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Byline: C. E. Hanson (1), J. P. Palutikof (2), M. T. J. Livermore (1), L. Barring (3), M. Bindi (4), J. Corte-Real (5), R. Durao (5), C. Giannakopoulos (6), P. Good (6), T. Holt (1), Z. Kundzewicz (7,8), G. C. Leckebusch (9), M. Moriondo (4), M. Radziejewski (7,10), J. Santos (11,12), P. Schlyter (13), M. Schwarb (14), I. Stjernquist (13), U. Ulbrich (9) Abstract: This paper provides an overview of the aims, objectives, research activities undertaken, and a selection of results generated in the European Commission-funded project entitled 'Modelling the Impact of Climate Extremes' (MICE) -- a pan-European end-to-end assessment, from climate model to impact model, of the potential impacts of climate change on a range of economic sectors important to the region. MICE focussed on changes in temperature, precipitation and wind extremes. The research programme had three main themes -- the evaluation of climate model performance, an assessment of the potential future changes in the occurrence of extremes, and an examination of the impacts of changes in extremes on six activity sectors using a blend of quantitative modelling and expert judgement techniques. MICE culminated in a large stakeholder-orientated workshop, the aim of which was not only to disseminate project results but also to develop new stakeholder networks, whose expertise can be drawn on in future projects such as ENSEMBLES. MICE is part of a cluster of three projects, all related to European climate change and its impacts. The other projects in the cluster are PRUDENCE (Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects) and STARDEX (Statistical and Regional Dynamical Downscaling of Extremes for European Regions). Author Affiliation: (1) Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK (2) Hadley Centre, Met Office, Fitzroy Road, Exeter, EX1 3PB, UK (3) Department of Physical Geography and Ecosystems Analysis, Geobiosphere Science Centre, Lund University, Solvegatan 12, 223 62, Lund, Sweden (4) Department of Agronomy and Land Management, University of Florence, Piazale delle Cascine 18, 50144, Florence, Italy (5) Instituto de Cienca Aplicada e Tecnologia, Faculdade de Ciencas, Universidade Lisboa, Campo Grande, 1749-016, Lisbon, Portugal (6) National Observatory of Athens, Institute of Environmental Research & Sustainable Development, 15236, P. Penteli, Athens, Greece (7) Research Centre for Agricultural and Forest Environment, Polish Academy of Sciences, Bukowska 19, 60809, Poznan, Poland (8) Potsdam Institute for Climate Impact Research, Telegrafenberg, 14412, Potsdam, Germany (9) Institute for Meteorology, Freie Universitat Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany (10) Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614, Poznan, Poland (11) Departamento de Fisica, Centro de Geofisica, Colegio Luis Antonio Verney, Universidade de Evora, R. Romao Ramalho 59, 7000, Evora, Portugal (12) Departamento de Fisica, Universidade de Tras-os-Montes e Alto Douro, Quinta dos Prados, Apartado 1013, 5000-911, Vila Real, Portugal (13) Department of Physical Geography and Quaternary Geology, Stockholm University, 106 91, Stockholm, Sweden (14) Institute of Geography, University of Bern, Hallerstr. 12, 3012, Bern, Switzerland Article History: Registration Date: 05/12/2006 Received Date: 15/02/2005 Accepted Date: 17/10/2006 Online Date: 23/03/2007

The Atlantic hurricane season and the European windstorm season are found to be negatively correlated in a seasonal forecast model. The probability of extremes occurring in both seasons is compared to the probability of extremes in each season being independent of one another. An above average Atlantic hurricane season is followed by an above average European windstorm season less often than if they were independent, consistent across three intensity measures. The El Niño–Southern Oscillation is found to be in the positive (negative) phase when hurricane activity is suppressed (enhanced) and European windstorm activity is enhanced (suppressed). A clear extratropical response in the seasonal forecast model to El Niño/La Niña provides a probable pathway for the observed correlation between the extreme event seasons. This result has important predictability implications for both the actuarial and seasonal forecasting communities. Plain Language Summary: On both sides of the Atlantic Ocean storms with extremely high wind speeds are a natural hazard, resulting in billions of dollars in damages and loss of life. During the late summer and autumn, hurricanes which form in the tropical ocean impact the Caribbean and United States. In the winter, windstorms form in the midlatitude regions primarily impacting Europe. These two seasons are traditionally considered to be unrelated. Here we present evidence that the two are linked through the climate system, specifically the El Niño–Southern Oscillation. An active Atlantic tropical cyclone season precedes an active European windstorm season less often than if the two seasons were independent. Future efforts to predict how damaging the upcoming European windstorm season may be should take this into account, and the insurance industry should be aware that these two risks are not independent. Key Points: Above average Atlantic hurricane seasons are followed by above average European windstorm seasons less often than if they were independentThe El Niño–Southern Oscillation is a consistent factor for both seasons, several months ahead of the European windstorm seasonThis has important predictability implications for both the actuarial and seasonal forecasting communities [ABSTRACT FROM AUTHOR]

This study aims to validate the widely used regional climate model COSMO-CLM driven by ERA-Interim reanalysis data with a spatial resolution of 55 km with respect to observed features of the intraseasonal variability of the Indian summer monsoon (ISM) during the period 1979 until 2011. One of these features is the northward propagation of the ISM intraseasonal oscillations. We find, that the temporal evolution between model and observation is in good agreement, while less agreement with respect to the strength is found. Furthermore, the model's capability to simulate observed dry and wet events on a weekly time-scale is investigated using the standardized precipitation index. In general, the model is capable to simulate these events with a similar magnitude at the same time. Observational based analyses show, that the coupling between atmospheric circulation anomalies and rainfall anomalies over India on the intraseasonal time scale is well represented by the model. The most important circulation anomalies for dry events are a lower tropospheric anti-cyclonic vortex over India and partly an upper tropospheric cyclonic vortex over the Pakistan region and vice versa for wet events. The model shows a slightly higher ability to simulate dry compared to wet events. Overall, this study shows that the current configuration of COSMO-CLM is able to simulate the key features of the intraseasonal variability of the Indian summer monsoon. Being aware of its limitation, COSMO-CLM is suitable to investigate possible changes of the intraseasonal variability of ISM under changed climate conditions in the past or in the future. [ABSTRACT FROM AUTHOR]

A refined model for the calculation of storm losses is presented, making use of high-resolution insurance loss records for Germany and allowing loss estimates on a spatial level of administrative districts and for single storm events. Storm losses are calculated on the basis of wind speeds from both ERA-Interim and NCEP reanalyses. The loss model reproduces the spatial distribution of observed losses well by taking specific regional loss characteristics into account. This also permits high-accuracy estimates of total cumulated losses, though slightly underestimating the country-wide loss sums for storm "Kyrill", the most severe event in the insurance loss records from 1997 to 2007. A larger deviation, which is assigned to the relatively coarse resolution of the NCEP reanalysis, is only found for one specific rather small-scale event, not adequately captured by this dataset. The loss model is subsequently applied to the complete re-analysis period to extend the storm event catalogue to cover years when no systematic insurance records are available. This allows the consideration of loss-intensive storm events back to 1948, enlarging the event catalogue to cover the re cent 60+ years, and to investigate the statistical characteristics of severe storm loss events in Germany based on a larger sample than provided by the insurance records only. Extreme value analysis is applied to the loss data to estimate the return periods of loss-intensive storms, yielding a return period for storm "Kyrill", for example, of approximately 15 to 21 years. [ABSTRACT FROM AUTHOR]

Extreme wind speeds and related storm loss potential in Europe have been investigated using multi-model simulations from global (GCM) and regional (RCM) climate models. Potential future changes due to anthropogenic climate change have been analysed from these simulations following the IPCC SRES A IB scenario. The large number of available simulations allows an estimation of the robustness of detected future changes. All the climate models reproduced the observed spatial patterns of wind speeds, although some models displayed systematic biases. A storm loss model was applied to the GCM and RCM simulated wind speeds, resulting in realistic mean loss amounts calculated from 20th century climate simulations, although the inter-annual variability of losses is generally underestimated. In future climate simulations, enhanced extreme wind speeds were found over northern parts of Central and Western Europe in most simulations and in the ensemble mean (up to 5%). As a consequence, the loss potential is also higher in these regions, particularly in Central Europe. Conversely, a decrease in extreme wind speeds was found in Southern Europe, as was an associated reduction in loss potential. There was considerable spread in the projected changes of individual ensemble members, with some indicating an opposite signature to the ensemble mean. Downscaling of the large-scale simulations with RCMs has been shown to be an important source of uncertainty. Even RCMs with identical boundary forcings can show a wide range of potential changes. The robustness of the projected changes was estimated using two different measures. First, the inter-model standard deviation was calculated; however, it is sensitive to outliers and thus displayed large uncertainty ranges. Second, a multimodel combinatorics approach considered all possible subensembles from GCMs and RCMs, hence taking into account the arbitrariness of model selection for multi-model studies. Based on all available GCM and RCM simulations, for example, a 25% mean increase in risk of loss for Germany has been estimated for the end of the 21st century, with a 90% confidence range of +15 to +35%. [ABSTRACT FROM AUTHOR]

A climatology of cyclones with a focus on their relation to wind storm tracks in the Mediterranean region (MR) is presented. Trends in the frequency of cyclones and wind storms, as well as variations associated with the North Atlantic Oscillation (NAO), the East Atlantic/West Russian (EAWR) and the Scandinavian variability pattern (SCAND) are discussed. The study is based on the ERA40 reanalysis dataset. Wind storm tracks are identified by tracking clusters of adjacent grid boxes characterised by extremely high local wind speeds. The wind track is assigned to a cyclone track independently identified with an objective scheme. Areas with high wind activity - quantified by extreme wind tracks - are typically located south of the Golf of Genoa, south of Cyprus, southeast of Sicily and west of the Iberian Peninsula. About 69% of the wind storms are caused by cyclones located in the Mediterranean region, while the remaining 31% can be attributed to North Atlantic or Northern European cyclones. The North Atlantic Oscillation, the East Atlantic/West Russian pattern and the Scandinavian pattern all influence the amount and spatial distribution of wind inducing cyclones and wind events in the MR. The strongest signals exist for the NAO and the EAWR pattern, which are both associated with an increase in the number of organised strong wind events in the eastern MR during their positive phase. On the other hand, the storm numbers decrease over the western MR for the positive phase of the NAO and over the centralMRduring the positive phase of the EAWR pattern. The positive phase of the Scandinavian pattern is associated with a decrease in the number of winter wind storms over most of the MR. A third of the trends in the number of wind storms and wind producing cyclones during the winter season of the ERA40 period may be attributed to the variability of the North Atlantic Oscillation. [ABSTRACT FROM AUTHOR]

A simple storm loss model is applied to an ensemble of ECHAM5/MPI-OM1 GCM simulations in order to estimate changes of insured loss potentials over Europe in the 21st century. Losses are computed based on the daily maximum wind speed for each grid point. The calibration of the loss model is performed using wind data from the ERA40-Reanalysis and German loss data. The obtained annual losses for the present climate conditions (20C, three realisations) reproduce the statistical features of the historical insurance loss data for Germany. The climate change experiments correspond to the SRES-Scenarios A1B and A2, and for each of them three realisations are considered. On average, insured loss potentials increase for all analysed European regions at the end of the 21st century. Changes are largest for Germany and France, and lowest for Portugal/Spain. Additionally, the spread between the single realisations is large, ranging e.g. for Germany from -4% to +43% in terms of mean annual loss. Moreover, almost all simulations show an increasing interannual variability of storm damage. This assessment is even more pronounced if no adaptation of building structure to climate change is considered. The increased loss potentials are linked with enhanced values for the high percentiles of surface wind maxima over Western and Central Europe, which in turn are associated with an enhanced number and increased intensity of extreme cyclones over the British Isles and the North Sea. [ABSTRACT FROM AUTHOR]