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.

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.

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.

Finland: Precipitation, mm per year: The latest value from 2021 is 536 mm per year, unchanged from 536 mm per year in 2020. In comparison, the world average is 1168 mm per year, based on data from 178 countries. Historically, the average for Finland from 1961 to 2021 is 536 mm per year. The minimum value, 536 mm per year, was reached in 1961 while the maximum of 536 mm per year was recorded in 1961.

This statistic shows the average monthly precipitation (in mm) in selected cities in Finland from 1981 to 2010. During the period under survey, the mean rain volume in Helsinki in July was 63 mm.

Precipitation in Finland increased to 681.94 mm in 2023 from 587.28 mm in 2022. This dataset includes a chart with historical data for Finland Average Precipitation.

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.

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 elevation) and water cover (sea and lake proximity), and subsequent leave-one-out cross-validation tests to assess model performance (for full process description, see Aalto et al. 2017; Heikkinen et

As of 2023, Helsinki had around 117 days with precipitation. The month with the most rainy days in Helsinki was December, with 16 days. The months with the least rainy days were March and August with three days.

Average monthly temperatures in Manaus, Brazil remain incredibly stable and warm throughout the year. This is characteristic of tropical climates, which see very little seasonal variation due to their proximity to the equator, as well as the self-regulatory nature of rainforest climates. In contrast, the examples of locations in the far north of Canada or in Finland are much further from the equator and are therefore much colder, and they also see the most seasonal variation.

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, Väliranta 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 identified from the CORINE CLC2018 land cover data which is available in Finland as a 20 x 20 m resolution raster data, by focusing on the CORINE category 4121 (‘Peatbogs’) which includes various open mires occurring in aapa mire and palsa mire zones, as well as in raised bogs zones. We excluded open mires occurring in the raised bogs zone but included CORINE Peatbog occurrences both from the aapa mire and palsa mire zones. This opted for this decision because open mires in aapa and palsa mire zones share several matching ecological features, and because palsa mires may provide suitable habitats for aapa mire species under warming climate.

The adjacent peatbog 20-m pixels in the aapa and palsa mire zones were merged and converted into contiguous peatland polygons. From these, polygons smaller than 10 ha in size were excluded because typically they show only limited number of ecological elements central to the representative aapa mires. These selected ≥10 ha peatland polygons formed the first study mire dataset, aapa mire complexes, or ‘aapa mires’ in short (i.e., the whole aapa mire ecosystem containing all embedded mire habitats therein). The second study mire dataset was constrained to include only the wettest parts of aapa mire complexes characterized by flarks, i.e., open water pools, referred here simply as ‘wet aapa mires’. These wet aapa mire occurrences are typically smaller than the whole aapa mire complexes and occur more sparsely in the landscape. Thus, the climatic exposure of wet aapa mires can be expected to be greater than that of aapa mire complexes. This will very likely cause elevated climate change adaptation challenges for habitat specialist species that require open water or permanently wet environments. The spatial data for the wet aapa mires were determined with the help of the topographic database developed by the National Land Survey of Finland (NLS), and the land cover class ‘Swamps classified as difficult, dangerous and impossible to cross’ therein.

Climate data: In the first phase, monthly average air temperature data for 1981–2010 were constructed at the 50 x 50 m spatial resolution across Finland, as described in Aalto et al. (2017) and Heikkinen et al. (2020, 2021). This was done by modelling the weather station data from 313 Fennoscandian stations together with variables of geographical location, local topography and water cover. Monthly precipitation data were developed by fitting kriging interpolation method to the data on 343 rain gauges, and the data on geographical location, topography and proximity to the sea. Based on the monthly temperature and precipitation data, six bioclimatic variables describing key ecological winter- and summer-time conditions for aapa mire ecosystems were calculated (cf. Parviainen and Luoto 2007, Ruuhijärvi 1988, Rydin and Jeglum 2006): (1) annual temperature sum above the base temperature of 5 °C (growing degree days, GDD5), (2) mean January temperature, (3) mean July temperature, (4) monthly climatic water balance calculated for May and (5) for July, and (6) annual precipitation sum. The two climatic water balance variables were calculated as the difference between the May - or July - total precipitation sum and the potential evapotranspiration (PET) in the corresponding month following Skov and Svenning (2004).

In the second step, the data based on an ensemble of 23 global climate models from the Coupled Model Intercomparison Project (CMIP5) archives (Taylor et al. 2012) were employed to develop future climate surfaces averaged for the years 2040–2069 and the two Representative Concentration Pathways (RCP4.5 and RCP8.5). The monthly air temperature and precipitation data in these climate surfaces were interpolated to match the 50 × 50 m grid, then the change predicted by the GCMs was added to the 1981–2010 climate data, and finally, the values for the six bioclimatic variables were recalculated for the 50-m resolution grid across the whole Finland.

In the third step, all the developed climate surface datasets were intersected by the spatial datasets of the two differently delimited aapa mire networks, i.e. ‘aapa mires’ and ‘wet aapa mires’. This allowed calculation of the climate change velocity metrics separately for the two types of aapa mires, namely, for both mire datasets by measuring the distance between climatically similar 50-m grid cells in the present and future climates by considering only locations with either (i) aapa mires, or (ii) wet aapa mires. Thus, matrix areas providing unsuitable habitat for aapa mire biodiversity were excluded and for both types of mires the distance from the present-day mire cell was linked to the nearest corresponding mire cell with similar future climatic conditions.

The climate data for the years 1981 – 2010 and the future time slice of 2040–2069 and the two Representative Concentration Pathways (RCP4.5 and RCP8.5), clipped to the

This study aimed at investigating the role of vegetation components, sedges, dwarf shrubs, and Sphagnum mosses, in methane fluxes of a boreal fen under natural and experimental water level drawdown conditions. We measured the fluxes during growing seasons 2001-2004 using the static chamber technique in a field experiment where the role of the ecosystem components was assessed via plant removal treatments. The first year was a calibration year after which the water level drawdown and vegetation removal treatments were applied. Under natural water level conditions, plant-mediated fluxes comprised 68 %-78% of the mean growing season flux (1.73 +/- 0.17 g CH4 m-2 month-1 from June to September), of which Sphagnum mosses and sedges accounted for one-fourth and three-fourths, respectively. The presence of dwarf shrubs, on the other hand, had a slightly attenuating effect on the fluxes. In water level drawdown conditions, the mean flux was close to zero (0.03 +/- 0:03 g CH4 m-2 month-1) and the presence and absence of the plant groups had a negligible effect.

The past decades have witnessed an increase in dissolved organic carbon (DOC) concentrations in the catchments of the Northern Hemisphere. Increases in terrestrial productivity may be a reason for the increases in DOC concentration. The aim of this study is to investigate the impacts of increased terrestrial productivity and changed hydrology following climate change on DOC concentrations. We tested and quantified the effects of gross primary production (GPP), ecosystem respiration (RE) and discharge on DOC concentrations in boreal catchments over three years. As catchment characteristics can regulate the extent of rising DOC concentrations caused by the regional or global environmental changes, we selected four catchments with different sizes (small, medium and large) and landscapes (forest, mire and forest-mire mixed). We applied multiple models: Wavelet coherence analysis detected the delay-effects of terrestrial productivity and discharge on aquatic DOC variations of boreal catchments; thereafter, the distributed-lag linear models (DLMs) quantified the contributions of each factor on DOC variations. Our results showed that the combined impacts of terrestrial productivity and discharge explained 62% of aquatic DOC variations on average across all sites, whereas discharge, GPP and RE accounted for 26%, 22% and 3%, respectively. GPP dominated the DOC variations in small catchments (<1 km2), but in large catchments, DOC variations were mainly dependent on discharge. The direction of the relation between GPP and discharge on DOC varied. Increasing RE always made a positive contribution to DOC concentration. This study demonstrated that terrestrial greening and changing hydrology caused by climate change did affect the DOC export from terrestrial to aquatic ecosystems, which improves the mechanistic understanding of surface water DOC regulation in boreal catchments under climate change.

Methods 1. Study site

Four catchments with different sizes (small, medium and large) and landscapes (forest, mire and forest-mire mixed) were studied. Hereafter, following the catchment named ‘S’, ‘M’ and ‘L’ state the catchment sizes while ‘forest’, ‘mire’ and ‘mix’ show the land cover types. Three sub-catchments locate in Krycklan, about 50 km northwest of the city of Umeå in northern Sweden (64°14′ N, 19°46′E) (Fig S1). In Krycklan, C2[S-forest] is covered by forest with the size of 0.14 km2; C4[S-mire] of 0.19 km2 is covered by 40.4% of wetlands, and the remainder is forest; C6[M-mix] of 1.3 km2 is constituted by 72.8% of forest, 24.1% of wetland and 3.1% of lakes (Table 1). The climate is characterised as a cold temperate humid type with persistent snow cover during the winter season. The 30-year mean annual temperature (1981-2010) is 1.8 C, January -9.5 ◦C, and July 14.7 ◦C. The mean annual precipitation is 614 mm, mean annual mean runoff is 311 mm, giving an annual average evapotranspiration of 303 mm (Laudon et al., 2013). The 40-year average duration of winter snow cover is 167 days, but this has been decreasing over time (Laudon et al., 2021). Yli-Nuortti (NT [L-mix]) is a catchment nearby Nuorttiaapa measuring station and located in Värriö, Finland (67°44′ N, 29°27′E) approximately 120 km north of the Arctic Circle close to the northern timberline (Fig S1). NT [L-mix] covers about 40 km2 with 25% of peatlands, and 5% of the area is covered by alpine vegetation on the top of the fells while the rest of the catchment is dominated by pine forests on glacial tills (Table 1). There are no lakes above the measurement station. According to the statistics of the Finnish Meteorological Institute (1981-2012), the mean annual air temperature is -0.5 ◦C. The mean temperature in January is -11.4 ◦C, and in July 13.1 ◦C. The mean annual precipitation is 601 mm. The average number of days with snow cover is 205-225 days (Pohjonen et al., 2008).

1. Sampling and laboratory DOC measurement

High-density polyethene bottles were used for collecting water samples. In Finland (NT [L-mix]), we sampled monthly in winter and fall, fortnightly in spring and every week in summer (2018-2020). In Sweden (C2[S-forest], C4[S-mire] and C6[M-mix]), water samples were collected monthly during winter, every two weeks during summer and fall, and every third day during the spring flood (2016-2018). Water samples were filtered immediately after sampling by a filtration system made of glass using Whatman GF/F Glass Microfiber Filters (pore size 0.45 μm), which had been rinsed by the sample water before filtration. All samples were frozen until further DOC analysis.

In Finland, DOC concentrations were determined by thermal oxidation coupled with infrared detection (Multi N/C 2100, Analytik Jena, Germany) following acidification with phosphoric acid. In Sweden, DOC concentrations were measured with Shimadzu TOC-5000 using catalytic combustion (Laudon et al., 2004).

1. Prediction of DOC based on real-time spectral absorbance

To monitor the real-time spectral absorbance, in-situ portable multi-parameter UV–Vis probes (spectro:lyser, S:CAN Messtechnik GmbH, Austria) were installed in Yli-Nuortti river on June 12, 2018, and in the Krycklan catchments on May 9, 2016. The spectro:lyser measures absorbance across the wavelengths from 220 to 732.5 nm at 2.5 nm intervals with a path length of 35 mm. The benefits of in-situ UV–Vis probe is to make high-frequency aquatic monitoring possible, especially during short-duration events or in remote areas (Avagyan et al., 2014; Rode et al., 2016; Zhu et al., 2020).

Principal component regression (PCR) was used to model the relationship between DOC concentration and absorbance. In the PCR model, absorbance values from 250 nm to 732.5 nm at 2.5 nm intervals (194 variables) were the independent variables. The dependent variables were the DOC concentrations measured in the lab from water samples collected in the respective days. The observations were split into a training and testing data set. The training set contained 75% of observations that were randomly selected from all samples (C2[S-forest], C4[S-mire], C6[M-mix]and NT [L-mix]), and the testing set contained the remaining 25% of observations. The PCR analyses were conducted with the 'pls' package (Mevik et al., 2019) in R (R Core Team, 2019). After the PCR model was built, hourly real-time spectral absorbances were used as input to predict hourly DOC concentrations. The hourly predicted DOC concentrations were aggregated into daily data for further analysis. The outlier values were automatically detected and corrected using the 'tsclean’ function of package ‘forecast’ (Hyndman & Khandakar, 2008) in R (R Core Team, 2019).

4.Water discharge

In Finland, water discharge was determined based on the continuous water depth measurements carried out by pressure sensors measuring the hydrostatic pressure (Levelogger, Solinst, Georgetown, Canada) in the bottom of the river, which was corrected by barometric pressure measurements (Barologger, Solinst, Georgetown, Canada). The water depth measurements were converted to flow rates using channel cross-section, water depth and manual flow rate measurements (Flow Tracker Handheld ADV, SonTek, CA, USA) carried out at sampling locations.

In Sweden, water discharge was computed hourly from water level measurements (using pressure transducers connected to Campbell Scientific dataloggers, USA or duplicate WT-HR water height data loggers, Trutrack Inc., New Zealand). Rating curves were derived based on discharge measurements using salt dilution or time-volume methods (Laudon et al., 2011).

1. Carbon fluxes

There are three measuring stations nearby our study sites where the C exchange between the terrestrial ecosystem and the atmosphere is continuously recorded by the EC technology (Medlyn et al., 2005). The EC data included GPP, RE and NEP. We assumed that NEP=-NEE (Black et al., 2007), and the value for RE and GPP was taken from day-time measurements (Aubinet et al., 2012).

In Finland, the Värriö measuring station SMEAR I (67°45′ N, 29°36′ E, 390m asl) is close to NT [L-mix]. Most of the area is dominated by 60-year-old Scots pine (Pinus sylvestris L.) forests, in addition to which there are also large wetlands and deep gorges in the surroundings(Vehkamäki et al., 2004, pp. 1998–2002)(Vehkamäki et al., 2004, pp. 1998–2002)(Vehkamäki et al., 2004, pp. 1998–2002). Flux data from SMEAR I was applied to NT [L-mix]. The flux data were collected from the Dynamic Ecological Information Management System (https://deims.org/b471311f-e819-4f6f-bbae-1ac86cd9777f). The processing pipeline differed from the two Swedish sites due to polar day (24 hours sunlight) during the growing season. More details about the whole process for data quality control are presented in Kulmala et al. (2019).

In Sweden, the Rosinedalsheden station (64°10′N, 19° 45′E, 145m asl) is located in a forest stand that consists of naturally regenerated 80-year-old Scots pine (Pinus sylvestris L.), and the soil is a deep deposit of sand and fine sand. The ground vegetation is dominated by blueberries (Vaccinium myrtillus L.) and lingonberries (Vaccinium vitis-idaea L.). Degerö station (64°11′N, 19°33′E, 270m asl) is situated on a highland between two major rivers, Umeälven and Vindelälven. The site is a nutrient-poor minerogenic mire dominated by flat mire lawn plant communities with bog mosses (Sphagnum balticum, Sphagnum majus and Sphagnum Lindbergii) dominating the bottom layer. The field layer is dominated by the cottongrass (Eriophorum vaginatum L.) and cranberry (Vaccinium oxycoccos L.), bog-rosemary (Andromeda polifolia L.), deergrass (Trichophorum cespitosum L.). Sedges (Carex spp.) occur more sparsely. C fluxes data from Rosinedalsheden were applied to C2[S-forest], and C6[M-mix] and C fluxes data from Degerö was used in C4[S-mire] (Table

[FI] Satelliittikuvilta seurataan Itämeren ja Suomen suurimpien järvien (79kpl) pintalämpötilaa jäättömiltä ja pilvettömiltä alueilta. Aineisto kattaa myös lähialueiden järviä, muun muassa Laatokan ja Peipsin.

Pintalämpötila on laskettu EUn Copernicus-ohjelman Sentinel-3 satelliittisarjan SLSTR-instrumentin (Sea and Land Surface Temperature Radiometer) aineistoista. Lämpötila on laskettu kahden termisen aallonpituusalueen havaintojen avulla käyttäen ns. Split Window-menetelmää. Pilvet tunnistetaan ja pilviset alueet poistetaan SLSTR LB1-aineiston pilventunnistustasojen sekä manuaalisen tarkastuksen avulla. Pintalämpötilahavainnot tehdään yöaikaan, jolloin ne vastaavat hyvin asemahavaintoja lähellä pintakerrosta. Kuvien maastoerotuskyky on noin 1x1 km.

Päivittäinen pintalämpötila-aineisto koostuu vuodesta 2017 alkaen tehdyistä päivittäisistä tulkinnoista. Vuodesta 2019 lähtien aineistoa on tuotettu ympäri vuoden jää- ja pilvitilanteen salliessa. Aineistossa on mukana ne päivät, joilta on saatavilla satelliittidataa.

Päivittäisistä havainnoista on laskettu lämpötilakoosteet eri ajanjaksoilta. Kesäkauden koosteet (1.6.-31.8.) on laskettu vuodesta 2018 alkaen. Vuosikoosteet (1.1.-31.12.) on laskettu vuodesta 2019 alkaen. Kuukausikooste lasketaan myös kuluvalta kuukaudelta, ja aineistoa on tuotettu 2019 alkaen.

Rajapintojen lämpötilakoosteet sisältävät lämpötilakoosteet sisältävät pikselikohtaiset tilastot eri kanavilta: keskiarvo, geometrinen keskiarvo, minimi, maksimi, sekä percentiilit P05-P95 ja erotus "P95-P05". Aineistosta kuukauden, kesäkauden ja vuoden keskiarvoja ja maksimeja voi selata Syken Tarkka-palvelussa, kausikoosteet teemassa.

Koosteineistoa on kehitetty projektissa Tailored downstream applications/products–from Copernicus to coastal and inland water monitoring. FPCUP-projekti on rahoitettu European Commission päätöksellä FPA no.: 275/G/GRO/COPE/17/10042.

Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0).

Käyttötarkoitus: Itämeren ja Suomen järvien pintalämpötilan seuranta.

[EN] Satellite observations are used to monitor the sea surface temperature (SST) in the Baltic Sea. Sea surface temperature dataset covers daily the non-cloudy ice-free sea areas and many of the largest lakes in Finland (79 lakes).

The dataset is based on the observations by Copernicus programme Sentinel-3 satellite series SLSTR ( Sea and Land Surface Temperature Radiometer) ) instrument. Finnish Environment Institute (Syke) has produced the dataset by using Split Window method and removed the areas covered by clouds. Clouds are identified by a combination of the basic and probabilistic cloud screening flags in the SLSTR L1B product. The spatial (ground) resolution is ~1 km x 1 km.

The daily observations cover years from 2017 on and annually mainly the period from April to October. From 2019 on, the dataset covers annually all non-cloudy and ice-free observations throughout the year.

The water surface temperature aggregates have been processed for periods which contain almost daily observations. Summer aggregates (June 1 - August 31) have been processed 2018 onwards. Yearly aggregates (January 1 - December 31) have been processed 2019 onwards. Monthly aggregates are processed also for the current month, and data has been produced 2019 onwards.

The water surface temperature aggregates at the WMS-server include pixel-specific statistics in different bands: mean, geometric mean, minimum, maximum, as well as percentiles P05-P95 and the difference “P95-P05”. Monthly, summer, and yearly average and maximum are available for browsing in Syke's Tarkka service Seasonal theme.

Dataset has been developed in project Tailored downstream applications/products–from Copernicus to coastal and inland water monitoring. The FPCUP project is financed by the European Commission under the FPA no.: 275/G/GRO/COPE/17/10042

WMS-palvelin / WMS service endpoint: https://geoserver2.​ymparisto.​fi/geoserver/eo/wms

WMS-tasot / WMS layers:

EO_MR_SLSTR_SST_SEASONAL_MONTH

EO_MR_SLSTR_SST_SEASONAL_SUMMER

EO_MR_SLSTR_SST_SEASONAL_YEAR

Kaukokartoitusseurantojen tuloksena syntynyt pintalämpötilakooste, joka pohjautuu Sentinel-3 SLSTR -satelliittikuvista johdettuihin päivittäisiin pintalämpötiloihin. Vuodesta 2018 eteenpäin prosessoitu veden pintalämpötila-koosteaineisto.

Prosessointihistoria: Pintalämpötilat on tulkittu Sentinel-3 SLSTR-satelliitti-instrumentin aineistoilta. Alkuperäinen satelliittidata on ladattu Euroopan avaruusjärjestön (ESA) latauspalveluista. Sykessä niistä on laskettu lämpötilatulkinnat.