FMI’s Climate Bulletin Research Letters Spring Issue 2019

1/2019

FMI’S CLIMATE BULLETIN

RESEARCH LETTERS

CONTENTS

FMI’S CLIMATE BULLETIN

RESEARCH LETTERS

1/2019

RESEARCH LETTERS

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-2341-

6408-IK-2019-01-RL

© FMI

PUBLISHER

Finnish Meteorological

Institute (FMI)

P.O. BOX 503

FI-00101 HELSINKI

www.ilmastokatsaus.fi

ilmastokatsaus@fmi.fi

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.

2 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2019

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


DOI: 10.35614/ISSN-2341-6408-IK-2019-03-RL


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


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.

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2019 | 5

DOI: 10.35614/ISSN-2341-6408-IK-2019-04-RL


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


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.


DOI: 10.35614/ISSN-2341-6408-IK-2019-05-RL

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


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



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.


DOI: 10.35614/ISSN-2341-6408-IK-2019-06-RL


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 TUOMENVIRTA 1 , MIKAEL HILDÉN 2 , SANNA LUHTALA 1 , KAROLIINA PILLI-SIHVOLA 1

1

Finnish Meteorological Institute, 2 Finnish Environment Institute

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.


DOI: 10.35614/ISSN-2341-6408-IK-2019-07-RL


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 HARJANNE 1,2 , TUUKKA RAUTIO 1,3

1

Finnish Meteorological Institute, 2 Aalto University School of Business, 3 Aalto University School of Engineering

FIG 1: Urban planning themed climate services workshop in the EU-MACS project.

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 climate

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.


DOI: 10.35614/ISSN.2341-6408-RL-2019-01

10.35614/ISSN-2341-6408-IK-2019-08-RL


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


FIG 1: Value chain segments in climate service provision and typical positions

of actors (source: Cortekar et al., 2017)

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 expertise

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/#


DOI: 10.35614/ISSN-2341-6408-IK-2019-09-RL


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 STRAHLENDORFF 1 , HEIKKI PAJUOJA 2 , HILPPA GREGOW 1

1

Finnish Meteorological Institute, 2 Metsäteho 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 estimated

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.


DOI: 10.35614/ISSN-2341-6408-IK-2019-10-RL


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 VAJDA 1 , OTTO HYVÄRINEN 1 , MATTI KÄMÄRÄINEN 1 , JUHA A. KARHU 1 , PEKKA NIEMI 2 , HILPPA GREGOW 1

1

Finnish Meteorological Institute, 2 Fingrid 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.


DOI: 10.35614/ISSN-2341-6408-IK-2019-11-RL


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


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-CM5A-

MR, MIROC-ESM, MRI-CGCM3, and

eight LS models: MIROC5, CCSM4,

MPI-ESM-MR, CNRM-CM5, EC-EARTH,

BCC-CSM1-1, NorESM1-M and GF-

DL-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 EN-

SEMBLES 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.


Ilmatieteen laitos

ilmastokatsaus@fmi.fi

www.ilmastokatsaus.fi

FMI’s Climate Bulletin Research Letters Spring Issue 2019

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