Petr Pišoft

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.

On the use of the CM-SAF cloud-data in the Czech Republic

The cloudiness represents one of the basic climate elements. It plays an important role in the global energy and water cycle, dominates the planetary albedo and takes part in many climate feedback processes. Its traditional man-made observation provides usually high-quality data. But it is partly influenced by the observer capabilities to see various cloud layers if lower layers are presented hiding the higher ones. Of course, the quality usually differs between observations made during day and by night. On the other side, manual observation is quite expensive if you want to have uninterrupted long-time series of high density network over large area. Here, you can benefit from satellite data that provide more complex view of the cloudiness above given area and definitely are less expensive. On the other hand, satellite data sustain some problems regarding its limited horizontal resolution that can lead to fact that some details can be hidden or not properly recognized. Some satellite information is provided on daily or larger time frequency only and this can be sometimes problem, too. Nowadays, due to better geometric resolution and more exact calibration, more and more applications for satellite data in climatology can be found and it has also sufficient length to be used in climate analysis as noted by Schulz1. In this paper we focus on cloudiness satellite data usage for the climatology purposes in the Czech Republic area. For this reason, data provided by Satellite Application Facility on Climate Monitoring (CM-SAF) supported by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) have been used. CM-SAF data products are distinguished between operational monitoring products and retrospectively produced data sets. We have used products, so far. Studies describing validation of cloud products or cloud detection schemes with synoptic or other kinds of data can be found in literature (e.g. Reuter2, Karlsson3, 4). The basic motivation for this work was to find answer whether it is possible to replace surface cloud observation by satellite measurements and/or in which case or under which circumstances. For this reason, features of operatively available daily products of CFC (Cloud Fractional Cover) and CTY (Cloud Type) were analyzed in the area of the Czech Republic territory.

On the aliasing of the solar cycle in the lower stratospheric tropical temperature

The double-peaked response of the tropical stratospheric temperature profile to the 11-year solar cycle (SC) has been well documented. However, there are concerns about the origin of the lower peak due to potential aliasing with volcanic eruptions or the El Nino Southern Oscillation (ENSO) detected using multiple linear regression (MLR) analysis. We confirm the aliasing using the results of the chemistry-climate model (CCM) SOCOLv3 obtained in the framework of the IGAC/SPAR Chemistry-Climate Model Initiative phase 1 (CCMI-1). We further show that, even without major volcanic eruptions included in transient simulations, the lower stratospheric response exhibits a residual peak when historical sea surface temperatures (SST)/sea ice coverage (SIC) are used. Only the use of climatological SSTs/SICs in addition to background stratospheric aerosols removes volcanic and ENSO signals and results in an almost complete disappearance of the modeled solar signal in the lower stratospheric temperature. We demonstrate that the choice of temporal sub-period considered for the regression analysis has a large impact on the estimated profile signal in the lower stratosphere: at least 45 consecutive years are needed to avoid the large aliasing effect of SC maxima with volcanic eruptions in 1982 and 1991 in historical simulations, reanalyses and observations. The application of volcanic forcing compiled for phase 6 of the Coupled Model Intercomparison Project (CMIP6) in the CCM SOCOLv3 reduces the warming overestimation in the tropical lower stratosphere and the volcanic aliasing of the temperature response to the SC, although it does not eliminate it completely.

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.