Judit Bartholy

Med-CORDEX initiative for Mediterranean climate studies

The Mediterranean is expected to be one of the most prominent and vulnerable climate change “hot spots” of the 21st century, and the physical mechanisms underlying this finding are still not clear. Furthermore complex interactions and feedbacks involving ocean-atmosphere-land-biogeochemical processes play a prominent role in modulating the climate and environment of the Mediterranean region on a range of spatial and temporal scales. Therefore it is critical to provide robust climate change information for use in Vulnerability/Impact/Adaptation assessment studies considering the Mediterranean as a fully coupled environmental system. The Med-CORDEX initiative aims at coordinating the Mediterranean climate modeling community towards the development of fully coupled regional climate simulations, improving all relevant components of the system, from atmosphere and ocean dynamics to land surface, hydrology and biogeochemical processes. The primary goals of Med-CORDEX are to improve understanding of past climate variability and trends, and to provide more accurate and reliable future projections, assessing in a quantitative and robust way the added value of using high resolution and coupled regional climate models. The coordination activities and the scientific outcomes of Med-CORDEX can produce an important framework to foster the development of regional earth system models in several key regions worldwide.

Validation of a high-resolution version of the regional climate model RegCM3 over the Carpathian basin

Abstract This paper presents a validation study for a high-resolution version of the Regional Climate Model version 3 (RegCM3) over the Carpathian basin and its surroundings. The horizontal grid spacing of the model is 10 km—the highest reached by RegCM3. The ability of the model to capture temporal and spatial variability of temperature and precipitation over the region of interest is evaluated using metrics spanning a wide range of temporal (daily to climatology) and spatial (inner domain average to local) scales against different observational datasets. The simulated period is 1961–90. RegCM3 shows small temperature biases but a general overestimation of precipitation, especially in winter; although, this overestimate may be artificially enhanced by uncertainties in observations. The precipitation bias over the Hungarian territory, the authors’ main area of interest, is mostly less than 20%. The model captures well the observed late twentieth-century decadal-to-interannual and interseasonal variability...

Analysis of regional climate change modelling experiments for the Carpathian Basin

In the last decade, Regional Climate Models (RCMs) nested in Global Climate Models (GCMs) have become essential tools to make climate projections with fine spatial resolution. In this paper, control runs of the RCMs RegCM and PRECIS are discussed and compared for the Central/Eastern European region. Both RCMs are three-dimensional, sigma-coordinate, primitive equation models, for the control experiments (1961-1990), they use initial and lateral boundary conditions from the European Centre for Medium-range Weather Forecast (ECMWF) reanalysis data sets (ERA-40). For the validation, monthly data sets of the Climatic Research Unit (CRU) of the University of East Anglia are used. According to the results, the model RegCM generally underestimates the temperature, while the model PRECIS overestimates it. The precipitation is generally overestimated by the RegCM simulations, and underestimated by the PRECIS simulations. In the case of PRECIS, a model experiment for the Central/Eastern European region for the 2071-2100 period is completed using the HadCM3 GCM outputs (A2 scenario) as boundary conditions. The results suggest that the significant temperature increase expected in the Carpathian Basin may considerably exceed the global warming rate. The climate of this region is expected to become wetter in winter and drier in the other seasons.

Evaluation of meteorological factors on sudden cardiovascular death

Climatic and seasonal triggering factors have received an increasing attention among risk factors of sudden cardiac death. The relationship between cold weather conditions and ischemic heart disease death is well established. In this study, there were 7450 (4967 males, 2483 females) cardiovascular death cases medico-legally autopsied between 1995 and 2004. In most of the cases (76%) cardiac death occurred at the scene, and 17% had acute ischemic heart disease. In order to examine the relationship between daily maximum, minimum and mean temperature, air humidity, air pressure, wind speed, global radiation and the daily numbers of death cases, statistical analysis were accomplished using correlation coefficients, and Box-Whisker-plot diagrams. A significant negative correlation was detected between daily mean temperature and cardiovascular mortality. A remarkable seasonal variation was found. Cold and dry weather may be an important risk factor in bringing on the onset of sudden cardiac death.

Relationship between suicidal cases and meteorological conditions

Meteorological factors are well known to modulate human health status and the rate of death cases. The suicidal rate might have been influenced by climatic and seasonal triggering factors. In this study 4918 suicidal cases (3099 male, 1819 female) in Budapest were investigated in connection with climatic data, as daily maximum, minimum temperature, and air humidity. The most frequent methods of suicide were intoxication, hanging and jumping. A mild seasonal variation was found, however, the rate of suicidal death was influenced by warm temperatures. Higher frequency of suicidal deaths was detected in warm weather with low relative humidity, which implies dominantly dry anticyclonic meteorological conditions. Our results suggest that the medico-legal investigation may help specific suicide prevention programme regarding to the climate change and meteorological conditions as potential risk factors of suicidal cases.

Fuzzy rule-based prediction of monthly precipitation

Abstract Monthly precipitation in Hungary is modeled using the Hess-Brezowsky atmospheric circulation pattern types and an ENSO index as forcing functions or inputs. The weakness of the statistical dependence between these individual inputs and precipitation prevents the use of a multivariate regression analysis for reproducing the probability distribution function of observed precipitation. In order to utilize the existing relationship between forcing functions and precipitation a fuzzy rule-based modeling technique is used. The first part of the observed input and precipitation data is used as the learning set to identify the fuzzy rules. Then, the second part of the data is used to validate the rules by comparing the frequency distributions of precipitation calculated respectively with the fuzzy rules and observed data. Example results are presented for two different climatic regions of Hungary. One of them represents a wetter climate while the other refers to the drier conditions of the Hungarian Plains. The fuzzy rule-based model reproduces the empirical frequency distributions in every season. However, as expected, the statistical prediction is better in winter, spring and fall than in the summer. The potential of the approach is important in climate change studies when the fuzzy rules obtained as described above can be used with input data stemming from GCM to predict regional/local precipitation reflecting GCM scenarios.

Effect of Climate Change on Regional Precipitation in Lake Balaton Watershed

SummaryA methodology is developed and applied to the area of Lake Balaton and its drainage basin, a region of Western Hungary, to estimate the space-time distribution of daily precipitation under climate change. Lake Balaton is the largest lake in Central and Western Europe; it has a central location in the country and its drainage basin covers about the 20% of Hungary (together with the Sió Canal). The methodology is based on an analysis of the semi-Markovian properties of atmospheric macrocirculation pattern types (MCP), and a stochastic linkage between daily (here 700 hPa) MCP types and daily precipitation events. Historical data and General Circulation Model (GCM) output of daily MCP corresponding to 1 · CO2 and 2 · CO2 scenarios are considered in this study. Time series of both local and areal precipitation corresponding for both scenarios are simulated and their statistical properties are compared. For the temperate continental climate of Western Hungary a slightly variable spatial response to climate change is obtained. Under 2 · CO2 conditions most of the local and the areal average precipitation suggests, a somewhat dryer precipitation regime in Western Hungary. The sensitivity of the results to the GCM utilized should be considered.

Present state of wind energy utilisation in Hungary: policy, wind climate, and modelling studies

Earlier renewable energy studies suggested that Hungary does not have considerable extractable wind power due to the low mean annual wind speed. Recent technological developments with the remarkable increase of tower height and rotor diameter of wind power stations made it possible to reevaluate wind energy consumption in continental regions such as the Carpathian Basin. This paper presents the policy changes of the Hungarian government concerning the joining of the country to the European Union planned in 2004. In order to support governmental efforts on renewable energy consumption a research project started on mapping potential wind resources of the country. It was essential to measure and analyse the flows in the lower 100-150 m of the boundary layer through vertical profile estimations. Then, serving the optimal siting the WAsP model has been applied to extrapolate the measured data at different regions of Hungary.

Bridging the gap between climate models and impact studies: the FORESEE Database

Studies on climate change impacts are essential for identifying vulnerabilities and developing adaptation options. However, such studies depend crucially on the availability of reliable climate data. In this study, we introduce the climatological database called FORESEE (Open Database for Climate Change Related Impact Studies in Central Europe), which was developed to support the research of and adaptation to climate change in Central and Eastern Europe: the region where knowledge of possible climate change effects is inadequate. A questionnaire‐based survey was used to specify database structure and content. FORESEE contains the seamless combination of gridded daily observation‐based data (1951–2013) built on the E‐OBS and CRU TS datasets, and a collection of climate projections (2014–2100). The future climate is represented by bias‐corrected meteorological data from 10 regional climate models (RCMs), driven by the A1B emission scenario. These latter data were developed within the frame of the ENSEMBLES FP6 project. Although FORESEE only covers a limited area of Central and Eastern Europe, the methodology of database development, the applied bias correction techniques, and the data dissemination method, can serve as a blueprint for similar initiatives.

CECILIA Regional Climate Simulations for Future Climate: Analysis of Climate Change Signal

Regional climate models (RCMs) are important tools used for downscaling climate simulations from global scale models. In project CECILIA, two RCMs were used to provide climate change information for regions of Central and Eastern Europe. Models RegCM and ALADIN-Climate were employed in downscaling global simulations from ECHAM5 and ARPEGE-CLIMAT under IPCC A1B emission scenario in periods 2021–2050 and 2071–2100. Climate change signal present in these simulations is consistent with respective driving data, showing similar large-scale features: warming between 0 and 3°C in the first period and 2 and 5°C in the second period with the least warming in northwestern part of the domain increasing in the southeastern direction and small precipitation changes within range of