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Thunderstorm observations in Finland – historical observations since 1887 — 4 Thunderstorm observations in Finland – 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 Issue 2 ISSN: 2341-6408 DOI: 10.35614/ISSN-23416408-IK-2019-12-RL
PUBLISHER Finnish Meteorological Institute (FMI) P.O. BOX 503 FI-00101 HELSINKI www.ilmastokatsaus.fi ilmastokatsaus@fmi.fi
2 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019
EDITOR IN CHIEF Hilppa Gregow
DESIGN Marko Myllyaho
EDITORIAL COMMITTEE Hadassa Hovestadt Tiina Ervasti
Please mention the source when citing the content. A DOI is available for each research letter article.
REVIEW BOARD ECRA members
© FMI
DOI: 10.35614/ISSN-2341-6408-IK-2019-13-RL Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
Thunderstorm observations in Finland – 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. 4 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019
DO I : 1 0. 3561 4/ I SS N - 2 341 - 6 4 0 8- I K- 2 01 9-1 4 -RL
Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
Thunderstorm observations in Finland – 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 Finnish Meteorological Institute
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 Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
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 Finnish Meteorological Institute
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 Tweekly average + b RRweekly average + c RHweekly 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_FORFIRE 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 6 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019
DO I : 1 0. 3561 4/ I SS N - 2 341 - 6 4 0 8- I K- 2 01 9-1 6-RL
Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
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 Finnish Meteorological Institute
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. FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019 | 7
DOI: 10.35614/ISSN-2341-6408-IK-2019-17-RL Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
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 Finnish Meteorological Institute
Up to 19 additional snow measurements were made in a 14-year period around the 15th 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 15th 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 15th 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 8 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019
DO I : 1 0. 3561 4/ I SS N - 2 341 - 6 4 0 8- I K- 2 01 9-1 8 -RL
Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
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 Finnish Meteorological Institute
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. FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019 | 9
DOI: 10.35614/ISSN-2341-6408-IK-2019-19-RL Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
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 Finnish Meteorological Institute
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 Meteo rological 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 10 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019
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Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
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 MÄKELÄ1, JULIANE EL ZOHBI2, JOUNI RÄISÄNEN3, TIINA ERVASTI1, ELISABETH VIKTOR2, RASMUS BENESTAD4, ABDELKADER MEZGHANI4, ANDREAS DOBLER4, OLLE RÄTY3, HILPPA GREGOW1 1 Finnish Meteorological Institute, 2Helmholtz-Zentrum Geesthacht, Climate Service Center Germany (GERICS), 3
University of Helsinki, 4Norwegian 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). FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019 | 11
DOI: 10.35614/ISSN-2341-6408-IK-2019-21-RL Received 10 Sep. 2019, accepted 13 Dec. 2019, published 19 Dec. 2019
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 12 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2019
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