Convective and stratiform precipitation characteristics in an ensemble of regional climate model simulations

We apply a recently proposed algorithm for disaggregating observed precipitation data into predominantly convective and stratiform, and evaluate biases in characteristics of parameterized convective (subgrid) and stratiform (large-scale) precipitation in an ensemble of 11 RCM simulations for recent climate in Central Europe. All RCMs have a resolution of 25 km and are driven by the ERA-40 reanalysis. We focus on mean annual cycle, proportion of convective precipitation, dependence on altitude, and extremes. The results show that characteristics of total precipitation are often better simulated than are those of convective and stratiform precipitation evaluated separately. While annual cycles of convective and stratiform precipitation are reproduced reasonably well in most RCMs, some of them consistently and substantially overestimate or underestimate the proportion of convective precipitation throughout the year. Intensity of convective precipitation is underestimated in all RCMs. Dependence on altitude is also simulated better for stratiform and total precipitation than for convective precipitation, for which several RCMs produce unrealistic slopes. Extremes are underestimated for convective precipitation while they tend to be slightly overestimated for stratiform precipitation, thus resulting in a relatively good reproduction of extremes in total precipitation amounts. The results suggest that the examined ensemble of RCMs suffers from substantial deficiencies in reproducing precipitation processes and support previous findings that climate models’ errors in precipitation characteristics are mainly related to deficiencies in the representation of convection.     

Assess hydrological responses to a warming climate at the Lysina Critical Zone Observatory in Central Europe

Climate warming is having profound effects on the hydrological cycle by increasing atmospheric demand, changing water availability, and snow seasonality. Europe suffered three distinct heat waves in 2019, and 11 of the 12 hottest years ever recorded took place in the past two decades, which will potentially change seasonal streamflow patterns and long-term trends. Central Europe exhibited six dry years in a row since 2014. This study uses data from a well-documented headwater catchment in Central Europe (Lysina) to explore hydrological responses to a warming climate. We applied a lumped parameter hydrologic model Brook90 and a distributed model Penn State Integrated Hydrologic Model (PIHM) to simulate long-term hydrological change under future climate scenarios. Both models performed well on historic streamflow and in agreement with each other according to the catchment water budget. In addition, PIHM was able to simulate lateral groundwater redistribution within the catchment validated by the groundwater table dynamics. The long-term trends in runoff and low flow were captured by PIHM only. We applied different EURO-CORDEX models with two emission scenarios (Representative Concentration Pathways RCP 4.5, 8.5) and found significant impacts on runoff and evapotranspiration (ET) for the period of 2071–2100. Results from both models suggested reduced runoff and increased ET, while the monthly distribution of runoff was different. We used this catchment study to understand the importance of subsurface processes in projection of hydrologic response to a warming climate.     

Assessing the water resources and ecological state of the Samur river (Eastern Caucasus)

The behavior of long-term changes in water resources of the transboundary Samur river is considered. It is found that there has been taking place a decrease in the river water flow rate over the course of the last four decades. Results from analyzing the ecological releases indicate that they would suffice to maintain the ecosystem of the river near its mouth in an acceptable state.     

Climate change scenarios of heat waves in Central Europe and their uncertainties

The study examines climate change scenarios of Central European heat waves with a focus on related uncertainties in a large ensemble of regional climate model (RCM) simulations from the EURO-CORDEX and ENSEMBLES projects. Historical runs (1970–1999) driven by global climate models (GCMs) are evaluated against the E-OBS gridded data set in the first step. Although the RCMs are found to reproduce the frequency of heat waves quite well, those RCMs with the coarser grid (25 and 50 km) considerably overestimate the frequency of severe heat waves. This deficiency is improved in higher-resolution (12.5 km) EURO-CORDEX RCMs. In the near future (2020–2049), heat waves are projected to be nearly twice as frequent in comparison to the modelled historical period, and the increase is even larger for severe heat waves. Uncertainty originates mainly from the selection of RCMs and GCMs because the increase is similar for all concentration scenarios. For the late twenty-first century (2070–2099), a substantial increase in heat wave frequencies is projected, the magnitude of which depends mainly upon concentration scenario. Three to four heat waves per summer are projected in this period (compared to less than one in the recent climate), and severe heat waves are likely to become a regular phenomenon. This increment is primarily driven by a positive shift of temperature distribution, but changes in its scale and enhanced temporal autocorrelation of temperature also contribute to the projected increase in heat wave frequencies.     

Recognition of shallow karst water resources and cave potentials using thermal infrared image and terrain characteristics in semi-arid regions of Iran

Shallow karst water resources and caves may influence land surface temperatures due to cold transfer property of rocks and evaporation from buried karst. The objective of this research was to develop a method for recognition of karst areas based on evaluating the surface characteristics that manifest itself by low land surface temperature in the satellite images. Investigation of thermal ETM⁺ image of the study region in Iran showed that parts of carbonate rocks that bear karst water are relatively cooler compared to areas with similar terrain conditions. Relational modeling provided useful information on spatial distribution of areas that have the potential to hold karst water resources and/or caves. Further inspection of ASTER images, along with geotechnical, geophysical and geological field surveys verified the approach. Significant correlation was found between electrical resistivity and thermal band values. The method may be used as a primary exploratory tool for shallow karst water explorations in similar areas.     

Performance of ALADIN-Climate/CZ over the area of the Czech Republic in comparison with ENSEMBLES regional climate models

Nowadays Regional Climate Models (RCMs) are increasingly used for downscaling of information from the coarse resolution of global climate models (GCMs) and they represent a more and more popular tool for assessment of future climate changes and their impacts at regional scales. In spite of continual progress of RCMs, their outputs still suffer from many uncertainties and biases. Therefore, it is necessary to assess their ability to simulate observed climate characteristics and uncertainties in their outputs before they are applied in subsequent studies. In the present study, the assessment of RCM performance in simulating climate in the reference period of 1961–1990 over the area of Czech Republic is presented. Furthermore, we focused on the intercomparison of the models’ results, mainly on the comparison of the Czech model ALADIN-Climate/CZ with outputs of other RCMs. Simulation of ALADIN-Climate/CZ in 25-km horizontal resolution, and thirteen RCM simulations from the ENSEMBLES project were assessed. Attention was paid especially to comparison of simulated and observed spatial and temporal variability of several climatic variables. The monthly and seasonal values of surface air temperature, precipitation totals and relative humidity were examined for evaluation of temporal variability and 30-year seasonal and monthly values with respect to spatial variability. Climate model performance was evaluated in several ways, namely by boxplots, maps of variability characteristics, skill scores based on mean square error and Taylor diagrams. Model errors detected by model evaluation depend on many factors (e.g. considered variables and their characteristics, area of analysis, time scale of interest and the method of assessment). On the basis of incorporated performance criteria model ALADIN-Climate/CZ belonged to a better group of RCMs in most cases. However, it was definitely the worst in simulating spring monthly means of air temperature and relative humidity in all seasons.     

Model ALADIN as regional climate model for Central and Eastern Europe

Results obtained with two versions of the Limited Area Model (LAM) ALADIN over differently sized integration domains (large, intermediate and small) in the European area are presented in order to investigate both the general model performance and the influence of domain choice on the quality of obtained results. The aim is also to illustrate the issues related to the strategy of selection of the optimal integration domain. Each of these studies has been performed with two versions of the ALADIN model: the first one is ALADIN-CLIMATE developed at CNRM/Météo-France, the second one is ALADIN-CLIMATE/CZ prepared at the Czech Hydrometeorological Institute (CHMI). This leaves us with total of six experiments forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 reanalysis data. The west Balkan domain covering Bulgaria is used as an evaluation region for investigation of the temporal and spatial properties of simulated precipitation and temperature fields. This region has been selected for its challenging orography making the results obtained here a valuable source for studies leading to further developments in climate modeling. It was found that size of the domain strongly affects the quality of obtained results. We have found that the largest domain reproduces the spatial characteristics of climate (such as bias) very well, but its use results in a poor representation of temporal aspects, which are however captured very well in experiments over both smaller domains. Our findings suggest that there is no optimal choice of domain size, securing the best results for both spatial and temporal evaluation.