Jiří Mikšovský

Performance of ENSEMBLES regional climate models over Central Europe using various metrics

We show the evaluation of ENSEMBLES regional climate models (RCMs) driven by reanalysis ERA40 over a region centered at the Czech Republic. Attention is paid especially to the model ALADIN-CLIMATE/CZ, being used as the basis of the new climate change scenarios simulation for the Czech Republic. The validation criteria used here are based on monthly or seasonal mean air temperature and precipitation. We concentrate not only on spatiotemporal mean values but also on temporal standard deviation, inter-annual variability, the mean annual cycle, and the skill of the models to represent the observed spatial patterns of these quantities. Model ALADIN-CLIMATE/CZ performs quite well in comparison to the other RCMs; we find its performance satisfactory for further use for impact studies. However, it is also shown that the results of evaluation of the RCMs’ skill in simulating observed climate strongly depend on the criteria incorporated for the evaluation.

Long-term variability of temperature and precipitation in the Czech Lands: an attribution analysis

Among the key problems associated with the study of climate variability and its evolution are identification of the factors responsible for observed changes and quantification of their effects. Here, correlation and regression analysis are employed to detect the imprints of selected natural forcings (solar and volcanic activity) and anthropogenic influences (amounts of greenhouse gases—GHGs—and atmospheric aerosols), as well as prominent climatic oscillations (Southern Oscillation—SO, North Atlantic Oscillation—NAO, Atlantic Multidecadal Oscillation—AMO) in the Czech annual and monthly temperature and precipitation series for the 1866–2010 period. We show that the long-term evolution of Czech temperature change is dominated by the influence of an increasing concentration of anthropogenic GHGs (explaining most of the observed warming), combined with substantially lower, and generally statistically insignificant, contributions from the sulphate aerosols (mild cooling) and variations in solar activity (mild warming), but with no distinct imprint from major volcanic eruptions. A significant portion of the observed short-term temperature variability can be linked to the influence of NAO. The contributions from SO and AMO are substantially weaker in magnitude. Aside from NAO, no major influence from the explanatory variables was found in the precipitation series. Nonlinear forms of regression were used to test for nonlinear interactions between the predictors and temperature/precipitation; the nonlinearities disclosed were, however, very weak, or not detectable at all. In addition to the outcomes of the attribution analysis for the Czech series, results for European and global land temperatures are also shown and discussed.

CMSAF Radiation Data: New Possibilities for Climatological Applications in the Czech Republic

Satellite Application Facility on Climate Monitoring (CMSAF) data have been studied in the Czech Republic for approximately 10 years. Initially, validation studies were conducted, particularly regarding the incoming solar radiation product and cloudiness data. The main focus of these studies was the surface incoming shortwave (SIS) radiation data. This paper first briefly describes the validation of CMSAF SIS data for the period of 1989–2009. The main focus is on the use and possible applications of CMSAF data. It is shown that maps of SIS radiation in combination with surface data may be useful for solar power plant operators as well as for assessing the climate variability in the Czech Republic during different years and seasons. This demonstrates that the CMSAF data can improve our understanding of local climate, especially in regions lacking traditional surface observations and/or in border regions with a scarcity of stations in the neighboring countryside. Furthermore, data from the recently released SARAH (Surface Solar Radiation Data Set-Heliosat) dataset (1983–2013) are also briefly described and their use for trend computing is demonstrated. Finally, an outlook is given in terms of further possibilities for using CMSAF data in the Czech Republic.

Global Patterns of Nonlinearity in Real and GCM-Simulated Atmospheric Data

We employed selected methods of time series analysis to investigate the spatial and seasonal variations of nonlinearity in the NCEP/NCAR reanalysis data and in the outputs of the global climate model HadCM3 of the Hadley Center. The applied nonlinearity detection techniques were based on a direct comparison of the results of prediction by multiple linear regression and by the method of local linear models, complemented by tests using surrogate data. Series of daily values of relative topography and geopotential height were analyzed. Although some differences of the detected patterns of nonlinearity were found, their basic features seem to be identical for both the reanalysis and the model outputs. Most prominently, the distinct contrast between weak nonlinearity in the equatorial area and stronger nonlinearity in higher latitudes was well reproduced by the HadCM3 model. Nonlinearity tends to be slightly stronger in the model outputs than in the reanalysis data. Nonlinear behavior was generally stronger in the colder part of the year in the mid-latitudes of both hemispheres, for both analyzed datasets.

Similarities within a multi-model ensemble: functional data analysisframework

Despite the abundance of available global and regi onal climate model outputs, their use for evaluatio n of past and future climate changes is often complicated by subs tantial differences between individual simulations, and the resulting uncertainties. In this study, we present a methodol ogy framework for the analysis of multi-model ensem bles based on functional data analysis approach. A set of two met rics that generalize the concept of similarity base d on the behaviour of 15 entire simulated climatic time series, encompassing both past and future periods, is introduced. As fa r as our knowledge, our method is the first to quantitatively assess simila rities between model simulations based on the tempo ral evolution of simulated values. To evaluate mutual distances of t he time series we used two semimetrics based on Euc lidean distances between the simulated trajectories and on differenc es in their first derivatives. Further, we introduc e an innovative way of visualizing climate model similarities based on a n etwork spatialization algorithm. Using the layout g raphs the data are 20 ordered on a 2-dimensional plane which enables an u n mbiguous interpretation of the results. The metho d is demonstrated using two illustrative cases of air temperature ove r th British Isles and precipitation in central Eu rope, simulated by an ensemble of EURO-CORDEX regional climate models and their driving global climate models over the 1971– 2098 period. In addition to the sample results, interpretational aspects of the applied methodology and its possibl e extensions are also discussed. 25

Time Evolution of Initial Errors in Lorenz’s 05 Chaotic Model

Initial errors in weather prediction grow in time and, as they become larger, their growth slows down and then stops at an asymptotic value. Time of reaching this saturation point represents the limit of predictability. This paper studies the asymptotic values and time limits in a chaotic atmospheric model for five initial errors, using ensemble prediction method (models data) as well as error approximation by quadratic and logarithmic hypothesis and their modifications. We show that modified hypotheses approximate the models time limits better, but not without serious disadvantages. We demonstrate how hypotheses can be further improved to achieve better match of time limits with the model. We also show that quadratic hypothesis approximates the models asymptotic value best and that, after improvement, it also approximates the models time limits better for almost all initial errors and time lengths.

Estimations of Initial Errors Growth in Weather Prediction by Low-dimensional Atmospheric Model

Initial errors in weather prediction grow in time. As errors become larger, their growth slows down and then stops at an asymptotic value. Time of reaching this value represents the limit of predictability. Other time limits that measure the error growth are doubling time τ d, and times when the forecast error reaches 95%, 71%, 50%, and 25% of the limit of predictability. This paper studies asymptotic value and time limits in a low-dimensional atmospheric model for five initial errors, using ensemble prediction method as well as error approximation by quadratic and logarithmic hypothesis. We show that quadratic hypothesis approximates the model data better for almost all initial errors and time lengths. We also demonstrate that both hypotheses can be further improved to achieve even better match of the asymptotic value and time limits with the model.