Precipitation and temperature statistics in high‐resolution regional climate models: Evaluation for the European Alps

Smiatek, Gerhard; Kunstmann, Harald; Knoche, R.; Marx, Andréas

doi:10.1029/2008jd011353

Cited by 92 publications

(82 citation statements)

References 53 publications

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“…Results on temperature signals are even more unambiguous with respect to the direction of change towards a significant rise in air temperature for both seasons and the whole of Austria. The latter findings equally concur with other studies on climate change in Europe, and particularly the Alps, which likewise show a significant increase in mean air temperature in all seasons and in all regions (e.g., [9][10][11][12][13][14][15][16]). …”

Section: Discussionsupporting

confidence: 79%

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Frequency Analysis of Critical Meteorological Conditions in a Changing Climate—Assessing Future Implications for Railway Transportation in Austria

Kellermann

Bubeck

Kundela³

et al. 2016

Climate

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Meteorological extreme events have great potential for damaging railway infrastructure and posing risks to the safety of train passengers. In the future, climate change will presumably have serious implications on meteorological hazards in the Alpine region. Hence, attaining insights on future frequencies of meteorological extremes with relevance for the railway operation in Austria is required in the context of a comprehensive and sustainable natural hazard management plan of the railway operator. In this study, possible impacts of climate change on the frequencies of so-called critical meteorological conditions (CMCs) between the periods 1961-1990 and 2011-2040 are analyzed. Thresholds for such CMCs have been defined by the railway operator and used in its weather monitoring and early warning system. First, the seasonal climate change signals for air temperature and precipitation in Austria are described on the basis of an ensemble of high-resolution Regional Climate Model (RCM) simulations for Europe. Subsequently, the RCM-ensemble was used to investigate changes in the frequency of CMCs. Finally, the sensitivity of results is analyzed with varying threshold values for the CMCs. Results give robust indications for an all-season air temperature rise, but show no clear tendency in average precipitation. The frequency analyses reveal an increase in intense rainfall events and heat waves, whereas heavy snowfall and cold days are likely to decrease. Furthermore, results indicate that frequencies of CMCs are rather sensitive to changes of thresholds. It thus emphasizes the importance to carefully define, validate, and-if needed-to adapt the thresholds that are used in the weather monitoring and warning system of the railway operator. For this, continuous and standardized documentation of damaging events and near-misses is a pre-requisite.

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Section: Discussionsupporting

confidence: 79%

“…The analyses on future temperature trends consistently show a marked increase in mean air temperature in all regions (e.g., [9][10][11][12][13][14][15][16]). According to Eitzinger et al [10], Gobiet et al [11], and Strauss et al [15], a significant annual mean temperature rise of approximately 1.6˝C until 2040 is expected.…”

Section: Introductionmentioning

confidence: 99%

Frequency Analysis of Critical Meteorological Conditions in a Changing Climate—Assessing Future Implications for Railway Transportation in Austria

Kellermann

Bubeck

Kundela³

et al. 2016

Climate

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“…For dynamical downscaling, regional climate models (RCMs) are capable of bridging the gap between large-scale GCM data and local-scale information to conduct climate studies. Nevertheless, the RCM simulations usually do not agree well with observations even if downscaled to high spatial resolutions (Smiatek et al, 2009;Teutschbein and Seibert, 2010). Thus, they might not be useful for deriving hydrological impacts on local scales directly (Graham et al, 2007a, b;Christensen et al, 2008;Bergström et al, 2001).…”

Section: Introductionmentioning

confidence: 97%

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Mao

Vogl

Laux

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over-or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer functionbased statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971-2000. The REG-NIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibration (1971-1985) and validation (1986-2000) period of equal length. Based on the estimated dependence structure (described by the Copula function) between WRF and REGNIE data and the identified respective marginal distributions in the calibration period, separately analyzed for the different seasons, conditional distribution functions are derived for each time step in the validation period. This finally allows to get additional information about the range of the statistically possible bias-corrected values. The results show that the Copula-based approach efficiently corrects most of the errors in WRF derived precipitation for all seasons. It is also found that the Copula-based correction performs better for wet bias correction than for dry bias correction. In autumn and winter, the correction introduced a small dry bias in the northwest of Germany. The average relative bias of daily mean precipitation from WRF for the validation period is reduced from 10 % (wet bias) to −1 % (slight dry bias) after the application of the Copula-based correction. The bias in different seasons is corrected from 32 % March-April-May (MAM), −15 % June-July-August (JJA), 4 % September-October-November (SON) and 28 % December-January-February (DJF) to 16 % (MAM), −11 % (JJA), −1 % (SON) and −3 % (DJF), respectively. Finally, the Copula-based approach is compared to the quantile mapping correction method. The root mean square error (RMSE) and the percentage of the corrected time steps that are closer to the observations are analyzed. The Copula-based correction derived from the mean of the sampled distribution reduces the RMSE significantly, while, e.g., the quantile mapping method results in an increased RMSE for some regions.

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“…In order to enhance process understanding and to reveal potential reasons for biases in atmospheric quantities, also surface energy fluxes (Hagemann et al, 2004;Lenderink et al, 2007;Markovic et al, 2008) and nonatmospheric state parameters such as terrestrial water storage (Greve et al, 2013; and snow cover (Räisänen and Eklund, 2012;Salzmann and Mearns, 2012;Steger et al, 2013) have been evaluated. In Europe, several studies explicitly focused on RCM evaluation over the Alps, a region subject to a complex topography and a strong spatial variability of near-surface climates (Frei et al, 2003;Haslinger et al, 2013;Kotlarski et al, 2010;Prömmel et al, 2010;Smiatek et al, 2009;Suklitsch et al, 2008Suklitsch et al, , 2011.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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Abstract. EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERAInterim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989-2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations.The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies nonnegligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 • C, while precipitation biases are typically located in the ±40 % range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and southeastern Europe reflect common model biases. For seasonal mean quantities averaged over large European subdomains, no clear benefit of an increased spatial resolution (12 vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different Published by Copernicus Publications on behalf of the European Geosciences Union. S. Kotlarski et al.: Regional climate modeling on European scalesconfigurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.

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Precipitation and temperature statistics in high‐resolution regional climate models: Evaluation for the European Alps

Cited by 92 publications

References 53 publications

Frequency Analysis of Critical Meteorological Conditions in a Changing Climate—Assessing Future Implications for Railway Transportation in Austria

Frequency Analysis of Critical Meteorological Conditions in a Changing Climate—Assessing Future Implications for Railway Transportation in Austria

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

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

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