparable quality as on well investigated larger scales as far as temperature is concerned. For precipitation, which is a much more demanding parameter, the quality is moderately degraded on smaller scales

Regional climate modeling on European scales : A joint standard evaluation of the EURO-CORDEX RCM ensemble

EURO-CORDEX, the European branch of the CORDEX initiative, is providing regional climate simulations at spatial resolutions of 12 and 50 km. An important aspect for both model developers and end-users of regional climate projections is the assessment of model performance and the quantification of bias ranges. Here we present an evaluation of the ERA-Interim-driven EURO-CORDEX ensemble for the period 1989-2008, focusing on the two standard variables near-surface air temperature and precipitation and using the E-OBS dataset as an observational reference. In total, nine 12 km and eight 50 km simulations are considered conducted with seven different models. Several performance metrics, mostly relying on monthly and seasonal mean values, are used to assess model performance on a European scale. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The evaluation reveals a satisfying model performance in many cases, but also considerable model biases for selected metrics and selected sub-domains of the continent. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the - 40 to +40 % range. For regional-scale averages, no clear benefit of an increased spatial resolution (12 km compared to 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble often correspond to those of the ENSEMBLES simulations, but several improvements in model performance can be identified, such as a less pronounced southern European warm summer bias. Bias ranges found for different configurations of one individual model can be of a similar magnitude as bias ranges across different models, demonstrating a strong sensitivity of the chosen combination of physics on model performance. The present overview study will be complemented by further evaluation efforts of the EURO-CORDEX community, addressing selected aspects of model performance such as the representation of extreme events in more detail