Weather categorization in climate change research: Preliminary results

Summary The results of an objective weather categorization are presented. The 9 meteorological variables recorded daily during winter seasons of 1961–66 at Prague-Clementinum represent the input dataset. The principal component analysis and a few clustering procedures have been evaluated. 5 component solution and the average linkage clustering method were found optimal. The winter days have been grouped, according to their meteorological character, into 14 clusters. The warm categories represent 20% of the time and the cold categories less than 15% of the days. The mean maps of 1000 hPa and 500 hPa are shown for a few selected categories. Clustering techniques applied to long-time instrumental series can provide a better basis for attempting to detect temperature changes which have taken place over a long time span.     

On the Integrability of Limited-Area Numerical Weather Prediction Model ALADIN over Extended Time Periods

This note summarizes results of the first integration of regional numerical weather prediction model ALADIN in a climate mode. The ALADIN model, developed in an international cooperation led by Météo France, is operationally used for weather prediction. The grid step of the model is 12 km; the integration domain covers a major part of Europe. A one-month-long run has been performed with this model on observed boundary conditions (represented by assimilations by the global model ARPEGE). It is demonstrated that no excessive error is generated and accumulated in the model during the integration; hence the model is integrable for extended time periods and may serve a basis for a development towards a regional climate model.     

A GCM Simulation of Heat Waves, Dry Spells, and Their Relationships to Circulation

Heat waves and dry spells are analyzed (i) at eightstations in south Moravia (Czech Republic), (ii) inthe control ECHAM3 GCM run at the gridpoint closest tothe study area, and (iii) in the ECHAM3 GCM run fordoubled CO₂ concentrations (scenario A) at thesame gridpoint (heat waves only). The GCM outputs arevalidated both against individual station data andareally representative values. In the control run, theheat waves are too long, appear later in the year,peak at higher temperatures and their numbers areunder- (over-) estimated in June and July (in August).The simulated dry spells are too long, and the annualcycle of their occurrence is distorted.Mid-tropospheric circulation, and heat waves and dryspells are linked much less tightly in the controlclimate than in the observed. Since mid-troposphericcirculation is simulated fairly successfully, wesuggest the hypothesis that either the air-masstransformation and local processes are too strong inthe model or the simulated advection is too weak. Inthe scenario A climate, the heat waves become a commonphenomenon: warming of 4.5 °C in summer(difference between scenario A and control climates)induces a five-fold increase in the frequency oftropical days and an immense enhancement of extremityof heat waves. The results of the study underline theneed for (i) a proper validation of the GCM outputbefore a climate impact study is conducted and (ii)translation of large-scale information from GCMs intolocal scales using downscaling and stochasticmodelling techniques in order to reduce GCMs' biases.     

Valleys and river benches from the viewpoint of morphotectonics

The geomorphological concepts on the formation of valleys and river benches applied until now have been based upon the viewpoints of dynamic and climatic geomorphology. This exogenous interpretation should be abandoned and an endogenous concept accepted, which is to say that rupturological views should serve as the base and exogenous processes considered a subsidiary.     

Simulation of Surface Air Temperature by GCMs,Statistical Downscaling and Weather Generator: Higher-Order Statistical Moments

The third and fourth statistical moments, that is, skewness and kurtosis, are compared for daily maximum temperature in summer and daily minimum temperature in winter between observations, outputs of two global climate models, four versions of statistical downscaling, and weather generator. The comparison is performed at six stations in central Europe. None of the simulation models can be considered as superior to the others. Causes of a good correspondence with and differences from observations are identified e.g. in the treatment of physics in the models, imperfections in physical parameterizations, or a linear transfer of properties from predictors onto predictands in statistical downscaling.