EURO-CORDEX Regional Climate Models Research Articles

Impacts of climate change and rising atmospheric CO2 on future projected reference evapotranspiration in Emilia-Romagna (Italy)

The continuous increase of atmospheric CO2 content mainly due to anthropogenic CO2 emissions is causing a rise in temperature on earth, altering the hydrological and meteorological processes and affecting crop physiology. Evapotranspiration is an important component of the hydrological cycle. Thus, understanding the change in evapotranspiration due to global warming is essential for better water resources planning and management and agricultural production. In this study, the effect of climate change with a focus on the combined effect of temperature and elevated CO2 concentrations on reference evapotranspiration (ETo) was evaluated using the Penman–Monteith equation. A EURO-CORDEX regional climate model (RCM) ensemble was used to estimate ETo in five locations in the Emilia-Romagna region (Northern Italy) during the period 2021–2050. Then, its projected changes in response to different CO2 concentrations (i.e., 372 ppm and 550 ppm) under two Representative Concentration Pathways (RCP) scenarios (i.e., RCP4.5 and RCP8.5) were analyzed. Simulation results with both scenarios, without increasing CO2 levels (372 ppm), showed that the annual and summertime ETo for all locations increased by an average of 4 to 5.4% with regard to the reference period 1981–2005, for an increase of air temperature by 1 to 1.5 °C. When the effect of elevated CO2 levels (550 ppm) was also considered in combination with projected changes in temperature, changes in both annual and summer ETo demand for all locations varied from − 1.1 to 2.2% during the 2021–2050 period with regard to the reference period 1981–2005. This shows that higher CO2 levels moderated the increase in ETo that accompanies an increase in air temperature.

Extreme wind projections over Europe from the Euro-CORDEX regional climate models

Extreme weather events represent one of the most visible and immediate hazards to society. Many of these types of phenomena are projected to increase in intensity, duration or frequency as the climate warms. Of these extreme winds are among the most damaging historically over Europe yet assessments of their future changes remain fraught with uncertainty. This uncertainty arises due to both the rare nature of extreme wind events and the fact that most model are unable to faithfully represent them. Here we take advantage of a 15 member ensemble of high resolution Euro-CORDEX simulations (∼12 km) and investigate projected changes in extreme winds using a peaks-over-threshold approach. Additionally we show that – despite lingering model deficiencies and inadequate observational coverage – there is clear added value of the higher resolution simulations over coarser resolution counterparts. Further, the spatial heterogeneity and highly localised nature is well captured. Effects such as orographic interactions, drag due to urban areas, and even individual storm tracks over the oceans are clearly visible. As such future changes also exhibit strong spatial heterogeneity. These results emphasise the need for careful case-by-case treatment of extreme wind analysis, especially when done in a climate adaptation or decision making context. However, for more general assessments the picture is more clear with increases in the return period (i.e. more frequent) extreme episodes projected for Northern, Central and Southern Europe throughout the 21st century. While models continue to improve in their representation of extreme winds, improved observational coverage is desperately needed to obtain more robust assessments of extreme winds over Europe and elsewhere.

The impact of climate change in wheat and barley yields in the Iberian Peninsula

The impact of climate change on wheat and barley yields in two regions of the Iberian Peninsula is here examined. Regression models are developed by using EURO-CORDEX regional climate model (RCM) simulations, forced by ERA-Interim, with monthly maximum and minimum air temperatures and monthly accumulated precipitation as predictors. Additionally, RCM simulations forced by different global climate models for the historical period (1972–2000) and mid-of-century (2042–2070; under the two emission scenarios RCP4.5 and RCP8.5) are analysed. Results point to different regional responses of wheat and barley. In the southernmost regions, results indicate that the main yield driver is spring maximum temperature, while further north a larger dependence on spring precipitation and early winter maximum temperature is observed. Climate change seems to induce severe yield losses in the southern region, mainly due to an increase in spring maximum temperature. On the contrary, a yield increase is projected in the northern regions, with the main driver being early winter warming that stimulates earlier growth. These results warn on the need to implement sustainable agriculture policies, and on the necessity of regional adaptation strategies.

A Weather-Type Classification and Its Application to Near-Surface Wind Climate Change Projections over the Adriatic Region

The main goal of this study is to present a recently developed classification method for weather types based on the vorticity and the location of the synoptic centers relative to the Adriatic region. The basis of the present objective classification, applied to the Adriatic region, is the subjective classification developed by Poje. Our algorithm considered daily mean sea-level pressure and 500 hPa geopotential height to define one out of 17 possible weather types. We applied the algorithm to identify which weather type was relevant in the generation of the two typical near-surface winds over the Adriatic region, namely Bora and Sirocco. Two high-resolution (0.11°) EURO-CORDEX regional climate models were used, SMHI-RCA4 and DHMZ-RegCM4, forced by several CMIP5 global climate models and analyzed for two 30-year periods: near-present day and mid-21st century climate conditions under the high-end Representative Concentration Pathway (RCP8.5) scenario. Bora and Sirocco days were extracted for each weather type and a distribution over the 30-year period was presented. Our results suggest that in the winter season, climate model projections indicate a reduction in the main cyclonic types relevant in the formation of Bora over the entire Adriatic region and an increase in the number of anticyclonic types relevant in Sirocco events. In contrast, for the summer season, an increase in the main anticyclonic Bora-related weather types is found in the ensemble over the northern Adriatic region.

Verification of the EURO-CORDEX RCM Historical Run Results over the Pannonian Basin for the Summer Season

In previous projects that focused on dynamical downscaling over Europe, e.g., PRUDENCE and ENSEMBLES, many regional climate models (RCMs) tended to overestimate summer air temperature and underestimate precipitation in this season in Southern and Southeastern Europe, leading to the so-called summer drying problem. This bias pattern occurred not only in the RCM results but also in the global climate model (GCM) results, so knowledge of the model uncertainties and their cascade is crucial for understanding and interpreting future climate. Our intention with this study was to examine whether a warm-and-dry bias is also present in the state-of-the-art EURO-CORDEX multi-model ensemble results in the summer season over the Pannonian Basin. Verification of EURO-CORDEX RCMs was carried out by using the E-OBS gridded dataset of daily mean, minimum, and maximum near-surface air temperature and total precipitation amount with a horizontal resolution of 0.1 degrees (approximately 12 km × 12 km) over the 1971–2000 time period. The model skill for selected period was expressed in terms of four verification scores: bias, centered root mean square error (RMSE), spatial correlation coefficient, and standard deviation. The main findings led us to conclude that most of the RCMs that overestimate temperature also underestimate precipitation. For some models, the positive temperature and negative precipitation bias were more emphasized, which led us to conclude that the problem was still present in most of the analyzed simulations.

Assessment of potential hydrological climate change impacts in Kastoria basin (Western Macedonia, Greece) using EUROCORDEX regional climate models

<p>The Mediterranean region is expected to present reduced availability of water resources due to climate change. This study aims to assess the potential hydrological responses to climate change in the Kastoria basin (Western Macedonia, Northern Greece) for the period 2019-2078. Climate projections from eight regional climate models from EURO-CORDEX were bias-adjusted using the linear scaling method. The bias-adjusted climate data were used to force the FeFLOW hydro-logical model to predict the discharge of the Kastoria aquifer towards lake Orestiada along with the projected groundwater level distribution. Precipitation (temperature) shows a tendency to decrease (increase) mainly in late spring to early autumn while increase (decrease) in the other sea-sons. Moreover, results indicate a significant increase in temperature and a slight decrease in precipitation towards 2078, while the predicted groundwater level of Kastoria aquifer will reduce slightly. However, the future hydrological behavior of the basin indicates a substantial reduction by approximately 15% of total water yield towards the end of the century.</p>

Projection of future changes in the summer thermal stress index in Romania using statistical downscaling and associated uncertainties

The main goal of this paper is to infer local-scale information on future changes in a complex index quantifying the direct discomfort felt by the human body under global warming. A statistical downscaling model based on the canonical correlation analysis (CCA-SDM) is used to project future changes in heat thermal stress (THI) at high spatial resolution across Romania and the frequency of extremely high THI values (FRTHI) over southern regions. The uncertainty associated with the statistically downscaled changes and validity of the CCA-SDM under future changed climate is assessed. The 2021-2050 and 2071-2100 time horizons are considered. Large-scale predictors (temperature at 850 hPa, specific humidity at 700 hPa) simulated by 6 CMIP5 (RCP4.5 and RCP8.5 scenarios) and 17 CMIP6 (SSP2-4.5 and SSP5-4.5 scenarios) global climate models (GCMs) are considered to estimate the GCM uncertainty. The uncertainty related to the downscaling technique is estimated by comparing the THI changes (derived directly through dynamical downscaling of 3 EURO-CORDEX regional climate models [RCMs]) with those obtained through the CCA-SDM (driven by the same GCM). The CCA-SDM is skilful in capturing the observed THI magnitude and temporal variability over periods with different climate regimes and less skilful in capturing the FRTHI magnitude. Future statistically downscaled THI changes exhibit an increase over the entire country, with magnitudes depending on the GCM driver and time horizon, the highest values being revealed over mountainous regions. Increases are also revealed for FRTHI changes. THI changes derived from RCMs show similar behaviours to those obtained through CCA-SDM driven by the same GCM. The 2 climate signals are closest for those RCMs that are more skilful in capturing the observed characteristics of THI variability, providing an increased confidence in the CCA-SDM transferability in future changed climate.

200\xa0years of equilibrium-line altitude variability across the European Alps (1901\u22122100)

Mountain glaciers are key indicators of climate change. Their response is revealed by the environmental equilibrium-line altitude (ELA), i.e. the regional altitude of zero mass balance averaged over a long period of time. We introduce a simple approach for distributed modelling of the environmental ELA over the entire European Alps based on the parameterization of ELA in terms of summer temperature and annual precipitation at a glacier. We use 200 years of climate records and forecasts to model environmental ELA from 1901 to 2100 at 5 arcmin grid cell resolution. Historical environmental ELAs are reconstructed based on precipitation from the Long-term Alpine Precipitation reconstruction (LAPrec) dataset and temperature from the Historical Instrumental climatological Surface Time series of the greater Alpine region (HISTALP). The simulations of future environmental ELAs are forced with high-resolution EURO-CORDEX regional climate model projections for the European domain using three different greenhouse gas emissions scenarios (Representative Concentration Pathways, RCP). Our reconstructions yielded an environmental ELA across the European Alps of 2980 m above sea level for the period 1901−1930, with a rise of 114 m in the period 1971−2000. The environmental ELA is projected to exceed the maximum elevation of 69%, 81% and 92% of the glaciers in the European Alps by 2071−2100 under mitigation (RCP2.6), stabilization (RCP4.5) and high greenhouse gas emission (RCP8.5) scenarios, respectively.

Evaluation of EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) historical simulations by high-quality observational datasets in southern Italy: insights on drought assessment

Abstract. Many recent studies indicate climate change as a phenomenon that significantly alters the water cycle in different regions worldwide, also implying new challenges in water resource management and drought risk assessment. To this end, it is of key importance to ascertain the quality of regional climate models (RCMs), which are commonly used for assessing at proper spatial resolutions future impacts of climate change on hydrological events. In this study, we propose a statistical methodological framework to assess the quality of the EURO-CORDEX RCMs concerning their ability to simulate historic climate (temperature and precipitation, the basic variables that determine meteorological drought). We then specifically focus on drought characteristics (duration, accumulated deficit, intensity, and return period) determined by the theory of runs at seasonal and annual timescales by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to the Sicily and Calabria regions (southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the former Regional Hydrographic Offices, whose density is considerably greater than observational gridded datasets available at the European level, such as E-OBS or CRU-TS. Results show that among the more skilful models able to reproduce, overall, precipitation and temperature variability as well as drought characteristics, many are based on the CLM-Community RCM, particularly in combination with the HadGEM2 global circulation model (GCM). Nevertheless, the ranking of the models may slightly change depending on the specific variable analysed as well as the temporal and spatial scale of interest. From this point of view, the proposed methodology highlights the skills and weaknesses of the different configurations and can serve as an aid for selecting the most suitable climate model for assessing climate change impacts on drought processes and the underlying variables.

European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP)

Abstract. In this study, we evaluate a set of high-resolution (25–50 km horizontal grid spacing) global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), developed as part of the EU-funded PRIMAVERA (Process-based climate simulation: Advances in high resolution modelling and European climate risk assessment) project, and from the EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) regional climate models (RCMs) (12–50 km horizontal grid spacing) over a European domain. It is the first time that an assessment of regional climate information using ensembles of both GCMs and RCMs at similar horizontal resolutions has been possible. The focus of the evaluation is on the distribution of daily precipitation at a 50 km scale under current climate conditions. Both the GCM and RCM ensembles are evaluated against high-quality gridded observations in terms of spatial resolution and station density. We show that both ensembles outperform GCMs from the 5th Coupled Model Intercomparison Project (CMIP5), which cannot capture the regional-scale precipitation distribution properly because of their coarse resolutions. PRIMAVERA GCMs generally simulate precipitation distributions within the range of EURO-CORDEX RCMs. Both ensembles perform better in summer and autumn in most European regions but tend to overestimate precipitation in winter and spring. PRIMAVERA shows improvements in the latter by reducing moderate-precipitation rate biases over central and western Europe. The spatial distribution of mean precipitation is also improved in PRIMAVERA. Finally, heavy precipitation simulated by PRIMAVERA agrees better with observations in most regions and seasons, while CORDEX overestimates precipitation extremes. However, uncertainty exists in the observations due to a potential undercatch error, especially during heavy-precipitation events. The analyses also confirm previous findings that, although the spatial representation of precipitation is improved, the effect of increasing resolution from 50 to 12 km horizontal grid spacing in EURO-CORDEX daily precipitation distributions is, in comparison, small in most regions and seasons outside mountainous regions and coastal regions. Our results show that both high-resolution GCMs and CORDEX RCMs provide adequate information to end users at a 50 km scale.

Future heat waves over the Mediterranean from an Euro-CORDEX regional climate model ensemble

Heat waves are among the most relevant extreme climatic events due to their effects on society, agriculture and environment. The aim of this work is to improve our understanding of heat waves over the Mediterranean basin during the 21st century from an ensemble of Regional Climate Models (RCMs). Focus has been placed on sensitivities to forcing global models, emissions scenarios and the RCM resolution, being the first work based on Euro-CORDEX simulations to fully analyze future heat waves in the Mediterranean. Heat wave features are studied with Warm Spell Duration Index (WSDI, duration) and Heat Wave Magnitude Index daily (HWMId, intensity). Results indicate a large increase by the end of the century in both intensity and length of heat waves from all emissions scenarios, global models, and regional models at any resolution. Exceptional heat waves observed early on the century could then become normal by the end of this period. Forcing global models and emissions scenarios play a major role. Clear added value on spatial distribution and heat wave indices are obtained from global to regional models dynamical downscaling, related to the important coastal or orographic aspects widely present over the Mediterranean.

Evaluation of the performance of Euro-CORDEX Regional Climate Models for assessing hydrological climate change impacts in Great Britain: A comparison of different spatial resolutions and quantile mapping bias correction methods

Regional Climate Models (RCMs) are an essential tool for analysing regional climate change impacts, such as hydrological change, as they provide simulations with more small-scale details and expected smaller errors than global climate models. There has been much effort to increase the spatial resolution and simulation skill of RCMs (i.e. through bias correction), yet the extent to which this improves the projection of hydrological change is unclear. Here, we evaluate the skill of five reanalysis-driven Euro-CORDEX RCMs in simulating precipitation and temperature, and as drivers of a hydrological model to simulate river flow on four UK catchments covering different physical, climatic and hydrological characteristics. We use a comprehensive range of evaluation indices for aspects of the distribution such as means and extremes, as well as for the structure of time series. We test whether high-resolution RCMs provide added value, through analysis of two RCM resolutions, 0.44° (50 km) and 0.11° (12.5 km), which are also bias-corrected employing the parametric quantile-mapping (QM) method, using the normal distribution for temperature, and the Gamma (GQM) and Double Gamma (DGQM) distributions for precipitation. The performance of these is considered for a range of meteorological variables and for the skill in simulating hydrological impacts at the catchment scale.In a small catchment with complex topography, the 0.11° RCMs clearly outperform their 0.44° version for precipitation and temperature, but when used in combination with the hydrological model, fail to capture the observed river flow distribution. In the other (larger) catchments, only one high-resolution RCM consistently outperforms its low-resolution version, implying that in general there is no added value from using the high-resolution RCMs in those catchments. Both resolutions produce river flow simulations that cover the observed flow duration curve, but the ensemble spread is large and therefore the simulations are difficult to use in practice. GQM decreases most of the simulation biases, except for extreme precipitation and high flows, which are further decreased by DGQM, which also reduces the multi-model simulation spread. Bias correction does not improve the representation of daily temporal variability measured by the Nash-Sutcliffe Efficiency Index, but it does for monthly variability, in particular when applying DGQM, which reduces most of the simulation biases. Overall, an increase in RCM resolution does not imply a better simulation of hydrology and bias-correction represents an alternative to ease decision-making.

Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (<approx. 30 cm), and large for the GCM driven ones (up to 200 cm), when averaged over all stations within 400 m of altitude difference with RCM orography. Some RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa. In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to their limited horizontal resolution. Detected shortcomings in the models depended on the considered snow variable, season and individual RCM.

Modelling Climate Change’s Impact on the Hydrology of Natura 2000 Wetland Habitats in the Vistula and Odra River Basins in Poland

Climate change is expected to affect the water cycle through changes in precipitation, river streamflow, and soil moisture dynamics, and therefore, present a threat to groundwater and surface water-fed wetland habitats and their biodiversity. This article examines the past trends and future impacts of climate change on riparian, water-dependent habitats within the special areas of conservation (SAC) of the Natura 2000 network located within Odra and Vistula River basins in Poland. Hydrological modelling using the Soil and Water Assessment Tool (SWAT) was driven by a set of nine EURO-CORDEX regional climate models under two greenhouse gas concentration trajectories. Changes in the duration of flooding and inundation events were used to assess climate change’s impact on surface water-fed wetland habitats. The groundwater-fed wetlands were evaluated on the basis of changes in soil water content. Information about the current conservation status, threats, and pressures that affect the habitats suggest that the wetlands might dry out. Increased precipitation projected for the future causing increased water supply to both surface water and groundwater-fed wetlands would lead to beneficial outcomes for habitats with good, average, or reduced conservation status. However, habitats with an excellent conservation status that are already in optimum condition could be negatively affected by climate change as increased soil water or duration of overbank flow would exceed their tolerance.

Climate projections of a multivariate heat stress index: the role of downscaling and bias correction

Abstract. Along with the higher demand for bias-corrected data for climate impact studies, the number of available data sets has largely increased in recent years. For instance, the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) constitutes a framework for consistently projecting the impacts of climate change across affected sectors and spatial scales. These data are very attractive for any impact application since they offer worldwide bias-corrected data based on global climate models (GCMs). In a complementary way, the CORDEX initiative has incorporated experiments based on regionally downscaled bias-corrected data by means of debiasing and quantile mapping (QM) methods. In light of this situation, it is challenging to distil the most accurate and useful information for climate services, but at the same time it creates a perfect framework for intercomparison and sensitivity analyses. In the present study, the trend-preserving ISIMIP method and empirical QM are applied to climate model simulations that were carried out at different spatial resolutions (CMIP5 GCM and EURO-CORDEX regional climate models (RCMs), at approximately 150, 50 and 12 km horizontal resolution) in order to assess the role of downscaling and bias correction in a multivariate framework. The analysis is carried out for the wet-bulb globe temperature (WBGT), a heat stress index that is commonly used in the context of working people and labour productivity. WBGT for shaded conditions depends on air temperature and dew-point temperature, which in this work are individually bias corrected prior to the index calculation. Our results show that the added value of RCMs with respect to the driving GCM is limited after bias correction. The two bias correction methods are able to adjust the central part of the WBGT distribution, but some added value of QM is found in WBGT percentiles and in the inter-variable relationships. The evaluation in present climate of such multivariate indices should be performed with caution since biases in the individual variables might compensate, thus leading to better performance for the wrong reason. Climate change projections of WBGT reveal a larger increase in summer mean heat stress for the GCM than for the RCMs, related to the well-known reduced summer warming of the EURO-CORDEX RCMs. These differences are lowered after QM, since this bias correction method modifies the change signals and brings the results for the GCM and RCMs closer to each other. We also highlight the need for large ensembles of simulations to assess the feasibility of the derived projections.

Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble

Abstract. Glaciers in the European Alps play an important role in the hydrological cycle, act as a source for hydroelectricity and have a large touristic importance. The future evolution of these glaciers is driven by surface mass balance and ice flow processes, of which the latter is to date not included explicitly in regional glacier projections for the Alps. Here, we model the future evolution of glaciers in the European Alps with GloGEMflow, an extended version of the Global Glacier Evolution Model (GloGEM), in which both surface mass balance and ice flow are explicitly accounted for. The mass balance model is calibrated with glacier-specific geodetic mass balances and forced with high-resolution regional climate model (RCM) simulations from the EURO-CORDEX ensemble. The evolution of the total glacier volume in the coming decades is relatively similar under the various representative concentrations pathways (RCP2.6, 4.5 and 8.5), with volume losses of about 47 %–52 % in 2050 with respect to 2017. We find that under RCP2.6, the ice loss in the second part of the 21st century is relatively limited and that about one-third (36.8 % ± 11.1 %, multi-model mean ±1σ) of the present-day (2017) ice volume will still be present in 2100. Under a strong warming (RCP8.5) the future evolution of the glaciers is dictated by a substantial increase in surface melt, and glaciers are projected to largely disappear by 2100 (94.4±4.4 % volume loss vs. 2017). For a given RCP, differences in future changes are mainly determined by the driving global climate model (GCM), rather than by the RCM, and these differences are larger than those arising from various model parameters (e.g. flow parameters and cross-section parameterisation). We find that under a limited warming, the inclusion of ice dynamics reduces the projected mass loss and that this effect increases with the glacier elevation range, implying that the inclusion of ice dynamics is likely to be important for global glacier evolution projections.

Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments

Abstract. Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing meteorological data are usually downscaled and bias corrected, most often by univariate approaches such as quantile mapping of individual variables, neglecting the relationships that exist between climate variables. In this study we test the hypothesis that the explicit consideration of the relation between air temperature and precipitation will affect hydrological impact modelling in a snow-dominated mountain environment. Glacio-hydrological simulations were performed for two partly glacierized alpine catchments using a recently developed multivariate bias correction method to post-process EURO-CORDEX regional climate model outputs between 1976 and 2099. These simulations were compared to those obtained by using the common univariate quantile mapping for bias correction. As both methods correct each climate variable's distribution in the same way, the marginal distributions of the individual variables show no differences. Yet, regarding the interdependence of precipitation and air temperature, clear differences are notable in the studied catchments. Simultaneous correction based on the multivariate approach led to more precipitation below air temperatures of 0 ∘C and therefore more simulated snowfall than with the data of the univariate approach. This difference translated to considerable consequences for the hydrological responses of the catchments. The multivariate bias-correction-forced simulations showed distinctly different results for projected snow cover characteristics, snowmelt-driven streamflow components, and expected glacier disappearance dates. In all aspects – the fraction of precipitation above and below 0 ∘C, the simulated snow water equivalents, glacier volumes, and the streamflow regime – simulations resulting from the multivariate-corrected data corresponded better with reference data than the results of univariate bias correction. Differences in simulated total streamflow due to the different bias correction approaches may be considered negligible given the generally large spread of the projections, but systematic differences in the seasonally delayed streamflow components from snowmelt in particular will matter from a planning perspective. While this study does not allow conclusive evidence that multivariate bias correction approaches are generally preferable, it clearly demonstrates that incorporating or ignoring inter-variable relationships between air temperature and precipitation data can impact the conclusions drawn in hydrological climate change impact studies in snow-dominated environments.

Similarities within a multi-model ensemble: functional data analysis framework

Abstract. Despite the abundance of available global climate model (GCM) and regional climate model (RCM) outputs, their use for evaluation of past and future climate change is often complicated by substantial differences between individual simulations and the resulting uncertainties. In this study, we present a methodological framework for the analysis of multi-model ensembles based on a functional data analysis approach. A set of two metrics that generalize the concept of similarity based on the behavior of entire simulated climatic time series, encompassing both past and future periods, is introduced. To our knowledge, our method is the first to quantitatively assess similarities between model simulations based on the temporal evolution of simulated values. To evaluate mutual distances of the time series, we used two semimetrics based on Euclidean distances between the simulated trajectories and based on differences in their first derivatives. Further, we introduce an innovative way of visualizing climate model similarities based on a network spatialization algorithm. Using the layout graphs, the data are ordered on a two-dimensional plane which enables an unambiguous interpretation of the results. The method is demonstrated using two illustrative cases of air temperature over the British Isles (BI) and precipitation in central Europe, simulated by an ensemble of EURO-CORDEX RCMs and their driving GCMs over the 1971–2098 period. In addition to the sample results, interpretational aspects of the applied methodology and its possible extensions are also discussed.

Forcings and projections of past and future wind speed over the Czech Republic

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 77:1-21 (2019) - DOI: https://doi.org/10.3354/cr01540 Forcings and projections of past and future wind speed over the Czech Republic Rudolf Brázdil1,2,*, Jiří Mikšovský2,3, Petr Štépánek2,4, Pavel Zahradníček2,4, Ladislava Řezníčková1,2, Petr Dobrovolný1,2 1Institute of Geography, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic 2Global Change Research Institute, Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, Czech Republic 3Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 180 00 Praha 8, Czech Republic 4Czech Hydrometeorological Institute, Brno Regional Office, Kroftova 43, 616 67 Brno, Czech Republic *Corresponding author: brazdil@sci.muni.cz ABSTRACT: Monthly, seasonal and annual wind-speed series from 119 meteorological stations in the Czech Republic indicate significant decreasing trends in the period 1961-2015. Attribution analysis, applying multiple linear regression, was used to identify wind-speed components related to natural and anthropogenic climate forcings and internally induced climate variability. A significant link to wind speeds was detected for the North Atlantic Oscillation index, as well as for the closely-related Central European Zonal index, especially during the winter. An influence from the East Atlantic/Western Russia Pattern was found during autumn and winter, especially in the eastern part of the country. Changes in large-scale circulation did not seem to be primarily involved in long-term wind stilling, despite a formal correlation between the stilling and anthropogenic forcing. Distinct geographical variations in the regression-estimated links suggest profound influences from interactions between local features of the measuring sites and large-scale climate-forming factors. In total, 11 Euro-CORDEX regional climate model (RCM) simulations for representative concentration pathways RCP4.5 and RCP8.5 were used for projection of annual and seasonal mean daily wind speeds for the Czech Republic for 1951-2100. Despite correction of the model biases for individual RCMs, these simulations largely underestimated the magnitude of declining observational trends in 1981-2010, with only annual, winter and spring values sharing the same trend for both RCPs. Linear trends in wind speeds calculated for 1981-2100 for both RCPs show a significant negative trend in summer, while significant positive trends in winter and spring wind speeds were recorded for RCP8.5. KEY WORDS: Wind speed · Climate forcings · Circulation indices · Attribution analysis · Windspeed projections · Regional climate models · Czech Republic Full text in pdf format Supplementary material NextCite this article as: Brázdil R, Mikšovský J, Štěpánek P, Zahradnícěk P, Rězníčková L, Dobrovolný P (2019) Forcings and projections of past and future wind speed over the Czech Republic. Clim Res 77:1-21. https://doi.org/10.3354/cr01540 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 77, No. 1. Online publication date: January 08, 2019 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2019 Inter-Research.

Detailed validation of EURO-CORDEX and Med-CORDEX regional climate model ensembles over the Carpathian Region

Detailed validation of EURO-CORDEX and Med-CORDEX regional climate model ensembles over the Carpathian Region