FMI’s Climate Bulletin Research Letters Spring Issue 2019

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The ECMWF monthly forecast predicted the Finnish heat wave in summer 2018 — 4 2018: An exceptionally warm thermal growing season in Finland — 5 2018: An exceptionally dry thermal growing season in Finland — 6 Thermal sensation studies with children at the Heureka Summer Science Camp — 7 Assessment of Weather and Climate Risks in Finland — 8 Re-thinking how climate services are talked about — 9 What makes a climate service useful? — 10 Bioenergy production condition indicator for managing risks to forestry for Copernicus Climate Change Service — 11 A freezing rain impact indicator tailored for the European energy sector — 12 Do high and low climate sensitivity GCMs show differences in projected precipitation changes in Finland? — 13

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS Volume 1 ISSN: 2341-6408 DOI: 10.35614/ISSN-23416408-IK-2019-01-RL © FMI

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 EDITORIAL COMMITTEE Hadassa Hovestadt Tiina Ervasti REVIEW BOARD ECRA members

DESIGN Marko Myllyaho Please mention the source when citing the content.

A DOI is available for each research letter.

DOI: 10.35614/ISSN-2341-6408-IK-2019-02-RL Received 28 Mar. 2019, accepted 11 June 2019, available online 20 June 2019

The ECMWF monthly forecast predicted the Finnish heat wave in summer 2018 The beginning and ending of the prolonged heat wave in mid-summer 2018 was predicted by the ECMWF ensemble forecasts 12-18 days in advance.

NATALIA KORHONEN, OTTO HYVÄRINEN, REIJA RUUHELA, ANNA LUOMARANTA, HILPPA GREGOW Finnish Meteorological Institute

In summer 2018, prolonged heat waves in many areas around the Northern Hemisphere led to record-breaking temperatures, severe droughts, crop failures, and forest fires (WMO, 2018).

In Fennoscandia, the dry and warm May 2018, was followed by a prolonged heat wave between 9th July and 12th August 2018, during which the weekly mean temperatures were mostly 1-6 degrees above average as depicted in Fig. 1, first column. The National Institute for Health and Welfare (THL) estimated that the heat wave caused 380 excess deaths (THL, 2018).

The heat waves in summer 2018 were in part caused by the weaker than average jet stream causing stationary high pressure systems. Further, it has been estimated that global warming more than doubled the probability of this heat wave to occur in many places in northern Europe (Otto, 2016; Schiermeier, 2018).

In Fig. 1 columns 2-5 show the weekly mean temperature outlooks by the monthly ensemble forecast of the European Centre for Medium-Range Weather Forecasts (ECMWF; Molteni et al., 2011). These ECMWF forecasts predicted both the beginning and the ending of higher than usual weekly mean temperatures during the heat wave about 12-18 days in advance.

Further, for the mature phase of the heat wave (time period 30th July to

FIG 1: Weekly temperature anomalies computed using ECMWF operational analysis and reanalysis for a given week (first column), ECMWF’s ensemble forecasts 1 to 4 weeks earlier (columns 2 to 5). The weekly mean anomalies are displayed relative to the past 20 year climate. The model anomalies are relative to the model climate computed from the model back-statistics. Blue and red areas are significant at 10% level, contours at 1% level. The areas where the ensemble forecast is not significantly different from the ensemble climatology are blanked. Figure source: ECMWF. 12th August), the forecasts succeeded in predicting the persistence of higher than usual weekly mean temperatures as much as 26-32 days in advance.

Acknowledgements: We thank ECMWF and the Academy of Finland (project number 303951, SA CLIPS) for their support.

Molteni, F., and Coauthors, 2011: The new ECMWF seasonal forecast system (System 4). ECMWF Tech. Memo. 656, 49 p.

National Institute for Health and Welfare (THL), 2018: URL: https://thl.fi/en/-/viime-kesan-helleaalto-lisasi-ikaantyneiden-kuolleisuutta-helteisiin-on-hyva-varautua-ajoissa Otto, F.E.L., 2016: The art of attribution, Nature Climate Change, 6, p. 342–343.

Schiermeier, Q. 2018: Droughts, heatwaves and floods: How to tell when climate change is to blame. Nature, 560, p. 20–22.

World Meteorological Organization (WMO), 2018: July sees extreme weather with high impacts, URL: https://public.wmo.int/en/media/news/july-sees-extreme-weather-high-impacts 4 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2019

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Received 28 Mar. 2019, accepted 11 June 2019, available online 20 June 2019

2018: An exceptionally warm thermal growing season in Finland The summer of 2018 saw exceptionally high temperatures leading to record-breaking effective temperature sum in large parts of Finland. Years with a similarly warm thermal growing season are expected to become more frequent in the future.

ILARI LEHTONEN, PENTTI PIRINEN Finnish Meteorological Institute

The effective temperature sum, or the growing degree day (GDD) sum is a widely used measure of the intensity of thermal growing season (TGS). TGS is defined to begin when the daily mean temperature rises above a selected threshold (5 °C in Finland) in spring and snow has melted from open areas.

In autumn, TGS terminates when daily mean temperature falls permanently below the same threshold. GDD sum is then calculated by summing the daily mean temperature excess above the threshold during TGS. The summer of 2018 was very warm in northern Europe leading to exceptionally high GDD sums. While GDD sum varies in a typical year between 1200 and 1500 °C days in southern Finland and in the north from 600 to 900 °C days, with the exception of highest elevated areas, GDD sum in 2018 exceeded these normal values widely by 300–400 °C days (Fig. 1).

Highest GDD sums in 2018 in Finland were more than 1900 °C days in the south, corresponding to typical values in Poland (Wypych et al., 2017). Over most of Finland GDD sum was record high in 2018. In Helsinki, the previous record from 2011 was exceeded by over 100 °C days but in Sodankylä in northern Finland, GDD sum remained short compared to the year 1937 (Fig. 2).

FIG 1: Growing degree day sum (°C days) of thermal growing season in Finland in 2018 (left) and averaged over the period 1981–2010 (right).

On a longer time scale, periodicity matching with the phase of the Atlantic multidecadal oscillation (AMO) (Polonskii, 2008) can be seen in the variability of GDD sums, including a rising trend after the 1980s both in Helsinki and Sodankylä (Fig. 2). During the last approximately 10 years, GDD sums have been on average even as high as the climate projections indicate for the mid-21st century (Ruosteenoja et al., 2011). Hence, it can be hypothesized that the rapid increase in GDD sums after the 1980s might be partly attributable to AMO and only partly to anthropogenic climate change. However, by the end of

FIG 2: Growing degree day sum (°C days) of thermal growing season in Helsinki and Sodankylä from 1900 onwards. Thin lines show the 30-year moving averages. Data prior to 1959 is not completely comparable with the rest of the period e.g., due to the different calculation scheme of daily mean temperature. the 21st century, GDD sums similar to those in 2018 are projected to become typical in Finland (Ruosteenoja et al., 2011, 2016). Moreover, as the climate warming continues, it will soon become very unlikely to have a cool TGS as evaluated by current climate statistics whereas the probability to have an anomalously warm TGS, like 2018, will increase rapidly (Ruosteenoja et al., 2016).

Polonskii, A. B., 2008: Atlantic multidecadal oscillation and its manifestations in the Atlantic-European region. Phys. Oceanogr., 18, 227–236.

Ruosteenoja, K., et al., 2011: Projected changes in thermal seasons and the growing season in Finland. Int. J. Climatol., 31, 1473–1487.

Ruosteenoja, K., et al., 2016: Projections for the duration and degree days of the thermal growing season in Europe derived from CMIP5 model output. Int. J. Climatol., 36, 3039–3055.

Wypych, A., et al., 2017: Variability of growing degree days in Poland in response to ongoing climate changes in Europe. Int. J. Biometeorol., 61, 49–59.

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DOI: 10.35614/ISSN-2341-6408-IK-2019-04-RL Received 28 Mar. 2019, accepted 11 June 2019, available online 20 June 2019

2018: An exceptionally dry thermal growing season in Finland In addition to high temperatures, the thermal growing season of 2018 in Finland was characterized by low precipitation, particularly in May and early June. Accompanied with warm and sunny weather, low precipitation lead to harsh dryness.

ILARI LEHTONEN, PENTTI PIRINEN Finnish Meteorological Institute

Precipitation deficit, defined as a difference between potential evaporation and precipitation, can be used as a measure for drought severity (e.g., Hao et al., 2018). At Finnish Meteorological Institute, potential evaporation is routinely calculated with the Penman-Monteith equation (Monteith, 1981) by using gridded daily weather data at 10 km × 10 km grid (Venäläinen and Heikinheimo, 2002). Comparable data goes back to 2003. Potential evaporation describes the amount of evaporation that would occur if a sufficient water source were available.

In the Finnish conditions, soil is typically moist in early spring. Evaporative demand is small during winter and melting snow provides additional moisture in spring. Hence, drought severity can be assessed by calculating cumulative precipitation deficit from the beginning of thermal growing season. In summer, potential evaporation usually exceeds precipitation leading to an increasing precipitation deficit towards the end of thermal growing season. On the driest years, however, severe drought may occur already during early summer. For spouting of cultivated plants and crops this may be specifically harmful.

The thermal growing season of 2018 in Finland was not only exceptionally warm but also very dry. Particularly in May and early June no rain

FIG 1: The maximum precipitation deficit (mm) during thermal growing season in 2018 until the end of June. fell in wide areas for several weeks.

As May was at the same time record warm and sunny, precipitation deficit started to accumulate rapidly and by the end of June, precipitation deficit had exceeded 200 mm virtually everywhere in southern and western Finland (Fig. 1). Compared to recent years, the maximum precipitation deficit until the end of June was mostly the largest (Fig. 2). On many years, the maximum precipitation deficit of the whole thermal growing season had remained smaller. Considering the whole summer, both at Vantaa in southern Finland and at Sodankylä in northern Finland, precipitation deficit was almost as high as in 2006 which

FIG 2: The maximum precipitation deficit (mm) during thermal growing season at Vantaa in southern Finland (top) and at Sodankylä in northern Finland (bottom) until the end of June (brown curves) and in July and August (green curves) during 2003–2018. has been generally considered the driest summer during the recorded history in Finland (Nordlund, 2006).

As potential evaporation is largely affected by temperature, droughts are expected to occur more frequently in the future due to global warming. In Finland, it has been estimated that as severe drought as occurred in the late 20th century once in a decade could occur in the late 21st century every second or third year (Ruosteenoja et al., 2018).

Hao, Z., et al., 2018: Seasonal drought prediction: Advances, challenges, and future prospects. Rev. Geophys., 56, 108–141.

Monteith, J. L., 1981: Evaporation and surface temperature. Quart. J. Roy. Meteor. Soc., 107, 1–27.

Nordlund, A., 2006: Laajasti kaikkien aikojen kuivin hellekesä. Ilmastokatsaus, 11, 4.

Ruosteenoja, K., et al., 2018: Seasonal soil moisture and drought occurrence in Europe in CMIP5 projections for the 21st century. Clim. Dyn., 50, 1177–1192.

Venäläinen, A., and Heikinheimo, M., 2002: Meteorological data for agricultural applications. Phys. Chem. Earth, 27, 1045–1050.

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Received 28 Mar. 2019, accepted 14 May 2019, available online 20 June 2019

Thermal sensation studies with children at the Heureka Summer Science Camp Finnish Meteorological Institute experts conducted a survey about the thermal perception of children during their participation in a summer camp week in 2018.

The study showed that children could express themselves in thermal comfort surveys.

ACHIM DREBS, REIJA RUUHELA, ANTTI MÄKELÄ Finnish Meteorological Institute

Thermal sensation studies with children are scarce due to difficulties to interview children and find suitable thermal conditions to carry out the survey (Rupp et al., 2015). As a part of the Horizon 2020 iSCAPE project (Improving the Smart Control of Air Pollution in Europe) (iSCAPE–project, 2018) living lab activities were embedded into summer activities aimed for children within the Science Camp of the Finnish Science Centre Heureka (HEUREKA, 2018).

A total of about 700 children taking part in the Camp were organized to groups based on their age; from 7 to 13 and from 14 to 16 years, respectively. For the younger children, there was every day of the week up to four different activities related to various fields of science, including also meteorology and climate. The FMI iSCAPE team assisted in designing this activity.

Furthermore, for the children at the age from 13 to 16 years, a unique program was organized in three weeks during the Camp.

During the summer camp, there was a period (31.7.–2.8.2018) with high daily air temperature, fair wind and clear sky conditions. This period was suitable to survey the children’s thermal sensations on consecutive days to find out if there are changes in thermal sensation between the hot days.

FIG 1: Results of the 3-day Heureka summer camp survey on thermal sensation, 31.7.–2.8.2018.

The structure of the survey was simple; only one question was asked: How did you experience the outdoor air temperature this morning on your way to the Camp? The answer was given by touching on a poll pad to a 5-step scale from 1 (red smiley = really uncomfortable) to 5 (green smiley = very comfortable). Up to 99 children participated in the survey. The percentage of collected answers lay between 85 and 90%. The results are presented in Fig. 1.

The share of uncomfortable thermal sensation increased during the study period together with increasing

temperature. The change from day one to day three was statistically significant with p-value = 0.04 (Chi-squared-test).

Acknowledgement: The iSCAPE project has received funding from the European Community’s H2020 Programme under Grant Agreement No. 689954.

Finnish Science Centre HEUREKA, 2018. Available at: https://www.heureka.fi iSCAPE–project, 2018: Improving the Smart Control of Air Pollution in Europe. Available at: https://www.iscapeproject.eu Rupp, R.F., et al., 2015: A review of human thermal comfort in the built environment. Energy and Buildings [Electronic journal], 105, 178–205.

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DOI: 10.35614/ISSN-2341-6408-IK-2019-06-RL Received 28 Mar. 2019, accepted 11 June 2019, available online 20 June 2019

Assessment of Weather and Climate Risks in Finland An assessment of hydro-meteorological and climatic risks was prepared for several sectors in Finland based on literature review and expert judgment. A governance model for organising future assessment of weather and climate risks was developed to support climate change adaptation and disaster risk management needs.

HEIKKI TUOMENVIRTA1, MIKAEL HILDÉN2, SANNA LUHTALA1, KAROLIINA PILLI-SIHVOLA1 Finnish Meteorological Institute, 2Finnish Environment Institute

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The hydro-meteorological and climatic risks were assessed as a combination of the hazard, exposure and vulnerability using the framework adopted by the Intergovernmental Panel on Climate Change (IPCC). (IPCC, 2012) The framework can deal with the influence of both the changing climate and socio-economic factors on risks. In the current climate, weather events pose identifiable risks to Finnish infrastructure, citizens and businesses, as demonstrated by recent events. In the future, the risks will change as climate change will affect frequency, severity and seasonal timing of adverse hydro-meteorological events. Risks are likely to increase, especially for ecosystems and infrastructure. Hydro-meteorological events and climate change outside Finland can indirectly affect Finnish society through global flows and movements of commodities, energy, finance and humans.

(Tuomenvirta et al., 2018) The management of weather and climate risks can be improved by introducing a common governance model for risk assessments. Such assessments should support adaptation policies and the implementation of risk reductions nationally and regionally. The proposed model uses timely and coherent information on societal development, hazards, exposure and

FIG 1: Governance model for weather and climate risk assessment. (Hildén, M., et al., 2018). Climatological hazards cover hydro-meteorological, other weather-related and climate change hazards. vulnerability (Fig. 1). This information is synthesized into climate risk assessments that are updated at regular intervals to meet the requirements of the Climate Act. (Hilden et al., 2018) The governance model is designed to deliver sector-specific risk assessments from interoperable basic data and scenarios; ultimately merging them into a national climate risk assessment. The model is also suited to guide regional and municipal risk assessments. It supports the assessment of the consequences of harmful

weather events, emerging risks and cross-border effects. The development of monitoring of risks factors, increasing coverage of geo-referenced data, information sharing between stakeholders, implementation of new technologies and maintaining longterm monitoring are all critical to the success of future risk assessments.

Acknowledgements: The work was funded by the Government’s Analysis, Assessment and Research Activities (SIETO-project).

IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp.

Tuomenvirta, H., et al., 2018: Sää- ja ilmastoriskit Suomessa – Kansallinen arvio. (Weather and Climate Risks in Finland – National Assessment; in Finnish, abstract in English). Prime Minister’s Office Finland. Publications of the Government’s analysis, assessment and research activities 43/2018. 107 p.

Hildén, M., et al., 2018: Assessing and monitoring hydrometeorological and climate risk is an investment in safety and well-being. Policy Brief. Prime Minister’s Office Finland. Article series of Government’s analysis, assessment and research activities 23/2018. 4 p.

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Received 28 Mar. 2019, accepted 11 June 2019, available online 20 June 2019

Re-thinking how climate services are talked about Climate service development faces an interesting paradox: User orientation is a fundamental aspect of climate services, yet the concept of climate services did not originate from the users and remains unclear for them.

ATTE HARJANNE1,2, TUUKKA RAUTIO1,3 Finnish Meteorological Institute, 2Aalto University School of Business, 3Aalto University School of Engineering

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Climate service has become an increasingly hot topic among European Union research and innovation policymakers and some academic circles. These services can be defined simply as providing climate information, data or products to someone. For example, these services can vary from forecasts to economic analyses (EU, 2015). Climate change adaptation and disaster risk reduction are perhaps the most typical contexts of climate services along with different uses of seasonal forecasts, and as a process, climate service development and delivery is focused on user needs.

The idea of climate services is not new, it emerged already in 1970’s (Harjanne 2017) but remained somewhat marginal until the introduction of the Global Framework for Climate Services (GFCS) by the World Meteorological Organization (WMO) in 2011 (WMO, 2011).

Recently, European Union has become a major promoter of climate services.

Guided largely by the European Research and Innovation Roadmap for Climate Services (EU, 2015), the union has funded over 30 projects concerning climate services research, innovation and development. Besides developing services, themselves, the project aims have included support in creating functional climate services markets. EU-MACS and MARCO are examples of such projects.

The Finnish Meteorological Institute (FMI) has been an active partner in cli-

FIG 1: Urban planning themed climate services workshop in the EU-MACS project. mate service research, development and innovation projects. In this work we have observed a major challenge regarding the engagement of users and potential users. It seems that many climate services are not a sufficiently attractive concept for most people outside the field. Sometimes the reason can be low prioritization of climate issues or low interest towards climate information in general, but there seems to be more to it. It may be that the idea of climate services does not address the stakeholders’ views well enough. This is also indicated by the observation that arranging research interviews has been easier than getting workshop or survey participants, but often it has turned out in the beginning of the interview that the interviewee has a faint if any understanding on what the climate services are.

This means we have a paradox in our hands. User orientation is a fundamental aspect of climate services, yet

climate services are not a user-oriented concept. Based on our experiences, it seems that in developing climate services it is worthwhile to take a step back and listen, with the aim to understand how climate risks are framed and conceptualized in different fields and industries, instead of offering uncustomized solutions. A prime example is the formation of Task Force on Climate-related Financial Disclosures (TFCD) that has presented a new, holistic approach on climate risk management in the finance sector (TFCD, 2017). In general, ethnographic methods could offer a fruitful path in future research work.

In the end, it is important to remember, that climate services are means for certain ends, not ends in themselves.

For more information about recent studies on climate services, see: EU-MACS (EUropean MArket for Climate Services) and MARCO (Market research for a Climate Services Observatory).

EU, 2015: A European Research and Innovation Roadmap for Climate Services. Publications Office of the European Union, Luxembourg.

Harjanne, A., 2017: Servitizing climate science–Institutional analysis of climate services discourse and its implications. Global Environmental Change 46 (2017) 1–16.

TFCD, 2017: Final Report - Recommendations of the Task Force on Climate-related Financial Disclosures, WMO, 2011: Climate Knowledge for Action: A Global Framework for Climate Services −Empowering the Most Vulnerable, The Report of the High-Level Taskforce for Climate Services, WMO Report No. 1065, Geneva, Switzerland.

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10.35614/ISSN-2341-6408-IK-2019-08-RL DOI: 10.35614/ISSN.2341-6408-RL-2019-01 Received 28 Mar. 2019, accepted 11 June 2019, available online 20 June 2019

What makes a climate service useful?

A climate service is useful if a user can identify benefits that can be appropriated, and which are expected to be larger than the costs of acquisition and use of the climate service, while users’ benefits should not go at the expense of third parties.

ADRIAAN PERRELS, ATTE HARJANNE, JUHA A. KARHU, VÄINÖ NURMI, KAROLIINA PILLI-SIHVOLA, TUUKKA RAUTIO, REIJA RUUHELA, HEIKKI TUOMENVIRTA Finnish Meteorological Institute

We understand ‘climate services’ (hereafter CS) as the transformation of climate related data, often together with other information, into customized information products, offered as such or visualized or embedded in consultancy and/or education (condensed version of EC Roadmap definition – EC 2015). This means that CS can entail quite different things, such as seasonal vs. adaptation oriented CS or qualitative guidelines vs. visualized model output.

Despite wide spread referencing to the Roadmap definition, actual development and provision of CS is very often still mainly driven by science & technology push rather than demand pull (Lourenço et al., 2016).

In the FMI led EU-MACS study factors that impede the uptake of CS in various sectors were assessed (Hoa et al., 2018). Among others CS are hitherto predominantly developed by public expert organisations, which by their very nature are less inclined to ponder their position in the CS value chain (Fig. 1), while their strengths and resources tend to emphasize the upstream part.

Public CS developers and providers should ponder their position in the value chain as well as those of other providers. The more downstream in the value chain the higher the (potential) value added of a CS gets, but also the more diverse the required exper-

FIG 1: Value chain segments in climate service provision and typical positions of actors (source: Cortekar et al., 2017)

tise and input information tends to be.

In turn this means that CS provision increasingly needs to be supported by proper business models, in order to have viable and societal beneficial CS. During the market and business model analysis the end-users’ benefit generation process and its functionality preconditions should get clear.

By then one should know what constitutes a useful CS – at least for that user. This clarification process should also enable the provider - in cooperation with the user - to provide an estimate of the amount of value the CS can create for the user.

Another point of attention is standardization of terms and product categories, which supports quality control and comparison. Where relevant, quality assurance should extend to non-climate data when moving along the value chain, and be clearly communicated.

Acknowledgements: EU-MACS received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 730500.

Cortekar, J. et al., 2017: Review and analysis climate service market conditions, EU-MACS Deliverable 1.1.

Hoa, E. et al., 2018: From generating to using climate services – How the EU-MACS and MARCO projects help to unlock the market potential, Climate Services, 11, 86–88.

Lourenço, T.C., et al., 2016: The rise of demand-driven climate services, Nature Climate Change, 6, 13–14.

Perrels, A. et al (2018): A Structured Analysis of Obstacles to Uptake of Climate Services and Identification of Policies and Measures to Overcome Obstacles so as to Promote Uptake, EU-MACS Deliverable 5.1, 02.12.2018. http://eu-macs.eu/outputs/# 10 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2019

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Received 28 Mar. 2019, accepted 11 June 2019, available online 20 June 2019

Bioenergy production condition indicator for managing risks to forestry for Copernicus Climate Change Service Novel services to support growth of bioeconomy and management of risks to forestry are crucially needed. Within the Copernicus Climate Change Service (C3S) project Clim4Energy, a proof of concept for a soil bearing indicator was developed and demonstrated. This indicator aimed to support the planning in timing winter harvesting operations for current machinery.

MIKKO STRAHLENDORFF1, HEIKKI PAJUOJA2, HILPPA GREGOW1 1 Finnish Meteorological Institute, 2Metsäteho Oy

In Finland, Bioeconomy is a vital element of societal welfare. For instance, pulp factories have grown to produce also energy, renewable fuel and other chemicals. Pulpwood is a major resource to be secured sustainably, but also economically. In 2014, the Finnish Government set an aim for the Bioeconomy sector to create 100 000 new jobs by 2025.

Mitigation goals and climate change impacts on the forestry sector are difficult to combine economically. Finland’s forests have been growing fast during recent decades due to increasing warming. At the same time the winter ground bearing has weakened and harvesting conditions have become worse (Gregow et al., 2011 and Siren M., 2000).

Within the project Clim4Energy, the Finnish Meteorological Institute and Metsäteho Oy co-developed an indicator, which is simple to use to assess winter conditions for harvesting operations. Soil bearing in boreal zone is known to be excellent with soil freezing to 20 cm depth or snow cover being 40 cm thick (Eeronheimo 1991). If one of the two is met, it awards good conditions. Poor conditions were estimat-

ed for less than 5 cm of frozen soil and less than 10 cm snow depth. Frozen soil depth input is not directly available in reanalysis (Dee et al., 2011) or numerical forecast data. Based on our evaluation, using model soil temperature at available depth layers (0–7 cm, 7–28 cm, 28–100 cm, 100–289 cm) is not realistic either – soil freezes too deep and too fast compared to observed freezing. An empirical equation for frozen soil depth (fsdt = prev + 0.0591 - (t2m - 273.15) * 0.079 - sd 0.0161 (Gregow et al., 2011)) based on relations from soil freezing observations to daily 2m air temperature (t2m in K) and snow depth (sd in m) is applicable and was used in our proof of concept.

The bioenergy production condition indicator shows the areas and timing of good conditions (value=2) both in time and space (Fig. 1). It also indicates when the conditions are poor (value=0) or uncertain (1).

The indicator was used when piloting seasonal forecasts of the bioenergy production conditions during winter 2016–2017. The demonstrations are available at http://c4e.fmi.fi/.

FIG 1: Pilot service at http://c4e.fmi.fi/ Map shows frozen soil depth on 24.12.2016 and graphs depicting mean and ensemble quantiles 20 and 80 for the indicators frozen soil depth and snow depth.

Acknowledgements: Copernicus C3S Clim4Energy project funded this effort.

Dee, D. P., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597.

Eeronheimo, O., 1991: Suometsien puunkorjuu. Folia Forestalia 779, 29 p.

Gregow H., et al.; Ilmatieteen laitos Raportteja 2011:5 Lumettoman maan routaolojen mallintaminen ja ennustettavuus muuttuvassa ilmastossa.

Kellomäki, S., et al., 2010: Model computations on the Climate Change Effects on Snow Cover, Soil Moisture and Soil Frost in the Boreal Conditions over Finland. Silvia Fennica 44(2).

Sirén, M., 2000: Metsätieteen aikakauskirja 2/2000; Turvemaiden puunkorjuun kehittäminen.

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DOI: 10.35614/ISSN-2341-6408-IK-2019-10-RL Received 28 Mar. 2019, accepted 14 May 2019, available online 20 June 2019

A freezing rain impact indicator tailored for the European energy sector A novel freezing rain impact indicator tailored for the European energy infrastructure was developed within the Copernicus Climate Change Service (C3S) CLIM4ENERGY project. The indicator is available for the users through the C3S Climate Data Store.

ANDREA VAJDA1, OTTO HYVÄRINEN1, MATTI KÄMÄRÄINEN1, JUHA A. KARHU1, PEKKA NIEMI2, HILPPA GREGOW1 1 Finnish Meteorological Institute, 2Fingrid Oy

Freezing rain (FZRA) is one of the costliest high impact winter phenomena causing substantial damages to energy infrastructure due to the heavy ice accumulation. Energy companies require knowledge on the climate change impact on severe freezing rain to be able to increase the preparedness of energy infrastructure. Driven by this need, a pan-European freezing rain impact indicator tailored for the energy sector was developed in the C3S CLIM4ENERGY project during 2015–2017. The indicator was co-designed and tested together with the Finnish power transmission grid operator, Fingrid Oy. All the energy indicators developed in the project are now publicly available through a visualization tool (http://c4e-visu.ipsl.upmc.fr/), providing a variety of maps, data, documentation, product evaluation and fact sheets.

The occurrence of FZRA events were deduced from precipitation, temperature and relative humidity values by applying a freezing rain detection algorithm (Kämäräinen et al. 2016). The severity is given in two intensity categories: a) 10 mm/24 h is aimed at catching severe events that danger distribution lines and b) 25 mm/24 h is catching extreme events that start damaging more resistant transmission lines and transformer stations.

ERA-Interim reanalysis data (Dee et al.

2011) and an ensemble of EURO-CORDEX regional climate models (Kotlarski et al., 2014) with medium (RCP4.5) and strong (RCP8.5) emission scenarios were used as input data. The indicator is presented

FIG 1: Change in the 30 year sum of freezing rain by 2050 exceeding 10 mm/24 h compared to the reference period 1971-2000 for a moderate emission scenario (RCP4.5). through a set of frequency maps, a variety of statistical analyses and graphs, time series of occurrence of events, freezing rain amounts. Since prevailing wind conditions can intensify the damage caused by ice accumulation, information on wind speed during the FZRA events is also provided, as this was specifically desired by our co-designer, Fingrid.

The results indicate that severe FZRA is a relatively rare phenomenon over Europe. The occurrence of FZRA events with impact for distribution and transmission networks is highest in south-eastern Europe and the southern coast of Norway where over 20 cases with 10 mm/24h and locally up to 5 cases with 25 mm/24h have occurred during 1981-2010. Most of the severe cases observed in the present climate are shortlived, their length not exceeding one day.

Climate projections predict a northward shift in the occurrence of severe FZRA

cases in Fennoscandia and Russia by 2050. A slight decrease in the occurrence is projected over central Europe.

The developed freezing rain impact indicator was validated through multiple processes, including validation of the FZRA detection using ERA-Interim data against SYNOP observations and comparing distributions of FZRA amounts from ERA-Interim and RCMs. In addition, the most severe FZRA cases detected from ERA-Interim were also validated.

The indicator allows both power distribution and transmission system operators to build resilience and develop prevention strategies for the safety of energy infrastructure in the future decades.

Acknowledgement: The study contributes to the C3S CLIM4ENERGY project funded by EC in the C3S program. We acknowledge the EURO-CORDEX dataset provided by SMHI, KNMI and GERICS.

Dee, D.P. et al., 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597.

Kotlarski, S., et al., 2014: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geoscientific Model Development, 7, 1297–1333.

Kämäräinen, M. et al., 2016: A method to estimate freezing rain climatology from ERA-Interim reanalysis over Europe. Nat. Hazards Earth Syst. Sci. 12 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2019

DO I : 1 0. 3561 4/ I SS N - 2 341 - 6 4 0 8- I K- 2 01 9-1 1 -RL

Received 28 Mar. 2019, accepted 14 May 2019, available online 20 June 2019

Do high and low climate sensitivity GCMs show differences in projected precipitation changes in Finland?

In this study, 13 bias corrected GCMs are divided into groups of high and low climate sensitivity and both groups’ projected precipitation changes are studied separately. It is shown that yearly precipitation increases much more with high climate sensitivity models.

JANI RÄIHÄ Finnish Meteorological Institute

Global climate models (GCMs) from the Coupled Model Intercomparison Phase 5 (CMIP5; van Vuuren et al., 2011) can be grouped by their sensitivity to feedback mechanisms such as water vapour and clouds; they are accordingly categorized into high climate sensitivity (HS) and low climate sensitivity (LS) models. Higher sensitivity to feedback mechanisms results in more pronounced changes in temperature and precipitation in the future climate (Mauritzen et al., 2017).

Sherwood et al. (2014) found that HS models are more consistent with observations than LS models.

This study uses simulation results of precipitation from 13 GCMs under the RCP8.5 scenario (Riahi at al., 2011). There are five HS models: CanESM2, GFDL-CM3, IPSL-CM5AMR, MIROC-ESM, MRI-CGCM3, and eight LS models: MIROC5, CCSM4, MPI-ESM-MR, CNRM-CM5, EC-EARTH, BCC-CSM1-1, NorESM1-M and GFDL-ESM2M. GCMs were downscaled following method 8 (M8), empirical quantile mapping bias correction method (Räty et al., 2014), conducted separately for each calendar month in 1981–2010 (baseline period) and 2071– 2100 (projection period). Observed

FIG 1: Mean annual precipitation change from 1981–2010 to 2071–2100. Grid points with significant change according to Wilcoxon signed-rank test at 5% level are marked with black dot. precipitation data for downscaling was provided in a 10 km x 10 km grid resulting from a kriging-interpolation procedure (Aalto et al., 2016).

There is a clear difference between HS and LS models in the magnitude of precipitation change from 1981–2010 to 2070–2100 (Fig. 1). HS models show an average relative change of +25% ranging from about +20% to +30%, while LS models reveal an average increase of only 13% ranging from 10% to 18%.

Changes are statistically significant in all of Finland for both HS and LS.

This study shows that climate sensitivity differences in GCMs have a large effect on the magnitude of projected precipitation changes in Finland. For some impact studies it might be useful to treat HS and LS models separately as Mauritzen et al. (2017) proposes.

Acknowledgements: I would like to thank Kimmo Ruosteenoja and Matti Kämäräinen from FMI for providing data and computer code that made this study possible.

Aalto, J. et al., 2016: New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. Journal of Geophysical Research: Atmospheres, 121, 3807–3823.

Mauritzen C. et al., 2017: On the relationship between climate sensitivity and modelling uncertainty. Tellus A: Dynamic Meteorology and Oceanography, 69, 1327765.

Riahi, K. et al., 2011: RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clim. Chang., 109, 33–57.

Räty, O. et al., 2014: Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations. Clim. Dynam., 42, 2287–2303.

Sherwood, S. C. et al., 2014: Spread in model climate sensitivity traced to atmospheric convective mixing. Nature, 505, 37–42. van Vuuren, D. P. et al., 2011: The representative concentration pathways: an overview. Clim. Chang., 109, 5–31.

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Ilmatieteen laitos ilmastokatsaus@fmi.fi www.ilmastokatsaus.fi