In January 2024, the monthly average temperature in Helsinki, the capital of Finland, was -6.8 degrees Celsius, and in Northern Finland in Sodankylä -16.3 degrees Celsius. In 2023, the winter period in Finland was not as cold as in the previous years. Finland as an attractive travel destination Finland is gaining popularity among international tourists. Known for its untouched natural landscapes and unique regions, it offers diverse experiences ranging from the metropolitan area of Helsinki to the northernmost point of Lapland. The travel and tourism industry is important for the growth of the Finnish economy. By 2029, the revenue generated by tourism is forecast to exceed 25 billion euros. Finns opted more for domestic holidays In the Nordic comparison, Finland had the lowest share of overnight stays of foreign tourists in 2022, while Denmark, Sweden, and Norway recorded significantly higher visitor numbers. In recent years, Finns have increasingly opted for domestic holidays, which illustrates emerging trends of local and climate-conscious tourism. Most non-resident tourists came from Germany, followed by the United Kingdom, Sweden, and Estonia.

The average temperature in the region of Central Finland in 2023 was measured at 4.1 degrees Celsius. While August was the warmest month with around 16 degrees Celsius, December accounted for the coldest month of that year.

The average temperature in the region of Lapland in 2023 was measured at 0.8 degrees Celsius. While August was the warmest month with around 14.6 degrees Celsius, December accounted for the coldest month of that year.

Temperature in Finland decreased to 2.87 celsius in 2023 from 3.27 celsius in 2022. This dataset includes a chart with historical data for Finland Average Temperature.

The annual average temperatures in Helsinki and Sodankylä in Finland showed an upward trend in selected years from 1950 to 2021. In Sodankylä, Northern Finland, the average annual temperature fell to -2 degrees Celsius in 1980. It peaked in 2021 at three degrees Celsius. From 2013 onwards, the average temperature did not drop below 0 degree Celsius. In Helsinki, the capital of Finland, the average temperature remained above 4.6 degrees Celsius throughout the period under survey. In 2021, the average temperature in Helsinki was measured at 6.5 degrees Celsius.

• W1 - Winter with high heating demand
• W2 - Winter with low heating demand
• W3 - Winter with the coldest individual day by average temperature
• S1 - Summer with the lowest cooling demand
• S2 - Summer with the highest heating demand
• S3 - Summer with the warmest individual day by average temperature

Selected years and the procedure for their selection are described in https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.energy.2024.131636.

Original weather data is downloaded for the selected years from Finnish Meteorological Institute's Open data repository: https://www.ilmatieteenlaitos.fi/havaintojen-lataus under CC BY 4.0 licence.

Future change in climate is based on Finnish Meteorological Institute's data used in creating Test Reference Year weather files (https://www.ilmatieteenlaitos.fi/energialaskenta-try2020) for which the climate change data is presented by Ruosteenoja et al. (2016).

The data is statistically downscaled through a method called morphing created by Belcher et al. (2005) with some parts using methods from Räisänen & Räty (2013) and Jylhä et al, (2015). Morphing was computationally conducted through created software https://github.com/japulk/Weather-Morphing-Tool For additional information please refer to original article or contact the authors.

This statistic shows the average monthly temperatures (in °C) in selected cities in Finland from 1981 to 2010. During the period under survey, the mean temperature in Helsinki in January was close to minus four degrees Celcius.

This study assesses spatio-temporal changes in inter-annual variability of temperature, precipitation and runoff for 1962-2014 at sub-basin scale in Finland. The analysis is based on 1) interpolated areal average temperature and total precipitation based on corrected observations, and 2) modelled runoff based on the areal averages, both prepared by the Finnish Environmental Institute (SYKE). Temporal changes in variability were analyzed by constructing moving window median absolute deviation time series at annual and seasonal scales. Sub-basins with similar patterns of temporal variability were identified using principal component analysis and agglomerative hierarchical clustering. Presence of monotonic trends in variability was tested. Distinct areas with similar patterns of statistically significant changes in variability were found. Decreases in temperature variability were found annually, in winter and in summer, respectively in many parts of Finland, the south and the north. Precipitation variability increased in autumn in northern Finland, and decreased annually as well as in winter and spring in northern and central parts of Finland. Runoff variability increased in winter in most parts of Finland and in summer in the central parts, as well as decreased in spring in southern Finland. Findings of this study describes hydro-climatic variability in Nordic conditions and hence potentially improves the possibility to adapt and predict the changes in hydro-climatic conditions, including weather extremes.

The Nordic Gridded Climate Dataset (NGCD) is a high resolution, observational, gridded dataset of daily minimum, maximum and mean temperatures and daily precipitation totals, covering Finland, Sweden and Norway. The time period covered begins in January 1961 and continues to the present. The dataset is regularly updated every 6 months, in March and in September. In addition, there are daily, provisional updates. Spatial interpolation methods are applied to observational datasets to create gridded datasets. In general, there are three types of such methods: deterministic (type 1), stochastic (type 2) and pure mathematical (type 3). NGCD applies both a deterministic kriging (type 1) interpolation approach and a stochastic Bayesian (type 2) interpolation approach to the same in-situ observational dataset collected by weather stations. For more details on the algorithms, users are advised to read the product user guide. The input data is provided by the National Meteorological and Hydrological Services of Finland, Norway and Sweden. The time-series used for Finland and Sweden are the non-blended time-series from the station network of the European Climate Assessment & Dataset (ECA&D) project. For Norway, time-series are extracted from the climate database of the Norwegian Meteorological Institute.

FI: Annual Surface Temperature: Change Since 1951 1980 data was reported at 1.923 Number in 2021. This records a decrease from the previous number of 3.305 Number for 2020. FI: Annual Surface Temperature: Change Since 1951 1980 data is updated yearly, averaging 1.387 Number from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 3.305 Number in 2020 and a record low of 0.064 Number in 1998. FI: Annual Surface Temperature: Change Since 1951 1980 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Finland – Table FI.OECD.GGI: Environmental: Climate Risk: OECD Member: Annual.

In case of precipitation and runoff, 95th percentile is used instead of maximum, omitting outliers with extremely high values.

This dataset contains files that show the climate change velocity metrics calculated for three climate variables across Finland. The climate velocities were used to study the magnitude of projected climatic changes in a nation-wide Natura 2000 protected area (PA) network (Heikkinen et al., 2020). Using fine-resolution climate data that describes the present-day and future topoclimates and their spatio-temporal variation, the study explored the rate of climatic changes in protected areas on an ecologically relevant, but yet poorly explored scale. The velocities for the three climate variables were developed in the following work, where in-depth description of the different steps in velocity metrics calculation and a number of visualisations of their spatial variation across Finland are provided:

Risto K. Heikkinen 1, Niko Leikola 1, Juha Aalto 2,3, Kaisu Aapala 1, Saija Kuusela 1, Miska Luoto 2 & Raimo Virkkala 1 2020: Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. https://doi.org/10.1038/s41598-020-58638-8

1 Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland

2 Department of Geosciences and Geography, University of Helsinki, FI-00014, Helsinki, Finland

3 Finnish Meteorological Institute, FI-00101, Helsinki, Finland

The dataset includes GIS compatible geotiff files describing the nine spatial climate velocity surfaces calculated across the whole of Finland at 50 m × 50 m spatial resolution. These nine different velocity surfaces consist of velocity metric values measured for each 50-m grid cell separately for the three different climate variables and in relation to the three different future climate scenarios (RCP2.6, RCP4.5 and RCP8.5). The baseline climate data for the study were the monthly temperature and precipitation data averaged for the period from 1981 to 2010 modelled at a resolution of 50-m, based on which estimates for the annual temperature sum above 5 °C (growing degree days, GDD, °C), the mean January temperature (TJan, °C) and the annual climatic water balance (WAB, the difference between annual precipitation and potential evapotranspiration; mm) were calculated. Corresponding future climate surfaces were produced using an ensemble of 23 global climate models for the years 2070–2099 (Taylor et al. 2012) and the three RCPs. The data for the three climate variables for 1981–2010 and under the three RCPs will be made available in separately via METIS - FMI's Research Data repository service (Aalto et al., in prep.).

The climate velocity surfaces included in the present data repository were developed using climate-analog approach (Hamann et al. 2015; Batllori et al. 2017; Brito-Morales et al. 2018), whereby velocity metrics for the 50-m grid cells were measured based on the distance between climatically similar cells under the baseline and the future climates, calculated separately for the three climate variables. In Heikkinen et al. (2020), the spatial data for the Natura 2000 protected areas were used to assess their exposure to climate change. The full data on N2K areas can be downloaded from the following link: https://ckan.ymparisto.fi/dataset/%7BED80465E-135B-4391-AA8A-FE2038FB224D%7D. However, note that the N2K areas including multiple physically separate patches were treated as separate polygons in Heikkinen et al. (2020), and a minimum size requirement of 2 hectares were requested. Moreover, the digital elevation model (DEM) data for Finland (which were dissected to Natura 2000 polygons to examine their elevational variation and its relationships to topoclimatic variation) can be downloaded from the following link: https://ckan.ymparisto.fi/en/dataset/dem25_astergdem25.

The coordinate system for the climate velocity data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summary of the key settings and elements of the study are provided below. A detailed treatment is provided in Heikkinen et al. (2020).

Code to the files (four files per each velocity layer: *.tif, *.tfw. *.ovr and .tif.aux.xml) in the dataset:

(a) Velocity of GDD with respect to RCP2.6 future climate (Fig 2a in Heikkinen et al. 2020). Name of the file: GDDRCP26.

(b) Velocity of GDD with respect to RCP4.5 future climate (Fig. 2b in Heikkinen et al. 2020). Name of the file: GDDRCP45.*

(c) Velocity of GDD with respect to RCP8.5 future climate (Fig. 2c in Heikkinen et al. 2020). Name of the file: GDDRCP85.*

(d) Velocity of mean January temperature with respect to RCP2.6 future climate (Fig. 2d in Heikkinen et al. 2020). Name of the file: TJanRCP26.*

(e) Velocity of mean January temperature with respect to RCP4.5 future climate (Fig. 2e in Heikkinen et al. 2020). Name of the file: TJanRCP45.*

(f) Velocity of mean January temperature with respect to RCP8.5 future climate (Fig. 2f in Heikkinen et al. 2020). Name of the file: TJanRCP85.*

(g) Velocity of climatic water balance with respect to RCP2.6 future climate (Fig. 2g in Heikkinen et al. 2020). Name of the file: WABRCP26.*

(h) Velocity of climatic water balance with respect to RCP4.5 future climate (Fig. 2h in Heikkinen et al. 2020). Name of the file: WABRCP45.*

(i) Velocity of climatic water balance with respect to RCP8.5 future climate (Fig. 2i in Heikkinen et al. 2020). Name of the file: WABRCP85.*

Note that velocity surfaces e and f include disappearing climate conditions.

Summary of the study:

Climate velocity is a generic metric which provides useful information for climate-wise conservation planning to identify regions and protected areas where climate conditions are changing most rapidly, exposing them to high rates of climate displacement (Batllori et al. 2017), causing potential carry-over impacts to community structure and ecosystem functions (Ackerly et al. 2010). Climate velocity has been typically used to assess the climatic risks for species and their populations, but velocity metrics can also be used to identify protected areas which face overall difficulties in retaining ecological conditions that promote present-day biodiversity.

Earlier climate velocity assessments have focussed on the domains of the mesoclimate (resolutions of 1–100 km) or macroclimate (>100 km scales), and fine-grained (<100 m) local climatic conditions created by variation in topography ('topoclimate'; Ackerly et al. 2010; 2020) have largely been overlooked (Heikkinen et al. 2020). This omission may lead to biased exposure assessments especially in rugged terrain (Dobrowski et al. 2013; Franklin et al. 2013), as well as a limited ability to detect sites decoupled from the regional climate (Aalto et al. 2017; Lenoir et al. 2017). This study provided the first assessment of the climatic exposure risks across a national PA (Natura 2000) network based on very fine-grained velocities of three established drivers of high latitude biodiversity.

The produce fine-grain climate velocity measures, 50-m resolution monthly temperature and precipitation data averaged for 1981–2010 were first developed, and based on it, the three bioclimatic variables (growing degree days, mean January temperature and annual climatic water balance) were calculated for the whole study domain. In the next phase, similar future climate surfaces were produced based on data from an ensemble of 23 global climate models, extracted from the CMIP5 archives for the years 2070–2099 and the three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5)26. In the final step, climate velocities for each the 50 x 50 m grid cells were measured using climate-analog velocity method (Hamann et al. 2015) and based on the distance between climatically similar cells under the baseline and future climates.

The results revealed notable spatial differences in the high velocity areas for the three bioclimatic variables, indicating contrasting exposure risks in protected areas situated in different areas. Moreover, comparisons of the 50-m baseline and future climate surfaces revealed a potential wholesale disappearance of current topoclimatic temperature conditions from almost all the studied PAs by the end of this century.

Calculation of climate change velocity metrics for the three climate variables

The overall process of calculation of climate velocities included three main steps.

(1) In the first step, we developed high-resolution monthly average temperature and precipitation data averaged over the years 1981–2010 and across the study domain at a spatial resolution of 50 × 50 m. This was done by building topoclimatic models based on climate data sourced from 313 meteorological stations (European Climate Assessment and Dataset [ECA&D]) (Klok et al. 2009). Our station network and modelling domain covered the whole of Finland with an additional 100 km buffer. However, it was also extended to cover large parts of northern Sweden and Norway for areas >66.5°N, as well as selected adjacent areas in Russia (for details see Heikkinen et al. 2020). This was done to capture the present-day climate spaces in Finland which are projected to move in the future beyond the country borders but have analogous climate areas in neighbouring areas; this was done to avoid developing a large number of velocity values deemed as infinite or unknown in the data for Finland.

The 50-m resolution average air temperature data were developed for the study domain using generalized additive modelling (GAM), as implemented in the R-package mgcv version 1.8–7 (R Development Core Team 2011; Wood 2011). In this modelling we utilised variables of geographical location (latitude and longitude, included as an anisotropic interaction), topography (elevation, potential incoming solar radiation, relative

Finland FI: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data was reported at 0.000 % in 2009. Finland FI: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data is updated yearly, averaging 0.000 % from Dec 2009 (Median) to 2009, with 1 observations. Finland FI: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Finland – Table FI.World Bank: Land Use, Protected Areas and National Wealth. Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.; ; EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank.; ;

This dataset includes 6 years of open access meteorological mast data and operational data of multiple turbines from Olos wind farm in Finland, and simultaneous and longer-term monthly icing time series from WIceAtlas database for reference. Olos is a complex terrain site with severe icing conditions during winter (IEA Ice Class 4).

The work was funded by an EU IRPWIND project.

Introduction

A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO₂ emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO₂ emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023).

Time series data

The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition).

The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018).

Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues.

The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO₂ release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively.

The pre-prepared input data are given in files, see Table 1 for descriptions.

Table 1. Description of input data files.

The R-scripts

The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO₂ emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R.

Guidance for running the R-scripts is given in the README.txt.

References

Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023.

LUKE Statistics

• https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.

• https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.

Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023).

Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014.

R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020.

Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.

This repository contains four zipped data files which contain (i) the spatial distribution of aapa mire complexes (���aapa mires���) and their wettest flark-dominated parts (���wet aapa mires���) situated in the aapa mire and palsa mire zones of Finland, as selected for the study by Heikkinen et al. (in review), (ii) values for the six bioclimatic variables (growing degree days, mean January and July temperature, annual precipitation, and May and July water balance) averaged for the years 1981���2010, and developed for the studied aapa mires and wet aapa mires using a 50 x 50 m lattice system, and (iii) values for the same six bioclimatic variables developed for future climates and the two types of study mires, based on the global climate models for 2040���2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5), and (iv) values of climate velocity metrics calculated for the six bioclimatic variables and the two types of study mires. These data provide the essential data employed in conducting the analysis in the following work: Risto K. Heikkinen1, Kaisu Aapala1, Niko Leikola1 and Juha Aalto2: Exposure of boreal aapa mires to climate change, in review. 1 Biodiversity Centre, Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland 2 Finnish Meteorological Institute, Weather and climate change impact research, Helsinki, Finland The data files are embedded in four compressed zip files (one of them including a geodatabase folder with files) which include several ArcGIS compatible tiff-raster or shape files. The names and contents of the four zipped files are as follows: (1) mires.zip ��� includes shape files describing the location and spatial configuration of the aapa mires (���Aapa_mires.shp���) and the wet aapa mires (���Wet_aapa_mires.shp���) included in the study, and the borders of different mire zones in Finland (���Mire_zones.shp���); (2) climate_data_aapa_mires.zip ��� includes 18 tiff raster files showing the values of the six bioclimatic variables in the studied aapa mires within the 50 x 50 m resolution grid. The data in this zipped file include climate data averaged for the years 1981 ��� 2010 and for the future time slice of 2040���2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5); (3) climate_data_wet_aapa_mires.zip ��� includes 18 tiff raster files showing the values of the six bioclimatic variables in the studied wet aapa mires within the 50 x 50 m resolution grid. Similarly as in (2), the data in this zipped file include climate data averaged for the years 1981 ��� 2010 and for the future time slice of 2040���2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5); (4) velocity_data_for_mires.zip ��� includes zipped geodatabase folder velocity_open_mires.gdb which, in turn, includes spatial ArcGIS surfaces for the climate change velocity metric calculated for all the six bioclimatic variables, and the two types of mires and the two RCPs. In the zipped files (2) and (3), first part of the names of the included files refer to one of the six bioclimatic variables as follows: GDD5 ��� growing degree days, PREC ��� annual precipitation, TEMP_Jan ��� mean January temperature, TEMP_July ��� mean July temperature, WAB_May ��� May water balance, WAB_July ��� July water balance; and the remaining part of the name indicates the time period, type of the RCP and that of the mire. It should be noted that these data are embargoed until the end of the SUMI project for which they were developed, i.e. 1.1.2023. The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summarization of the key settings of the study is provided below. A detailed treatment is included in the manuscript Heikkinen et al. (in review). Once the manuscript is accepted for publication an updated link will be provided. Study system: Aapa mires are waterlogged, peat-accumulating EU Habitats Directive priority habitats whose ecological conditions and biodiversity values may be jeopardized by climate change. Aapa mires depend on the surface water flows from the surroundings which makes them sensitive to hydrological alterations and falling water tables caused by land use (ditching for peatland drainage) as well as climate change (Gong et al. 2012, Sallinen et al. 2019). This sensitivity of aapa mires and their biodiversity to increasing temperatures and decreasing water balance and precipitation can be of particular concern as they occur in northern hemisphere, in areas where the largest climatic changes are projected to take place (AMAP 2017, V��liranta et al. 2017. Kolari et al. 2021). In the study by Heikkinen et al. (in review), we assess the climate exposure of these habitats by developing velocity metrics for both the aapa mire complexes (���aapa mires���) and their wettest flark-dominated parts (���wet aapa mires���) in Finland. Aapa mire data: Occurrences of aapa mires were ident...

We collected relevant observational and measured annual-resolution time series dealing with climate in northern Europe, focusing in Finland. We analysed these series for the reliability of their temperature signal at annual and seasonal resolutions. Importantly, we analysed all of the indicators within the same statistical framework, which allows for their meaningful comparison. In this framework, we employed a cross-validation procedure designed to reduce the adverse effects of estimation bias that may inflate the reliability of various temperature indicators, especially when several indicators are used in a multiple regression model. In our data sets, timing of phenological observations and ice break-up were connected with spring, tree ring characteristics (width, density, carbon isotopic composition) with summer and ice formation with autumn temperatures. Baltic Sea ice extent and the duration of ice cover in different watercourses were good indicators of winter temperatures. Using combinations of various temperature indicator series resulted in reliable temperature signals for each of the four seasons, as well as a reliable annual temperature signal. The results hence demonstrated that we can obtain reliable temperature information over different seasons, using a careful selection of indicators, combining the results with regression analysis, and by determining the reliability of the obtained indicator.

The dataset contains information on the prevailing weather conditions on the roads produced by the Finnish Transport Agency's road weather system. There are nearly 500 weather and weather-observing road weather stations along the roads. Most stations are located in the coastal region and southern Finland. Road weather stations provide data every 10-15 minutes from various road surface sensors on road surface conditions and meteorological sensors on the prevailing weather. Due to the location of the road weather stations, the reliability of the weather sensor data and the comparability of the data between the stations are weaker than with the weather observation stations of the Finnish Meteorological Institute, which are located in the most meteorologically representative locations. At road weather stations, the main focus is on weather measurement and the stations are therefore positioned using different methods than the weather stations.

Finland FI: Official Development Assistance: % of Total ODA: Climate Change Adaptation data was reported at 22.740 % in 2021. This records an increase from the previous number of 15.970 % for 2020. Finland FI: Official Development Assistance: % of Total ODA: Climate Change Adaptation data is updated yearly, averaging 18.440 % from Dec 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 38.610 % in 2017 and a record low of 14.560 % in 2014. Finland FI: Official Development Assistance: % of Total ODA: Climate Change Adaptation data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Finland – Table FI.OECD.GGI: Environmental: Environmental Policy, Taxes and Transfers: OECD Member: Annual.

Meteorological observations for every 10 minutes from Saariselkä, Finland, collected by an automatic weather station during the winter 2011-2012.