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

2014 ◽  
Vol 7 (4) ◽  
pp. 1297-1333 ◽  
Author(s):  
S. Kotlarski ◽  
K. Keuler ◽  
O. B. Christensen ◽  
A. Colette ◽  
M. Déqué ◽  
...  

Abstract. EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-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 configurations 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.

2014 ◽  
Vol 7 (1) ◽  
pp. 217-293 ◽  
Author(s):  
S. Kotlarski ◽  
K. Keuler ◽  
O. B. Christensen ◽  
A. Colette ◽  
M. Déqué ◽  
...  

Abstract. EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatio-temporal 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 dataset 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 sub-domains 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 non-negligible 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 south-eastern Europe reflect common model biases. For seasonal mean quantities averaged over large European sub-domains, no clear benefit of an increased spatial resolution (12 km 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 configurations 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.


2021 ◽  
Author(s):  
Jonathan Meyer ◽  
Shih-Yu (Simon) Wang ◽  
Robert Gillies ◽  
Jin-Ho Yoon

<p>The western U.S. precipitation climatology simulated by the NA-CORDEX regional climate model ensembles are examined to evaluate the capability of the 0.44<sup>° </sup>and 0.22<sup>° </sup>resolution<sup></sup>ensembles to reproduce 1) the annual and semi-annual precipitation cycle of several hydrologically important western U.S. regions and 2) localized seasonality in the amount and timing of precipitation. Collectively, when compared against observation-based gridded precipitation, NA-CORDEX RCMs driven by ERA-Interim reanalysis at the higher resolution 0.22<sup>° </sup>domain resolution dramatically outperformed the 0.44<sup>°</sup> ensemble over the 1950-2005 historical periods. Furthermore, the ability to capture the annual and semi-annual modes of variability was starkly improved in the higher resolution 0.22° ensemble. The higher resolution members reproduced more consistent spatial patterns of variance featuring lower errors in magnitude—especially with respect to the winter-summer and spring-fall seasonality. A great deal of spread in model performance was found for the semi-annual cycles, although the higher-resolution ensemble exhibited a more coherent clustering of performance metrics. In general, model performance was a function of which RCM was used, while future trend scenarios seem to cluster around which GCM was downscaled.</p><p><br>Future projections of precipitation patterns from the 0.22° NA-CORDEX RCMs driven by the RCP4.5 “stabilization scenario” and the RCP8.5 “high emission” scenario were analyzed to examine trends to the “end of century” (i.e. 2050-2099) precipitation patterns. Except for the Desert Southwest’s spring season, the RCP4.5 and RCP8.5 scenarios show a consensus change towards an increase in winter and spring precipitation throughout all regions of interest with the RCP8.5 scenario containing a greater number of ensemble members simulating greater wetting trends. The future winter-summer mode of variability exhibited a general consensus towards increasing variability with greatest change found over the region’s terrain suggesting a greater year-to-year variability of the region’s orographic response to the strength and location of the mid-latitude jet streams and storm track. Increasing spring-fall precipitation variability suggests an expanding influence of tropical moisture advection associated with the North American Monsoon, although we note that like many future monsoon projections, a spring “convective barrier” was also apparent in the NA-CORDEX ensembles.</p>


2021 ◽  
Author(s):  
Katrin Ziegler ◽  
Felix Pohl ◽  
Felix Pollinger ◽  
Heiko Paeth

<p>Adapting the impacts of climate change is a great challenge. To facilitate forest adaptation long-term and forward-looking decisions must be made today since they have to be valid for several decades. Therefore, fundamental knowledge of the future climate and of tree species which are more resilient to the future climate than trees growing in the forests today is necessary.</p><p>To give local foresters a basis for their decisions, we use the so-called analogue region method. With this method we aim to find regions in Europe which currently have the same climate as it is projected in a specific reference region for different future scenarios. For the projections, the model runs of the regional climate model REMO are used. As an example of finding analogue regions, we selected the forest region Steigerwald in North Bavaria. We use different climatic and forest specific indices and data preparation methods to test the influence of varying indices and methods on the resulting regions. After identifying the respective analogue regions, we analyze which tree species are growing currently in these regions by using the EU-Forest data set.</p>


2020 ◽  
Author(s):  
Kevin Sieck ◽  
Christine Nam ◽  
Laurens M. Bouwer ◽  
Diana Rechid ◽  
Daniela Jacob

Abstract. This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C.


2013 ◽  
Vol 17 (11) ◽  
pp. 4323-4337 ◽  
Author(s):  
M. A. Sunyer ◽  
H. J. D. Sørup ◽  
O. B. Christensen ◽  
H. Madsen ◽  
D. Rosbjerg ◽  
...  

Abstract. In recent years, there has been an increase in the number of climate studies addressing changes in extreme precipitation. A common step in these studies involves the assessment of the climate model performance. This is often measured by comparing climate model output with observational data. In the majority of such studies the characteristics and uncertainties of the observational data are neglected. This study addresses the influence of using different observational data sets to assess the climate model performance. Four different data sets covering Denmark using different gauge systems and comprising both networks of point measurements and gridded data sets are considered. Additionally, the influence of using different performance indices and metrics is addressed. A set of indices ranging from mean to extreme precipitation properties is calculated for all the data sets. For each of the observational data sets, the regional climate models (RCMs) are ranked according to their performance using two different metrics. These are based on the error in representing the indices and the spatial pattern. In comparison to the mean, extreme precipitation indices are highly dependent on the spatial resolution of the observations. The spatial pattern also shows differences between the observational data sets. These differences have a clear impact on the ranking of the climate models, which is highly dependent on the observational data set, the index and the metric used. The results highlight the need to be aware of the properties of observational data chosen in order to avoid overconfident and misleading conclusions with respect to climate model performance.


2016 ◽  
Vol 9 (3) ◽  
pp. 1143-1152 ◽  
Author(s):  
Olivier Giot ◽  
Piet Termonia ◽  
Daan Degrauwe ◽  
Rozemien De Troch ◽  
Steven Caluwaerts ◽  
...  

Abstract. Using the regional climate model ALARO-0, the Royal Meteorological Institute of Belgium and Ghent University have performed two simulations of the past observed climate within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The ERA-Interim reanalysis was used to drive the model for the period 1979–2010 on the EURO-CORDEX domain with two horizontal resolutions, 0.11 and 0.44°. ALARO-0 is characterised by the new microphysics scheme 3MT, which allows for a better representation of convective precipitation. In Kotlarski et al. (2014) several metrics assessing the performance in representing seasonal mean near-surface air temperature and precipitation are defined and the corresponding scores are calculated for an ensemble of models for different regions and seasons for the period 1989–2008. Of special interest within this ensemble is the ARPEGE model by the Centre National de Recherches Météorologiques (CNRM), which shares a large amount of core code with ALARO-0. Results show that ALARO-0 is capable of representing the European climate in an acceptable way as most of the ALARO-0 scores lie within the existing ensemble. However, for near-surface air temperature, some large biases, which are often also found in the ARPEGE results, persist. For precipitation, on the other hand, the ALARO-0 model produces some of the best scores within the ensemble and no clear resemblance to ARPEGE is found, which is attributed to the inclusion of 3MT. Additionally, a jackknife procedure is applied to the ALARO-0 results in order to test whether the scores are robust, meaning independent of the period used to calculate them. Periods of 20 years are sampled from the 32-year simulation and used to construct the 95 % confidence interval for each score. For most scores, these intervals are very small compared to the total ensemble spread, implying that model differences in the scores are significant.


2019 ◽  
Vol 12 (12) ◽  
pp. 5229-5249 ◽  
Author(s):  
Emmanuele Russo ◽  
Ingo Kirchner ◽  
Stephan Pfahl ◽  
Martijn Schaap ◽  
Ulrich Cubasch

Abstract. Due to its extension, geography and the presence of several underdeveloped or developing economies, the Central Asia domain of the Coordinated Regional Climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, regional climate models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for the present day, testing the effects of singular changes in the model physical configuration and their mutual interaction with the simulation of monthly and seasonal values of three variables that are important for impact studies: near-surface temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo, taking into consideration the ratio of forest fractions, and the soil conductivity, taking into account the ratio of liquid water and ice in the soil, allows one to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The analyses also show that improvements in model performance are not achievable for all domain subregions and variables, and they are the result of a compensation effect in the different cases. The proposed better performing configuration in terms of mean climate leads to similar positive improvements when considering different observational data sets and boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.


2014 ◽  
Vol 6 (1) ◽  
pp. 147-164 ◽  
Author(s):  
B. Geyer

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and climate changes since 1948, e.g., in frequencies of extremes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges, etc.) over many decades. The acronym coastDat stands for the set of consistent ocean and atmospheric data, where the atmospheric data where used as forcing for the reconstruction of the sea state. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013; doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for the entire European continent, including the Baltic Sea and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with the regional climate model COSMO-CLM (CCLM) and a horizontal grid size of 0.22 degree in rotated coordinates. Global reanalysis data of NCEP1 were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.


2015 ◽  
Vol 8 (10) ◽  
pp. 8387-8409
Author(s):  
O. Giot ◽  
P. Termonia ◽  
D. Degrauwe ◽  
R. De Troch ◽  
S. Caluwaerts ◽  
...  

Abstract. Using the regional climate model ALARO-0 the Royal Meteorological Institute of Belgium has performed two simulations of the past observed climate within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The ERA-Interim reanalysis was used to drive the model for the period 1979–2010 on the EURO-CORDEX domain with two horizontal resolutions, 0.11 and 0.44 °. ALARO-0 is characterised by the new microphysics scheme 3MT, which allows for a better representation of convective precipitation. In Kotlarski et al. (2014) several metrics assessing the performance in representing seasonal mean near-surface air temperature and precipitation are defined and the corresponding scores are calculated for an ensemble of models for different regions and seasons for the period 1989–2008. Of special interest within this ensemble is the ARPEGE model by the Centre National de Recherches Météorologiques (CNRM), which shares a large amount of core code with ALARO-0. Results show that ALARO-0 is capable of representing the European climate in an acceptable way as most of the ALARO-0 scores lie within the existing ensemble. However, for near-surface air temperature some large biases, which are often also found in the ARPEGE results, persist. For precipitation, on the other hand, the ALARO-0 model produces some of the best scores within the ensemble and no clear resemblance to ARPEGE is found, which is attributed to the inclusion of 3MT. Additionally, a jackknife procedure is applied to the ALARO-0 results in order to test whether the scores are robust, by which we mean independent of the period used to calculate them. Periods of 20 years are sampled from the 32 year simulation and used to construct the 95 % confidence interval for each score. For most scores these intervals are very small compared to the total ensemble spread, implying that model differences in the scores are significant.


Sign in / Sign up

Export Citation Format

Share Document