Impact of extreme weather on critical infrastructure. Deliverable D2.1. Definition of different EWIs, to support the management of European CI

Transcription

1 This project is co-funded by the European Union under the 7 th Framework Programme Impact of extreme weather on critical infrastructure Deliverable D2.1 Definition of different EWIs, to support the management of European CI

2 2 D2.1 DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF

3 Project Information Grant agreement number Project acronym Project full title INTACT Start date of project 1 May 2014 Duration Partners Impact of extreme weather on critical infrastructure Capability Project 36 months TNO, CMCC, DELTARES, FAC, DRAGADOS, HRW, PANTEIA, NGI, CSIC, UNU-EHS, ULSTER, VTT Document information Work package Deliverable Title Version 1.0 Date of submission 30 April 2015 Main Editor(s) Contributor(s) Reviewer(s) This document should be referenced as WP2: Climate and Extreme Weather Deliverable D2.1: Definition of different EWIs, to support the management of European CI Edoardo Bucchignani and Jose Manuel Gutierrez Myriam Montesarchio, Alessandra Lucia Zollo, Guido Rianna, Maialen Iturbide, Sixto Herrera, Paola Mercogliano Unni Eidsvig (NGI), Peter Petiet (TNO) E. Bucchignani and J.M. Gutierrez (2015), Definition of different EWIs, to support the management of European CI, INTACT Deliverable D2.1, project co-funded by the European Commission under the 7th Frame-work Programme, Classification This report is: Draft Final X Confidential Restricted Public X History Version Issue Date Status Distribution April 2015 Draft Consortium April 2015 Final Project Officer Security Sensitivity Assessment According classification? If NO, please explain YES Member of Security Scrutiny Board Peter Petiet 3

4 Contents Contents... 4 Tables and figures... 6 Glossary... 7 Executive summary Introduction The INTACT project Aim of the document Reading guide Description of methodology Definitions and understanding Definition of extreme weather events Climate changes and Extreme events Impact of extreme events on infrastructures Changing climate and infrastructure vulnerability Diagnosis and detection of extreme events Modelling of Extreme events Extreme Weather Indicators (EWI) Statistical modelling - Generalized Extreme Values (GEV) Statistical modelling Trend Analysis Description of conventional observational datasets adopted in the activity ECA&D blended dataset E-OBS EURO4M-APGD Spain WATCH-Forcing-Data-ERA-Interim (WFDEI) Examples of application Conclusions and future work

5 7 References

6 Tables and figures Table 3.1 Indicators based on temperature and precipitation Table 3.2 Indicators based on wind, snow and humidity Table 4.1 Overview of observational datasets Table 4.2 List of the WFDEI variables considered in the INTACT project Figure 1.1 WP2 Functional architecture Figure 2.1 Representation of Probability Density Function of temperature: effect of increase in (a) mean, (b) variance and (c) both Figure 5.1 Examples of EWI for precipitation, wind and temperature, significant trends, evaluated averaging over the entire Europe, for the time period , for the ECA&D data set Figure 5.2 Map of the trend over Europe at a local scale for some temperature EWI reported in Table 3.1 over the period using the ECA&D dataset Figure 5.3 Map of the trend over Europe at a local scale for some temperature EWI reported in Table 3.1 over the period using the ECA&D dataset Figure 5.4 Map of the trend over Europe at a local scale for some precipitation EWI reported in Table 3.1 over the period using the ECA&D dataset Figure 5.5 Maps of 50-years return value of precipitation, snow depth, wind speed and temperatures of the ECA&D dataset for the period Figure years return value of precipitation, snow depth, wind speed and temperatures of the ECA&D dataset for the period Figure 5.7 Map of the trend over Europe at a local scale for some precipitation EWI reported in Table 3.1 over the period using the WFDEI dataset Figure 5.8 Map of the trend over Europe at a local scale for some precipitation EWI reported in the Table 3.1 over the period using the WFDEI dataset

7 Glossary CI CCI CEH CORDEX CLIVAR EC ECA&D EU ESSEM ETCCDI EUPORIAS EW EWE EWI GCM GEV FP6 FP7 JCOMM IPCC PDF POT RCM VALUE Critical Infrastructure WMO s Commission for Climatology Centre for Ecology and Hydrology Coordinated Regional Downscaling Experiment Climate Variability and Predictability European Commission European Climate Assessment & Dataset European Union Earth System Science and Environmental Management Experts of CCl/CLIVAR/JCOMM Team on Climate Change Detection and Indices European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales (EU FP7) Extreme Weather Extreme Weather Event Extreme Weather Indicator General Circulation Model Generalized Extreme Values Sixth Framework Programme Seventh Framework Programme Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology Intergovernmental Panel on Climate Change Probability Density Function Peaks-over-threshold Regional Climate Model Validating and Integrating Downscaling Methods for Climate Change Research (ESSEM COST action) WATCH Water and Global Change (EU FP6) 7

8 WCRP WFDEI WMO WP World Climate Research Program WATCH-Forcing-Data-ERA-Interim World Meteorological Organization Work Package 8

9 Executive summary The EU FP7 project INTACT aims to support governments and managers of critical infrastructure to reduce the risks caused by extreme weather by providing information, methods, tools and examples of good practices. The identification of the Extreme Weather Indicators (EWIs) represents the first goal of the Work Package (WP) 2. They will be analyzed over Europe with a special focus on the critical infrastructures (CI) identified in the frame of WP5. It is very important to emphasize that EWIs represent a concise way to characterize the expected changes, in frequency and intensity, of weather induced hazards. Climate changes are the only drivers of the change considered in WP2. As reported in different literature works, some kinds of extreme weather events might become more frequent and severe across the globe under the effect of global warming. The evaluation of hazard changes in the next decades is the first step to provide indications about the future risk posed by EWE and related hazard to CI. More specifically, hazards considered by WP2 are mainly related to precipitation (incl., snowfall) winds and temperature. This deliverable describes the activity aimed to define appropriate EWIs for the characterization of extreme events according to definitions and thresholds critical for infrastructures. Definitions and guidance on climate change indicators adapted to user needs are provided, in order to meet scientific standards with a focus on extreme events in a European climate change context. The characterization of these extremes is performed using datasets provided from different sources or case studies. Existing indices (e.g. ETCCDI, are reviewed, while new specific EWIs, tailored to users needs, are developed. Thus, beyond the typical extreme indices defined from temperature and precipitation, multi-parameter indices are introduced, focusing on other parameters, such as wind speed or humidity. This extension will allow to synthesize the combination effects of meteorological variables into EWIs in order to better support the management of CI in Europe. The specific study objectives will be defined according to the quality of the available data. To this aim observed data (specific from case studies, or generic like the interpolated grids Iberia02, E-OBS, MAP, etc.) and databases will be considered. 9

10 1 Introduction 1.1 The INTACT project Resilience of Critical Infrastructure (CI) to Extreme Weather Events (EWE), such as heavy rainfall, drought or icing, is one of the most demanding challenges for both government and society. Extreme Weather (EW) is a phenomenon that causes severe threats to the well-functioning of CI. The effects of various levels of EW on CI will vary throughout Europe. These effects are witnessed through changes in seasons and extreme temperatures (high and low), humidity (high and low), extreme or prolonged precipitation (for example rain, fog, snow, and ice) or prolonged lack thereof (drought), extreme wind or lack of wind, and thunderstorms. The increased frequency and intensity of EWE can cause events such as flooding, drought, ice formation and wild fires which present a range of complex challenges to the operational resilience of CI. The economic and societal relevance of the dependability and resilience of CI is obvious: infrastructure malfunctioning and outages can have far reaching consequences and impacts on economy and society. The cost of developing and maintaining CI is high if they are expected to have a realistic functional and economic life (50+ years). Hence, future EWE has to be taken into account when considering protection measures, mitigation measures and adaption measures to reflect actual and predicted instances of CI failures. The INTACT project will address these challenges and bring together innovative and cutting edge knowledge and experience in Europe in order to develop and demonstrate best practices in engineering, materials, construction, planning and designing protective measures as well as crisis response and recovery capabilities. All this will culminate in the INTACT Reference Guide, the decision support system that facilitates cross-disciplinary and cross-border data sharing and provides for a forum for evidence based policy formulation. The objectives of the INTACT project are to: Assess regionally differentiated risk throughout Europe associated with extreme weather; Identify and classify, on a Europe wide basis, CI and to assess the resilience of such CI to the impact of EWE; Raise awareness of decision-makers and CI operators about the challenges (current and future) EW conditions may pose to their CI; and, Indicate a set of potential measures and technologies to consider and implement, be it for planning, designing and protecting CI or for effectively preparing for crisis response and recovery. 10

11 1.2 Aim of the document In this section, the general objectives of WP2, and specifically of this deliverable, are defined. WP2 aims to define appropriate EW Indicators (EWIs) characterizing the relevant critical factors for the different infrastructures, presenting both recent trends and projections of EWIs over the 21st century. Historical trends are presented considering observational datasets. Then performances of climate model simulations in reproducing the different EWI are estimated and finally, future projections of the different EWIs under different climate change scenarios are provided. Figure 1.1shows the functional architecture of WP2. Figure 1.1 WP2 Functional architecture This deliverable aims: to provide a general overview about the impact of extreme events on infrastructures, considering a changing climate; to define appropriate EWIs characterizing the relevant critical factors for different infrastructures; to provide a description of all the observational datasets used; to provide some examples of applications that will be developed in the frame of WP Reading guide This document is organized as follows. Chapter 2 provides some basic definitions (such as the definition of Extreme Weather Events), an overview of the impacts of Extreme Events on Infrastructures, the problems connected with the design of infrastructures under a changing climate and the related vulnerability. Indeed, a typical procedure to design infrastructures is to take into account extreme values from the past historical information on climate extremes and to assume stationary mean states, but 11

12 such measures could not be able to guarantee safety conditions in the future. Chapter 3 provides a description of Extreme Weather Indicators which refer to moderate extremes (typically occurring several times every year) and of Statistical Modelling, used to evaluate intensity and frequency of rare events (that lie far in the tails of the probability distribution of variables, for example events that occur once in 20 years). Chapter 4 provides a list and a general description of the observational datasets used in the present work, with large emphasis on the resolution and on the data potentiality. In Chapter 5, some examples of applications are shown and analysed, while the main Conclusions are reported in Chapter Description of methodology The main aim of WP2 of INTACT project is the definition of appropriate EW Indicators (EWI) characterizing the relevant critical factors for different infrastructures, analysing present trends and future climate projections for the 21 st century. One of the aims of the World Meteorological Organization (WMO) is to provide a regular monitoring of the occurrence of extreme weather and climate events. In particular, the WMO s Commission for Climatology (CCl), the World Climate Research Program (WCRP) with Climate Variability and Predictability (CLIVAR), and the Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology (JCOMM) have developed tools for the analysis of computed statistics (indices). Moreover, the Experts of CCl/CLIVAR/JCOMM Team on Climate Change Detection and Indices (ETCCDI) has provided a core set of 27 extreme indices for temperature and precipitation, to assess changes in extreme climate events. In order to achieve the WP2 main aim, existing indices from ETCCDI are reviewed, while new EWI s, tailored to the user s needs, are developed. In particular, in the frame of task 2.1, multiparameter indices are defined, focusing not only on temperature and precipitation, but also on other variables, as wind and humidity. In this way, it is possible to synthesize the combined effects of meteorological variables into EWIs in order to better support the management of CI in Europe. More specifically, indicators presented in Chapter 3 will be evaluated using the observational datasets described in Chapter 4. 12

13 2 Definitions and understanding 2.1 Definition of extreme weather events Climate extremes and temporal deviations of weather characteristics from the norm have an important role and strong impact on the natural environment and economic activities (Klein Tank et al, 2009). For example, it is well known that too much or too little precipitation (e.g. flood or drought) poses severe challenges to the society. So it is very important to detect how extremes have changed in the past, if a change of them is expected in the future and what this change could be (e.g. changes in the intensity and/or frequency). In this view, it is important to develop a procedure to characterize and quantify extreme events; however, there is not a universally accepted methodology for this purpose. There is not a unique definition of extreme, since it can describe either a characteristic of a climate variable or that of an impact (Stephenson, 2008). In the case of a variable related with weather or climate (e.g. temperature or precipitation), an extreme can be defined as a value located in the tails of the variable s distribution, occurring infrequently. A useful criterion is also the evaluation of the ratio of the intensity of an anomaly in relation to climatic variability: it is generally agreed that extreme events are those exceeding two standard deviations in long term observational series (Kislov and Krenke, 2009). In the case of an impact, it is more difficult to define the extreme, since generally there is not a unique way to quantify it. Moreover, a rare climate event does not necessarily causes damages; for example, a strong wind over the Ocean generally does not result in any damage while, on the other side, floods might be caused by not very unusual precipitation, especially in heavily urbanized basins (Peterson et al. 2012). Weather conditions which are unfavourable but normal for a particular area (e.g. Siberian frosts or long periods without precipitation in the desert) cannot be regarded as EWE (Kislov and Krenke, 2009). The IPCC Assessment Report 4 (IPCC, 2007) defines an extreme climatic event as one that is rare within its statistical reference distribution at a particular place and time. Definitions of rare vary, but an extreme weather event would normally be as rare as or rarer than the 10 th or 90 th percentile of the observed Probability Density Function (PDF). By definition, the characteristics of what is called extreme weather may vary from place to place. Single extreme events cannot be simply and directly attributed to anthropogenic climate change, as there is always a finite chance that the event in question might have occurred naturally. When a pattern of extreme weather persists for some time, such as a season, it may be classed as an extreme climate event, especially if it yields an average or total that is itself extreme (e.g. drought or heavy rainfall over a season). The IPCC Special Report on managing the risks of extreme events defines an extreme as the occurrence of a value of a variable above or below a threshold value near the upper (or lower) ends of the range of observed values of the variable (IPCC, 2012). Extreme events are defined not only with respect to their low frequency, but also with respect to the intensity. For events characterized by relatively small or large values (i.e. events that have large 13

14 magnitude deviations from the norm), one need to take into account that not all intense events are rare. For example, low cumulative precipitations are often far from the mean precipitation but can still occur quite frequently. Also severity is a criterion used in climate science to classify events as extreme: events that result in large socio-economic losses. Severity is a complex criterion because damaging impacts can occur in the absence of a rare or intense climatic event, for example thawing of mountain permafrost leading to rock falls and mud-slides. 2.2 Climate changes and Extreme events Design of infrastructures is generally performed under the hypothesis that climate is stationary, meaning that physical variables could vary from day to day, but always around an unchanging mean state. Information about weather extreme values on a specific area are generally taken from historical series: in particular, values corresponding to a fixed return value of a variable in the historical dataset are generally considered the normative value for design. However, this approach could be no more adequate, since it is evident that climate changes are unequivocal and will alter the mean, variability and extremes. The warming of the climate system in recent decades is evident from observations and is mainly related to the increase of anthropogenic greenhouse gas concentrations (IPCC, 2012). As a consequence, also precipitation will be altered, since a warmer atmosphere will hold more water vapour, resulting in heavier rains or, on the other side, in strong drought due to larger water absorption from soil and vegetation. A changing climate may lead to changes in the frequency, intensity, spatial extent, duration and timing of weather and climate extremes. Climate changes are usually assessed in terms of averages climate properties rather than on variability or extremes, but often these last ones have more impacts on the society than averages values (Katz and Brown, 1992). As climate extreme will change, it is likely that risks for infrastructure failure will increase worldwide, since extreme weather conditions become more variable and regionally more intense. Figure 2.1 (a, b, c) explains how extreme events can be defined as the tails of a PDF; in particular, the PDF of daily temperature tends to be approximately a Gaussian. More specifically, Figure 2.1(a, b, c) shows respectively how hot and cold extremes are affected by changes in the mean, variance and in both. Temperature and precipitation extremes have been studied on global, regional and national scales (Alexander et al, 2006). On the global scale, the most comprehensive analyses on temperature and precipitation extremes are discussed in the Fourth Assessment Report of IPCC (IPCC, 2007). Some recent changes in the pattern of extremes have been significant. Over Europe, observed trends to longer heat waves and fewer extremely cold days have been registered. For example, since 1960, the mean heat wave intensity over the Eastern Mediterranean area increased by a factor of five (How et al, 2013), suggesting that the heat wave characteristics in this region have increased at higher rates than previously reported (Kuglitsch et al, 2010). Furthermore, flood damage has increased substantially. 14

15 However observations alone do not provide conclusive and general proof to how climate change affects flood frequency. Figure 2.1 Representation of Probability Density Function of temperature: effect of increase in (a) mean, (b) variance and (c) both. Source: Impact of extreme events on infrastructures Infrastructures include transportation systems (bridges, roads, and motorways), urban buildings, energy, water and communication systems, health-care systems and in general those sections intended to deliver services to support the human quality of life (Wilbanks and Fernandez, 2012). Critical Infrastructures (CI) provide fundamental functions to sustain the society (such as transnational connecting networks) and a breakdown of a CI could lead to significant economic losses and high number of human deaths. Moreover, a CI may rely on resources provided by other infrastructures. For these reasons, the protection of CI from disasters is an important priority task for all countries. It is of great importance for regional and local institutions to be aware of present and future climate extremes related risks with regard to the development of adaptation strategies (Hokstad et al, 2012). Climate-related extremes generally produce large impact on infrastructures, especially on those with insufficient design. Infrastructures may become inadequate under the effects of severe extremes: for example, the capacity of sewerages may be affected by intense rainfalls, as well as industrial installations containing dangerous materials. Many villages in the world are dependent on wide 15

16 infrastructure networks for power, water, transport and telecommunications, which are exposed to a wide range of extreme events, especially because modern logistic systems are intended to minimize redundancies (IPCC, 2012). Transport infrastructure is vulnerable to extreme value of temperature, precipitation and wind, which can have impact on roads, rail, and airports; impacts on ports can have serious implications on international trade, since more than 80% of international trade in goods is carried by sea (UNCTAD, 2009). Coastal inundation may affect terminals, storage areas and intermodal facilities especially on small islands, where transportation facilities are generally located in low elevation coastal zones. The design of many infrastructures, for example those related to transportation, water and energy, require the availability of climatic data related to extreme events: for example, high precipitation might affect the resilience of roads and bridges. The main aim, in fact, is to avoid damages to structures due to extreme events during the whole lifetime of the infrastructures, and in the same time to limit the costs for the realization of them. For the design of infrastructures, engineers account for climate extremes that occur only infrequently and are not expected to recur each year. For example, design rainfalls for sewage systems are derived estimating long period return values of maximum amount of rainfall within 1, 2, 6, 12 and 24 hours (Zhang and Zwiers, 2013), making use of powerful statistical tools based on extreme value theory, to aid the analysis of historical series (Coles, 2001): such tools have been developed to infer extreme values that might occur beyond the range of the observed sample, such as the estimation of the 100-years return value on the basis of a 50 year series of historical values. Impact of extreme events is being investigated in the frame of EUPORIAS (EUPORIAS, 2014) and VALUE (VALUE, 2013) projects. EUPORIAS (European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales) has been funded by the European commission under the 7 th framework program, with the aim of developing and delivering reliable predictions of the impacts of future climatic conditions on a number of key sectors (water, energy, health, transport, agriculture and tourism), on timescales from seasons to years. VALUE (Validating and Integrating Downscaling Methods for Climate Change Research) is a COST Action whose aim is to provide a European network to validate and develop downscaling methods and improve the collaboration between the research communities and with stakeholders. The Action systematically compares the different downscaling approaches and assesses temporal variability from sub-daily to decadal time scales including climate change, extreme events, spatial coherence and variability, and inter-variable consistency together with the related uncertainties. In particular, a WP is devoted to carry out an inventory of extreme definition, a validation phase and development of downscaling methods for spatially extended extremes. The characterization of EW is performed according with thresholds critical for infrastructures. Absolute thresholds are suitable in order to monitor extreme events that affect human society and the natural environment, while percentile thresholds are specific of the sites, since they are expressions of anomalies relative to the local climate. Moreover, specific threshold values related to stakeholder s and user s needs are considered. 16

17 2.4 Changing climate and infrastructure vulnerability The severity of climate impacts on infrastructures will vary across Europe according to specific locations and their geophysical risk exposure, the existing adaptive capacity and resilience, and the level of economic development. Evaluation of potential effects of climate change on infrastructure is still very limited and further research and development will be required to support decision-making. Experiences over the past periods have shown how vulnerable infrastructures can be to the types of EWE that are projected to be more intense and more frequent with future climate change. Evaluation of vulnerability of infrastructures requires the analysis of several climatic elements and their impact on the resilience. The world s largest reinsurance company, Munich Re, calculated that more than 90 percent of all disasters and 65 percent of associated economic damages were weather and climate related (Munich Re, 2011). Insurance generally report an increase in the number of weather-related events, which have caused significant losses, for example, wind-storms and floods in Europe. However, there is still insufficient information about the extent to which these changes can be found in wind and precipitation observations and whether they are driven by global warming. Some of the hazard-driven increases of events may have been hidden by human prevention actions, in particular in the case of flood, as these can be influenced much more by preventive measures than wind-storm losses. Analysis of vulnerability can be done from a global or local view, for example by assessing the vulnerability of a city, a river basin or a specific piece of infrastructure. Vulnerabilities and impacts are issues beyond physical infrastructures themselves: the true consequences of impacts involve not only the costs associated with the replacement of affected infrastructures but also social and environmental effects, since supply chains are interrupted, economic activities are suspended, social well-being is threatened (Wilbanks and Fernandez, 2012). Vulnerability alarms tend to be focused on EWE associated with climate change that can interfere with infrastructure services, often cascading across different infrastructures due to wide interdependencies, especially where populations and activities are concentrated in urban areas. Vulnerabilities are larger when infrastructures are subject to multiple stresses, when they are located in areas vulnerable to EWE and if climate change is severe. These risks are greater for infrastructures that are located near particularly climate-sensitive environmental features, such as coastlines, rivers, storm tracks and vegetation in arid areas. A larger risk is expected for infrastructures already stressed by age or by demand levels that exceed what they were designed to supply (Wilbanks and Fernandez, 2012). The consequences of climate change will be different, depending on the kinds of infrastructures: The consequences for transport infrastructure such as rail, roads, shipping and aviation will differ from region to region. In particular, the projected increase in the frequency and intensity of EWE such as heavy rain, snowfall, extreme heat and cold, drought and reduced visibility can increase negative impacts on transport infrastructures, causing damages and economic losses, transport disruptions and delays (European Commission, 2013). 17

18 Climate changes will have effects on energy transmission, distribution, generation and demand. In fact, the generation of electrical energy is affected by efficiency reduction due to climate change (e.g. decreasing availability of cooling water for electricity generators). However, in some parts of Europe, increased precipitation or more wind may also lead to better opportunities for hydropower or wind energy generation. Furthermore, extreme weather periods, such as heat waves or cold spells, will cause higher energy demand peaks, causing overstress of energy infrastructure (European Commission, 2013). Buildings and infrastructures can be vulnerable because of their design (e.g. low resistance to storms) or location (e.g. in flood-prone areas, landslides, avalanches). Many European cities have been built along a river, and these rivers will respond to extreme rainfall or snowmelt events with extreme discharges, threatening the cities with floods (European Commission, 2013). Implications of climate change for infrastructures can be examined by assessing historical data with extreme weather events and by simulating future conditions, including both individual events and either a series of extreme events in a short time period or the combination of an extreme weather event with another type of threat at the same time (Wilbanks and Kates, 2010). As explained in Sec. 3.1, Regional Climate Models are used to provide high resolution climate projections on the area of interest over the 21 st century, with different emission scenarios. According with WMO recommendations ( average values over 30-years periods are used, as they are long enough to filter out any interannual variation or anomalies, but also short enough to be able to show longer climatic trends. Generally the current climate period is calculated over (or ) time period, while the following time horizons are selected for future projections: 1) (short range) 2) (medium range) 3) (long range) These time periods are selected to provide insight into changes in climatic parameters over this century and to be representative of future time frames for planning infrastructure design or rehabilitation cycles. Future vulnerability assessments are related to time frames that match the design or remaining service life of existing infrastructures of the service life for new infrastructure. Generally, data necessary for the vulnerability assessments are available and scenarios of climate change are possible for almost all climate indicators. However, the quality or usefulness of the data (both observed and predicted) varies greatly, as well as their level of confidence. Indeed, the data from the models must be validated by the scientific community. As research continues and additional validation work is done, more products become available, although this depends largely on the needs expressed by end-users and the importance given to produce this information. One of the main problems linked to data availability arises from a lack of observed data, namely for events that are very localized in time and space which, by nature, are rare. 18

19 3 Diagnosis and detection of extreme events 3.1 Modelling of Extreme events Assessment of trends and changes in weather extremes is complicated and harder to predict because they are rare events, related to sharp changes in climatic system and result from its nonlinear nature (Kislov and Krenke, 2009). The main tool for providing insights into possible future climate changes is the climate modelling. Climate models are mathematical models that simulate the climate system s behaviour based on the fundamental laws of physics. General Circulation Models (GCMs) simulate planet-wide climate dynamics: they are powerful instruments to simulate the response of the global climate system to external forcing (Giorgi, 2005), however they are generally unsuitable to simulate local climate, since they are characterized by resolutions generally around or coarser than 100 km, which is too poor for impact studies, since many important phenomena occur at spatial scales of few tens of km. Moreover, GCMs do not account for vegetation variations, complex topography and coastlines, which are important aspects of the physical response governing the regional climate change signal. One of the most effective tools, providing high resolution climate analysis through dynamical downscaling, is represented by Regional Climate Models (RCM) (Giorgi and Mearns, 1991), able to provide an accurate description of climate variability on local scale. Moreover, RCMs show the capability to provide a detailed description of climate extremes (Rummukainen, 2010; Soares et al, 2012). A very relevant research question would be the determination of changes of extreme events expected under anthropogenic climate change in future climate model simulations. The capabilities of RCMs were assessed over Europe in the framework of several European projects, such as PRUDENCE (Christensen and Christensen, 2007) and ENSEMBLES (Van Der Linden and Mitchell, 2009). In recent years, the WCRP Coordinated Regional Downscaling Experiment (CORDEX) project (Giorgi et al, 2009) has been established to provide a global coordination of regional climate downscaling for improved climate change adaptation policy and impact assessment: EURO-CORDEX is the European branch of the CORDEX initiative. To date there is a lack of an exhaustive validation of EURO-CORDEX data in terms of extreme values. Preliminary analysis of climate projections collected in EURO-CORDEX project show that in the future a significant increase in the frequency of events, such as heavy rainfall, heat waves and droughts, is expected (Jacob et al, 2014; Vautard et al, 2013). Confidence in projections of future changes in the severity and frequency of such events will increase if the mechanisms of changes can be identified and understood. Equally important is the rigorous quantification of the uncertainties in these projections, including the natural variability of the climate system, the limitations in climate models and the statistical methods used to analyse their output (Wehner, 2013). Several works highlight evidences that RCM skill in simulating the spatial and temporal characteristics of rainfall increases with increasing model resolution (Maraun et al., 2010); moreover, a higher grid spacing 19

20 is expected to improve parameters such as meso-scale circulations and precipitation intensity distribution at daily scale (Kotlarski et al, 2014). The main inconvenience of regional climate models is that they are computationally demanding, this feature gives some constraints on the resolution, domain size, number of experiments and duration of simulations that can be conducted. Overall, a general agreement in the research community is emerged about the likely future pattern of extreme weather events in Europe: heat waves will become more frequent while the number of cold spells and frost days are likely to decrease. Southern Europe and the Mediterranean Region will be affected by a combination of a reduction in annual precipitation and an increase in average temperatures (Giorgi and Lionello, 2008). High intensity and extreme precipitation are expected to become more frequent over the 21 st century. The increased frequency is estimated to be larger for more extreme events, but will vary from region to region (Beninston et al, 2007). Numerical tools are being used in the evaluation of extreme events simulated in regional climate models, in the characterization of the influence of large scale atmospheric circulation variations on extreme precipitation and in the detection of anthropogenic influence on temperature extremes (Zwiers et al, 2011) 3.2 Extreme Weather Indicators (EWI) The ETCCDI core set of 27 extreme indices for temperature and precipitation ( ) has been extended to other variables (wind, snow, humidity, etc.) within the ECA&D project ( These indices highlight various characteristics of extremes, including frequency, amplitude and persistence (Klein Tank et al, 2009) and are widely used to assess future changes (e.g. Fischer et al. 2013). Some indices involve calculation of the number of days in a year/season exceeding specific thresholds. On the one hand, percentile thresholds are specific of the sites, since they are expressions of anomalies relative to the local climate. For example, the number of days with daily minimum temperature below the 10th percentile value in the base period depends on the season (e.g. a minimum temperature of - 10 C could be considered not an extreme in winter, but would be very extreme in other seasons) and is also related to the geographical location. On the other hand, absolute thresholds are suitable in order to monitor extreme events that affect human society and the natural environment. Examples of such indices are the following: maximum 24 hours precipitation amount, widely used in engineering applications to infer design values for engineered structures, or the number of frost days per year (i.e. minimum temperature below 0 C). Such indices may not be applicable everywhere on the Earth, since the phenomena may not occur in some places (e.g. a frost day would never occur in the tropics), however they have a long history in many applications. To build the final EWI dictionary to be used in the framework of the INTACT project, we have considered several sources. Although, most of the indices are included in the extended list defined within the ECA&D project, we have also considered some indicators used by the Weather Meteorological Services 20

21 to establish weather alerts and warnings, and other combined indicators. The definition and details of the indices considered are included in Table 3.1and Table 3.2: Table 3.1 Indicators based on temperature and precipitation Temperature Description Units FD Number of frost (TN<0ºC) days Days SU Number of summer (TX>25ºC) days Days ID Number of icing (TX<0ºC) days Days TR Number of tropical (TN>20ºC) nights Days GSL Growing season length Days TXx Maximum value of daily maximum temperature ºC TNx Maximum value of daily minimum temperature ºC TXn Minimum value of daily maximum temperature ºC TNn Minimum value of daily minimum temperature ºC TN10p Number of days when TN < 10th percentile days TX10p Number of days when TX < 10th percentile days TN90p Number of days when TN > 90th percentile days TX90p Number of days when TX > 90th percentile days WSDI Warm-spell duration index days CSDI Cold-spell duration index days DTR Daily temperature range ºC FTD Number of days with temperature zero-crossings (Frost-thaw cycles) days HW Number of heat waves (TX>35ºC) days days Precipitation Description Units Rx1day Maximum 1-day precipitation mm Rx5day Maximum consecutive 5-day precipitation mm SDII Simple precipitation intensity index mm RR1 Annual count of days when PRCP 1mm days R10mm Annual count of days when PRCP 10mm days R20mm Annual count of days when PRCP 20mm days RNNmm Annual count of days when PRCP NNmm days CDD Maximum length of dry spell (consecutive days with RR < 1mm) days CWD Maximum length of wet spell (consecutive days with RR 1mm) days R95pTOT Annual total PRCP when RR > 95p mm R99pTOT Annual total PRCP when RR > 99p mm PRCPTOT Annual total precipitation in wet days mm RAI Number of days when TN > 90th percentile 1 RDI Number of days when TX > 90th percentile mm Combined Description Units DW Number of dry (RR<0.1 mm)-warm (TG>75 th percentile) days days DC Number of dry (RR<0.1 mm)-cold (TG<25 th percentile) days days WW Number of wet (RR<0.1 mm)-warm (TG>75 th percentile) days days WC Number of wet (RR<0.1 mm)-cold (TG<25 th percentile) days days FRD Number of freezing rain (TX<0ºC y RR>0.5 mm) days 21

22 These indicators allow the analysis of the interannual variability and trend of the mean and extreme weather regimes, both in the historical period and the future projections given by the models. Note that the models have important bias, most of them non-systematic, and, then, the effect of these biases should be taken into account in the calculation of the indices, mainly in the case of absolute thresholddependent indicators. In this sense, percentile threshold are more robust than the absolute ones. Table 3.2 Indicators based on wind, snow and humidity. Wind Description Units FG Monthly average of daily mean wind speed m/s FGx1day Yearly maximum of daily mean wind speed m/s FGXx1day Yearly maximum of daily maximum wind speed of gusts m/s FG05 Number of days with wind speed > 5 m/s days FG10 Number of days with wind speed > 10 m/s days FG15 Number of days with wind speed > 15 m/s Days FG25 Number of days with wind speed > 25 m/s Days FGNN Number of days with wind speed > NN m/s Days Snow Description Units SD1 Number of days with snow cover Days SD010 Number of days with snow depth 0-10 cm Days SD1020 Number of days with snow depth cm Days SDx1day Yearly maximum snowfall Mm SDratio Average total annual / seasonal snowfall 1 Combined Description Units HU90 Number of days when the relative humidity (daily mean) is above 90% and mean temperature > 10 ºC Days 3.3 Statistical modelling - Generalized Extreme Values (GEV) The descriptive indices developed by ETCCDI refer to moderate extremes that typically occur several times every year. On the other side, but complementary, intensity and frequency of rare events, in terms of return periods and values, are evaluated using the extreme value theory. This approach allows estimating the intensity of events which occur once in a given period, typically 50, 100 or 1000 years. To this aim, two general approaches are possible: 22 Peaks-over- threshold method (POT), which adjusts the occurrence and intensity of events above a predefined threshold. Explicit extreme value theory based on Generalized Extreme Value (GEV), which is, by the extreme value theorem, the limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables. Once fitted, the model provides a cumulative distribution function, F(x), which can be used to estimate return values for different time spans (T), usually expressed in years, which are typically used to design, maintain and adapt the infrastructures. Changes in these return values lead to changes in the intensity

23 of extreme events. For example, where extremes increase, the impact will be a reduction in the effective return period event that existing structures were built to withstand. In this case, return periods and values are estimated for a climatological period, typically 30 years, and, then, the possible changes found are referred to this time-scale. To this aim, for each location and variable the parameters of the GEV distribution (location, scale and shape) were estimated using the method of maximum log-likelihood and, then, the return values for 50 and 100 years were obtained. Equivalent analyses using the POT approach leads to similar results and, then, for the sake of the simplicity, has not been included in the present document. 3.4 Statistical modelling Trend Analysis In order to analysis the historical evolution of the EWI described in Table 3.1and Table 3.2, linear regression models (least squared method) were fitted against time to obtain the values of linear trend and to calculate the change between the end and beginning of the studied period. Furthermore, the Mann-Kendall non-parametric test was used for detecting the statistical significance of trend (Kendall, 1975). A significance P-level <0.05 (95% of confidence) was set to reject the null hypothesis of the test, that is, no trend in data. This confidence threshold was used in all the figures shown in the present document. 23

24 4 Description of conventional observational datasets adopted in the activity The characterization of extreme weather events is performed using datasets provided from different sources or case studies. This specific study objective will be defined according to the quality of the available data. This Chapter provides a list and a general description of the observational datasets used in the present work, with large emphasis on the resolution and on the data potentiality. Using these observational datasets we can evaluate the indicators presented in Chapter 3. Each dataset mentioned in Table 4.1 will be described in the next sections. Table 4.1 Overview of observational datasets Dataset ECA&D E-OBS EURO4M- APGD Spain02 WFDEI Spatial characteristic Temporal characteristics Spatial Extent Resolution Period Resolution Europe and Mediterranean Europe and Mediterranean European Alps and adjacent flatland Peninsular Spain and the Balearic islands Worldwide Land areas Point stations 0.25º (~25 km) 5x5 km 0.11º, 0.22º and 0.44º different periods for different stations Daily Daily Daily Daily 0.5ºx0.5º hourly, 6- hourly and daily Parameters Maximum, minimum and mean temperature, precipitation amount, mean sea level pressure, cloud cover, humidity, snow depth, sunshine duration, mean wind speed, maximum wind gust, wind direction. Maximum, minimum and mean temperature, precipitation and mean sea level pressure Precipitation (rainfall plus snow water equivalent) Maximum, minimum and mean temperature, precipitation Temperature, wind speed, surface pressure, specific humidity, long- and short-wave downwards surface radiation, rainfall and snowfall rate. 24

25 4.1 ECA&D blended dataset The European Climate Assessment & Dataset (ECA&D, project was initiated by the ECSN ( in 1998 to provide of high quality climate data, products and services to the European user community. This project is receiving actually data from 66 participants for 62 countries, containing series of observations for 12 elements at meteorological stations throughout Europe and the Mediterranean, most of them publicly available for non-commercial research and education ( Although both blended and non-blended ECA series are available (Klein Tank et al, 2002), only blended series are further analysed in ECA&D and used for gridding and, then, this is the data set considered within the INTACT project. Blended series are series that are near-complete by infilling from nearby stations and using synoptical messages (more details in Meteorological observations are taken at many stations across Europe, each day. To minimize the effects of changes over time in the way the measurements were made, rigorous quality control is applied before the data is used to analyse extremes. The blended series have been tested for homogeneity, which is relevant to assess the quality of each series for climate change research. The meteorological parameters included in this data set are daily values of maximum, minimum and mean temperature, precipitation amount, mean sea level pressure, cloud cover, humidity, snow depth, sunshine duration, wind speed and direction, and maximum wind gust. Note that the observational network and spatial coverage depend strongly on the variable considered. On the one hand, the availability of several meteorological parameters at a local scale and for the European and Mediterranean region is the main advantage of this dataset because it allows analyse in deep the trend of all this variables, and the indices derived from them, in the historical period. On the other hand, this dataset has been used to build E-OBS (Haylock et al. 2008, van den Besselaar et al. 2011), also considered for INTACT, and, thus, the analysis of both dataset lets study the impact/effect of the gridding process in the extreme events at a local scale. 4.2 E-OBS The E-OBS dataset (Haylock et al., 2008) is a European daily high-resolution (0.25 ) gridded dataset for precipitation, mean, maximum and minimum temperature for the period , developed in the frame of EU ENSEMBLES project (Van Der Linden and Mitchell, 2009) with the aim of using it for validation of Regional Climate Models and for climate change studies. It was constructed through interpolation of ECA&D station data (the most complete collection of station data over Europe); the number of stations used for the interpolation differs in time and by variable, in particular the period has the highest density, with more precipitation than temperature stations. Additional station data were obtained from other research projects, such as STARDEX (Haylock et al., 2006), or by 25