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.     

Interpolation of climate variables and temperature modeling

Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4°?×?4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables.     

The climate tourism potential of Alpine destinations using the example of Sonnblick, Rauris and Salzburg

The climate tourism potential of a region can be described by methods used in human biometeorology and applied climatology. Frequency analyses based on complex thermal bioclimatic indices (e.g. physiologically equivalent temperature) and diagrams of precipitation patterns based on thresholds offer new approaches of visualisation. An integral approach for tourism climatologic analyses is provided by the climate?Ctourism/transfer?Cinformation?Cscheme that also bases on frequency distributions of relevant factors and parameters which are important for a destination. The knowledge about the vertical variability of tourism climatologic factors is of high importance because of the several kinds of tourism activities affected by weather. The same holds for a quantification of extreme events like heat waves because of their possible effects on health and recreation over a year's course. The results show that the vertical gradient of bioclimatic and tourism-related parameters can be of value when developing strategies of adaption to climate change.     

Some evidence on European monsoon existence

Large-scale European atmospheric circulation induced by temperature differences between the continent and the North Atlantic Ocean causes thermodynamic and climatic conditions that initiate a European monsoon. In Eastern Europe, the rainy season occurs in early summer, and the dry season occurs in winter. In Western Europe, the rainy season is in the early winter and the dry season is in the spring. This precipitation trend, as well as other climatic features, suggests the existence of a European monsoon.     

Assessment of Indian summer monsoon simulation by Community Atmosphere Model (CAM3)

Seasonal prediction of Indian Summer Monsoon (ISM) has been attempted for the current year 2011 using Community Atmosphere Model (CAM) developed at the National Centre for Atmospheric Research (NCAR). First, 30?years of model climatology starting from 1981 to 2010 has been generated to capture the variability of ISM over the Indian region using 30 seasonal simulations. The simulated model climatology has been validated with different sets of observed climatology, and it was observed that the simulated climatological rainfall is affected by model bias. Subsequently, a bias correction procedure using the Tropical Rainfall Measuring Mission (TRMM) 3B43 rainfall has been proposed. The bias-corrected rainfall climatology shows both spatial and temporal variability of ISM satisfactorily. Further, four sets of 10-member ensemble simulations of ISM 2009 and 2010 have been performed in hindcast mode using observed sea surface temperature (SST) and persistence of April SST anomaly, and it has been found that the bias-corrected model rainfall captures the seasonal variability of ISM reasonably well with some discrepancies in these two contrasting monsoon years. With this positive background, the seasonal prediction of ISM 2011 has been carried out in forecast mode with the assumption of persistence of May SST anomaly from June through September 2011. The model assessment shows an 11% deficiency in All-India Rainfall (AIR) of ISM 2011. In particular, the monthly accumulated rains are predicted to be 101% (17.6?cm), 86% (24.3?cm), 83% (21.0?cm) and 95% (15.5?cm) of normal AIR for the months of June, July, August and September, respectively.     

Estimating reliability and resolution of probability forecasts through decomposition of the empirical score

Proper scoring rules provide a useful means to evaluate probabilistic forecasts. Independent from scoring rules, it has been argued that reliability and resolution are desirable forecast attributes. The mathematical expectation value of the score allows for a decomposition into reliability and resolution related terms, demonstrating a relationship between scoring rules and reliability/resolution. A similar decomposition holds for the empirical (i.e. sample average) score over an archive of forecast–observation pairs. This empirical decomposition though provides a too optimistic estimate of the potential score (i.e. the optimum score which could be obtained through recalibration), showing that a forecast assessment based solely on the empirical resolution and reliability terms will be misleading. The differences between the theoretical and empirical decomposition are investigated, and specific recommendations are given how to obtain better estimators of reliability and resolution in the case of the Brier and Ignorance scoring rule.     

Analyses of possible changes in intense and extreme wind speeds over northern Europe under climate change scenarios

Dynamical downscaling of ECHAM5 using HIRHAM5 and RCA3 for a northern European domain focused on Scandinavia indicates sustained extreme wind speeds with long recurrence intervals (50?years) and intense winds are not likely to evolve out of the historical envelope of variability until the end of C21st. Even then, significant changes are indicated only in the SW of the domain and across the central Baltic Sea where there is some evidence for relatively small magnitude increases in the 50?year return period wind speed (of up to 15%). There are marked differences in results based on the two Regional Climate Models. Additionally, internal (inherent) variability and initial conditions exert a strong impact on projected wind climates throughout the twenty-first century. Simulations of wind gusts by one of the RCMs (RCA3) indicate some evidence for increased magnitudes (of up to +10%) in the southwest of the domain and across the central Baltic Sea by the end of the current century. As in prior downscaling of ECHAM4, dynamical downscaling of ECHAM5 indicates a tendency towards increased energy density and thus wind power generation potential over the course of the C21st. However, caution should be used in interpreting this inference given the high degree of wind climate projection spread that derives from the specific AOGCM and RCM used in the downscaling.     

Connection of the stratospheric QBO with global atmospheric general circulation and tropical SST. Part II: interdecadal variations

The interdecadal variation of the association of the stratospheric quasi-biennial oscillation (QBO) with tropical sea surface temperature (SST) anomalies (SSTA) and with the general circulation in the troposphere and lower stratosphere is examined using the ERA40 and NCEP/NCAR reanalyses, as well as other observation-based analyses. It is found that the relationship between the QBO and tropical SSTA changed once around 1978–1980, and again in 1993–1995. During 1966–1974, negative correlation between the QBO and NINO3.4 indices reached its maximum when the NINO3.4 index lagged the QBO by less than 6?months. Correspondingly, the positive correlations were observed when the NINO3.4 index led the QBO by about 11–13?months or lagged by about 12–18?months. However, maximum negative correlations were shifted from the NINO3.4 index lagging the QBO by about 0–6?months during 1966–1974 to about 3–12?months during 1985–1992. During 1975–1979, both the negative and positive correlations were relatively small and the QBO and ENSO were practically unrelated to each other. The phase-based QBO life cycle composites also confirm that, on average, there are two phase (6–7?months) delay in the evolution of the QBO-associated anomalous Walker circulation, tropical SST, atmospheric stability, and troposphere and lower stratosphere temperature anomalies during 1980–1994 in comparison with those in 1957–1978. The interdecadal variation of the association between the QBO and the troposphere variability may be largely due to the characteristic change of El Ni?o-Southern Oscillation. The irregularity of the QBO may play a secondary role in the interdecadal variation of the association.     

A Hierarchical Hybrid SVM Method for Classification of Remotely Sensed Data

The focus of this work is on developing a new hierarchical hybrid Support Vector Machine (SVM) method to address the problems of classification of multi or hyper spectral remotely sensed images and provide a working technique that increases the classification accuracy while lowering the computational cost and complexity of the process. The paper presents issues in analyzing large multi/hyper spectral image data sets for dimensionality reduction, coping with intra pixel spectral variations, and selection of a flexible classifier with robust learning process. Experiments conducted revealed that a computationally cheap algorithm that uses Hamming distance between the pixel vectors of different bands to eliminate redundant bands was quite effective in helping reduce the dimensionality. The paper also presents the concept of extended mathematical morphological profiles for segregating the input pixel vectors into pure or mixed categories which will enable further computational cost reductions. The proposed method’s overall classification accuracy is tested with IRS data sets and the Airborne Visible Infrared Imaging Spectroradiometer Indian Pines hyperspectral benchmark data set and presented.