FMI’s Climate Bulletin Research Letters Autumn Issue 2019

2/2019

FMI’S CLIMATE BULLETIN

RESEARCH LETTERS

CONTENTS

FMI’S CLIMATE BULLETIN

RESEARCH LETTERS

2/2019

RESEARCH LETTERS

Thunderstorm observations in Finland –

historical observations since 1887 — 4


lightning location data 2002–2018 — 5

Developing a model for forest fire risk

forecast at sub-seasonal scale in Finland — 6

Simulating a 2014 wildfire in Lieksa (Finland) using

the Canadian Prometheus fire growth simulation model — 7

14 years of extended snow depth and snow water

equivalent measurements in eastern parts of Finland — 8

A comparative analysis of climate

change denialism in Europe — 9

Managing Development of a Novel Climate

Service Application called the “DECM App” — 10

Co-design process used in the

development of the “DECM App” — 11

The effect of atmospheric winds on

recent temperature changes in Finland — 12

Information for authors — 13

FMI’S CLIMATE BULLETIN:

RESEARCH LETTERS

Volume 1

ISSN: 2341-6408

DOI: 10.35614/ISSN-2341-

6408-IK-2019-12-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 2/2019

DOI: 10.35614/ISSN-2341-6408-IK-2019-13-RL

Received 10 Sep. 2019, accepted 13 Dec. 2019, available online 19 Dec. 2019


historical observations since 1887

Historical time series of occurrence of thunderstorms in Finland reveals a large

year-to-year variation. On average, there are annually 134 000 cloud-to-ground

flashes in Finland.

TERHI K. LAURILA, ANTTI MÄKELÄ

Finnish Meteorological Institute

Thunderstorms and related phenomena,

such as lightning, wind gusts and heavy

rainfall, can lead to considerable damage,

economic losses and even cause fatalities.

Therefore, the research of thunderstorms

is important not only from meteorological

perspective but also for the

safety and preparedness of the society.

In Finland, thunderstorm observations

have been recorded since 1887

when human observations of thunder

days were started. An automatic flash

counter network was established in

1960 which enabled the detection of

the number of cloud-to-ground flashes.

Since the lightning location system became

operative at Finnish Meteorological

Institute in 1998, also the lightning

strike point and other lightning statistics

have been recorded (Mäkelä et al.

2010). Hence, currently there are 132

years of thunder day observations and

59 years of flash number observations to

investigate the long-term occurrence of

thunderstorms in Finland. For more details

on the observations, see Tuomi and

Mäkelä (2008) and Mäkelä et al. (2010).

A thunder day is defined as a day when

lightning is observed in an observation

site. Time series of thunder days show a

large annual and decadal variation (Fig.

1a). High-activity periods occurred, for example,

during 1934–1940 and 1984–1988.

In contrast, years 1950–1955 had low activity

in thunder days. There is no clear trend

in the long-term variation; however, there

FIG 1: a) Average annual number of thunder days, b) average annual cloud-toground

flash density (flashes/100 km -2 ). Red line is the 10-year running mean.

seems to be a periodicity in the high and

low thunderstorm activity years. The average

annual number of thunder days for

the period 1887–2018 in Finland was 10,7.

Annual number of cloud-to-ground

flashes in Finland has also a large annual

variation (Fig. 1b). The years with

the highest annual amount of lightning

were 1972, 1988 and 2003 whereas the

lowest number of flashes occurred in

1996, 2015 and 2017. On average, there

are annually 134 000 cloud-to-ground

flashes in Finland, and the average annual

flash density is 0,37 km -2 yr -1 . It

should be noted that even during the

years of low lightning activity, individual

thunderstorms may have reached

high intensity; in fact, typically a major

part of annual lightning in Finland is

from a few intense thunderstorm days.

Mäkelä, A., et al., 2010: A decade of high-latitude lightning location: Effects of the evolving location network in Finland, Journal of Geophysical

Research: Atmospheres, 115(D21), DOI: 10.1029/2009JD012183

Tuomi, T.J., and Mäkelä A., 2008: Thunderstorm climate of Finland 1998–2007, Geophysica, 44(1–2), 67–80.


DOI: 10.35614/ISSN-2341-6408-IK-2019-14-RL



lightning location data 2002–2018

Lightning location data from an extensive period can be used to characterise

the typical occurrence of thunderstorms and lightning. In Finland, most of the

lightning occurs in the western parts.

ANTTI MÄKELÄ, TERHI K. LAURILA


Thunderstorms cause annually high societal

impacts worldwide and also in the

Northern Europe. Nowadays, lightning location

systems and the data they provide

are an essential part of weather services.

In addition to the real-time observations

of lightning occurrence, the data enables

the analysis of, for example, the local

thunderstorm climatology.

In this study, we present the updated

cloud-to-ground flash density and thunder

day statistics of Finland from the period

2002–2018; from this period, the lightning

location system can be considered homogeneous

with respect to cloud-to-ground

lightning. The lightning location system

of the Finnish Meteorological Institute

(FMI) is at present practically the same

as described in earlier studies (Mäkelä et

al. 2010; Mäkelä et al. 2014). The main difference

compared to the previous studies

is the longer data period, extending now

to almost twenty years. We note that the

FMI’s location system was established already

in 1998, but the coverage of all of

Finland was established in 2002. Cloud-toground

flash density and the average annual

number of thunder days are the most

commonly used parameters for describing

the occurrence of lightning and thunderstorms.

Flash density is calculated by the

sum of observed lightning in spatial grid

during the data period and dividing the

sum by the data period in years. The thunder

day number is originally defined as the

FIG 1: Annual average cloud-to-ground flash density (km -2 yr -1 ; left) and annual

average number of thunder days (right) in 2002–2018.

number of days per year that lightning is

heard at an observation site (WMO, 1956).

Although the original definition is based

on human observations, the number can

be calculated also from lightning location

data as explained in Mäkelä et al. (2014).

The occurrence of cloud-to-ground

lightning and thunder days (Fig. 1) show

both similar and dissimilar spatial features.

Firstly, the south-north distribution is largely

similar, indicating that most of the thunderstorm

and lightning activity take place in

the southern half of the country. Indeed, the

division between Northern and Southern

Finland is very sharp. This is likely related to

the shorter summer (i.e., convective) season

in the North. Secondly, there is a sharp contrast

between the sea and land areas, and

especially the Gulf of Bothnia seems to mitigate

the occurrence of deep moist convection

and formation of thunderstorms. Over

the Gulf of Finland, the effect is not as clear,

suggesting that this water area is too narrow

for mitigating thunderstorms coming

from the South. The largest dissimilarity in

the average annual occurrence of lightning

and thunder days is that the regions of largest

values do not necessarily overlap. For

example, some locations near the southern

coast show high amount of thunder days

although the amount of lightning does not

peak there; this suggests that there are differences

in the intensity of individual thunderstorms,

i.e., in some places the thunderstorms

do not produce as much lightning as

others (and vice versa).

Mäkelä, A., et al., 2010: A decade of high-latitude lightning location: Effects of the evolving location network in Finland, Journal of Geophysical

Research: Atmospheres, 115(D21), DOI: 10.1029/2009JD012183

Mäkelä, A., et al., 2014: Nordic Lightning Information System: Thunderstorm climate of Northern Europe for the period 2002–2011, Atmos. Res.,

139, 46–61, DOI: 10.1016/j.atmosres.2014.01.008

WMO, World Meteorological Organization (WMO), 1953: World distribution of thunderstorm days, WMO Rep. 21, 204 pp., Geneva, Switzerland.

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019 | 5

DOI: 10.35614/ISSN-2341-6408-IK-2019-15-RL


Developing a model for forest fire risk

forecast at sub-seasonal scale in Finland

Extending the fire risk forecasts to sub-seasonal scale allows rescue services and other

authorities to prepare for potential forest fires earlier in advance, which can reduce the

negative impact caused by forest fires. A statistical model forecasting potential fire risk

for boreal forest conditions on sub-seasonal scale was developed and evaluated.

CECILIA WOLFF, ANDREA VAJDA, OTTO HYVÄRINEN


Since 1996, the Finnish Meteorological Institute

(FMI) operationally monitors favourable

conditions for potential forest fires

and issues short-range warnings in Finland

based on the Finnish Forest Fire Index (FFI)

(Venäläinen and Heikinheimo 2003, Vajda

et al. 2014). Forest fire risk predictions at

the sub-seasonal scale are still rare and

have not been studied at FMI yet.

A statistical model originating from the

FFI, which is determined from the volumetric

soil moisture (V), was developed

with the aim to estimate the fire risk on

sub-seasonal scale. To decide which meteorological

parameters to include in the

new model, a cross-validation using the

validation set approach was first performed.

Further, a linear regression was

applied on an interpolated gridded dataset

for the summer season 2003 to 2015

over Finland. The dataset consists of computed

V (12 UTC) and 12 UTC observations

of 2m temperature (T), relative humidity

(RH) and daily accumulated precipitation

(RR). As surface parameters change

much slower than atmospheric parameters,

the one-week moving average of surface

parameters was used. The final statistical

model is as follows:

log(V) = a T weekly average

+ b RR weekly average

+ c RH weekly average

+ d

The constants were determined by

least-square fitting to the data; a = -0.34,

FIG 1: Verification metrics (bias, correlation and RMSE/MEAN) for predicted

FFIs and the input parameters (T, RR and RH). The weekly mean metric is calculated

for various lead weeks (weeks 1 to 6) for the time period 2008–2016.

b = 0.08, c = 0.03 and d = 5.81. Last,

the V is converted to FFI and scaled to

range between 1 (wet) and 6 (dry).

The model was evaluated for Finnish

conditions using re-forecast data from

ECMWF’s extended range forecast system

(ENS) (ECMWF 2016) for the time period

2008 to 2016. The predicted FFI and the

input parameters were verified against an

observation dataset using standard verification

methodologies (Fig. 1). The predicted

potential fire risk (FFI>4.0) was underestimated

by the model. Temperature

(correlation: 0.92 to 0.26) has the highest

skill of the parameters, but the skill of both

RR (correlation: 0.57 to -0.13) and RH (correlation:

0.76 to -0.08) is lower, showing a

significant drop after the first lead week.

Following the evaluation, model improvements

have been done, e.g. including an

adjustment period of one week aiming to

estimate the current soil conditions. It is

worth noting that no bias adjustment has

been applied to the data.

The developed sub-seasonal forest fire

risk forecast is being piloted with the Regional

State Administrative Agency from

Northern Finland during summer season

2019. The 6-week fire risk outlooks are

produced operationally and delivered to

the end-user as probability forecast twice

a week. Based on the performance of the

model during the pilot season and the

feedback from the end-users the new statistical

model will be further improved.

Acknowledgements: The work was

funded within the ERA4CS Joint call. For

more information about the SERV_FOR-

FIRE project:

https://servforfire-era4cs.eu/.

ECMWF, 2016: IFS documentation, CY43R2, Part V: Ensemble Prediction System, ECMWF, pp. 23. Available online at www.ecmwf.int/sites/default/

files/elibrary/2016/17118-part-v-ensemble-prediction-system.pdf

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

Venäläinen A. and Heikinheimo M., 2003: The Finnish forest fire index calculation system. In Early Warning Systems for Natural Disaster Reduction,

Zschau J, Kuppers A (eds). Springer: Berlin; 645–648


DOI: 10.35614/ISSN-2341-6408-IK-2019-16-RL


Simulating a 2014 wildfire in Lieksa

(Finland) using the Canadian Prometheus

fire growth simulation model

Fire spread modelling programs could bring many benefits to the rescue services. Information

about the potential spread of an ongoing wildfire, better planning of prescribed burning and a

tool for estimating the vulnerability of high importance locations to wildfires are some of the

potential benefits of such a system. In this case study, the Canadian Prometheus model has

been demonstrated using open data from Finland.

JOONAS KOLSTELA


Prometheus is a Canadian wildland fire

growth simulator, which is based on the Canadian

Forest Fire Danger Rating System

(CFFDRS) and its two subsystems, the Fire

Weather Index (FWI) system, and the Fire

Behaviour Prediction (FBP) system. The FWI

subsystem is used to calculate the effects of

fuel moisture and wind on fire behaviour

(Van Wagner, 1987). The FBP subsystem

uses information of the 16 different fuel

classes, weather, topography, foliar moisture

content and the type and duration of the

fire to estimate the different statistics of the

modelled fire (Forestry Canada, 1992).

During the summer of 2014, a wildfire of

10 hectares was ignited in the Lieksa municipality

of Finland, due to a tree falling on a

powerline. The workers of Lieksa fire station

gave estimates of the ignition location and

time and of the final fire perimeter.

In order to model this wildfire using Prometheus,

information about elevation, fuel

grid, place and time of the ignition were required,

together with information about the

different firebreaks in the area and weather

data for the duration of the fire. The 2m resolution

Digital Elevation Model (DEM) of the

area and the vectorized firebreaks (roads,

rivers, swampland) were downloaded from

the National Land Survey of Finland (NLS)

spatial database. The weather data was

downloaded from the Finnish Meteorological

Institute (FMI) weather database. The

FIG 1: The different fuel classes and physical features which were used in the

simulation. The main results of the simulation are the fire perimeters with a temporal

resolution of one-hour. The fuel grid was provided by Arbonaut Oy Ltd.

ignition time and location were provided by

the Lieksa municipality fire station. The fuel

grid (Fig. 1) was created by Arbonaut Oy

Ltd by classifying the different fuel classes

using NLS LiDAR data and Finnish Forest

centre forest resource data.

The first simulations produced unrealistic

fire perimeters which were wrong

in shape and size compared to the real

events. By adding swamplands, roads and

rivers as firebreaks, and by choosing more

suitable fuel classes to represent the different

vegetation types found in the area,

the simulation produced a more realistic

final perimeter of 11,7 hectares (Fig. 1).

This case study shows that the different

types of data required to run a Prometheus simulation

are high quality and open data in Finland.

However, it would be important to gather

information about fire spread related values in

Finnish fuel types in order to ensure the accuracy

of the used fuel models. This could be a

great opportunity for a Nordic co-operation for

collecting information from real and prescribed

burns in different fuel types.

Acknowledgements: I would like to thank

FMI, Arbonaut Oy Ltd for providing me with

the fuel grid to be used in the Prometheus

simulation, and the workers of Lieksa fire

station for providing details of the wildfire.

Forestry Canada Fire Danger Group, 1992: Development and Structure of the Canadian Forest Fire Behavior Prediction System. Forestry Canada,

Headquarters, Fire Danger Group and Science and Sustainable Development Directorate, Ottawa. Information Report ST-X-3. 64 pp.

Van Wagner, C.E., 1987: Development and structure of the Canadian Forest Fire Weather Index System. Can. For. Serv., Ottawa, ON. For. Tech. Rep. 35. 37 pp.


DOI: 10.35614/ISSN-2341-6408-IK-2019-17-RL


14 years of extended snow depth and

snow water equivalent measurements

in eastern parts of Finland

Snow cover and snow depth are important climatic parameters in the boreal zone.

Besides their relevance as climate change indicators, they play important roles in

regional forest ecology, agriculture and water management.

ACHIM DREBS, JUHA KERSALO


Up to 19 additional snow measurements

were made in a 14-year period around

the 15 th of March on two successive

days. The measurements stood in the

tradition of a one hundred year observation

series, where hundreds of snow

depth (SD) and snow water equivalent

(SWE) measurements were conducted

by volunteers to amend the official

snow cover observations provided by

FMI and its predecessors (Solantie,

2000). The snowpack measurements

were made with a yardstick, SWE was

measured using a scale.

The measuring sites were categorized

by three different properties.

First, the sites were divided topographically

in lower sites (8 sites, heights between

110–180 a.m.s.l.) and upper sites

(11 sites, heights between 210–260

a.m.s.l.). Second, sites were divided

in open spaces (field or meadow, less

than 30 trees/ha, 9 sites), half-open

spaces (swamp, bog, and open courtyards,

30–150 trees/ha, 6 sites) and

sheltered space (pine forest, more than

150 trees/ha, 5 sites). The third property

reflects the roughness of the sites in

even (9 sites) and uneven (11 sites). This

classification leads to higher scattering

of the measurements results for uneven

sites. All sites were documented with

maps and areal pictures. The area of

the measuring sites is shown in Fig. 1.

FIG 1: Area of extended snow measurements

in Eastern Finland, red square.

Source: Uwe Dedering, CC BY-SA 4.0

Concerning the snow depth and the

SWE on the 15 th of March, five winters out

of the 14-year period can be regarded as

snow-rich winters, eight as average winters,

and two as winters with little snow.

Among other climatic factors like mean

air temperature and precipitation, the snow

depth and snow water equivalent depend

on the prevailing wind conditions during

the cold season (Solantie and Drebs, 2001).

The additional measurements were

analyzed graphically and statistically. Results

showed that with no exception, SD

and SWE at upper measuring sites were

greater than at lower measuring sites. Furthermore,

snow quantities at lower sites

remained more or less constant, while they

increased at higher elevations (Fig. 2).

FIG 2: Snow cover depth on the 15 th of

March in Eastern Finland from 2004–

2018 (blue: 11 upper sites; red: overall

mean; orange: linear trend of the

overall mean; yellow: 8 lower sites).

The snowiest areas in the examined

region were the Maanselkä-Naulavaara

area, the area at the Kuhmo-Nurmes regional

administrative border, the Lieksa-Kivivaara

area at the state border

to Russia, and the Karjalanselkä at the

Kaavi-Juuka region. Here, snow cover

with more than 80 cm depth was observed.

The less snowy areas covered

almost the whole Lake Pielinen district

between Valtimo and Lieksa, especially

the easterly shorelines of Lake Pielinen,

snow depth values peaked at 50 cm.

The analysis of SWE revealed an increase

of 30–40mm during the observation

period.

Solantie, R., 2000: Snow depth on January 15th and March 15th in Finland 1919-98, and its implications for soil frost and forest ecology.

Meteorological Publication, 42, Finnish Meteorological Institute, ISBN 951-697-520-8

Solantie, R., Drebs, A., 2001: Lumensyvyys ja Lumipeitteen vesiarvo 15.3. joulu-maaliskuun keskilämpötilan ja geostrofisten lounais- ja kaakkoistuulien

erotuksen funktiona (in Finnish with a summary in English). Meteorological Publication, 45, Finnish Meteorological Institute, ISBN 951-697-541-0


DOI: 10.35614/ISSN-2341-6408-IK-2019-18-RL


A comparative analysis of climate

change denialism in Europe

Almost one in four Europeans can be identified as having denialists attitudes towards

climate change. People whose identity and worldview are more threatened by societal

changes associated with climate change are more inclined to deny it.

SANNA ERKAMO


Different types of climate change

denialism include the disbelief in

the existence, anthropogenic nature,

or seriousness of climate change

(Rahmstorf 2004). The unifying

assumption is that nothing can or

should be done to mitigate climate

change. This study examines the

prevalence of these types of climate

change denialism by country, and

which individual and country-level

factors explain climate change denialism

in Europe.

On average, 5 % of Europeans

believe that climate is probably or

definitely not changing (trend), 6 %

believe that climate change is entirely

or mainly caused by natural

processes (attribution) and 10 % believe

that climate change will have

more positive than negative effects

on people across the world (impact).

In addition, 3 % believe in more than

one of these. Among the countries

studied, climate change denialism is

most prevalent in Russia and Lithuania,

and lowest in Spain and Iceland

(Fig. 1).

Overall, those who deny climate

change in Europe are more likely to

be low-income, male, less educated,

older, more conservative, more

opposed to income distribution

and more anti-immigration. Climate

change denialism is also more prevalent

in countries with higher carbon

dioxide emissions per capita, more

conservative attitudes and where a

greater share of the country's GDP

comes from agriculture and industry.

Results are based on descriptive analysis

and a logistic random intercepts

model, using data from European Social

Survey and the World Bank.

According to anti-reflexivity thesis,

climate change denialism is the

result of opposing societal change

(McCright & Dunlap 2010). More

precisely it means resisting reflexive

modernization, which implies changes

in the industrial structure, the labor

market and social roles due to

the emergence of new environmental

risks such as climate change (Beck

1992). These societal changes and the

means to mitigate climate change,

such as reducing industrial production

and fossil fuel consumption, limiting

free consumption and providing

aid to people in other countries,

threaten more the identity and worldview

of older working-class men with

conservative, anti-immigration and

anti- income distribution attitudes.

Kahan et. al. (2007) clarify this

with their theory of identity-protective

cognition. When the risks to society

are explained by actions that

FIG 1: Prevalence of different types

of climate change denialism in European

countries.

are important to a person's identity,

this causes cognitive dissonance, or

discomfort and people are more inclined

to deny climate change than to

change their identity to ease this discomfort.

Beck, U. 1992: Risk Society: Towards a New Modernity. London: SAGE Publications Ltd.

Kahan, D. M., Braman, D., Gastil, J., Slovic, P., Mertz, C. K. 2007: Culture and identity-protective cognition: Explaining the white-male effect in risk

perception, Journal of Empirical Legal Studies, 4(3), 465–505.

McCright, A. M., Dunlap, R. E., 2010: Anti-reflexivity: The American conservative movement’s success in undermining climate science and policy,

Theory, Culture and Society, 27(2), 100–133.

Rahmstorf, S. 2004: The climate sceptics, Weather catastrophes and climate change, 76–83.


DOI: 10.35614/ISSN-2341-6408-IK-2019-19-RL


Managing Development of a Novel Climate

Service Application called the “DECM App”

Co-creating an operationally running novel climate service application required effective

project management in a Copernicus Climate Change Service project, called the Data Evaluation

for Climate Models (DECM, 51_Lot4) https://climate.copernicus.eu/node/243/.

HILPPA GREGOW, ANTTI MÄKELÄ, ANNA SALONEN


The Data Evaluation for Climate Models

(DECM) project was an EU-funded Copernicus

Climate Change Service (C3S)

project running for a 30 months period

ending in February 2019. In DECM, The

Finnish Meteorological Institute (FMI)

was the contractor working for ECMWF

(European Centre for Medium-Range

Weather Forecasts) with seven sub-contractors

(GERICS, OMSZ, DMI, MetNo,

Climate Data Factory, Helsinki University,

CSC Finland). DECM team prepared deliverables

covering reports, surveys, tutorials,

webinars, web pages, blog pages

and most importantly an application for

evaluating the data of the climate models.

This is called the DECM App (Fig. 1).

Efforts were especially needed in generating

evaluation and quality control

framework of the CMIP5 and CORDEX

datasets and in complementing the development

of the user-relevant tools.

The reports concentrated on the data

collection and intercomparing, scientific

gap analysis as well as the climate

service user needs. The synthesis of all

written deliverables formed the basis of

an evaluation and quality control (EQC)

framework for climate model data for

the Copernicus Climate Data Store

(CDS). To allow fast and interactive development,

github, R-shiny Apps, Wordpress,

Google docs and Slack services

were applied during the project.

FIG 1: Example of usage of the DECM App.

Advancing the use of climate services

cannot be done without direct interaction

with users and without understanding the

real needs and preferences (Harjanne and

Rautio, 2019, Ervasti et al. 2018, Gregow et

al. 2019). Related to the user interaction,

the DECM project was monitored by two

specific key performance indicators: That

user requirements are 100% present in the

DECM App and that the DECM App is 100%

compatible with the climate data store

(CDS) of the C3S. Here, however, we faced

a big challenge since the CDS was under

construction in parallel with our project.

To be able to follow the CDS development

carefully and to avoid overlap of the parallel

C3S projects, we had to interact with

five other C3S projects at the same time.

All in all, in DECM, effective project

management played a key role for reaching

the operational goals in time. We organized

28 monthly progress monitoring

telco’s with subcontractors and 18 quarterly

progress and review meetings with

ECMWF. In total 20 management deliverable

and 11 milestone reports were produced.

We were present in the advisory

board meetings and essential meetings

of the C3S sister projects. DECM was

also presented in the European Meteorological

Society (EMS) conference, the

Euro-Cordex annual meetings in 2017–

2018 and in the General Assemblies of

C3S. The final DECM App is running at

https://decm.copernicus-climate.eu/. It

was successfully launched in February 2019.

Ervasti, T., et al., 2018: Mapping users’ expectations regarding extended-range forecasts, Adv. Sci. Res., 15, 99–106, DOI: 10.5194/asr-15-99-2018

Gregow, H., et al., 2019: Preparing for peat production seasons in Finland and experimenting with long range impact forecasting, Climate Services, 14, 37–50,

DOI: 10.1016/j.cliser.2019.04.003

Harjanne and Rautio, 2019: Rethinking how climate services are talked about, FMI Climate Bulletin – Research Letters, 1, DOI:

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


DOI: 10.35614/ISSN-2341-6408-IK-2019-20-RL


Co-design process used in the

development of the “DECM App”

For a well-functioning and user-friendly operational climate service, it is important

to consider the user needs and technical possibilities. Here, we describe the

co-design process involving users that took place in a C3S contract.

ANTTI MÄKELÄ 1 , JULIANE EL ZOHBI 2 , JOUNI RÄISÄNEN 3 , TIINA ERVASTI 1 , ELISABETH VIKTOR 2 , RASMUS BENESTAD 4 ,

ABDELKADER MEZGHANI 4 , ANDREAS DOBLER 4 , OLLE RÄTY 3 , HILPPA GREGOW 1

1

Finnish Meteorological Institute, 2 Helmholtz-Zentrum Geesthacht, Climate Service Center Germany (GERICS),

3

University of Helsinki, 4 Norwegian Meteorological Institute

The main challenges in the usability of

climate projections in climate change

adaptation and impact research are

the nature of the data, the non-trivial

access to the information embedded

in the climate data (Benestad et al.,

2017), and the interpretation of the climate

data quality (Zahid et al., 2019).

The Copernicus Climate Change Service

(C3S) is already providing access

to climate projection data. However,

merely the access to the data is not

enough to establish wider usage;

guidance on selecting the right data

for the users’ specific purposes is indispensable.

Within C3S, as part of the “Data

Evaluation for Climate Models -

DECM” contract, researchers from

various institutions joined efforts to

develop a prototype of a web-application

called “DECM App” that

is currently running on a virtual machine

hosted by C3S and accessible at

https://decm.copernicus-climate.eu/.

The functions and usability of the

“DECM App” originated i) from the

co-design process involving the project

experts and volunteer pilot users

(Fig. 1) ii) through on-line and iii) live

demonstration sessions. The first version

of the App was launched in 2017

and it was developed into its final version

during 2018. A two-way feedback

FIG 1: Description of the co-design process of the DECM App in 2018.

mechanism helped improving the

content and functionalities to enable

quick and easy evaluation and quality

control of climate projection data

(global and regional), even in specific

areas.

In spring 2018, the University of

Helsinki tested the “DECM App” in the

academic course “Greenhouse effect,

climate change, and impacts” involving

18 students, who received four

research questions focusing on the

usability of the App. The co-design

process continued with a total of eight

on-line demonstration sessions that

included a chance to give anonymous

feedback after each session. From

there, the prototype was developed

further according to the user feedback.

Also, a blog page was created

to inform the (pilot) users about the

progress of new developments. By the

end of the pilot year 2018, the “DECM

App” had reached a total of 585

unique users, over 2000 sessions, and

more than 4000 page views. In February

2019, the “DECM App” was included

in the C3S Climate Data Store

collection and was officially opened

for European and global use.

Based on the reassuring experiences

from the DECM contract and feedback

from the users, we recommend

the prescribed approach for climate

service development in general.

Benestad, R., et al., 2017: New vigour involving statisticians required to overcome ensemble fatigue, Nature Climate Change, DOI: 10.1038/NCLIMATE3393.

Zahid, M., et al., 2019: What does quality mean to climate data users/providers and how to enable them to evaluate the quality of climate model

data and derived products? Handbook of Climate Services (in final revision).


DOI: 10.35614/ISSN-2341-6408-IK-2019-19-RL


The effect of atmospheric winds on

recent temperature changes in Finland

Weather in Finland strongly depends on irregular variations in atmospheric winds. Changes

in wind conditions have also affected some longer-term climate trends, such as the recent

(1979–2018) lack of warming in June and the very large warming in December. Subtracting

this effect from the observations leaves a robust residual warming in all months of the year.

JOUNI RÄISÄNEN

Institute for Atmospheric and Earth System Research / Physics, University of Helsinki

Variations in atmospheric circulation (i.e.,

winds near the surface and higher in the

atmosphere) can either amplify or counteract

the warming caused by increased

greenhouse gas concentrations (Saffioti

et al. 2016). Here, the effect of atmospheric

circulation on recent (1979–2018)

monthly mean temperature trends in Finland

is studied. The method, based on

tracing the origin of air before it arrives in

Finland, is described in Räisänen (2019).

Two study areas in Finland were selected,

“South” (61–62°N, 23–25°E) and

“North” (67–68°N, 26–28°E). Based on

the E-OBS 19.0e data set (Haylock et al.

2008), the annual mean temperature increased

by 1.9°C in South and 2.7°C in

North, in terms of a linear trend over the

1979–2018 period. However, the trends

varied from month to month (red bars in

Figs. 1a–b). In particular, June mean temperatures

decreased slightly in South

and remained nearly constant in North.

There was also a distinct local minimum

in warming in October in South, sandwiched

between larger warming in September

and November. By contrast, a

sharp maximum in warming occurred in

December particularly in North.

The lack of warming in June was due

to a negative contribution from circulation

change, i.e., an increase in northerly winds.

If acting alone, this would have cooled the

June mean temperature by about 1.6°C

in South and 2.2°C in North (blue bars in

FIG 1: Trends in monthly mean temperature from 1979 to 2018 in (a, c) South and

(b, d) North. In (a) and (b), the red bars show the observed temperature trends

and the blue bars the best-estimate circulation-related trends. In (c) and (d),

the residual trends are given. The 5–95% error bars for the circulation-related

trends and the residual trends are derived as detailed in Räisänen (2019).

Figs. 1a–b). Circulation changes also clearly

reduced the warming in South in October,

while amplifying the warming in November

and December in both two areas.

Figs. 1c–d show the difference between

the observed and the circulation-related

trends. In both areas, the annual cycle of

this “residual warming” is much smoother

than that of the observed warming.

Although there is still a large contrast

between larger warming in winter and

smaller warming in summer and early fall,

the residual warming is robustly positive

in all months of the year. Thus, the temperatures

that are observed nowadays in

Finland tend to be systematically higher

than they would have been under similar

wind conditions four decades ago.

Haylock, M.R., et al., 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119

Räisänen, J. 2019: Effect of atmospheric circulation on recent temperature changes in Finland. Climate Dynamics, DOI: 10.1007/s00382-019-04890-2

Saffioti, C., et al., 2016: Reconciling observed and modelled temperature and precipitation trends over Europe by adjusting for circulation variability. Geophys. Res. Lett., 43, 8189–8198


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Ilmatieteen laitos

ilmastokatsaus@fmi.fi

www.ilmastokatsaus.fi

FMI’s Climate Bulletin Research Letters Autumn Issue 2019

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