FMI’s Climate Bulletin Research Letters spring issue 2020

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Editorial — 4 Operational risks of sand and dust storms in aviation and solar energy: the DustClim approach — 6 User needs for extreme wind information in the forest, insurance and offshore energy sectors — 8 Sectoral-based indices for creating future climate services — 10 Urban climate services: climate impact projections and their uncertainties at city scale — 12 Climate services for drought and fire danger estimation on various time scales — 14 Information for authors — 17

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS Volume 2 Issue 1 ISSN: 2341-6408 DOI: 10.35614/ISSN-23416408-IK-2020-01-RL

PUBLISHER Finnish Meteorological Institute (FMI) P.O. BOX 503 FI-00101 HELSINKI www.ilmastokatsaus.fi ilmastokatsaus@fmi.fi

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EDITOR IN CHIEF Hilppa Gregow

DESIGN Marko Myllyaho

EDITORIAL COMMITTEE Hadassa Hovestadt Tiina Ervasti

Please mention the source when citing the content.

A DOI is available for each research letter article.

REVIEW BOARD ECRA members

© FMI

ED I TOR I A L

Celeberating Climate Services This is the second year of the FMI’s Climate Bulletin Research Letters. The first issue of 2020 celebrates the Europe-wide ERA4CS projects and their results.

The world of research and science is often crowded with abbreviations and acronyms. This issue is dominated by one: ERA4CS – which is the nickname for the ERA-NET Consortium “European Research Area for Climate Services”. ERA4CS aims to support the research and development of climate services in Europe and thus promote more efficient solutions to cope with current and future climate variability.

Recently it hasn’t only been about the data anymore, neither solely about the information. Next step of climate services is to transform the data and information into knowledge and understanding of climate, climate change and the expected impacts. Demand for guidance in the use of climate knowledge is increasing throughout Europe.

ERA4CS projects are based on reliable climate information and cover variety of activities, such as building tools and developing methods to produce, transfer, communicate and use these novel climate services. Some projects focus on urban needs, others on various high-priority sectors. Most of the projects cooperate and co-develop climate services together with their potential users. User demands and ease of use are the driving forces behind the transfer from data provision to service-based approach.

In total, there are 26 funded ERA4CS projects running in Europe. For several years many research institutes, universities and other national organizations have worked towards efficient climate services with their European colleagues. In the summer of 2020 the results are starting to blossom. In this Research Letters issue you will find compact short papers on results of five ERA4CS projects.

In the past years during these projects, the world has seen devastating wild fires, violent sand storms and cases of extreme winds. All these pose a threat to people, property and functions of society. And all are issues that the ERA4CS projects presented in this issue are aiming to tackle.

Welcome to read and enjoy the brand new issue of the FMI’s Climate Bulletin Research Letters!

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HILPPA GREGOW Editor in chief, FMI’s Climate Bulletin Research Letters

ACKNOWLEDGMENT The members of the executive committee of the European Climate Research Alliance (ECRA) have been acting as the reviewers for the FMI’s Climate Bulletin Research Letters since the beginning.

ECRA is an association of 23 leading European research institutions. ECRA’s objective is to bring together, expand and optimise expertise in climate research through a bottom-up approach.

Read more >> ECRA website

ERA4CS projects are co-developed by many participants.

We wish to acknowledge experts in leading roles from the different institutes

ERA4CS PROJECT DUSTCLIM

INDECIS

SERV_FORFIRE

URCLIM

WINDSURFER

RESEARCHERS IN LEADING ROLES (PRINCIPAL INVESTIGATOR* AND/OR WP LEADER)

PARTICIPATING ORGANIZATIONS

SARA BASART 1* FRANCESCA BARNABA 2, ENZA DI TOMASO1, PAOLA FORMENTI3, LUCIA MONA 2, ADRIAAN PERRELS 4, ENRIC TERRADELLAS 5, ATHANASIOS VOTSIS 4, ERNEST WERNER HIDALGO5

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BARCELONA SUPERCOMPUTING CENTER (BSC), SPAIN CONSIGLIO NAZIONALE DELLE RICERCHE (CNR), ITALY CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE (CNRS), FRANCE 4 FINNISH METEOROLOGICAL INSTITUTE (FMI), FINLAND 5 STATE METEOROLOGICAL AGENCY (AEMET), SPAIN

ENRIC AGUILAR 1* GERARD VAN DER SCHRIER 2 SERGIO VICENTE-SERRANO3 RICHARD ALLAN4 LILIANA VELEA 5 YOLANDA LUNA6 ALI NADIR ARSLAN7 YVAN CABALLERO8 ROBERTO COSCARELLI9 MANUEL DEL JESUS10 ERIK ENGSTRÖM11 PATRICK FOURNET12 ALBERT SORET13 RICARDO TRIGO14 MIREK TRNKA15 HANS VAN DE VYVER16

URV-C3 (UNIVERSITY ROVIRA I VIRGILI / CENTER FOR CLIMATE CHANGE, C3) KNMI (ROYAL NETHERLANDS METEOROLOGICAL INSTITUTE) IPE/CSIC (INSTITUTO PIRENAICO DE ECOLOGÍA / CONSEJO SUPERIOR DE INVESTIGACIONES CIENTÍFICAS) 4 UREAD (UNIVERSITY OF READING) 5 METEORO (NATIONAL METEOROLOGICAL ADMINISTRATION) 6 AEMET (AGENCIA ESTATAL DE METEOROLOGÍA) 7 FMI (FINNISH METEOROLOGICAL INSTITUTE) 8 BRGM/D3E (BRGM / WATER DIVISION) 9 IRPI (CNR-DTA) (NATIONAL RESEARCH COUNCIL OF ITALY - DEPARTMENT OF EARTH SYSTEMS SCIENCE AND ENVIRONMENTAL TECHNOLOGIES — RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION) 10 UC/IHC (UNIVERSIDAD DE CANTABRIA / ENVIRONMENTAL HYDRAULICS INSTITUTE “IH CANTABRIA”) 11 SMHI (SWEDISH METEOROLOGICAL AND HYDROLOGICAL INSTITUTE) 12 MET EIREANN (DEPARTMENT OF THE ENVIRONMENT, COMMUNITY AND LOCAL GOVERNMENT ) 13 BSC (BARCELONA SUPERCOMPUTER CENTER (BSC)/EARTH SCIENCES DEPARTMENT) 14 FFCUL (FUNDAÇÃO DA FACULDADE DE CIENCIAS DA UNIVERSIDADE DE LISBOA UNIVERSIDADE DE LISBOA) 15 GCRI (GLOBAL CHANGE RESEARCH INSTITUTE, CZECH ACADEMY OF SCIENCES) 16 RMI (ROYAL METEOROLOGICAL INSTITUTE OF BELGIUM METEOROLOGICAL AND CLIMATOLOGICAL RESEARCH)

ROSA LASAPONARA1 MONICA PRONTO1 VASSILIKI VARELA 2 ANDREA VAJDA 3 OLIVIER CERDAN4 MIROSLAV TRNKA 5 PETER VAN VELTHOVEN6

CNR –DTA (ITALY) NATIONAL CENTER FOR SCIENTIFIC RESEARCH ”DEMOKRITOS” (GREECE) FINNISH METEOROLOGICAL INSTITUTE (FINLAND) 4 BUREAU DE RECHERCHES GÉOLOGIQUES ET MINIÈRES (FRANCE) 5 CZECHGLOBE - GLOBAL CHANGE RESEARCH INSTITUTE OF THE CZECH ACADEMY OF SCIENCES (CZECH REPUBLIC) 6 THE ROYAL NETHERLANDS METEOROLOGICAL INSTITUTE (NETHERLANDS)

VALÉRY MASSON 1*, BERT VAN SCHAEYBROECK 2, RAFIQ HAMDI2, ZENAIDA CHITU3, LILIANA VELEA 3, ERWAN BOCHER4, BÉNÉDICTE BUCHER5, SIDONIE CHRISTOPHE 5, ARNAUD LE BRIS 5, ADRIAAN PERRELS 6, CARL FORTELIUS 6, EN WICHERS SCHREUR7

MÉTÉO FRANCE, FRANCE ROYAL METEOROLOGICAL INSTITUTE OF BELGIUM (RMI), BELGIUM METEO ROMANIA, ROMANIA 4 CNRS, LAB-STICC, VANNES, FRANCE 5 UNIVERSITÉ GUSTAVE EIFFEL, ENSG, UNIVERSITÉ PARIS EST, LASTIG, IGN, SAINT-MANDÉ, FRANCE 6 FINNISH METEOROLOGICAL INSTITUTE, FINLAND 7 KONINKLIJK NEDERLANDS METEOROLOGISCH INSTITUUT, NETHERLANDS

LEN SHAFFREY 1, PANOS ATHANASIADIS2, ØYVIND BREIVIK 3, ARI VENÄLÄINEN4, PAULA CAMUS 5, PATRICK FOURNET6, BEN WICHERS SCHREUR7, GEORGE EMMANOUIL 8

UNIVERSITY OF READING (UREAD) CENTRO EUROMEDITERRANEO SUI CAMBIAMENTI CLIMATICI (CMCC) NORWEGIAN METEOROLOGICAL INSTITUTE (MET) 4 FINNISH METEOROLOGICAL INSTITUTE (FMI) 5 UNIVERSIDAD DE CANTABRIA – INSTITUTO DE HIDRÁULICA AMBIENTAL DE CANTABRIA (UC-IHC) 6 MET ÉIREANN (METEI) 7 ROYAL NETHERLANDS METEOROLOGICAL INSTITUTE (KNMI) 8 NATIONAL CENTRE FOR SCIENTIFIC RESEARCH – DEMOKRITOS (NCRSD)

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DOI: 10.35614/ISSN-2341-6408-IK-2020-02-RL Received 3 Mar. 2020, accepted 30 Apr. 2020, published 25 May 2020

Operational risks of sand and dust storms in aviation and solar energy: the DustClim approach Sand and dust storms (SDS) entail short- and long-term operational threats for aviation and solar energy production. The ERA4CS DustClim project is assisting these sectors in understanding and reducing SDS-related risks, by producing a state-of-the-art reanalysis climatology and a set of specialized impact analysis products with industry partners.

ATHANASIOS VOTSIS1, FRANCESCA BARNABA2, SARA BASART3, ENZA DI TOMASO3, PAOLA FORMENTI4, ANDERS LINDFORS¹, LUCIA MONA⁵, TUUKKA RAUTIO¹, YIJUN WANG¹, ERNEST WERNER HIDALGO⁶ 1 Finnish Meteorological Institute (FMI), Finland ²Consiglio Nazionale delle Ricerche-Istituto di Scienze dell’Atmosfera e del Clima (CNR-ISAC), Italy ³Barcelona Supercomputing Center (BSC), Spain ⁴LISA, UMR CNRS 7583, Université Paris-Est-Créteil, Université de Paris, Institut Pierre Simon Laplace (IPSL), Créteil, France 5 Consiglio Nazionale delle Ricerche-Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), Italy ⁶State Meteorological Agency (AEMET), Spain

Sand and dust storms (SDS) are an important threat to health, property, equipment, and the economy in many countries, whereas the scales of the threat can be considerable (Fig. 1). In many cases, the risks include loss of lives, substantial disruptions of activities and operations, and economic losses. Some of the risks are expected to increase due to a combination of climatic, social, and technological trends.

DustClim (Dust Storms Assessment for the development of user-oriented Climate Services in Northern Africa, Middle East and Europe) aims to provide reliable information on SDS for developing dust-related services for the air quality, aviation and solar energy sectors. In this paper, we present our approach to developing purpose-specific products that help the aviation and solar energy production industry in understanding and reducing SDS-related risks. We finish with a brief overview of the products.

DustClim is producing a state-ofthe-art dust reanalysis over the domain of Northern Africa, the Middle East, and Europe at an unprecedented high

FIG 1: A sand storm developing over Camp Bastion in Afghanistan, May 2014 (source: Cpl Daniel Wiepen) spatial resolution (10km x 10km) using the state-of-art NMMB-MONARCH model and its advanced data assimilation capabilities (Di Tomaso et al. 2017) as well as quality dust satellite products and their respective uncertainties.

The resulting reanalysis is at present the most advanced basis to understand the long-term risks of operating in risky sand and dust environments.

To exploit this potential in a way that better covers the needs of aviation operations and solar energy production, we identified SDS-related ‘objective threats’ by referring to operation-

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al manuals, regulations and standards, scientific literature, and other technical documentation, the results of which have been in a continuous interplay with the development of the SDS reanalysis climatology, including the evaluation of uncertainty. This allowed to give the necessary focus to both the envisioned products and reanalysis setup. Next, we initiated an ongoing dialogue with industry partners, aiming to expose our product concepts to flexible, expert peer-review, and potential co-refinements of the products.

Our development methodology (Fig. 2

top) has ensured that the products are (a) grounded in state-of-the-art science, (b) are motivated by risks encountered by operations and regulatory standards, and (c) are critically modified into case-relevant knowledge for end-users. Fig. 2 (bottom) summarizes the first round of products.

SDS affect aviation operations mainly through reduced visibility and mechanical effects. Reduced visibility affects mainly navigation and approach/ landing/take-off, route planning, and ground support operations. Mechanical effects include the scouring, corrosion, erosion, or abrasion of aircraft surfaces, instrumentation, and engines and have implications for safety, certification standards, and maintenance schedules and costs (Baddock et al. 2013; Clarkson and Simpson 2017). We address visibility effects through products that indicate (a) exceedances of instrument flight rules, visual flight rules, and low-visibility procedures thresholds for 14 flight levels and various temporal aggregations and (b) implications for airport closures according to their current instrument approach capacity. We address mechanical effects by providing measures of the accumulated exposure of aircraft and engine to particle concentrations for approx. 70000 flight routes and 14 flight levels, which is primarily related to engine abrasion.

In solar energy production, SDS reduce the capacity of solar panels to produce energy through two main channels: by reducing the incoming solar irradiance directly (through absorption and scattering) or indirectly (through dust-induced cloud formation and/or cloud modification) and by depositing material on the solar panel. Deposition affects all three main solar energy technologies: photovoltaics (PV), concentrating solar power (CSP), and concentrating photovoltaics (CPV). Dust

FIG 2: Pillars of DustClim’s product development process (top) and overview of products (bottom) for aviation and solar energy deposited on transparent or glass materials decreases transmissivity capacity, whereas dust deposited on mirror materials decreases reflectance (Sarver et al. 2013; Mejia et al 2013). These effects relate to simple blockage of the incoming irradiance, but also to chemical interaction with the panel’s surface material, which, in addition to blockage, has corrosive effects (Sarver et al.

2013). We focus on deposition effects on solar PV panels, providing products that (a) indicate dry and wet deposition on panel surface, (b) subsequent energy output reduction communicated as a soiling index, (c) optimal cleaning frequency for operating with profit, and (d) investment risk per location.

Acknowledgements: DustClim is supported by the European Commission under the ERA4CS action - Joint Call on Researching and Advancing Climate Services Development, Grant Agreement no. 690462 – ERA4CS – H2020-SC5-2014-2015/H2020SC5-2015-one-stage. Dr Paul Ginoux and Dr Antonis Gkikas are gratefully acknowledged for their collaboration in the development of the project. The World Meteorological Organization (WMO) Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) Regional Center for Northern Africa, the Middle East and Europe as well as the EU COST Action inDust (CA16202) are also acknowledged for providing indispensable support to the user interaction activities.

Baddock, M. C., et al., 2013: Aeolian dust as a transport hazard, Atmos. Environ., 71, 7–14. DOI: https://doi.org/10.1016/j.atmosenv.2013.01.042 Clarkson, R., and Simpson, H., 2017: Maximising Airspace Use During Volcanic Eruptions: Matching Engine Durability against Ash Cloud Occurrence, NATO STO AVT-272 Specialists Meeting on “Impact of Volcanic Ash Clouds on Military Operations” Volume: 1.

Di Tomaso, E., et al., 2017: Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107–1129, DOI: https://doi.org/10.5194/gmd-10-1107-2017 Mejia, F., et al., 2013: The effect of dust on solar photovoltaic systems, Energy Procedia, 49, 2370–2376. DOI: https://doi.org/10.1016/j.egypro.2014.03.251 Sarver, T., et al., 2013: A Comprehensive Review of the Impact of Dust on the Use of Solar Energy: History, Investigations, Results, Literature, and Mitigation Approaches, Renew. Sust. Energ. Rev., 22, 698–733, DOI: https://doi.org/10.1016/j.rser.2012.12.065 FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2020 | 7

DOI: 10.35614/ISSN-2341-6408-IK-2020-03-RL Received 28 Feb. 2020, accepted 30 Apr. 2020, published 25 May 2020

User needs for extreme wind information in the forest, insurance and offshore energy sectors To be able to tailor extreme wind information in order to address the needs of various endusers, a synthesised understanding summarizing these needs is required. In this article we summarize the user needs for extreme wind information in the forestry, insurance and off­ shore energy sectors. For the offshore energy, wave risk information needs are also included.

ARI VENÄLÄINEN1, ILARI LEHTONEN1, MIKKO LAAPAS1, HILPPA GREGOW1, LEN SHAFFREY2, ØYVIND BREIVIK3,4, PAULA CAMUS5 1 Finnish Meteorological Institute Reading University Norwegian Meteorological Institute 4 University of Bergen 5 IH Cantabria 2

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Extreme winds pose major risks to life, property and forestry, while extreme ocean waves can impact on offshore infrastructures and coastal communities. In the ERA4CS project “WIND and wave Scenarios, Uncertainty and climate Risk assessments for Forestry, Energy and Reinsurance” (WINDSURFER), the consortium partners have co-developed new methods, tools and assessments of extreme wind and wave risk with a focus on the selected three key sectors: insurance, forestry and offshore energy. This article summarizes the user needs identified in the project for extreme wind information in these three key sectors. For the offshore energy, wave risk information needs are also included.

Forestry. Wind is the dominant abiotic cause of forest damages in Europe (Seidl et al. 2014; Reyer et al. 2017; Gregow et al. 2017).

The mapping of user needs for the forestry sector was done as an outcome of a stakeholder workshop that was held as a part of the project “Sustainable, climate-neutral and resource-efficient forest-based bioeconomy” (FORBIO) (https://www.uef.fi/web/forbio).

The identified forestry sector’s needs for meteorological and climate data consists of: a) forecasts and warnings which include information on sustained and gust wind speed and wind direction with spatial resolution of EU NUTS 3 (https://ec.europa.eu/ eurostat/web/nuts/background) or higher.

Forecasts should: a) contain information

FIG 1: 10-year return levels of maximum wind speed (m s-1) during period of frozen (left) and unfrozen (middle) soil in pine forests growing on peatlands. The difference between the two maps (FROZEN-UNFROZEN) is shown on the right.

Adapted from Laapas et al., (2019). about the reliability (skill) of the forecast, b) an estimate about the possible magnitude of damage caused by the (forecasted) storm, c) climate scenarios that cover both near future and extend until the end of this century, and include information on the possible change in extreme wind speeds and possible change of prevailing wind direction, d) climate scenarios should include information also on other factors

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such as soil frost and soil wetness which influence wind throw risk, e) information on the occurrence, frequency and strength of past storms during the recent decades and openly available high spatial resolution GIS datasets, for example, depicting 10-year return level of maximum wind speed under frozen and unfrozen soil conditions (Fig. 1).

The skill of forecast varies from a weather condition to another. In item a) mentioned

needs to have an estimate about the forecast skill that can be understood and that the forecaster includes it into the forecasts an estimate about the possibility to make accurate forecasts under the prevailing weather conditions.

Insurance sector. The damage to infrastructure, property and life from damaging windstorms still account for the largest sources of insurance loss in Europe. For example, the three windstorms (Anatol, Lothar, Martin) that struck in Dec 1999 inflicted €18 billion of insured losses to property and infrastructure across Europe.

To better assess the risks from European Windstorms, the insurance sector requires improved information to assess windstorm risk. The results for the insurance sector are based on the understanding gained in the Copernicus Climate Change Services (C3S) SECTEUR project on user needs for Climate Services. For insurance, the primary need for information is for European Windstorm risk, especially spatial maps of maximum wind gusts for major historical storms. This is for assessing wind damage and/or for evaluating industry pricing models (known as ‘catastrophe models’). There is a growing need for information on climate change and insurance risk, which is largely driven by financial regulators (such as the Bank of England for the London market).

Energy sector and offshore infrastructures. Extreme waves driven by strong winds can have substantial impacts on the energy sector and offshore infrastructures.

For example, a windstorm on 12 December 1990 generated a significant wave height of about 12 m in the central North Sea (close to the 100-year return level), causing extensive damage on the Ekofisk platforms. Estimating the probabilities of extreme wind and wave events requires observations to be supplemented or combined with atmospheric and wave models, such as the recently completed NORA10EI wind and wave hindcast (see Fig. 2, from Haakenstad

FIG 2: Map of 100-year return values of 10-m wind speed (upper panels) and significant wave height (lower panels) for the period 1979–2017. Estimated based on NORA10 are shown on the left, panels (a) and (c). The corresponding estimates for NORA10EI are found on the right, panels (b) and (d). The NORA10EI based estimates are higher than the NORA10 (Reistad et al., 2011) estimates, particularly in wind speed (up to 5 m s-1 difference), less so in significant wave height. Adapted from Haakenstad et al., (2020). et al., 2020). Here we compare the 100year return values of NORA10EI (Haakenstad et al., 2020) with the older NORA10 hindcast (Reistad et al., 2011), the former funded by WINDSURFER. NORA10EI uses ERA-Interim on the boundaries for consistent forcing (Dee et al., 2011). The differences are generally quite modest, except for the region south of the Faroe Islands (where the 100-year return estimate of the wind speed is 5 m s-1 higher), but it is clear that the choice of boundary and initial conditions can significantly influence the return values from high-resolution hindcasts.

In the case of offshore energy, return levels of extreme events with very low probabilities (long return periods) are essential in-

formation. Regulators and owners require consideration of 1,00-year to 10,000-year events. Hindcasts and reanalyses are useful for these calculations as the relatively short observational time series available hinder calculation of robust estimates for such extreme events. One can also consider using simulated cases, e.g. a large ensemble of climate model simulations to expand the available amount of data (Breivik et al., 2014, Meucci et al., 2018).

Acknowledgements: We thank Strategic Research Council of the Academy of Finland project Forbio (project number 314224), and the ERA4CS Windsurfer project. ØB is grateful to Equinor for additional funding of the offshore hindcast archives.

Breivik, Ø., et al., 2014: Wind and Wave Extremes over the World Oceans From Very Large Ensembles, Geophys. Res. Lett., 41(14).

Dee, D., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137(656).

Gregow, H., et al., 2017: Increasing large scale windstorm damage in Western, Central and Northern European forests, 1951-2010, Scientific Reports, 7, 46397.

Haakenstad, H. et al., 2020: NORA10EI: A revised regional atmosphere-wave hindcast for the North Sea, the Norwegian Sea and the Barents Sea, Int. J. Climatol., DOI: https://doi.org/10.1002/joc.6458 Laapas, M., et al., 2019: The 10-year return levels of maximum wind speeds under frozen and unfrozen soil forest conditions in Finland. Climate, 7, 62.

Meucci, A., et al., 2018: Wind and Wave Extremes from Atmosphere and Wave Model Ensembles, J. Climate, 31(21).

Reistad, M., et al., 2011: A high-resolution hindcast of wind and waves for the North Sea, the Norwegian Sea, and the Barents Sea, J. Geophys. Res., 116.

Reyer C., et al., 2017: Are forest disturbances amplifying or cancelling out climate change-induced productivity changes in European forests? Environ. Res. Lett., 12, 034027.

Seidl, R., et al., 2014: Increasing forest disturbances in Europe and their impact on carbon storage, Nature Clim. Change, 4(9), 806–810.

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2020 | 9

DOI: 10.35614/ISSN-2341-6408-IK-2020-04-RL Received 2 Mar. 2020, accepted 30 Apr. 2020, published 25 May 2020

Sectoral-based indices for creating future climate services Climate services are defined as the provision of climate information in a way that assists decision making by individuals and organizations. Climate indices are useful synthetic measures, easily comprehensible for the stakeholders, managers, end-users and the public in general.

ALI NADIR ARSLAN1, ANDREA VAJDA1, OTTO HYVÄRINEN1, KATRIINA VEIJOLA1, SERGIO M. VICENTE-SERRANO2, LILIANA VELEA3, ENRIC AGUILAR4 Finnish Meteorological Institute (FMI) Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC) 3 National Meteorological Adminstration (MeteoRo) 4 University Rovira I Virgili (URV)

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The project “Integrated approach for the development across Europe of user oriented climate indicators for GFCS high-priority sectors: agriculture, disaster risk reduction, energy, health, water and tourism”, INDECIS, constitutes a pan-European effort for developing an integrated approach to produce a set of relevant climate indices targeting the high priority sectors of the World Meteorological Organization’s Global Framework for Climate Services (GFCS) on agriculture, disaster risk reduction, energy, health, water plus tourism. The INDECIS consortium (see www.indecis.eu) includes 16 institutions from 12 countries and intends to maximize the benefits achievable from the use of observational data across Europe to develop climate indicators and climate services useful to assess the effects of climate variability, including extreme events, and climate change over socioeconomic systems. INDECIS consists of seven Work Packages (WPs) which address (a) identification and preparing a catalog of climate data sets and portal, (b) data quality and homogeneity, (c) definition and implementation of indices, (d) evaluation of gridded datasets, and (e) communication of climate services developed in the project.

The INDECIS project prepared a comprehensive catalogue of a broad range of standard climate indices on the targeted sectors. The list of the full catalogue of climate indices can be found in the report, entitled, “Report on the Inventory and Catalog of Indices Datasets”. The software suite for indices calculation can be found together with all reports from the webpage of the project (www.indecis.eu).

Non-climatic data covering a wide array of sectorial data in Europe, mainly focusing on agriculture, human health, water resources, energy and tourism were collected. These datasets include different statistics, spanning a broad range of specific sectorial information, such as forest fires, reservoir storages, landslides, mortality/morbidity, hydropower production, road accidents, crop yields, phenological indicators, economic and human losses, water availability, groundwater quality, tourism nights, etc. The purpose of collecting sectoral data is to assess the calculated climate indices and create future climate services that can provide local and national European decision makers and stakeholders with information and tools required for better adaptation and planning to climate change. The sectorial data in Finland were gathered from the Finn-

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ish tourist board, Statistics Finland, the Finnish Ministry of economic affairs and employment, Visit Finland, Natural resources institute in Finland.

The details of the sectoral data gathered in other European countries are available and can be accessed from the project webpage. Based on data availability, the sectorial data were provided at different temporal scales (e.g. daily, monthly, and annual) and at various spatial scales (e.g. hydrological catchment, administrative divisions [e.g. counties, governments], and national level). As an example, statistics related to tourism sector in Finland are available via the Statistical Service Rudolf, in collaboration with Visit Finland and Statistics Finland.

We will continue to assess the usefulness of the climate indices using the sectorial data available focusing on tourism, agriculture and energy. The prediction skill of seasonal forecasts over the Northern latitude is gradually improving; summer temperature has shown perfect to marginally useful skill (Weisheimer and Palmer 2014, Wehrli et al. 2017), while precipitation a lower although not negligible skill (Weisheimer and Palmer 2014, Mishra et al. 2017). Driven by the need of end-users for tailored seasonal forecast products, we assessed

the applicability of seasonal forecast outputs in agriculture and winter tourism, and developed and piloted a set of seasonal climate indices for the targeted sectors in Finland. The seasonal forecast indices were selected, designed and tested together with end-users and were developed using the SEAS5 seasonal forecast system provided by ECMWF (Johnson et al.

2019). The quality of forecasts was assessed using re-forecast (or hindcast) data for the period 1993-2016, and the substantial systematic biases from raw model outputs were reduced by applying various bias-adjustment methods (Vajda and Hyvärinen, submitted to ASR). Six temperature and precipitation-based indices serving the agriculture, i.e. mean temperature, growing season progress, growing degree days, cold spell, total precipitation, dry conditions were co-designed with the Central Union of Agricultural Producers and Forest Owners from Finland. The computed indices were compiled into seasonal climate outlooks (Fig. 1) consisting of three monthly maps (lead month 0, 1 and 2) and piloted with more than 200 farmers during June-October 2019, followed by another pilot phase during April-October 2020. In addition to the forecast performance analysis performed at the end of the first pilot, a feedback survey was conducted with the farmers in order to gather their opinion about the usability of the newly developed outlooks. Similar approach was applied in the development process of the five seasonal indices targeting ski-resorts: mean temperature, probability of snow cover, snow depth, conditions for artificial snow production, occurrence of maximum wind speed. The Finnish

FIG 1: Seasonal climate outlooks describing the development of growing season lead months 0, 1 and 2 issued in July 2019. The categories behind normal and ahead describe the development of growing season compared to climatology. Probabilities (%) within these categories expressed through colour shades describe the proportion of members lying in the tercile categories (below 33%, 33-66% and above 66%) of the forecast (source: Vajda and Hyvärinen, submitted, 2020).

Ski Association and six ski resorts from Finland were involved in the design of seasonal indices and the on-going testing phase during November 2019-April 2020. The developed seasonal forecast services might contribute to the creation of tailored climate services for the end-users. Involving the users in co-designing and testing of seasonal forecast products provided us qualitative information both on the requirements concerning both the content and visualization of the new forecast products and their usability and value.

Acknowledgements: This work is conducted under INDECIS project part of ERA4CS, an ERA-NET initiated

by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

Johnson, S. J., et al., 2019: SEAS5: The new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, DOI: https://doi.org/10.5194/gmd-12-1087-2019 Mishra, N., Prodhomme, C. and Guemas, V., 2019: Multi-model skill assessment of seasonal temperature and precipitation forecast over Europe, Clim. Dyn., 52, 4207-4225, DOI: https://doi.org/10.1007/s00382-018-4404-z Vajda, A. and Hyvärinen, O., 2019: Development of seasonal climate outlooks for agriculture in Finland, submitted to ASR Special Issue “19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019”.

Wehrli, K., Bhend, J. and Liniger, M. A., 2017: Systematic quality assessment of an operational seasonal forecasting system, Technical Report MeteoSwiss, 263, 52 pp.

Weisheimer, A. and Palmer, T. N., 2014: On the reliability of seasonal climate forecasts, J. Roy. Soc. Interface, 11, DOI: https://doi.org/10.1098/rsif.2013.1162 FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2020 | 11

DOI: 10.35614/ISSN-2341-6408-IK-2020-05-RL Received 2 Mar. 2020, accepted 11 May 2020, published 25 May 2020

Urban climate services: climate impact projections and their uncertainties at city scale In many cities across Europe, both urban authorities and private actors have made strong commitments to adapt to future climate changes. Although a lot of climate information is available at the global and regional scale, this is often not the case at the local urban scale. Moreover, such information should account for a wide range of uncertainty factors ranging from global to city-scale development scenarios to uncertainties due to model errors. In an effort to lay the methodological groundworks for reliable urban climate services, URCLIM explores a compound handling of these uncertainties for various European cities and applies it to the assessment of adaptation measures.

BERT VAN SCHAEYBROECK1, BÉNÉDICTE BUCHER3, ZENAIDA CHITU2, SIDONIE CHRISTOPHE3, CARL FORTELIUS4, RAFIQ HAMDI1, VALÉRY MASSON5, ADRIAAN PERRELS4, BEN WICHERS SCHREUR6 1 Royal Meteorological Institute of Belgium (RMI), Belgium 2 Meteo Romania, Romania 3 Univ Gustave Eiffel, LASTIG, IGN, ENSG, France 4 Finnish Meteorological Institute, Finland 5 Météo France, France 6 Koninklijk Nederlands Meteorologisch Instituut, Netherlands

The URCLIM project and the uncertainty issue - Climate change will be strongly felt in cities through increased health and socio-economic risks. This originates from the strong urban exposure due to high density in population, infrastructure and emissions, but also from increased risks due to more extreme weather conditions. Moreover, the urban heat island effect (UHI; Oke et al., 2017) leads to temperatures that are typically higher within the city than in the surrounding rural areas.

Especially during heat waves, the absence of night-time cooling poses a threat to the most vulnerable in the population. Following the Paris Agreement, cities worldwide committed to introduce sustainable climate-resilient solutions, for instance

through the Covenant of Mayors.

In order for these cities to develop well-informed plans for climate action, reliable information is required (Bader et al., 2018). Such information necessitates an accurate evaluation of uncertainties owing to the uncertain pace of global warming, uncertainty in urban development, and shortfalls in the impact evaluation tools for local scales. By combining the expertise in urban climate, geomatics, urban mapping, socio-economics and health impacts, URCLIM aims to realize a proof of concept for integrated Urban Climate Services (UCS) for the metropolitan areas of Helsinki, Toulouse, Brussels, Amsterdam, and Bucharest obtained through a close collaboration with city authorities.

12 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2020

URCLIM objectives & purposes The objective of developing UCS at the scale pertinent for urban planners is currently being established through 1) the production of high-resolution urban maps for incorporation in urban climate models, obtained using a generic processing chain that is easily generalizable to other cities, (https://github.com/orbisgis/geoclimate), 2) development of methodological best practices for urban impacts (see next paragraph), 3) assess the separate and combined impact of adaptation strategies and climate-change and city-development scenarios, and, 4) co-creation of innovative 3D visualization and data interaction tools.

Methods & uncertainty exploration URCLIM aims to distil UCS information from raw data taken from state-of-the-art

climate projections and observational datasets. To estimate uncertainties, it is necessary to take a close look at the data-generation process. Indeed, depending on the impact different steps are required, each of which is associated with uncertainty factors. URLIM has considered these uncertainties in detail for UHI, floods, air quality, human comfort and socio-economic impacts as for instance shown in Fig. 1.

One can distinguish the uncertainties that the scientists aim to minimize (observational, model or analysis uncertainties) from those associated with the unknown future including scenarios for greenhouse gases as well as city-context scenarios or intervention scenarios. Each step can also be categorized by one of the components of risk: hazards (orange colour in Fig. 1), exposure or vulnerability (green). A major URCLIM achievement is the development of a methodology for uncertainty propagation from the global to the urban scale. More specifically, a computationally-cheap method for producing decades-long physically-coherent climate simulations, obtained by downscaling the CORDEX ensemble of regional climate model projections down to kilometre resolution.

Prospects & Follow-up - URCLIM provides a proof-of-concept for science-based Urban Climate Services for a few cities with a strong focus on generalizability and aimed at the most prominent urban impacts. Generaliz-

FIG 1: Steps necessary to arrive at the raw data necessary to distil UCS information. A few uncertainty factors affecting this process and investigated in URCLIM, specified in the table are indicated in the scheme.

ability is ensured by means of opensource and open-access tools to generate high-resolution urban maps, the use of freely available CORDEX data and smart visualization tools, in close collaboration with city actors.

Acknowledgements: This work received funding from EU’s H2020 Research and Innovation Program under Grant Agreement number 690462.

Oke, T., et al., 2017: Urban Climates. Cambridge, Cambridge University Press, DOI: https://doi.org/10.1017/9781139016476 Bader, D. A., et al., 2018: “Urban Climate Science,” in Climate Change and Cities, eds. C. Rosenzweig, P. Romero-Lankao, S. Mehrotra, S. Dhakal, S. Ali Ibrahim, and W. D. Solecki (Cambridge: Cambridge University Press), 27–60 FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2020 | 13

DOI: 10.35614/ISSN-2341-6408-IK-2020-06-RL Received 2 Mar. 2020, accepted 1 May 2020, published 25 May 2020

Climate services for drought and fire danger estimation on various time scales Wildfires are critical natural hazards, both in the Mediterranean and boreal regions of Europe, causing significant environmental and economic damages and losses.

Operational drought and fire risk forecast services on sub-seasonal, seasonal and climatic scale allow fire protection authorities to increase preparedness and response in drought and fire related emergencies and to develop mitigation and adaptation strategies in these regions.

ANDREA VAJDA¹, CECILIA WOLFF1, OTTO HYVÄRINEN¹, MASSIMILIANO PASQUI², VASSILIKI VARELA³, PETER VAN VELTHOVEN⁴, FOLMER KRIKKEN⁴, MONICA PROTO⁵, ROSA LASAPONARA⁵ ¹Finnish Meteorological Institute ²Italian National Research Council – Institute for the BioEconomy ³National Centre for Scientific Research ”Demokritos” ⁴The Royal Netherlands Meteorological Institute ⁵Italian National Research Council – Institute of Methodologies for Environmental Analysis

The ERA4CS SERV_FORFIRE project aims to develop operational services for fire assessment, monitoring and mitigation strategies from seasonal up to climatic time scale for different ecosystems and geographic areas ranging from Southern to Northern Europe. The tailored drought, fire and post-fire products have been implemented with the involvement of users, i.e. national and local authorities and decision makers in order to increase the user uptake and improve the preparedness level and efficiency of fire authorities in risk management. The usefulness of the developed services was demonstrated through applications both in Southern and Northern Europe. Four of the implemented and demonstrated drought and fire risk forecasting products and their related case studies are introduced in this paper. The selected case studies cover various time scales ranging from sub-seasonal to seasonal and climatic scale, all of them providing essential information on the meteorological conditions triggering wildfires in the present and future climate.

FIG 1: Standardized precipitation index (3-month) empirical seasonal forecasts are disseminated through the WebGIS application based on open source solutions customized to integrate different datasets and share maps and graphs of drought indices with researchers, decision makers and other stakeholders (https://drought.climateservices.it/en/).

Southern Europe and the Mediterranean basin are considered possible hot spots for drought and its consequences for fire risk and water security (Prudhomme et al. 2014). To reduce the temporal gap existing between the drought onset and development, and the response in managing the related emergencies, the Italian National

14 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2020

Research Council developed a seasonal forecasting system for drought for the Mediterranean basin to provide a semi-automatic, timely and comprehensive operational service for decision making, water authorities, researchers and stakeholders (Fig. 1).

The seasonal forecasting component of the system is based on an empirical

Probability of forest fire risk (%) > 95 % 80 - 95 % 60 - 80 % 40 - 60 % 20 - 40 % 5 - 20 % < 5%

FIG 2: An example of 6-week forest fire risk climate outlook issued to the end-users for testing on July 22, 2019.

approach that predicts meteorological drought up to three months in advance using the standardized precipitation index, from large-scale observed climate indices. This empirical system adopts a physically based statistical approach which uses a multivariate regression model to estimate future anomalies. Predictors are selected from observed atmospheric (Seasonally Varying NH Annular Mode, Arctic Oscillation, North Atlantic Oscillation, and Modified zonal index) and oceanic sea surface anomaly patterns (Atlantic Multidecadal Oscillation, Multivariate ENSO Index among others) as described in details in Magno et al. 2018.

To extend the fire warning services run by the Finnish Meteorological Institute, a novel six-week forest fire danger outlook prototype was developed and piloted with Finnish end-users, i.e. the Regional State Administrative Agency (AVI) from Northern Finland and the Finnish Rescue Services from North Karelia during the fire season 2019. The sub-seasonal fire forecast product would complement the already existing forest fire risk monitoring system and the regularly issued short-range forest fire warnings. To predict the probability of sub-seasonal fire danger a statistical model originating from the Finnish Forest Fire Index (FFI) was developed and tested (Wolff et al. 2019). The Finnish Forest Fire Index (FFI) is determined by estimating the volumetric moisture of a 60 mm thick surface

FIG 3: Seasonal Severity Rating values (top) and number of days per year with FWI greater than 50 (bottom) for past and near-future 10-year time period in Eastern Attica, Greece, calculated from an ensemble of two EURO-CORDEX regional climate models under RCP4.5. layer (Vajda et al. 2014) and it is already used by FMI in the operational fire monitoring and warning system.

The six-week forest fire outlooks (Fig. 2) were produced using as input the ECMWF EPS system data and observed daily FFI values to spin-up the index and delivered to the users twice a week through an operational service.

According to the feedback received from the end-users, the outlooks were

very useful when planning in advance the fire survey flights. The product is further improved and iterated again with the users during the fire season 2020.

Eastern Attika (Greece), is a wildland-urban interface area frequently affected by forest fires. Fire risk estimation for the area and its change in the future was performed by the National Centre for Scientific

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2020 | 15

Norrland

Svealand

Götaland

W@H 1C EC-Earth 1C CESM 1C models 1C avg EC Earth 2C CESM 2C models 2C avg Risk ratio [-] FIG 4: Risk ratios for maximum July-August FWI values as high as observed in 2018 for the different regions for multiple climate models. The risk ratios are calculated as following: first the return times of this specific event for the pre-industrial, current and future climate were calculated. If this event becomes more likely in a current or future climate it has lower return times. The risk ratio is computed by dividing the return time of the pre-industrial climate by the return time for e.g. current climate and indicates how much more likely such an event will occur in the current climate (red) and future climate (yellow) relative to pre-industrial climate.

Research “Demokritos” computing and mapping the Canadian FWI system components (Van Vagner 1987) on a daily basis during the fire seasons (May-October) for ten years, for the past (2006-2015) and near future (2036-2045) climate. The Canadian Fire Weather Index (FWI) represents the potential fire line intensity and it is a good indicator of general fire danger. The Daily Severity Rating (DSR), calculated as an exponential function of FWI, is a numeric rating of the difficulty of controlling fires, whereas Seasonal Severity Rating (SSR) is the averaged DSR during a fire season.

These FWI components and their maps (Fig. 3) were produced using an ensemble of two high resolution (12.5 km) EURO-CORDEX regional climate models with medium emission scenario (RCP4.5).

In the summer of 2018 forest fires raged through large parts of Sweden.

To answer the question to what extent

climate change has influenced such extreme event a climate attribution analysis was performed by The Royal Netherlands Meteorological Institute by studying the Canadian FWI in multiple observational and climate datasets. Analysis showed that the maximum FWI in July 2018 had return times of ~24 years. The climate models pointed to a ~10% increased risk for such events in the current climate relative to pre-industrial climate.

For the future climate (2°C warming) a roughly 2 times increased risk for such events relative to pre-industrial climate was found, which is mainly attributed to the increase in temperature. In summary, a small but positive role of global warming up to now in the 2018 forest fires in Sweden, but a more robust increase in the risk for such events in the future was found (Fig. 4).

The climate services implemented and demonstrated through the

presented case studies provide science-based, useful tools for end-users and decision makers in the fire-prone regions facilitating their preparedness and development of adaptation strategies on various timescales.

Acknowledgements: The study contributes to the ERA4CS SERF_ FORFIRE project (grant 690462), an ERA-NET initiated by JPI Climate with co-funding from the European Union.

Magno, R., et al., 2018: Semi-automatic operational service for drought monitoring and forecasting in the Tuscany region, Geosciences, 8(2), 49, DOI: https://doi.org/10.3390/geosciences8020049 Prudhomme, C., et al., 2014: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, Proceedings of the National Academy of Sciences, 111(9), 3262-3267 Vajda A., et al., 2014: Assessment of forest fire danger in a boreal forest environment: description and evaluation of the operational system applied in Finland, Meteorological Applications, 21(4), 879–887, DOI: https://doi.org/10.1002/met.1425 Van Wagner, C. 1987: Development and Structure of the Canadian Forest Fire Weather Index System, Technical Report 35, Canadian Forestry Service, Ottawa, Ontario.

Wolff C., Vajda A., and Hyvärinen O., 2019: Developing a model for forest fire risk forecast at sub-seasonal scale in Finland, FMI’s Climate Bulletin: Research Letters, 1(2), 6, DOI: https://doi.org/10.35614/ISSN-2341-6408-IK-2019-15-RL 16 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2020

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