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.

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 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.

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.

Global Temperature: Daily Normal: Finland: Muonio Alamuonio data was reported at 4.700 Degrees Celsius in 16 May 2025. This records an increase from the previous number of 4.500 Degrees Celsius for 15 May 2025. Global Temperature: Daily Normal: Finland: Muonio Alamuonio data is updated daily, averaging -5.000 Degrees Celsius from Dec 2023 (Median) to 16 May 2025, with 522 observations. The data reached an all-time high of 14.400 Degrees Celsius in 19 Jul 2024 and a record low of -14.100 Degrees Celsius in 30 Jan 2025. Global Temperature: Daily Normal: Finland: Muonio Alamuonio data remains active status in CEIC and is reported by Climate Prediction Center. The data is categorized under Global Database’s Finland – Table FI.CPC.GT: Environmental: Global Temperature: Daily Normal.

Background: The current coronavirus disease 2019 (COVID-19) is spreading globally at an accelerated rate. There is some previous evidence that weather may influence the incidence of COVID-19 infection. We assessed the role of meteorological factors including temperature (T) and relative humidity (RH) considering the concentrations of two air pollutants, inhalable coarse particles (PM10) and nitrogen dioxide (NO2) in the incidence of COVID-19 infections in Finland, located in arctic-subarctic climatic zone.Methods: We retrieved daily counts of COVID-19 in Finland from Jan 1 to May 31, 2020, nationwide and separately for all 21 hospital districts across the country. The meteorological and air quality data were from the monitoring stations nearest to the central district hospital. A quasi-Poisson generalized additional model (GAM) was fitted to estimate the associations between district-specific meteorological factors and the daily counts of COVID-19 during the study period. Sensitivity analyses were conducted to test the robustness of the results.Results: The incidence rate of COVID-19 gradually increased until a peak around April 6 and then decreased. There were no associations between daily temperature and incidence rate of COVID-19. Daily average RH was negatively associated with daily incidence rate of COVID-19 in two hospital districts located inland. No such association was found nationwide.Conclusions: Weather conditions, such as air temperature and relative humidity, were not related to the COVID-19 incidence during the first wave in the arctic and subarctic winter and spring. The inference is based on a relatively small number of cases and a restricted time period.

Global Temperature: Daily Average: Finland: Muonio Alamuonio data was reported at 3.450 Degrees Celsius in 16 May 2025. This records a decrease from the previous number of 4.000 Degrees Celsius for 15 May 2025. Global Temperature: Daily Average: Finland: Muonio Alamuonio data is updated daily, averaging -1.150 Degrees Celsius from Dec 2023 (Median) to 16 May 2025, with 521 observations. The data reached an all-time high of 21.500 Degrees Celsius in 29 Jun 2024 and a record low of -39.550 Degrees Celsius in 04 Jan 2024. Global Temperature: Daily Average: Finland: Muonio Alamuonio data remains active status in CEIC and is reported by Climate Prediction Center. The data is categorized under Global Database’s Finland – Table FI.CPC.GT: Environmental: Global Temperature: Daily Average.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

This statistic shows the average temperature in selected cities in Finland in 2023. During the period under survey, the mean temperature in Helsinki was 7.1 degrees Celsius. The corresponding figure in Sodankylä in the Lapland region was 0.3 degrees Celsius.

Data package contains daily observations from Kilpisjärvi weather station, run by Finnish Meterorological Institute. Measurements are: mean temperature, mean lowest temperature, mean highest temperature, precipiation and snow depth.

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.

The study was part of the ELASTINEN project funded by the Prime Minister's Office Finland whose aim was to produce information and seek solutions to improve weather and climate risk management. The survey was conducted by a reasearch consortium consisting of the Finnish Meteorological Institute, University of Helsinki, Finnish Environment Institute, Natural Resources Institute Finland, National Institute for Health and Welfare, and Gaia Consulting. The survey charted the effects of weather and climate on the activities and risk management of Finnish organisations. The study surveyed sources of weather and climate information and views on the usefulness of these sources in risk management. A further topic under study was improvement of weather and climate risk prevention and preparedness. Significance of different weather conditions and phenomena in the activities of the organisation were charted (e.g. severe weather phenomena occurring in Finland) as well as potential positive impacts of climate change on the organisation and the most significant harmful weather phenomena for the organisation. With regard to management of weather and climate risks, the respondents were asked how these risks were managed in the organisation's activities, who was responsible for managing these risks in the organisation, what the most important motivators for managing the risks were, how big the risks were in the organisation compared to other risks, and whether the organisation had experienced a realisation of some weather or climate risk. Use and awareness of different sources of weather and climate information were canvassed (e.g. long-term forecasts of the Finnish Meteorological Institute, climate change scenarios of the IPCC) as well as opinions on the usefulness of some sources of information in terms of climate risk management (e.g. scientific publications, social media, websites on weather and climate). The respondents were also asked whether their organisation collected data on the effects of exceptional weather phenomena and climate change on the organisation's activities and whether these data were available to outsiders. Finally, relating to improvement of risk preparedness, the respondents were asked how significant they regarded different factors that hinder the management of climate risks (e.g. organisation's limited resources, lack or problems of technological solutions) and how significant different ways of improving weather and climate risk management were for the organisation. Background variables included the type, field, and size of organisation, primary area of operations as well as the respondent's position and area of responsibility in the organisation, and work experience in the field.

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.

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 statistic shows the average temperatures (in °C) in selected cities in Finland from 1981 to 2010. During the period under survey, the mean temperature in Helsinki was 5.9 degrees Celcius.

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

The data includes computational and measured daily values from the Finnish Meteorological Institute's weather observation station. The calculated values are the daily average air temperature and the daily precipitation. Measured values include minimum and maximum air temperature, minimum ground temperature and snow depth. The extremes of air temperature are measured at 06 and 18 UTC and the minimum ground temperature at 06 UTC. These represent extreme values over the previous 12-hour period (18-06 and 06-18 UTC). The data includes computational and measured daily values from the Finnish Meteorological Institute's weather observation station. The calculated values are the daily average air temperature and the daily precipitation. Measured values include minimum and maximum air temperature, minimum ground temperature and snow depth. The extremes of air temperature are measured at 06 and 18 UTC and the minimum ground temperature at 06 UTC. These represent extreme values over the previous 12-hour period (18-06 and 06-18 UTC). The data includes computational and measured daily values from the Finnish Meteorological Institute's weather observation station. The calculated values are the daily average air temperature and the daily precipitation. Measured values include minimum and maximum air temperature, minimum ground temperature and snow depth. The extremes of air temperature are measured at 06 and 18 UTC and the minimum ground temperature at 06 UTC. These represent extreme values over the previous 12-hour period (18-06 and 06-18 UTC). The data includes computational and measured daily values from the Finnish Meteorological Institute's weather observation station. The calculated values are the daily average air temperature and the daily precipitation. Measured values include minimum and maximum air temperature, minimum ground temperature and snow depth. The extremes of air temperature are measured at 06 and 18 UTC and the minimum ground temperature at 06 UTC. These represent extreme values over the previous 12-hour period (18-06 and 06-18 UTC). The data includes computational and measured daily values from the Finnish Meteorological Institute's weather observation station. The calculated values are the daily average air temperature and the daily precipitation. Measured values include minimum and maximum air temperature, minimum ground temperature and snow depth. The extremes of air temperature are measured at 06 and 18 UTC and the minimum ground temperature at 06 UTC. These represent extreme values over the previous 12-hour period (18-06 and 06-18 UTC). The data includes computational and measured daily values from the Finnish Meteorological Institute's weather observation station. The calculated values are the daily average air temperature and the daily precipitation. Measured values include minimum and maximum air temperature, minimum ground temperature and snow depth. The extremes of air temperature are measured at 06 and 18 UTC and the minimum ground temperature at 06 UTC. These represent extreme values over the previous 12-hour period (18-06 and 06-18 UTC).