bioclimatology and natural hazards · 2013-07-18 · bioclimatology provides an integrated,...

Bioclimatology and Natural Hazards

Katarına Strelcova · Csaba Matyas · AxelKleidon · Milan Lapin · Frantisek Matejka ·Miroslav Blazenec · Jaroslav Skvarenina ·Jan Holecy (Eds.)

Bioclimatologyand Natural Hazards

EditorsAss. Prof. Dr. Katarına StrelcovaFaculty of ForestryTechnical University ZvolenT.G. Masaryka 24960 53 [email protected]

Dr. Axel KleidonMax-Planck-Institute for BiogeochemistryHans-Knoell-Str. 1007745 [email protected]

Dr. Frantisek MatejkaGeophysical InstituteSlovak Academy of SciencesDubravska cesta 9845 28 [email protected]

Prof. Dr. Jaroslav SkvareninaFaculty of ForestryTechnical University ZvolenT.G. Masaryka 24960 53 [email protected]

Prof. Dr. Csaba MatyasInstitute of Environmental and Earth SciencesFaculty of ForestryWest Hungarian UniversityAdy Endre Str. 5Sopron [email protected]

Prof. Dr. Milan LapinFaculty of Mathematics, Physics & InformaticsComenius University in BratislavaMlynska dolina - F1842 48 [email protected]

Dr. Miroslav BlazenecInstitute of Forest EcologySlovak Academy of SciencesSturova 2960 53 [email protected]

Prof. Dr. Jan HolecyFaculty of ForestryTechnical University ZvolenT.G. Masaryka 24960 53 [email protected]

ISBN: 978-1-4020-8875-9 e-ISBN: 978-1-4020-8876-6

DOI 10.1007/978-1-4020-8876-6

Library of Congress Control Number: 2008936211

c© Springer Science + Business Media B.V. 2009No part of this work may be reproduced, stored in a retrieval system, or transmittedin any form or by any means, electronic, mechanical, photocopying, microfilming, recordingor otherwise, without written permission from the Publisher, with the exceptionof any material supplied specifically for the purpose of being enteredand executed on a computer system, for exclusive use by the purchaser of the work.

Cover images c© 2008 Jupiter Images Corporation

Printed on acid-free paper

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Foreword

Man-made changes in the climate system, consisting of atmosphere, hydrosphere,cryosphere, lithosphere and biosphere represent a most serious challenge not onlyto the planet’s ecosystems and their natural environment but to human civilizations.While the Earth system will undoubtedly adapt, human infrastructures and societalorganization may be questioned if no action is taken in time to buffer unavoidableconsequences related to climate change. As a reaction, scientific disciplines such asbioclimatology, genetics, hydrology, bio-hydrology and eco-physiology are now con-sidered an important part of forestry, agriculture, water management, environmentalprotection, and natural hazards control (e.g., droughts, floods, windstorms, weatherextremes, and wild fires). Bioclimatology provides an integrated, interdisciplinaryframework for dealing with contemporary challenges of natural hazards. Bioclima-tology has also the potential to assess and predict extreme weather events in a verycomplex way.

Bioclimatology will help in better understanding the causes and impacts of naturalhazards and ways how to prevent them. Improved knowledge of natural hazards is avital prerequisite for the implementation of integrated resource management. It pro-vides a useful framework for combating current climate variability and for adaptingto ongoing climate change.

Climate change explains the occurrence of extreme weather in Central and EasternEurope (CEE). Today, the increases in precipitation and soil moisture variability andincreased temperature are the most important single issue that needs to be addressed.The assessment of impacts caused by extreme weather situations such as heat waves,droughts, floods, windstorms, etc., is even more complicated. Atmospheric GeneralCirculation Models (GCMs) are currently used to predict such situations. These mod-els need to be adjusted to provide downscaled outputs using localized scenarios ofselected extreme events. Some of the designed GCM scenarios of extreme weathersituations need to be modified according to analogues.

The shift of vegetation zones is the most investigated and obvious response ofecosystems to climate change. Forecasting the shifts of vegetation zones in responseto weather extremes and ongoing climate change is based on climatically determinedactual distribution models, or so-called “bioclimatic envelopes”. Bioclimatic mod-elling is based on the concept that distributional patterns depend on the physiologicaltolerance of populations to climatic effects besides ecological and life history fac-tors. These limits are genetically determined and thus more or less fixed. Geneticallyregulated plasticity enables the adaptation of individuals and populations to changingenvironments without any change in the inherited genetic resources. Natural selection

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vi Foreword

eliminates the genotypes of low fitness from a population, thus adjusting its genepool towards better adaptation. It is important to realise that the adaptive response ofecosystems to environmental stress is ultimately regulated by genetics and that bio-climatic modelling has to consider genetically set adaptation mechanisms in plants asimportant parts of ecosystems.

This book presents a carefully edited and reviewed selection of papers from theInternational Scientific Conference on Bioclimatology and Natural Hazards held inSlovakia at the Polana Biosphere Reserve on September 17–20, 2007. There 250participants from the 14 different countries of Europe discussed recent research onthe interactions between meteorological, climatological, hydrological and biologi-cal processes in the atmosphere and terrestrial environment. All contributing authorscome from renowned scientific research institutions and universities in Europe andspecialise in issues of climate change, soil-plant-atmosphere interactions, hydrologiccycle, ecosystems, biosphere, and natural hazards. From the total of 215 conferencecontributions, the 25 most important issues have been selected for this book to high-light a spectrum of topics associated with climate change and weather extremes andtheir impact on different sectors of the national economy.

Most of the presented papers point out that the damage caused by the occurrenceof extreme climate events and its impact on ecosystems seems to have substantiallyincreased over the past decades. Some of these climate extremes can induce disas-trous effects. For instance, drought and windstorms can act as promoters of windthrows and can result in increased population sizes of different kinds of insects. Thisin turn can have effects on landscape wild fire occurrence and enhance the vulnera-bility of ecosystems and their resilience. The vulnerability and the impacts of disas-ter on ecosystems and society are influenced by many factors. The combination ofmethods and knowledge from various academic disciplines provide efficient set oftools and procedures to reduce the vulnerability of ecosystems by strengthening theirresilience. The contributions reflect the diversity and the interdisciplinary character ofthe research concerning the occurrence of natural hazards. Some contributions reportresults of research in the fields of severe storms, heavy precipitation and floods, soilerosion and degradation resulting from the destruction of forest by wild fire as wellas results of modeling the impacts of natural hazards on tree growth.

The editors gratefully acknowledge the enthusiastic support and constructive sug-gestions made by many colleagues and friends. We express our sincere thanks to allreviewers of the manuscript.

Katarına StrelcovaCsaba MatyasAxel KleidonMilan Lapin

Frantisek MatejkaMiroslav Blazenec

Jaroslav SkvareninaJan Holecy

Contents

Part I EXTREME EVENTS, RISKS AND CLIMATE VARIABILITY

What Climate Can We Expect in Central/Eastern Europe by 2071–2100? . . . 3J. Bartholy, R. Pongracz, Gy. Gelybo and A. Kern

Detected and Expected Trends of Extreme Climate Indices for theCarpathian Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15R. Pongracz, J. Bartholy, Gy. Gelybo and P. Szabo

Precipitation Trend Analysis for Central Eastern Germany 1851–2006 . . . . . 29S. Hansel, S. Petzold and J. Matschullat

Some Facts on Extreme Weather Events Analysis in Slovakia . . . . . . . . . . . . . . 39M. Lapin, I. Damborska, P. Fasko, L. Gaal and M. Melo

Wind Risk Assessment in Urban Environments: The Case of Falling TreesDuring Windstorm Events in Lisbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55A. Lopes, S. Oliveira, M. Fragoso, J.A. Andrade and P. Pedro

Ozone Air Pollution in Extreme Weather Situation – Environmental Riskin Mountain Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75S. Bicarova and P. Fleischer

Part II DROUGHT, FLOODS AND ECOSYSTEM RESPONSES

Physiological Drought – How to Quantify it? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89V. Novak

Occurrence of Dry and Wet Periods in Altitudinal Vegetation Stages ofWest Carpathians in Slovakia: Time-Series Analysis 1951–2005 . . . . . . 97J. Skvarenina, J. Tomlain, J. Hrvol′ and J. Skvareninova

Thermodynamics, Irreversibility, and Optimality in Land SurfaceHydrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107A. Kleidon, S. Schymanski and M. Stieglitz

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Winter Snow Supply in Small Mountain Watershed as a Potential Hazardof Spring Flood Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119M. Hrıbik, A. Majlingova, J. Skvarenina and D. Kyselova

Mapping of Gumbel Extreme Value Distribution Parameters forEstimation of Design Precipitation Totals at Ungauged Sites . . . . . . . . . . 129S. Kohnova, J. Parajka, J. Szolgay and K. Hlavcova

Flood Prevention and Nature Conservation – Interdisciplinary Evaluationof Land Use Scenarios for an Agricultural Landscape . . . . . . . . . . . . . . . 137E. Richert, S. Bianchin, H. Heilmeier, M. Merta and Ch. Seidler

Part III FOREST BIOCLIMATOLOGY, NATURAL HAZARDSAND MODELLING

Risk Assessment of the Tatra Mountains Forest . . . . . . . . . . . . . . . . . . . . . . . . . . 145P. Fleischer, M. Koren, J. Skvarenina and V. Kunca

Modeling Natural Disturbances in Tree Growth Model SIBYLA . . . . . . . . . . . 155M. Fabrika and T. Vaculciak

Insect Pests as Climate Change Driven Disturbances in Forest Ecosystems . . 165T. Hlasny and M. Turcani

Genetic Background of Response of Trees to Aridification at the XericForest Limit and Consequences for Bioclimatic Modelling . . . . . . . . . . . 179Cs. Matyas, L. Nagy and E. Ujvari Jarmay

Seasonal Changes in Transpiration and Soil Water Content in a SprucePrimeval Forest During a Dry Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197F. Matejka, K. Strelcova, T. Hurtalova, E. Gomoryova and L′. Ditmarova

Assessment of Water Deficiency in Forest Ecosystems: Can a Simple Modelof Forest Water Balance Produce Reliable Results? . . . . . . . . . . . . . . . . . 207P. Balaz, K. Strelcova, M. Blazenec, R. Pokorny and Z. Klimankova

Forest Fire Vulnerability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219J. Tucek and A. Majlingova

The Paradigm of Risk and Measuring the Vulnerability of Forestby Natural Hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231J. Holecy

Part IV SOIL AND AGRICULTURE BIOCLIMATOLOGY, NATURALHAZARDS AND RESPONSES

Responses of Soil Microbial Activity and Functional Diversityto Disturbance Events in the Tatra National Park (Slovakia) . . . . . . . . . 251E. Gomoryova, K. Strelcova, J. Skvarenina, J. Bebej and D. Gomory

Contents ix

Capacities of Modelling to Assess Buffer Strip Efficiency to Reduce SoilLoss During Heavy Rainfall Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261M. Kandler, I. Barlund, M. Puustinen and C. Seidler

The Influence of Climate Change on Water Demands for Irrigation ofSpecial Plants and Vegetables in Slovakia . . . . . . . . . . . . . . . . . . . . . . . . . . 271V. Barek, P. Halaj and D. Igaz

Climate Change Impact on Spring Barley and Winter Wheat Yields onDanubian Lowland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283J. Takac and B. Siska

Emissions from Agricultural Soils as Influenced by Changeof Environmental Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289J. Horak and B. Siska

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297

Contributors

Jose Alexandre Andrade Geosciences Department, University of Evora, Portugal,[email protected]

Peter Balaz Forest Research Institute, National Forest Centre, T.G. Masaryka 22,960 92 Zvolen, Slovakia, [email protected]

Vilam Barek Department of Landscape Engineering, Faculty of Horticulture andLandscape Engineering, Slovak University of Agriculture, Hospodarska 7, 949 76Nitra, Slovakia, [email protected]

Ilona Barlund University of Kassel, Kassel, Germany, [email protected]

Judith Bartholy Department of Meteorology, Eotvos Lorand University, Pazmanyst. 1/a, H-1117 Budapest, Hungary, [email protected]

Juraj Bebej Technical University in Zvolen, Faculty of Forestry, Zvolen, Slovakia,[email protected]

Sylvi Bianchin Technische Universitat Bergakademie Freiberg, InterdisciplinaryEnvironmental Research Centre, Freiberg, Germany, [email protected]

Svetlana Bicarova Geophysical Institute, Slovak Academy of Sciences, Meteorolog-ical Observatory Stara Lesna, 059 60 Tatranska Lomnica, Slovakia, [email protected]

Miroslav Blazenec Institute of Forest Ecology, Slovak Academy of Sciences,Zvolen, Slovakia, [email protected]

Ingrid Damborska Faculty of Mathematics, Physics and Informatics, ComeniusUniversity, Bratislava, Slovakia, [email protected]

L′ubica Ditmarova Institute of Forest Ecology, Slovak Academy of Sciences,Zvolen, Slovakia, [email protected]

Marek Fabrika Technical University in Zvolen, Faculty of Forestry, T.G. Masaryka24, 960 53 Zvolen, Slovakia, [email protected]

Pavol Fasko Slovak Hydrometeorological Institute, Bratislava, Slovakia,[email protected]

Peter Fleischer Research Station of the Tatra National Park, State Forest of TANAP,059 60 Tatranska Lomnica, Slovakia, [email protected]

Marcelo Fragoso Centre of Geographical Studies, University of Lisbon,[email protected]

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xii Contributors

Ladislav Gaal Faculty of Civil Engineering, Slovak University of Technology,Bratislava, Slovakia

Gyorgyi Gelybo Department of Meteorology, Eotvos Lorand University, Budapest,Hungary, [email protected]

Dusan Gomory Technical University in Zvolen, Faculty of Forestry, Zvolen,Slovakia, [email protected]

Erika Gomoryova Technical University in Zvolen, Faculty of Forestry, T.G.Masaryka 24, 960 53 Zvolen, Slovakia, [email protected]

Peter Halaj Department of Landscape Engineering, Faculty of Horticultureand Landscape Engineering, Slovak University of Agriculture, Nitra, Slovakia,[email protected]

Stephanie Hansel Technical University, Bergakademie Freiberg, InterdisciplinaryEnvironmental Research Centre, Brennhausgasse, 14, D-09599 Freiberg, Germany,[email protected]

Hermann Heilmeier Technische Universitat Bergakademie Freiberg,Interdisciplinary Environmental Research Centre, Freiberg, Germany,[email protected]

Tomas Hlasny Forest Research Institute, National Forest Centre, T.G. Masaryka 22,960 92 Zvolen, Slovakia; Czech University of Life Sciences, Faculty of Forestry andWood Sciences, Department of Forest Protection and Game Management, Kamycka1176, Prague 6 – Suchdol 165 21, Czech Republic

Kamila Hlavcova Department of Land and Water Resources Management, Facultyof Civil Engineering, Slovak University of Technology Bratislava, Bratislava,Slovakia, [email protected]

Jan Holecy Technical University in Zvolen, Faculty of Forestry, T.G. Masaryka 24,960 53 Zvolen, Slovakia, [email protected]

Jan Horak Department of Biometeorology and Hydrology, Slovak AgriculturalUniversity, Hospodarska 7, 949 01 Nitra, Slovakia, [email protected]

Matus Hrıbik Technical University in Zvolen, Faculty of Ecology and Environmen-tal Sciences, T.G. Masaryka 24, 960 53 Zvolen, Slovakia, [email protected]

Jan Hrvol′ Faculty of Mathematics, Physics and Informatics, Comenius University,Bratislava, Slovakia, [email protected]

Tat′jana Hurtalova Geophysical Institute, Slovak Academy of Sciences, Bratislava,Slovakia, [email protected]

Dusan Igaz Department of Biometeorology and Hydrology, Faculty of Horticultureand Landscape Engineering, Slovak University of Agriculture, Nitra, Slovakia,[email protected]

Matthias Kandler International Graduate School Zittau, Markt 23, D-02763 Zittau,Germany, [email protected]

Aniko Kern Department of Meteorology, Eotvos Lorand University, Budapest,Hungary, [email protected]

Contributors xiii

Axel Kleidon Biospheric Theory and Modelling Group, Max-Planck-Institut furBiogeochemie, Jena, Germany, [email protected]

Zdenka Klimankova Laboratory of Plants Ecological Physiology, Institute ofSystems Biology and Ecology, Academy of Sciences of the Czech Republic, Brno,Czech Republic

Silvia Kohnova Department of Land and Water Resources Management, Facultyof Civil Engineering, Slovak University of Technology, Radlinskeho 11, 813 68Bratislava, Slovakia, [email protected]

Milan Koren Research Station of the Tatra Nationl Park, State Forest of TANAP,Tatranska Lomnica, Slovakia

Vladimır Kunca Technical University in Zvolen, Faculty of Ecology and Environ-mental Sciences, Zvolen, Slovakia, [email protected]

Daniela Kyselova Slovak Hydrometeorological Institute, Regional Center BanskaBystrica, Slovakia, [email protected]

Milan Lapin Faculty of Mathematics, Physics and Informatics, Comenius Univer-sity, Mlynska dolina - F1, 842 15 Bratislava, Slovakia, [email protected]

Antonio Lopes Centre of Geographical Studies, University of Lisbon, 1600-214Lisbon, Portugal, [email protected]

Andrea Majlingova Technical University in Zvolen, Faculty of Forestry, Zvolen,Slovakia, [email protected]

Frantisek Matejka Geophysical Institute, Slovak Academy of Sciences, Dubravskacesta 9, 845 28 Bratislava, Slovakia, [email protected]

Jorg Matschullat Technische Universitat Bergakademie Freiberg, InterdisciplinaryEnvironmental Research Centre, Freiberg, Germany, [email protected]

Csaba Matyas Institute of Environmental Sciences, Faculty of Forestry, WestHungarian University, Ady Endre Str. 5, Sopron 9400, Hungary, [email protected]

Marian Melo Faculty of Mathematics, Physics and Informatics, Comenius Univer-sity, Bratislava, Slovakia, [email protected]

Mariusz Merta International Graduate School Zittau, Zittau, Germany,[email protected]

Laszlo Nagy Forest Research Institute Experimental Station, Sarvar, Hungary,[email protected]

Viliam Novak Institute of Hydrology, Slovak Academy of Sciences, Racianska 75,831 02 Bratislava, Slovakia, [email protected]

Sandra Oliveira Centre of Geographical Studies, University of Lisbon,[email protected]

Juraj Parajka Institute of Hydraulics, Hydrology and Water Resources Manage-ment, Vienna University of Technology Vienna, Austria, [email protected]

Pedro Pedro Lisbon City Council, RSBL, [email protected]

Sylvia Petzold Technische Universitat Bergakademie Freiberg, InterdisciplinaryEnvironmental Research Centre, Freiberg, Germany, [email protected]

xiv Contributors

Radek Pokorny Laboratory of Plants Ecological Physiology, Institute of SystemsBiology and Ecology, Academy of Sciences of the Czech Republic, Brno, CzechRepublic, [email protected]

Rita Pongracz Department of Meteorology, Eotvos Lorand University, Pazmany st.1/a, H-1117 Budapest, Hungary, [email protected]

Markku Puustinen Finnish Environment Institute, Helsinki, Finland,[email protected]

Elke Richert Technische Universitat Bergakademie Freiberg, InterdisciplinaryEnvironmental Research Centre, Freiberg, Germany, [email protected]

Stan Schymanski Biospheric Theory and Modelling Group, Max-Planck-Institut furBiogeochemie, Jena, Germany, [email protected]

Christina Seidler International Graduate School Zittau, Zittau, Germany,[email protected]

Bernard Siska Department of Biometeorology and Hydrology, Slovak AgriculturalUniversity, Nitra, Slovakia, [email protected]

Jaroslav Skvarenina Technical University in Zvolen, Faculty of Forestry, T.G.Masaryka 24, 960 53 Zvolen, Slovakia, [email protected]

Jana Skvareninova Technical University in Zvolen, Faculty of Ecology andEnvironmental Sciences, Zvolen, Slovakia, [email protected]

Marc Stieglitz Department of Civil and Environmental Engineering, GeorgiaInstitute of Technology, Atlanta, Georgia, USA

Katarına Strelcova Technical University in Zvolen, Faculty of Forestry, Zvolen,Slovakia, [email protected]

Peter Szabo Department of Meteorology, Eotvos Lorand University, Budapest,Hungary, [email protected]

Jan Szolgay Department of Land and Water Resources Management, Faculty ofCivil Engineering, Slovak University of Technology Bratislava, Bratislava, Slovakia,[email protected]

Jozef Takac Soil Science and Conservation Research Institute, Gagarinova 10, 82713 Bratislava, Slovakia, [email protected]

Jan Tomlain Faculty of Mathematics, Physics and Informatics, Comenius Univer-sity, Bratislava, Slovakia, [email protected]

Jan Tucek Technical University in Zvolen, Faculty of Forestry, T.G. Masaryka 24,960 53 Zvolen, Slovakia, [email protected]

Marek Turcani Czech University of Life Sciences, Faculty of Forestry andWood Sciences, Department of Forest Protection and Game Management, Prague6 – Suchdol, Czech Republic, [email protected]

Eva Ujvari-Jarmay Forest Research Institute Experimental Station, Matrafured,Hungary, [email protected]

Tomas Vaculciak Technical University in Zvolen, Faculty of Forestry, Zvolen,Slovakia, [email protected]

Reviewers

Jan Bednar Department of Meteorology and Environmental Protection, Faculty ofMathematics and Physics of Charles University, Prague, Czech Republic

Istvan Bogardi University of Nebraska-Lincoln, USA

Pavel Cudlın Institute of Systems Biology and Ecology, Academy of Sciences ofthe Czech Republic

Wilfried Endlicher Humboldt-Universitat zu Berlin, Institute of Geography, Geog-raphy of Climates and Environmental Climatology, Germany

Jurgen Friedel University of Natural Resources and Applied Life Sciences, Vienna,Austria

Igantavicius Gytautas Vilnius University, Environmental Studies Centre, Latvia

Tove Heidman Danish Institute of Agricultural Sciences, Department of Agroecol-ogy, Research Centre Foulum, Denmark

Jaroslav Holusa The Forestry and Game Management Research Institute, Prague,Czech Republic

Dusan Huska Slovak Agricultural University, Nitra, Slovakia

Paul Jarvis School of GeoSciences, University of Edinburgh, Scotland, UK

Jan Kouba Czech University of Life Sciences Prague, Faculty of Forestry and WoodSciences, Department of Forest Management, Czech Republic

Jan Kysely Institute of Atmospheric Physics, Academy of Sciences of theCzech Republic

Viliam Novak Institute of Hydrology, Slovak Academy of Sciences, Slovakia

Ladislav Paule Technical University in Zvolen, Faculty of Forestry, Slovakia

Edward Pierzgalski Warsaw Agricultural University, Poland

Kalman Rajkai Research Institute for Soil Science and Agricultural Chemistry ofthe Hungarian Academy of Sciences, Hungary

Christian-D. Schonwiese Institut fur Atmosphare und Umwelt (IAU) der Johann-Wolfgang Goethe Universitat Frankfurt am Main, Germany

Miloslav Sır Institute of Hydrodynamics, Academy of Sciences of theCzech Republic

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xvi Reviewers

Miroslav Tesar Institute of Hydrodynamics, Academy of Sciences of theCzech Republic

Jozef Zwolinski Department of Industrial Region Forest Management, ForestResearch Institute, Poland

Part IEXTREME EVENTS, RISKS AND CLIMATE VARIABILITY

What Climate Can We Expect in Central/Eastern Europeby 2071–2100?

J. Bartholy, R. Pongracz, Gy. Gelybo and A. Kern

Keywords Regional climate change · Temperature ·Precipitation · Central/Eastern Europe · Regionalclimate model

Introduction

According to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change (IPCC)Working Group I, published on 2 February 2007(IPCC 2007), the key processes influencing theEuropean climate can be summarized as follows: (i)water vapour transport from low to high latitudes hasincreased; (ii) variation of atmospheric circulationhas changed on interannual as well as longer timescales; (iii) snow cover during winter has reduced in thenortheastern part of the continent; (iv) the soil has driedin summer in the Mediterranean and Central Europeanregions. For instance, the heat wave in summer 2003 inEurope can be considered as a consequence of a longperiod of anticyclonic weather (Fink et al. 2004), whichcoincided with a severe drought in the region (Blacket al. 2004). For Europe, it is likely that the increaseof annual mean temperature will exceed the globalwarming rate in the twenty-first century. The largestincrease is expected in winter in northern Europe(Benestad 2005) and in summer in the Mediterraneanarea. Minimum temperatures in winter are very likelyto increase more than the mean winter temperature innorthern Europe (Hanssen-Bauer et al. 2005), while

J. Bartholy (B)Department of Meteorology, Eotvos Lorand University,Pazmany st. 1/a, H-1117 Budapest, Hungarye-mail: [email protected]

maximum temperature values in summer are likely toincrease more than the mean summer temperature insouthern and Central Europe (Tebaldi et al. 2006). Forprecipitation, the annual sum is very likely to increasein northern Europe (Hanssen-Bauer et al. 2005) anddecrease in the Mediterranean area. Central Europeis located at the boundary of these large regions.Different seasonal trends can therefore be expected,namely, precipitation is likely to increase in winter,while it decreases in summer. In case of summerdrought events, the risk is likely to increase both inCentral Europe and in the Mediterranean area dueto a decrease in the mean summer precipitation andan increase in spring evaporation (Pal et al. 2004;Christensen and Christensen, 2004). As a consequenceof the European warming, the length of the snowseason and the accumulated snow depth are very likelyto decrease over the entire continent (IPCC 2007).

Global climate models (GCMs) are inappropriate todescribe regional climate processes due to their coarsespatial resolution. GCM outputs may therefore be mis-leading to compose regional climate change scenariosfor the twenty-first century (Mearns et al. 2001). Inorder to determine better estimations for regional cli-mate parameters, fine resolution regional climate mod-els (RCMs) can be used. RCMs are limited-area mod-els nested in GCMs, so the initial and the boundaryconditions of RCMs are provided by the GCM outputs(Giorgi 1990). Due to computational constrains the do-main of an RCM does not cover the entire globe, some-times not even a continent. On the other hand, theirhorizontal resolution can be as fine as 5–10 km.

The PRUDENCE (Prediction of Regional scenariosand Uncertainties for Defining EuropeaN Climatechange risks and Effects) project involved 21 Europeanresearch institutes and universities and was completed

K. Strelcova et al. (eds.), Bioclimatology and Natural Hazards, 3DOI 10.1007/978-1-4020-8876-6 1, c© Springer Science+Business Media B.V. 2009

4 J. Bartholy et al.

in 2004 in the context of the European Union 5thframework programme. The primary objectivesof PRUDENCE were to provide high-resolutionclimate change scenarios of 50 km × 50 km forEurope for 2071–2100 using dynamical downscalingmethods with RCMs with the reference periodof 1961–1990 and to explore the uncertainty inthese projections (Christensen 2005). Results of thePRUDENCE project are disseminated widely viaInternet (http://prudence.dmi.dk) and several othermedia, and thus, they support socioeconomic andpolicy-related research studies and decisions.

In the frame of PRUDENCE project, the follow-ing sources of climate uncertainty were studied (Chris-tensen 2005):

1. Sampling uncertainty: Simulated climate is consid-ered as an average over 30 years (2071–2100, witha reference period of 1961–1990).

2. Regional model uncertainty: The RCMs use differ-ent techniques to discretize the differential equa-tions and to represent physical processes on sub-grid scales.

3. Emission uncertainty: The RCM runs used twoIPCC-SRES emission scenarios, namely, the A2and B2. Twenty-two experiments from the PRU-DENCE simulations considered the A2 scenario,while only eleven of them used the B2 scenario.

4. Boundary uncertainty: The RCMs were run withboundary conditions from different GCMs. Most ofthe PRUDENCE simulations used the HadAM3Hmodel as the driving GCM. Only a few of themused the ECHAM4 model or the ARPEGE model(Deque et al. 2005).

Detailed intercomparison and analysis of the resultsof the PRUDENCE project is published in a specialissue of the journal Climatic Change edited by Chris-tensen et al. (May 2007). In this chapter, we focused onour region, and therefore the regional climate changeprojections are summarized for Central/Eastern Europeusing the outputs of all available PRUDENCE simula-tions. The results of the expected temperature changeby the end of the twenty-first century are discussed, aswell as the expected change of precipitation. More de-tailed results are presented for Hungary.

Data

The adaptation of RCMs with 10–25 km horizontalresolution is currently proceeding in the frame of

EU-funded projects: CECILIA (Central and EasternEurope Climate Change Impact and VulnerabilityAssessment) and CLAVIER (Climate Change andVariability: Impact on Central and Eastern Europe).In Hungary, the Department of Meteorology at theEotvos Lorand University (Bartholy et al. 2006a,2006b) and the Hungarian Meteorological Service(Horanyi 2006) play an active and important role inregional climate modelling. Since these EU projectswill end in June 2009, results of the RCM experimentsare expected within 1–3 years. However, impactstudies and end-users need and would like to haveaccess to climate change scenario data much earlier.In order to fulfil this instant demand with preliminaryinformation, outputs of the PRUDENCE simulationsare evaluated and can be offered for Central/EasternEurope. For the A2 scenario outputs of 16 RCMexperiments, while for the B2 scenario, only outputsof 8 RCM simulations are available. Since the projectPRUDENCE used only these two emission scenarios,no other scenario is discussed in this chapter. The A2scenario projects a very heterogeneous world with anemphasis on family values and local traditions, whilethe B2 scenario considers a world with an emphasison local solutions to economic and environmentalsustainability (IPCC 2007). The projected CO2

concentrations may reach 850 ppm (in the A2scenario) and 600 ppm (in the B2 scenario) by the endof the twenty-first century (IPCC 2007), which aremore than double of the pre-industrial concentrationlevel (280 ppm).

Table 1 lists the name of the contributing institutes,the RCMs, the driving GCMs and the availablescenarios we used in the composite maps. Compositemaps of expected temperature and precipitation changecover Central/Eastern Europe (44.75◦–55.25◦N,9.75◦–27.25◦E). The climate projections of PRU-DENCE are available for the end of the twenty-firstcentury (2071–2100) using the reference period of1961–1990.

Temperature Projectionsfor Central/Eastern Europe

Composite maps of the mean expected seasonal tem-perature change are shown for both A2 and B2 sce-narios in Fig. 1. In order to represent the uncertaintyof these composites, standard deviation values of the

What Climate Can We Expect in Central/Eastern Europe by 2071–2100? 5

Table 1 List of RCMs withtheir driving GCMs used inthe composite analysis

Institute RCM Driving GCM Scenario

Danish MeteorologicalInstitute

HIRHAMHIRHAM

HadAM3HECHAM5

A2, B2A2

HIRHAM highresolution

HadAM3H A2

HIRHAM extrahigh resolution

HadAM3H A2

Hadley Centre of the UKMet Office

HadRM3P(ensemble/1)

HadAM3P A2, B2

HadRM3P(ensemble/2)

HadAM3P A2

ETH (EidgenossischeTechnische Hochschule)

CHRM HadAM3H A2

GKSS (Gesellschaft furKernenergieverwertungin Schiffbau undSchiffahrt)

CLMCLM improved

HadAM3HHadAM3H

A2A2

Max Planck Institute REMO HadAM3H A2Swedish Meteorological RCAO HadAM3H A2, B2

and Hydrological Institute RCAO ECHAM4/OPYC B2UCM (Universidad

Complutense Madrid)PROMES HadAM3H A2, B2

International Centre forTheoretical Physics

RegCM HadAM3H A2, B2

Norwegian MeteorologicalInst.

HIRHAM HadAM3H A2

KNMI (KoninklijkNederlandsMeteorologisch Inst.)

RACMO HadAM3H A2

Meteo–France ARPEGE HadCM3 A2, B2ARPEGE ARPEGE/OPA B2

RCM model results are also determined and mappedfor all seasons (Fig. 2). Similar to the global and theEuropean climate change results, larger warming canbe expected for the A2 scenario in Central/Eastern Eu-rope than for the B2 scenario. The largest tempera-ture increase is expected in summer, while the small-est increase in spring. The expected summer warm-ing ranges in Hungary are 4.5–5.1◦C and 3.7–4.2◦Cfor the A2 and B2 scenarios, respectively. In case ofspring, the expected temperature increase in Hungaryis 2.9–3.2◦C (for A2 scenario) and 2.4–2.7◦C (for B2scenario). On the basis of seasonal standard deviationfields (Fig. 2), the largest uncertainty of the expectedtemperature change occurs in summer for both emis-sion scenarios.

Figure 3 summarizes the expected mean seasonalwarming for Hungary in case of A2 and B2 scenar-ios. In general, the expected warming by 2071–2100 ismore than 2.5◦C and less than 4.8◦C for all seasons andfor both scenarios. The expected temperature changesfor the A2 scenario are larger than that for the B2scenario. The smallest difference is expected in spring(0.6◦C), while the largest difference is expected in win-

ter (1◦C). The largest temperature increase is expectedin summer, 4.8◦C (A2) and 4.0◦C (B2). The smallesttemperature increase is expected in spring (3.1◦C and2.5◦C in case of A2 and B2 scenarios, respectively).

In order to evaluate the model performance, thetemperature bias is determined for each RCM outputfield using the GCM-driven simulations for the refer-ence period (1961–1990) and the CRU (Climate Re-search Unit of the University of East Anglia) database(New et al. 1999). In general, the RCM simulationsslightly overestimate the temperature in most of theCentral/Eastern European regions; however, small un-derestimation can be seen in the southwestern part ofthe selected domain, at the mountainous areas of theAlps. The temperature bias does not exceed 1.5◦C.

Similar to the mean temperature, expected seasonalwarming of daily maximum and minimum tempera-tures is summarized for Hungary in Fig. 4 The largestwarming is expected in summer for both scenarios: incase of maximum temperature the interval of the ex-pected increase is 4.9–5.3◦C (A2) and 4.0–4.4◦C (B2),while in case of minimum temperature these intervalsare 4.2–4.8◦C (A2) and 3.5–4.0◦C (B2). The expected


Fig. 1 Seasonal temperaturechange (◦C) expected by2071–2100 forCentral/Eastern Europe usingthe outputs of 16 and 8 RCMsimulations, A2 (left panel)and B2 (right panel)scenarios


Fig. 2 Standard deviation ofseasonal temperature change(◦C) expected by 2071–2100for Central/Eastern Europeusing the outputs of 16 and 8RCM simulations, A2 (leftpanel) and B2 (right panel)scenarios


Fig. 3 Expected seasonal increase of mean temperature (◦C)for Hungary (temperature values of the reference period, 1961–1990, represent the seasonal mean temperature in Budapest)

Fig. 4 Expected seasonal increase of daily minimum and maxi-mum temperatures (◦C) for Hungary (temperature values of thereference period, 1961–1990, represent the seasonal mean tem-perature in Budapest)

increase of mean temperature in summer is between theexpected warming of the maximum temperature andthat of the minimum temperature. Summarizing the ex-pected mean seasonal increase of daily extreme tem-perature for Hungary, the entire interval of the expectedwarming includes values from 2.4 to 5.1◦C, which is0.4◦C larger than in case of the mean temperature. Thelargest temperature increases are expected in summerfor both scenarios, which is not surprising when theabove results are considered. The expected increase ofmaximum temperature is generally not smaller than theexpected increase of minimum temperature, winter be-ing the only exception.

In order to provide a better overview on the spatialstructure of expected temperature changes (both meanand extremes) for Central/Eastern Europe by the end ofthe twenty-first century, Table 2 summarizes the spatialdifferences of warming for summer and winter. In sum-mer, zonal structure of warming (i.e. increasing valuesfrom north to south) can be detected for all parame-ters. The zonal temperature change difference valuesfor the entire region exceed 1.4◦C, and in general, theyare larger for the A2 scenario than for the B2 sce-

nario. The largest south–north difference is expectedfor maximum temperature, the A2 scenario (1.9◦C).In winter, generally a meridional structure of warmingis expected (i.e. increasing values from west to east).Similar to the summer values, the winter temperaturechange difference values are larger for the A2 than forthe B2 scenario. The meridional difference values ex-ceed 1.0◦C (except maximum temperature, in case ofthe B2 scenario). The largest difference value is ex-pected for the minimum temperature, the A2 scenario(1.8◦C). In spring and autumn, the meridional temper-ature change difference values are smaller and do notexceed 0.8◦C and 0.4◦C, respectively.

Precipitation Projections forCentral/Eastern Europe

Similar to the temperature projections, compositemaps of mean expected seasonal precipitation change(Fig. 5) and standard deviations (Fig. 6) are mapped forboth A2 and B2 scenarios for the 2071–2100 period.The annual precipitation sum is not expected to changesignificantly in this region (Bartholy et al. 2003),but this does not hold for seasonal precipitation.According to the results shown in Fig. 5, summerprecipitation is very likely to decrease (also, slightdecrease of autumn precipitation is expected), whilewinter precipitation is likely to increase considerably(somewhat less increase is expected in spring).

In summer, the projected precipitation decrease ex-ceeds 25% (A2) and 10% (B2). The largest decrease(exceeding 35% and 10% in case of A2 and B2 scenar-ios, respectively) is expected at the southern part of theselected region. In winter, the expected precipitationincrease exceeds 20% (A2) and 15% (B2). The largestprecipitation increase is expected in the Transdanubiansubregion located in Hungary (more than 35% and 20%in case of A2 and B2 scenarios, respectively). Fur-thermore, large increase can be expected in the north-eastern part of the selected region. As the compositemaps of Fig. 5 suggest, the precipitation is expectedto increase in spring in most of the selected region,except the southernmost subregions. This increase issomewhat less than the expected increase for winter.Note that in the eastern part of Central/Eastern Europe,the expected seasonal change is larger in case of theB2 than in the A2 scenario (about 10–18% and 5–8%, respectively). The expected precipitation change


Table 2 Spatial differences of expected summer and wintertemperature change for Central/Eastern Europe for 2071–2100(zonal difference is positive in case of increasing change from

north to south, while the meridional difference is positive in caseof increasing change from west to east)

Scenario Summer (J-J-A) Winter (D-J-F)

Mean temperature A2 Zonal: +1.6◦C Meridional: +1.4◦CB2 Zonal: +1.4◦C Meridional: +1.0◦C

Maximum temperature A2 Zonal: +1.9◦C Meridional: +1.4◦CB2 Zonal: +1.6◦C Meridional: +0.8◦C

Minimum temperature A2 Zonal: +1.4◦C Meridional: +1.8◦CB2 Zonal: +1.4◦C Meridional: +1.4◦C

in autumn is between –5% and +5%, which is notsignificant for the B2 scenario. For the A2 scenario,the expected precipitation decrease exceeds 10% in theeastern part of the selected region. The expected de-crease is smaller in autumn than in summer. Based onthe seasonal standard deviation values, the largest un-certainty of precipitation change is expected in sum-mer, especially in case of the A2 scenario when thestandard deviation of the RCM results exceeds 20% inthe Carpathian basin.

Figure 7 summarizes the expected seasonal changeof precipitation for Hungary for the A2 and the B2scenarios. Black and grey arrows indicate increase anddecrease of precipitation, respectively. According tothe reference period (1961–1990) the wettest seasonwas summer, then, less precipitation was observed inspring, even less in autumn and the driest season waswinter. If the projections are realized then the annualdistribution of precipitation will be totally restructured,namely, the wettest seasons will be winter and spring(in this order) for both scenarios. The driest season willbe the summer in case of the A2 scenario and autumnin case of the B2 scenario. On the basis of the projec-tions, the annual difference between seasonal precipi-tation amounts is expected to decrease significantly (byhalf) for the B2 scenario (which implies more similarseasonal amounts), while it is not expected to changefor the A2 scenario (nevertheless, the wettest and thedriest seasons are completely changed).

In order to evaluate the model performance, the pre-cipitation bias is determined for all the RCM outputfields using the GCM-driven simulations for the refer-ence period (1961–1990) and the CRU database (Newet al. 1999). The RCM simulations overestimate theprecipitation in most of the Central/Eastern Europeanregion; however, slight underestimation can be seen inthe southwestern part of the region. In Hungary, thebias does not exceed 15% in absolute values (Bartholyet al. 2007).

Discussion of Country-BasedTemperature and PrecipitationProjections Relative to 1◦C GlobalWarming

The target period of the PRUDENCE simulationscovers the end of the twenty-first century (2071–2100).Thus, the above results presented for Central/EasternEurope provide climate projections for this period. Onthe other hand, impact studies would require regionalclimate change scenarios for earlier periods, preferablyfor the next few decades. The only information sourcecurrently available with fine (i.e. 50 km) horizontalresolution for all the European countries is a specialcomprehensive assessment based on the PRUDENCEsimulations (Christensen 2005). This country-basedanalysis is conducted for both the mean temperaturevalues and the precipitation amounts. In order to avoidthe specific characteristics of the A2 or B2 scenario,a pattern-scaling technique has been applied, in whichthe changes are expressed relative to a 1◦C globalwarming. Uncertainties in the estimates of projectedchanges are due to the use of different GCMs andRCMs as well as natural variability. As a result, meanand standard deviation of 25 estimates of temperatureand precipitation change are provided for each country.Furthermore, these main statistical parameters are usedto fit a normal probability distribution function for theprojected change. Table 3 summarizes the mean, thestandard deviation, the 5th and the 95th percentilesof the annual, the winter and the summer projectedtemperature changes for the Central/Eastern Europeancountries. Table 4 summarizes the same in case ofprecipitation.

For annual and seasonal temperature changes(Table 3) the expected increase in the Central/EasternEuropean countries is larger than the global 1◦C


Fig. 5 Seasonal precipitationchange (%) expected by2071–2100 forCentral/Eastern Europe usingthe outputs of 16 and 8 RCMsimulations, A2 and B2scenarios


Fig. 6 Standard deviation ofseasonal precipitation change(%) expected by 2071–2100for Central/Eastern Europeusing the outputs of 16 and 8RCM simulations, A2 and B2scenarios


Table 3 Statistical characteristics of expected increase of temperature (◦C) for Central/Eastern European countries relative to 1◦Cglobal warming using 25 RCM simulations (Christensen 2005)

Year Winter (D-J-F) Summer (J-J-A)

Country Mean ± SD 5th and 95thpercentiles

Mean ± SD 5th and 95thpercentiles


Czech Republic 1.3 ± 0.4 0.6–2.0 1.3 ± 0.4 0.6–1.9 1.5 ± 0.6 0.5–2.4Hungary 1.4 ± 0.3 0.9–1.9 1.3 ± 0.3 0.8–1.9 1.7 ± 0.4 1.0–2.4Poland 1.3 ± 0.3 0.8–1.8 1.3 ± 0.3 0.8–1.8 1.3 ± 0.4 0.6–2.0Slovakia 1.4 ± 0.4 0.7–2.0 1.3 ± 0.4 0.7–1.9 1.6 ± 0.6 0.6–2.5South Germany 1.3 ± 0.4 0.6–2.0 1.1 ± 0.4 0.5–1.7 1.6 ± 0.6 0.6–2.5North Germany 1.2 ± 0.4 0.6–1.8 1.1 ± 0.4 0.5–1.8 1.3 ± 0.5 0.6–2.0

warming, which implies that this region is quitesensitive to the global environmental change. Ingeneral, the projected mean summer regional warming(1.3–1.7◦C) is larger than the mean annual temperatureincrease (1.2–1.4◦C), while the expected mean winter(1.1–1.3◦C) warming is smaller than that. In case ofPoland, the expected temperature increases are equalfor summer and for winter, which are also equal tothe projected annual warming (1.3◦C). In the CzechRepublic, the expected summer warming (1.5◦C) islarger than the projected temperature increase in winter(1.3◦C) and in the whole year (1.3◦C). The largeststandard deviation values are expected in summer,while the winter and the annual values are equal incase of all Central/Eastern European countries. The5th percentiles of the expected regional warming arebetween 0.5 and 1.0◦C. The 95th percentiles of theprojected annual temperature increase are between 1.8and 2.0◦C, and for winter and summer 1.7–1.9◦C and2.0–2.5◦C, respectively.

According to the results presented in Table 4, theannual amount of precipitation in the Central/EasternEuropean countries is not expected to change signifi-cantly. The mean values are between −0.6 and +0.7%,and the standard deviation values are less than 2.4%.On the other hand, considerable precipitation decrease

Fig. 7 Expected seasonal change of mean precipitation (mm)for Hungary (increasing or decreasing precipitation is also in-dicated in percent). Precipitation values of the reference period,1961–1990, represent the seasonal mean precipitation amount inBudapest

by 4.0–8.2% and increase by 4.5–9.0% are projectedfor the summer and winter seasons, respectively. Theseresults confirm the conclusions drawn from the precip-itation maps in the previous section, which implies thatthe expected shift in the annual distribution of precipi-tation starts quite early, that is when the global warm-ing reaches 1◦C.

Conclusions

On the basis of our research results shown in this chap-ter, the following conclusions can be drawn:

Table 4 Statistical characteristics of expected increase of precipitation (%) for Central/Eastern European countries relative to 1◦Cglobal warming using 25 RCM simulations (Christensen 2005)

Year Winter (D-J-F) Summer (J-J-A)

Country Mean± SD 5th and 95thpercentiles



Czech Republic +0.1 ± 2.0 –3.5–3.5 +4.5 ± 3.3 –0.9–10.0 –4.9 ± 5.2 –13.4–3.6Hungary –0.3 ± 2.2 –3.9–3.4 +9.0 ± 3.7 3.0–15.0 –8.2 ± 5.3 –16.9–0.5Poland +0.7 ± 2.1 –2.6–4.1 +6.0 ± 3.5 0.2–11.8 –4.0 ± 4.9 –12.0–4.1Slovakia –0.6 ± 2.4 –4.5–3.3 +7.5 ± 4.4 0.3–14.8 –7.3 ± 5.8 –16.8–2.2South Germany –0.3 ± 1.6 –2.8–2.3 +5.1 ± 2.9 0.3– 9.9 –6.4 ± 4.7 –14.0–1.3North Germany +0.3 ± 1.7 –2.7–2.9 +6.0 ± 4.1 –0.7–12.6 –7.3 ± 4.9 –15.4–0.9


� Expected seasonal temperature increase for Cen-tral/Eastern Europe for the A2 scenario is largerthan for the B2 scenario, which is in good agree-ment with the expected global and European cli-mate change results (IPCC 2007). The largest andthe smallest warming are expected in summer andin spring, respectively.

� In summer, a zonal structure of projected warm-ing with increasing values from north to south canbe expected for all temperature parameters, whilein winter, a meridional structure of warming is ex-pected with increasing values from west to east.In spring and autumn, the spatial difference valuesof projected temperature change are much smaller;they do not exceed 0.8◦C and 0.4◦C, respectively.

� In the reference period of 1961–1990, the RCMsimulations slightly overestimate the temperaturein most of the Central/Eastern European region. Asmall region of underestimation can be seen in thesouthwestern part of the selected domain, at themountainous areas of the Alps. The temperaturebias does not exceed 1.5◦C.

� In Hungary, for all the four seasons and for bothscenarios, the expected warming by 2071–2100 isbetween 2.5 and 4.8◦C. The largest temperatureincrease is projected for summer, 4.8◦C (A2) and4.0◦C (B2), and the smallest seasonal warming isexpected in spring, 3.1◦C (A2) and 2.5◦C (B2). Thesmallest difference between the A2 and B2 scenar-ios is projected for spring (0.6◦C), while the largestis projected for winter (1◦C).

� The largest increase of maximum and minimumtemperatures in Hungary is expected in summerfor both scenarios. For maximum temperature, theinterval of the expected warming is 4.9–5.3◦C (A2)and 4.0–4.4◦C (B2). For minimum temperature,these intervals are 4.2–4.8◦C (A2) and 3.5–4.0◦C(B2). In general, the expected increase of maximumtemperature is not smaller than the expectedincrease of minimum temperature with theexception of the winter season.

� The annual precipitation sum is not expected tochange significantly in the Central/Eastern Euro-pean region, but this does not hold for seasonal pre-cipitation sums. Summer precipitation is very likelyto decrease; furthermore, slight decrease of autumnprecipitation is expected. On the other hand, winterprecipitation is likely to increase considerably, andslight increase in spring is also expected.

� The projected summer precipitation decreasein Hungary is 24–33% (A2) and 10–20% (B2),while the expected winter precipitation increase is23–37% (A2) and 20–27% (B2).

� In case of Hungary, the wettest season was summer,while the driest season was winter in the referenceperiod (1961–1990). If the projections are realizedthen the annual distribution of precipitation will betotally restructured: namely, the wettest season isfound in winter in both scenarios; the driest seasonis found in summer in the A2 scenario and in au-tumn in the B2 scenario.

Acknowledgments Climate change data have been providedthrough the PRUDENCE data archive, funded by the EU throughcontract EVK2-CT2001-00132. Research leading to this chapterhas been supported by the following sources: the HungarianAcademy of Sciences under the program 2006/TKI/246 titledAdaptation to Climate Change, the Hungarian National ResearchDevelopment Program under grants NKFP-3A/082/2004 andNKFP-6/079/2005, the Hungarian National Science ResearchFoundation under grants T-049824, K-67626 and K-69164,the Hungarian Academy of Science and the Hungarian PrimeMinister’s Office under grant 10.025-MeH-IV/3.1/2006, theHungarian Ministry of Environment and Water under theNational Climate Strategy Development project and theCECILIA project of the European Union Number 6 programme(contract no. GOCE-037005).

References

Bartholy J, Pongracz R, Matyasovszky I, Schlanger V (2003)Expected regional variations and changes of mean and ex-treme climatology of Eastern/Central Europe. In: CombinedPreprints CD-ROM of the 83rd AMS Annual Meeting. Paper4.7, 10pp

Bartholy J, Pongracz R, Torma Cs, Hunyady A (2006a) Regionalclimate model PRECIS and its adaptation at the Departmentof Meteorology, Eotvos Lorand University. In: 31. Meteoro-logical Scientific Days – Dynamical climatological researchon objective estimation of regional climate change (ed.:Weidinger, T.) Hungarian Meteorological Service, Budapest.99–114 (in Hungarian)

Bartholy J, Pongracz R, Torma Cs, Hunyady A (2006b) Regionalclimate change scenarios for the Carpathian Basin. In: Biocli-matology and water in the land (eds.: Lapin, M. and Matejka,F.) CD-ROM. FMFI Comenius University, Slovakia. 9pp

Bartholy J, Pongracz R, Gelybo Gy (2007) Regional climatechange expected in Hungary for 2071–2100. Applied Ecol-ogy and Environmental Research, 5, 1–17

Benestad RE (2005) Climate change scenarios for northernEurope from multi-model IPCC AR4 climate sim-ulations. Geophysical Research Letters, 32, L17704,doi:10.1029/2005GL023401


Black E, Blackburn M, Harrison G, Hoskins BJ, Methven J(2004) Factors contributing to the summer 2003 Europeanheatwave. Weather, 59, 217–223

Christensen JH (2005) Prediction of Regional scenarios and Un-certainties for Defining European Climate change risks andEffects – Final Report. DMI. 269p

Christensen OB, Christensen JH (2004) Intensification of ex-treme European summer precipitation in a warmer climate.Global and Planetary Change, 44, 107–117

Christensen JH, Carter TR, Rummukainen M, Amanatidis G(guest editors) (2007) Prediction of regional scenarios anduncertainties for defining European climate change risks andeffects: The PRUDENCE project. Climatic Change, 81, Sup-plement 1, 371p

Deque M, Jones RG, Wild M, Giorgi F, Christensen JH, HassellDC, Vidale PL, Rockel B, Jacob D, Kjellstrom E, de CastroM, Kucharski F, van den Hurk B (2005) Global high reso-lution versus Limited Area Model climate change scenariosover Europe: results from the PRUDENCE project. ClimateDynamics, 25, 653–670. doi:10.1007/s00382-005-0052-1

Fink AH, Brucker T, Kruger A, Leckebusch GC, Pinto JG,Ulbrich U (2004) The 2003 European summer heatwavesand drought – synoptic diagnostics and impacts. Weather, 59,209–216

Giorgi F (1990) Simulation of regional climate using a limitedarea model nested in a general circulation model. Journal ofClimate, 3, 941–963

Hanssen-Bauer I, Achberger C, Benestad RE, Chen D, FørlandEJ (2005) Statistical downscaling of climate scenarios overScandinavia: A review. Climate Research, 29, 255–268

Horanyi A (2006) Dynamical climatological research on regionalscales: International and Hungarian review. In: 31. Meteoro-logical Scientific Days – Dynamical climatological researchon objective estimation of regional climate change (ed.:Weidinger, T.) Hungarian Meteorological Service, Budapest.62–70. (in Hungarian)

IPCC (2007) Climate Change 2007: The Physical Science Basis.Contribution of Working Group I to the Fourth AssessmentReport of the Intergovernmental Panel on Climate Change.(eds.: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis,M., Averyt, K.B., Tignor, M., Miller, H.L.), Cambridge Uni-versity Press, Cambridge, United Kingdom and New York,NY, USA, 996pp

Mearns LO, Hulme M, Carter TR, Leemans R, Lal M,Whetton PH (2001) Climate scenario development. In: Cli-mate Change 2001: The Scientific Basis. (eds.: Houghton,J. et al.) Intergovernmental Panel on Climate Change, Cam-bridge University Press, New York. 739–768

New M, Hulme M, Jones P (1999) Representing twentieth-century space-time climate variability. Part I: Developmentof a 1961–90 mean monthly terrestrial climatology. Journalof Climate, 12, 829–856

Pal JS, Giorgi F, Bi X (2004) Consistency of recent Europeansummer precipitation trends and extremes with future re-gional climate projections. Geophysical Research Letters, 31,L13202, doi:10.1029/2004GL019836

Tebaldi C, Hayhoe K, Arblaster JM, Meehl GE (2006) Going tothe extremes: an intercomparison of model-simulated histor-ical and future changes in extreme events. Climatic Change,79, 185–211

Detected and Expected Trends of Extreme Climate Indicesfor the Carpathian Basin

R. Pongracz, J. Bartholy, Gy. Gelybo and P. Szabo

Keywords Climate extreme index · Daily precipita-tion · Daily temperature · Carpathian basin · Trendanalysis

Introduction

Regional climatological effects of global warming maybe recognized not only in shifts of mean temperatureand precipitation but also in the frequency andintensity changes of different climate extremes. Ajoint WMO-CCl (World Meteorological OrganizationCommission for Climatology)/CLIVAR (a project ofthe World Climate Research Programme addressingClimate Variability and Predictability) working groupformed in 1998 on climate change detection (Karlet al. 1999); one of its task groups aimed to identifyclimate extreme indices (Peterson et al. 2002) andcompleted a climate extreme analysis on all partsof the world where appropriate data were available(Frich et al. 2002). The main results of this workinggroup appeared in the IPCC Assessment Reports(2001, 2007). Klein Tank and Konnen (2003) analyzedextreme climate indices on continental scale forEurope (86 and 151 stations were used in case oftemperature and precipitation time series, respectively)for the second half of the twentieth century. Theirresults give a general overview on the European scalebut they are not detailed enough for the Carpathianbasin. In this chapter, trend analysis of extremetemperature and precipitation indices is discussed for

R. Pongracz (B)Department of Meteorology, Eotvos Lorand University,Pazmany st. 1/a, H-1117 Budapest, Hungarye-mail: [email protected]

the Carpathian basin for both the past few decades andfor the last decades of the twenty-first century.

The next section of this chapter presents thedatabase and the definition of the extreme climateindices. Then, the Detected Trend of Extreme ClimateIndices for the Carpathian Basin section discusses thetrend analysis of extreme temperature and precipitationindices detected in the Carpathian basin in the secondhalf of the twentieth century. The Future Trends ofExtreme Climate Indices for the Carpathian Basinsection compares the extreme climate indices for theperiods 1961–1990 and 2071–2100 using simulateddaily temperature and precipitation datasets. Finally,Conclusions section discusses the main findings of thischapter.

Data and Methodology

In order to compile a global climate database suitablefor extreme analysis, the WMO-CCl/CLIVAR taskgroup on extreme indices contacted the national me-teorological services and collected daily precipitation,minimum, maximum, and mean temperature timeseries for the period 1946–1999. In addition to thedatasets from the national meteorological services,further data sources, namely, the National Oceanic andAtmospheric Administration (NOAA) and NationalClimatic Data Center (NCDC) datasets (Petersonand Vose 1997), the European Climate AssessmentDataset (ECAD) (Klein Tank et al. 2002b), and dailymeteorological time series for Australia (Trewin 1999),have been included. All these datasets have beenquality controlled and adjusted for inhomogeneities.Then, in order to accept a given observation station,the following general criteria have been used: (i) from

K. Strelcova et al. (eds.), Bioclimatology and Natural Hazards, 15DOI 10.1007/978-1-4020-8876-6 2, c© Springer Science+Business Media B.V. 2009

16 R. Pongracz et al.

the entire 1946–1999 period data must be availablefor at least 40 years, (ii) missing data cannot be morethan 10%, (iii) missing data from each year cannotexceed 20%, and (iv) in each year, more than 3 monthsconsecutive missing values are not allowed.

The WMO-CCl/CLIVAR task group decided to mapstation data instead of gridded database since extremeevents (e.g., local floods and droughts, heat waves, lo-cal cold spells) often occur on local scale, but on theother hand, they are all important part of global cli-mate patterns, which could disappear in case of a spa-tial data interpolation. Therefore, maps showing the de-tected trends and presented in this chapter use similartechnique applying station-based analysis. Since sim-ulations of regional climate models (RCMs) use andresult in gridded datasets, in case of analyzing the ex-pected future tendencies, we used grid-based maps.

For the evaluation of recent tendency of climate,extreme indices in the Carpathian basin 32 meteoro-logical stations have been used (Fig. 1). Datasets for21 Hungarian stations were provided by the HungarianMeteorological Service, while datasets for 11 stationslocated in the neighboring countries are freely avail-able via Internet from the ECAD (Klein Tank 2003).Two basic constraints are taken into account during theselection of the stations: (i) covering the area of theCarpathian basin with the best spatial homogeneity andthe best representation of the main climatic subregions,(ii) minimal number of missing data.

Our datasets are compiled for 1901–2001. However,our previous analyses (Pongracz and Bartholy 2000)suggest that precipitation and temperature tendenciesof the last quarter of the twentieth century and thesecond half of the century are significantly different.Because of this and due to the limited temporal extentof the time series, the analysis presented in this chap-ter for the Carpathian basin has been accomplished for1946–2001.

Table 1 lists the main extreme precipitation andtemperature indices (12 and 14, respectively) that theWMO-CCl/CLIVAR task group identified and sug-gested for global and regional climate extreme analy-sis. Indices listed in Table 1 include a few precipitation-related parameters, which do not indicate extremeconditions. They belong to the index type annual num-ber of precipitation days exceeding a given threshold(i.e., 5, 1, 0.1 mm). We accomplished the trend analysisfor these latter indices, because they add importantcharacteristics to regional precipitation conditions.

Detected Trend of Extreme ClimateIndices for the Carpathian Basin

First, extreme climate indices related to daily tempera-ture values are analyzed. On the basis of our previousstudy of time series of mean and extreme temperatureparameters for the Carpathian basin, a strong warmingtendency was detected from the middle of the 1970s(Pongracz and Bartholy 2000). Therefore, the entire1961–2001 period has been separated into two subpe-riods, namely, 1961–1975 and 1976–2001. The trendanalysis has been accomplished for these subperiods(Bartholy and Pongracz 2006).

Summary of the trend analysis of the extremetemperature indices is presented in Fig. 2. Thedistribution of the trend coefficients, determined foreach station, can be seen. The whisker plot diagramsprovide statistical characteristics of the decadal trendsfor each extreme temperature index, for the twosubperiods (1961–1975 and 1976–2001). Oppositesign of trend coefficients may indicate warming andcooling tendencies. For instance, negative coefficientsof the number of cold days (T×10) and positivecoefficients of the number of hot days (T×30GE)both indicate warming climate. Therefore, warmingand cooling tendencies are indicated by differentbackground colors (gray and white, respectively). Incase of the intra-annual extreme temperature range(ETR) index, hatched background is used since thesign of the trend coefficient is not directly connectedto a warming or cooling tendency.

According to the results, warming trends aredetected in the first subperiod in case of extremeindices indicating negative temperature extremes (i.e.,T×10, Tn10, FD, T×0LT, Tn−10LT), while coolingtrends are detected in case of positive temperatureextremes (i.e., HWDI, T×90, SU, T×30GE, T×35GE,Tn20GT). In case of Tn90, the regional mean trendcoefficient is not significant, both warming and coolingtrends have been detected. In the last quarter of thetwentieth century, warming tendencies are dominant.Warming trend is detected in case of nine indices,namely, HWDI, T×90, Tn10, Tn90, SU, T×30GE,T×35GE, Tn20GT, Tn−10LT, while the regionalmean trend coefficient is not significant in case ofT×10, FD, and T×0LT.

In this chapter, detailed analysis is presented for thelast quarter of the twentieth century when the largest

Detected and Expected Trends of Extreme Climate Indices for the Carpathian Basin 17

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