Temperature in Russia increased to -2.63 celsius in 2024 from -2.82 celsius in 2023. This dataset includes a chart with historical data for Russia Average Temperature.

This dataset contains Russian Historical Soil Temperature Data. This data set is a collection of monthly and annual average soil temperatures measured at Russian meteorological stations. Data were recovered from many sources and compiled by staff at the University of Colorado, USA, and the Russian Academy of Sciences in Puschino, Russia. Soil temperatures were measured at depths of 0.02 to 3.2 m using bent stem thermometers, extraction thermometers, and electrical resistance thermistors. Data coverage extends from the 1800s through 1990, but is not continuous. Data are not available for all stations for the entire period of coverage. For example, data collection began at many stations in the 1930s and 1950s, and not all stations continued taking measurements through 1990. This research was supported by the National Science Foundation (NSF) Office of Polar Programs (OPP) awards OPP-9614557, OPP-9907541, and OPP-0229766. Data are available as tar.gz files.

Over the past several decades, many climate datasets have been exchanged directly between the principal climate data centers of the United States (NOAA's National Climatic Data Center (NCDC)) and the former-USSR/Russia (All-Russian Research Institute for Hydrometeorological Information-World Data Center (RIHMI-WDC)). This data exchange has its roots in a bilateral initiative known as the Agreement on Protection of the Environment (Tatusko 1990). CDIAC has partnered with NCDC and RIHMI-WDC since the early 1990s to help make former-USSR climate datasets available to the public. The first former-USSR daily temperature and precipitation dataset released by CDIAC was initially created within the framework of the international cooperation between RIHMI-WDC and CDIAC and was published by CDIAC as NDP-040, consisting of data from 223 stations over the former USSR whose data were published in USSR Meteorological Monthly (Part 1: Daily Data). The database presented here consists of records from 518 Russian stations (excluding the former-USSR stations outside the Russian territory contained in NDP-040), for the most part extending through 2010. Records not extending through 2010 result from stations having closed or else their data were not published in Meteorological Monthly of CIS Stations (Part 1: Daily Data). The database was created from the digital media of the State Data Holding. The station inventory was arrived at using (a) the list of Roshydromet stations that are included in the Global Climate Observation Network (this list was approved by the Head of Roshydromet on 25 March 2004) and (b) the list of Roshydromet benchmark meteorological stations prepared by V.I. Kodratyuk, Head of the Department at Voeikov Main Geophysical Observatory. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/russia_daily518.html

Global Temperature: Daily Temperature Departure from Normal: Russian Federation: Utta data was reported at -0.850 Degrees Celsius in 16 May 2025. This records an increase from the previous number of -3.650 Degrees Celsius for 15 May 2025. Global Temperature: Daily Temperature Departure from Normal: Russian Federation: Utta data is updated daily, averaging 1.700 Degrees Celsius from Sep 2023 (Median) to 16 May 2025, with 596 observations. The data reached an all-time high of 15.750 Degrees Celsius in 16 Mar 2025 and a record low of -15.600 Degrees Celsius in 28 Feb 2025. Global Temperature: Daily Temperature Departure from Normal: Russian Federation: Utta data remains active status in CEIC and is reported by Climate Prediction Center. The data is categorized under Global Database’s Russian Federation – Table RU.CPC.GT: Environmental: Global Temperature: Daily Temperature Departure from Normal.

This dataset contains Russian summary of day data for 223 Russian stations, beginning as early as 1881 and continuing through 1989. Information in each day's summary includes maximum, minimum, and average temperatures and preciptation total.

The results of the International Permafrost Association's International Polar Year Thermal State of Permafrost (TSP) project are presented based on field measurements from Russia during the IPY years (2007-09) and collected historical data. Most ground temperatures measured in existing and new boreholes show a substantial warming during the last 20 to 30 years. The magnitude of the warming varied with location, but was typically from 0.5°C to 2°C at the depth of zero annual amplitude. Thawing of Little Ice Age permafrost is ongoing at many locations. There are some indications that the late Holocene permafrost has begun to thaw at some undisturbed locations in northeastern Europe and northwest Siberia. Thawing of permafrost is most noticeable within the discontinuous permafrost domain. However, permafrost in Russia is also starting to thaw at some limited locations in the continuous permafrost zone. As a result, a northward displacement of the boundary between continuous and discontinuous permafrost zones was observed. This data set will serve as a baseline against which to measure changes of near-surface permafrost temperatures and permafrost boundaries, to validate climate model scenarios, and for temperature reanalysis.

Monthly 30-year "normal" dataset covering the conterminous U.S., including the Russian River watershed, averaged over the climatological period 1981-2010. Contains spatially gridded average monthly and average annual precipitation, maximum temperature, and minimum temperature at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset was heavily peer reviewed, and is available free-of-charge on the PRISM website. The dataset was downloaded from the PRISM website in 2019

Method for quantitative reconstruction of mean July air temperatures (Tjuly). The quantitative reconstruction of mean July air temperatures (TJuly) is based on calibration chironomid data sets for lakes from northern Russia (Nazarova et al., 2015, doi:10.1016/j.gloplacha.2014.11.015). Mean July air temperatures were inferred using a North Russian (NR) chironomid-based temperature inference model (WA-PLS, 2 component; r 2 boot = 0.81; RMSEP boot =1.43 °C) based on a modern calibration data set of 193 lakes and 162 taxa from East and West Siberia (61-75°N, 50-140 °E, T July range 1.8 - 18.8 °C). The mean July air temperature of the lakes for the calibration data set was derived from New et al. (2002, doi:10.3354/cr021001). The TJuly NR model was previously applied to palaeoclimatic inferences in Europe, arctic Russia, East and West Siberia, and demonstrated a high reliability of the reconstructed parameters. The chironomid-inferred TJuly were corrected to 0 m a.s.l. using a modern July air temperature lapse rate of 6 oC km-1. Chironomid-based reconstructions were performed in C2 version 1.7. The chironomid data was square-rooted to stabilize species variance. To assess the reliability of the chironomid-inferred TJuly reconstruction, we calculated the percentage abundances of the fossil chironomids that are rare or absent in the modern calibration data set. A taxon is considered to be rare in the modern data when it has a Hill N2 below 5. Optima of the taxa that are rare in modern data are likely to be poorly estimated. Goodness-of-fit statistics derived from a canonical correspondence analysis (CCA) of the modern calibration data and down-core passive samples with TJuly as the sole constraining variables was used to assess the fit of the analyzed down-core assemblages to TJuly. This method shows how unusual the fossil assemblages are in respect to the composition of the training set samples along the temperature gradient. Fossil samples with a residual distance to the first CCA axis larger than the 90th and 95th percentile of the residual distances of all the modern samples were identified as samples with a 'poor fit' and a 'very poor fit' with the reconstructed variable (TJuly). CCA was performed using CANOCO 5. In the evaluation of goodness-of-fit, the CCA scaling focused on inter-sample distances with Hill's scaling selected to optimize inter-sample relationships.

Mapping Russian Wetlands and Estimating Methane Fluxes

Introduction

Wetlands are crucial in regulating the Earth’s climate, acting as both carbon sinks and significant methane sources. Russian wetlands represent one of the largest and most diverse wetland complexes globally, extending across biomes from Arctic tundra to boreal forests. Despite their importance, these wetlands remain underexplored, particularly in terms of their spatial distribution and greenhouse gas contributions. This dataset provides a detailed typological map of Russian wetlands and accompanying methane flux estimates, representing the most comprehensive methane emissions dataset for Russian wetlands to date. The maps and calculations were developed in Google Earth Engine (GEE) through a combination of multi-seasonal Landsat composites, PALSAR radar imagery, and extensive field-based validation data from peatland sites across Western Siberia.

Data Overview

Input Layers

The wetland mapping relied on seasonal Landsat composites (spring, summer, fall) and PALSAR radar data to capture the distinct structural and hydrological characteristics of each wetland type. Additional layers, such as GMTED topographic slope and Hansen’s TreeCover, were included to exclude non-wetland areas and to enhance the classification by distinguishing forested from non-forested wetlands.

Training Points

A comprehensive training site database was created, integrating field knowledge, high-resolution imagery, and georeferenced photos. Approximately 2,450 representative points were selected to capture 12 primary wetland types across Russia, with each point validated against high-resolution imagery to ensure accuracy. Points were collected to represent the wide-ranging wetland ecosystems in Russia, from open water and patterned bogs to swampy and forested fens, providing robust ground-truth data for training the classification model.

Random Forest Classifier

The random forest classifier was chosen for its capacity to handle large datasets and complex relationships among input layers. Optimized for Landsat and PALSAR inputs, the classifier used over 100 trees, each making independent predictions based on subsets of data, which were averaged to produce the final classification. This ensemble approach minimized overfitting, a crucial factor for the varied ecological regions across Russia.

Russian Wetlands Map

The final Russian Wetlands Map encompasses 12 wetland types, detailing their distribution and extent across the country:

• Total Wetland Area: 173.96 million hectares of mapped wetlands, capturing diverse ecosystems, including bogs, fens, and swampy areas.

• Open Water Area: Lakes, rivers, and smaller water bodies within wetland zones were separately mapped, totaling 42.6 million hectares.

Emission Modeling and Ecosite Analysis

Ecosite Proportions for Methane Emission Modeling

Each wetland type was further divided into ecosite units representing distinct, smaller areas with uniform hydrological and geochemical properties. This level of detail enabled precise methane emission estimates by capturing the variability within complex wetland ecosystems. For instance, ridges and hollows within patterned bogs exhibit unique methane emission dynamics due to differences in vegetation and water levels. Ecosite proportions for methane emission were calculated from 20-30 representative field sites per wetland type, capturing the typical area breakdown of each wetland type across Russia.

Methane Emission Period Calculation

To estimate seasonal methane emission periods across Russia’s climatic zones, the average summer temperature (Bio10) parameter from WorldClim data was used. Bio10 values reflect seasonal variation in emission potential, correlating with longer and warmer summers in southern regions versus shorter, cooler summers in the north. Using these data, an emission period was calculated for each 50 km x 50 km grid cell based on a regression model derived from Western Siberia data:
Emission Period (hours) = 303 * Bio10 – 675

This equation, which explained 98% of the variation in emission duration, provided a dynamic method for estimating emission periods across Russia’s diverse landscape.

Methane Emission Estimates

Calculation Approach

Methane emission estimates were derived from a multi-step approach that incorporated ecosystem-specific emission factors, ecosystem area, and the estimated emission period:

1. Ecosystem Area Calculation: Area estimates for each ecosite type were derived from field-based proportions applied to the classified wetland map.

2. Emission Period: Calculated for each grid cell based on Bio10 data, varying continuously across climatic zones.

3. Methane Flux Values: Based on quantiles from field measurements within three main zones (Tundra, Northern Taiga, and Southern Taiga) to account for natural variability in methane emissions.

Using this approach, methane emissions were calculated for each 50 km per 50 km grid cell, factoring in the unique emission characteristics of each wetland type and zone. This produced a spatially detailed estimate of methane fluxes, reflective of the temperature and vegetation gradients across Russia.

Resulting National Estimate

• Total Annual Methane Emissions: 11.39 MtCH₄ per year from all mapped wetland areas.

• Open Water Contributions: 2.54 MtCH₄ per year from open water bodies, including intra-wetland lakes and rivers.

Data Highlights

• High-resolution wetland classification covering 173.96 million hectares across diverse wetland ecosystems.

• Detailed methane emission data derived from multi-year field measurements and validated against climatic data, providing spatially continuous methane flux estimates across Russia.

• 50x50 km² grid cell calculations, accounting for methane emission rates, emission periods, and ecosystem proportions for each cell.

This dataset serves as an essential tool for environmental scientists, climate modelers, and conservationists, supporting further research into wetland carbon dynamics, climate mitigation strategies, and regional land-use planning. The high resolution data availbale at url: https://code.earthengine.google.com/d6a9d4045255fd84298777e56a38ae03

This data set provides two data files in text format (.txt). One file contains a long time series of biomass measurements made between 1954 and 1983 on a virgin meadow steppe in the Central-Chernozem V.V. Alyekhin Natural Reserve, Kursk Region, Russia. The second file contains monthly and annual climate data for the study site for the period 1947-1983.Above-ground live biomass measurements were made at biweekly to monthly intervals over the entire 30-year time series. Discontinuous measurements of above-ground standing dead matter and litter biomass (1956-1983) and below-ground live and dead biomass (1972-1973 and 1981-1983) were also made. Cumulative ANPP was estimated at the end of the growing season (1956-1963 and 1972-1973) and monthly (1982-1983). Averaged over the time series, above-ground live phytomass, standing dead, and litter biomass were estimated to be 362, 344, and 424 g/m2 (dry matter weight), respectively, while below-ground phytomass and mortmass were 910 and 1,370 g/m2 (dry matter weight), respectively. ANPP was estimated to be 774 g/m2/yr and BNPP was estimated to be 1,700 g/m2/yr for a TNPP estimate of 2,474 g/m2/yr. The study site is one of eight major grassland types of Eurasia which encompass an extremely wide climatic gradient in the direction of increasing maximum summer temperatures and continentality and decreasing precipitation in a north-west to the south-east band of steppes in the European and Asian parts of the former USSR (Commonwealth of Independent States). Kurst, on rich loamy chernozem soil, is one of the most productive upland grassland ecosystems of Russia with annual mean maximum/minimum temperatures of 24.8/-14.4 C and annual mean precipitation of 582.7 mm for the period 1947-1983. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996.

The presented database is a set of hydrological, meteorological, environmental and geometric values for Russia Federation for the period from 2008 to 2020.

Database consist of next items:

• Point geometry for hydrological observation stations from Roshydromet network across Russia
• Geometry of the catchment for correspond observation station point
• Daily hydrological values
  • Water level
    • In relative representation (sm)
    • In meters of Baltic system (m)
  • Water discharge
    • as an observed value (qms/s)
    • as a layer (mm/day)
• Daily meteorological values
  • Maximum and minimum daily temperatures (°C) from ERA5 and ERA5-Land
  • Total precipitation (mm/day) from ERA5, ERA5-Land, IMERG v06, GPCP v3.2 and MSWEP
  • Different kind of evaporation (mm/day) corresponded to each variable calculated in GLEAM model
• Set of hydro-environmental characteristics derived from HydroATLAS database

Each variable derived from the grid data was calculated for each watershed, taking into account the intersection weights of the watershed contour geometry and grid cells.

Coordinates of hydrological stations were obtained from resource of Federal Agency for Water Resources of Russia Federation—AIS GMVO

To calculate the contours of the catchment areas, a script was developed that builds the contours in accordance with the rasters of flow directions from MERIT Hydro. To assess the quality of the contour construction, the obtained value of the catchment area was compared with the archival value from the corresponded table from AIS GMVO. The average error in determining the area for 2080 catchments is approximately 2%

To derive values for different hydro-environmental values from HydroATLAS were developed approach which calculate aggregated values for catchment, leaning on type of variable: qualitative (Land cover classes, Lithological classes etc.) Or quantitive (Air temperature, Snow cover extent etc.). Every quantitive variable were calculated as mode value for intersected sub-basins and target catchment, e.g. most popular attribute from sub-basins will describe whole catchment which are they relating. Quantitative values were calculated as mean value of attribute from each sub-basin. More detail could be found in publication.

Files are distributed as follows:

Each file has some connection with the unique identifier of the hydrological observation post. Files in netcdf format (hydrological and meteorological series) are named in response to identifier.

Every file which describe geometry (point, polygon, static attributes) has and column named gauge_id with same correspondence.

• attributes/static_data.csv – results from HydroATLAS aggregation
• geometry/russia_gauges.gpkg – coordinates of hydrological observation stations

  • 	gauge_id	name_ru	name_en	geometry
    0	49001	р. Ковда – пос. Софпорог	r.Kovda - pos. Sofporog	POINT (31.41892 65.79876)
    1	49014	р. Корпи-Йоки – пос. Пяозерский	r.Korpi-Joki - pos. Pjaozerskij	POINT (31.05794 65.77917)
    2	49017	р. Тумча – пос. Алакуртти	r.Tumcha - pos. Alakurtti	POINT (30.33082 66.95957)

• geometry/russia_ws.gpkg – catchments polygon for each hydrological observation stations

  • 	gauge_id	name_ru	name_en	new_area	ais_dif	geometry
    0	9002	р. Енисей – г. Кызыл	r.Enisej - g.Kyzyl	115263.989	0.230	POLYGON ((96.87792 53.72792, 96.87792 53.72708...
    1	9022	р. Енисей – пос. Никитино	r.Enisej - pos. Nikitino	184499.118	1.373	POLYGON ((96.87792 53.72708, 96.88042 53.72708...
    2	9053	р. Енисей – пос. Базаиха	r.Enisej - pos.Bazaiha	302690.417	0.897	POLYGON ((92.38292 56.11042, 92.38292 56.10958...

  • Column ais_diff is corresponded to % error in area definition
• nc_all_q
  • netcdf files for hydrological observation stations which has no missing values on discharge for 2008-2020 period
• nc_all_h
  • netcdf files for hydrological observation stations which has no missing values on level for 2008-2020 period
• nc_all_q_h
  • netcdf files for hydrological observation stations which has no missing values on discharge and level for 2008-2020 period
• nc_concat
  • data for all available geometry provided in dataset

More details on processing scripts which were used for development of this database can be found in folder of GitHub repository where I store results for my PhD dissertation

05.04.2023 – Significant data changes. Removed catchments and related files that have more than ±15% absolute error in calculated area relative to AIS GMVO information. Now these are data for 1886 catchments across the Russia.

17.05.2023 – Significant data changes. Major review of parsing algorithm for AIS GMVO data. Fixed the way of how 0.0xx values were read. Use previous versions with caution.

11.10.2023 – Significant data changes. Added 278 catchments for CIS region from GRDC resource. Calculate meteorological and environmental attributes for each catchment. New folder /nc_all_q_h with no missing observations on discharge and level. Now these are data for 2164 catchments across CIS.

This data release contains monthly 270-meter gridded Basin Characterization Model (BCMv8) climate inputs and hydrologic outputs for Russian River (RR). Gridded climate inputs include: precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters. Monthly historical variables from water years 1896 to 2019 are summarized into water year files and long-term average summaries for water years 1981-2010. Four future climate scenarios were spatially downscaled from 6 kilometers to 270 meters, and run through the BCMv8 using the same model parameters. The future climate scenarios are all Representative Concentration Pathway (RCP) 8.5 and include: CanESM2, CNRM-CM5, HadGEM2-ES, and MIROC5 from California's Forth Climate Change Assessment. Future climate scenarios span from water year 2007 to 2099, and monthly variables were summarized by water year and the average 2070 to 2099 period. Streamflow for each calibration basin was calculated using a post processing Excel spreadsheet and BCMv8 recharge and runoff, and are provided in tabular comma separated .csv files. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (.asc).

This dataset includes data files provided by the Arctic and Antarctic Research Institute through our grant "Spatial and Temporal Variability of the Arctic Mixed Layer from Russian and American Data", National Science Foundation (NSF) Grant #OPP-9708635. Although this was not a SHEBA grant, the data has a strong relevance to SHEBA, because it helps put the SHEBA ocean data in historical perspective. The data set includes: Beaufort ML Quadrangle that gives mixed layer salinity and temperature from Russian hydrographic stations in a rectangle around the start position of the SHEBA drift. NPstations_2&12&22&31 includes derived mixed layer properties and representative profiles from North Pole Drifting Stations 2, 21, 22, and 31. These are in the general area of the SHEBA drift. Mixed_Layer_Depths_1970s includes mixed layer depths at Russian stations taken in the 1970s in the Arctic Ocean. Also included are files that contain averages of temperature and salinity from 148 historical Russian oceanographic stations within 100 kilometers of the SHEBA drift track for the depths indicated. Anomaly files are a collection of oceanographic stations of temperature and salinity anomalies relative to the average temperature and salinity of those stations that recorded a measurement at each given depth. The collection was chosen from Russian North Pole Station and Sever Program measurements between 1949 and 1989 within 100 kilometers of the SHEBA drift track.

Together with the Russian Academy of Sciences, IIASA's Forestry (FOR) project has released a CD-ROM titled Land Resources of Russia, Version 1.1, containing socioeconomic and biophysical data sets on important targets of international conventions — climate change, wetlands, desertification, and biodiversity. The CD-ROM, a country-scale integrated information system, supports sustainable use of land resources in line with Chapter 10 of Agenda 21 (UNCED) and makes a contribution to the Rio+10 Summit.

The Project's analysis of land resources are crucial for doing full greenhouse gas (or carbon) accounting. Integrated land analyses are also important for the introduction of sustainable forest management. FOR's land analyses concentrate on Russia, which is used as a case study for full carbon and greenhouse accounting.

Russia's area of forests, called here the forest zone, covers about 1180 million ha or 69% of the land of the country. The forested area (forests forming closed stands) occupies some 765 million ha constituting 65% of the forest zone. Forests are elements of a land-cover mosaic that direct the features of landscapes, ecosystems, vegetation and land uses. The FOR project attempts to overcome the traditional approach of just considering the direct utilities of forests. Instead, FOR operates with a holistic view of forests in a fully-fledged land concept. Integrated analysis of the land requires extended databases that includes various data for the total land operated in the form of GIS-based tools.

The land databases on Russia are the most comprehensive ever assembled, inside or outside of Russia. The databases have been enriched by remotely sensed data, biogeochemical functionality (carbon analysis), and institutional frameworks. The data included on the CD-ROM have been specially selected and filtered to meet the following criteria: (1) completeness: to meet a variety of the analysis tasks; (2) complexity: to describe a diversity of the task aspects; (3) consistency: to provide compatible results; to be ata compatible scale and, to provide a compatible time horizon; and (4) uniformity: to allow them to be standardized and formatted according to modern data handling routines.

The following databases and coverages are included on the CD-ROM and are available for download:

Socioeconomic Database -- Describes the social environment of each administrative region in Russia with close to 7000 parameters. The data cover the years 1987-1993. Coverages in this section include:

(1) Socioeconomic Statistical Database. This database provides the following statistical data sets: Population; Labor and Salary; Industry; Agriculture; Capital Construction; Communication and Transport; State Trade and Catering; Utilities and Services; Health Care and Sport; Education and Culture; Finance; Public Consumption; Industrial Production; Interregional Trade; Labor Resources; Supply of Materials; Environmental Protection; Foreign Trade; and Price Indices.

(2) Population Database. Adapted from Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IFPRI); and World Resources Institute (WRI). 2000. Gridded Population of the World (GPW), Version 2, this coverage contains population densities for 1995 on a 2.5 degree grid. Data were adjusted to match United Nations national population estimates for 1995.

(3) Administrative Oblasts, Cities & Towns Database. Oblasts coverage contains 92 polygons, 88 of which contain Oblast names, the other four represent waterbodies. The cities coverage contains 37 cities identified by name.

(4) Transportation Database. The statistical data sets and maps cover the transport routes of the railway, road, and river networks spanning the entire country. Railways and roads are classified by type and status, and major rivers are named. Map coverages (line data) were created from the Digital Chart of the World, using the 1993 version at the 1:1,000,000 scale.

Natural Conditions Database. This section of the CD-ROM contains the basic land characteristics. This database provides specialists and scientists in research institutes and international agencies with the capability to perform scientific analysis with a Geographic Information System. These data describe land characteristics that might be applied in various ways, such as individual items (e.g., temperature, elevation, vegetation community, etc.), in combination (e.g., forest-temperature associations, soil spectra for land use types, etc.), and as aggregations based on a conceptual framework of a different level of complexity (e.g., ecosystem establishment, human-induced land cover transformation, biochemical cycle analysis, etc.). Coverage includes:

(1) Climate Database. Temperature (annual and seasonal) and Precipitation... Visit https://dataone.org/datasets/Land_Resources_of_Russia%2C_Version_1.1.xml for complete metadata about this dataset.

The spatial distribution of the data were first interpolated by near-distance interpolation method based on Baseline Meteorological Dataset of Siberia (BMDS, 77stations), and then averaged on the annual.

Current mean January and mean winter temperatures near the places of determination of the isotopic composition of ice wedge veinlets in the north of the permafrost zone of Russia from 1930 to 2019 are presented. The meteorological observations over the past 80 years and the δ¹⁸О values in ice wedges for each region of the permafrost zone were analyzed in order to identify the most obvious and stable dependence of the isotopic composition and winter temperature. The correlation of the isotopic composition of ice wedges and the temperature of the cold period allows to create more detailed quantitative paleoreconstructions of climate change within the modern permafrost zone of Siberia. Data was submitted and proofread by Yurij K Vasil'chuk and Lyubov Bludushkina at the faculty of Geography, department of Geochemistry of Landscapes and Geography of Soils, Lomonosov Moscow State University.Funding was received from the Russian Science Foundation (Award nr. 19-17-00126, field studies and isotope analysis) and the Russian Foundation for Basic Research (Award nr. 18-05-60272, methodology of the study and calculations).

The Russian weather stations included in this analysis are Krenkel Observatory, Nagurskaya, Rudolf Island and Ostrov Victoria. The temperature data from the stations have undergone both manual and automatic quality controls in several stages. The data were initially manually controlled at the weather station by the observers and have later undergone several rounds of manual and automatic quality control including consistency checks and outlier tests. Tests to identify large errors and suspicious observations in the temperature series included logical tests using differences between maximum, minimum and mean temperature. To identify outliers, Grubbs’ criterion was used where values exceeding ±2.5 standard deviation from the monthly mean were marked and examined. A modified Tietjen-Moore test, was sometimes used to test outliers. All suspicious values were examined by experts at AARI (Arctic and Antarctic Research Institute), RIHMI-WDC (All-Russia Research Institute of Hydrometeorological Information - World Data Center) or SPSU (Saint Petersburg State University) who made the final decision on whether to keep or reject the value. The temperature series were also compared to series from neighboring stations to identify possible systematic errors giving shifts in the data series. The homogenized temperature series from Krenkel Observatory also includes data from the weather station Bukhta Thikaya and has been carefully scrutinized as described by Ivanov et al. 2021*.*Svalbard Airport, Ny-Ålesund and Hopen are weather stations intended for forecasting and climate analysis and the data from these stations undergo extensive quality control (QC) when being stored in MET Norway’s database. Quality control has been performed mostly manually until 2005 when an automatic QC routine was put into use that includes several consistency tests such as step tests and threshold tests, in addition to manual inspection of values flagged as suspicious by the system. There have been several changes in instrumentation and location at all three stations leading to breaks in the homogeneity of the series. More details on quality control, station changes, and homogeneity can be found in Førland et al. 2011, Nordli et al. 2015 & 2020, Gjelten et al. 2016, and Hanssen-Bauer et al. 2019. During a time span of nearly thirty years automatic weather stations (AWS) have been in operation on the northern and eastern islands of Svalbard. The instruments and station infrastructure have varied much during those years. During the early years the data were not stored in MET Norway’s database, and there was no quality control. There were also problems with the regularity of the data, in particular many stations were destroyed by polar bears. In 1996, no data of accepted quality reached MET Norway. However, in 2010 a new setup of stations was developed, which improved data quality and significantly reduced the number of missing data. Hence, almost all our work on data control for this study was related to data before the autumn of 2010.

The Russian weather stations included in this analysis are Krenkel Observatory, Nagurskaya, Rudolf Island and Ostrov Victoria. The temperature data from the stations have undergone both manual and automatic quality controls in several stages. The data were initially manually controlled at the weather station by the observers and have later undergone several rounds of manual and automatic quality control including consistency checks and outlier tests. Tests to identify large errors and suspicious observations in the temperature series included logical tests using differences between maximum, minimum and mean temperature. To identify outliers, Grubbs’ criterion was used where values exceeding ±2.5 standard deviation from the monthly mean were marked and examined. A modified Tietjen-Moore test, was sometimes used to test outliers. All suspicious values were examined by experts at AARI (Arctic and Antarctic Research Institute), RIHMI-WDC (All-Russia Research Institute of Hydrometeorological Information - World Data Center) or SPSU (Saint Petersburg State University) who made the final decision on whether to keep or reject the value. The temperature series were also compared to series from neighboring stations to identify possible systematic errors giving shifts in the data series. The homogenized temperature series from Krenkel Observatory also includes data from the weather station Bukhta Thikaya and has been carefully scrutinized as described by Ivanov et al. 2021*.*Svalbard Airport, Ny-Ålesund and Hopen are weather stations intended for forecasting and climate analysis and the data from these stations undergo extensive quality control (QC) when being stored in MET Norway’s database. Quality control has been performed mostly manually until 2005 when an automatic QC routine was put into use that includes several consistency tests such as step tests and threshold tests, in addition to manual inspection of values flagged as suspicious by the system. There have been several changes in instrumentation and location at all three stations leading to breaks in the homogeneity of the series. More details on quality control, station changes, and homogeneity can be found in Førland et al. 2011, Nordli et al. 2015 & 2020, Gjelten et al. 2016, and Hanssen-Bauer et al. 2019. During a time span of nearly thirty years automatic weather stations (AWS) have been in operation on the northern and eastern islands of Svalbard. The instruments and station infrastructure have varied much during those years. During the early years the data were not stored in MET Norway’s database, and there was no quality control. There were also problems with the regularity of the data, in particular many stations were destroyed by polar bears. In 1996, no data of accepted quality reached MET Norway. However, in 2010 a new setup of stations was developed, which improved data quality and significantly reduced the number of missing data. Hence, almost all our work on data control for this study was related to data before the autumn of 2010.

Northern peatlands represent one of the largest carbon pools in the biosphere the carbon they store are increasingly vulnerable to perturbations from climate and land-use change. Meteorological observations directly at peatland areas in Siberia are unique and rare, while peatlands characterized by a specific local climate. This paper presents a hydrological and meteorological dataset collected at the Mukhrino peatland, Khanty–Mansi Autonomous Okrug – Yugra, Russia over the period of 08 May 2010 to 31 December 2019. Hydrometeorological data collected from stations located at the small pine-shrub-Sphagnum ridge and Scheuchzeria-Sphagnum hollow at the ridge–hollow complexes of ombrotrophic peatland. Monitored meteorological variables include air temperature, air humidity, atmospheric pressure, wind speed and direction, incoming and reflected photosynthetically active radiation, net radiation, soil heat flux, precipitation (rain), and snow depth. The gap-filling procedure based on the gaussian process regression model with exponential kernel was developed to obtain a continuous time series. For the record from 2010 to 2019, the average mean annual air temperature site was −1.0 ◦C, with a mean monthly temperature of the warmest month (July) recorded as 17.4 ◦C and for the coldest month (January) −21.5 ◦C. The average net radiation was about 35.0 W m^-2, the soil heat flux was 2.4 and 1.2 W m^-2 for the hollow and the ridge sites, respectively.

DATASETS:

meteo_MFC_raw.dat - Raw data collected from the automated weather station at the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). Note: The time step differs during measured period 01.01.2010 – 15.07.2012: 15 minutes; 15.07.2012 – 20.06.2014: 1 hour; 20.06.2014 – 31.12.2020: 30 minutes.

meteo_MFC_qq_1h.dat – Quality controlled data collected from the automated weather station at the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). The time step is 60 minutes. The missing data denoted by “NA”.

meteo_MFC_gapfilled_1h.dat – Quality controlled and gap-filled hydrometeorological data for the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). The time step is 60 minutes. The missing data denoted by “NA”.

meteo_MFC_raw.dat parameters:

1. date - Date and time ( DD/MM/YYYY hh:mm:ss ).

2. ta_H - Air temperature at 2 m, hollow ( oC ).

3. ta_R - Air temperature at 2 m, ridge ( oC ).

4. rh_H - Relative air humidity at 2 m, hollow ( % ).

5. rh_R - Relative air humidity at 2 m, ridge ( % ).

6. vp_H - Water vapor pressure at 2 m, hollow ( kPa ).

7. vp_R - Water vapor pressure at 2 m, ridge ( kPa ).

8. ws_10m - Wind speed at 10 m ( m s-1 ).

9. wd_10m - Wind direction at 10 m ( deg ).

10. ws_2m - Wind speed at 2 m ( m s-1 ).

11. wd_2m - Wind direction at 2 m ( deg ).

12. stdwd_10m - Standard deviation of wind direction at 10 m for the period of measurement ( m s-1 ).

13. stdwd_2m - Standard deviation of wind direction at 2 m for the period of measurement ( m s-1 ).

14. ipar_H - Incoming PAR, hollow ( µmol m-2 s-1 ).

15. ipar_R - Incoming PAR, ridge ( µmol m-2 s-1 ).

16. rpar_H - Reflected PAR, hollow ( µmol m-2 s-1 ).

17. rpar_R - Reflected PAR, ridge ( µmol m-2 s-1 ).

18. nr_H - Net radiation balance, hollow ( Uncalibrated ).

19. nr_R - Net radiation balance, ridge ( Uncalibrated ).

20. shf_H - Soil heat flux, hollow ( Uncalibrated ).

21. shf_R1 - Soil heat flux, ridge, site 1 ( Uncalibrated ).

22. shf_R2 - Soil heat flux, ridge, site 2 ( Uncalibrated ).

23. ts_2cm_R1 - Soil temperature at 2 cm, ridge, site 1 ( oC ).

24. ts_5cm_R1 - Soil temperature at 5 cm, ridge, site 1 ( oC ).

25. ts_10cm_R1 - Soil temperature at 10 cm, ridge, site 1 ( oC ).

26. ts_20cm_R1 - Soil temperature at 20 cm, ridge, site 1 ( oC ).

27. ts_50cm_R1 - Soil temperature at 50 cm, ridge, site 1 ( oC ).

28. ts_2cm_R2 - Soil temperature at 2 cm, ridge, site 2 ( oC ).

29. ts_5cm_R2 - Soil temperature at 5 cm, ridge, site 2 ( oC ).

30. ts_10cm_R2 - Soil temperature at 20 cm, ridge, site 2 ( oC ).

31. ts_20cm_R2 - Soil temperature at 20 cm, ridge, site 2 ( oC ).

32. ts_50cm_R2 - Soil temperature at 50 cm, ridge, site 2 ( oC ).

33. ts_2cm_H1 - Soil temperature at 2 cm, hollow, site 1 ( oC ).

34. ts_5cm_H1 - Soil temperature at 5 cm, hollow, site 1 ( oC ).

35. ts_10cm_H1 - Soil temperature at 10 cm, hollow, site 1 ( oC ).

36. ts_20cm_H1 - Soil temperature at 20 cm, hollow, site 1 ( oC ).

37. ts_50cm_H1 - Soil temperature at 50 cm, hollow, site 1 ( oC ).

38. ts_2cm_H2 - Soil temperature at 2 cm, hollow, site 2 ( oC ).

39. ts_5cm_H2 - Soil temperature at 5 cm, hollow, site 2 ( oC ).

40. ts_10cm_H2 - Soil temperature at 20 cm, hollow, site 2 ( oC ).

41. ts_20cm_H2 - Soil temperature at 20 cm, hollow, site 2 ( oC ).

42. ts_50cm_H2 - Soil temperature at 50 cm, hollow, site 2 ( oC ).

43. T_cont - Temperature at data logger ( oC ).

44. batt_1 - Battery output voltage at data logger 1 ( V ).

45. batt_2 - Battery output voltage at data logger 2 ( V ).

46. batt_2 - Battery output voltage at data logger 3 ( V ).

meteo_MFC_qq_1h.dat and meteo_MFC_gapfilled_1h.dat parameters:

1. date - Date and time ( DD/MM/YYYY hh:mm:ss ).

2. ta_H - Air temperature at 2 m, hollow ( oC ).

3. ta_R - Air temperature at 2 m, ridge ( oC ).

4. vp_H - Water vapor pressure at 2 m, hollow ( kPa ).

5. vp_R - Water vapor pressure at 2 m, ridge ( kPa ).

6. ipar_H - Incoming PAR, hollow ( µmol m-2 s-1 ).

7. ipar_R - Incoming PAR, ridge ( µmol m-2 s-1 ).

8. rpar_H - Reflected PAR, hollow ( µmol m-2 s-1 ).

9. rpar_R - Reflected PAR, ridge ( µmol m-2 s-1 ).

10. alb_H - Albedo PAR, hollow ( [] ).

11. alb_R - Albego PAR, ridge ( [] ).

12. nr_H - Net radiation balance, hollow ( W m-2 ).

13. nr_R - Net radiation balance, ridge ( W m-2 ).

14. shf_H - Soil heat flux, hollow ( W m-2 ).

15. shf_R1 - Soil heat flux, ridge, site 1 ( W m-2 ).

16. shf_R2 - Soil heat flux, ridge, site 2 ( W m-2 ).

17. ws_10m - Wind speed at 10 m ( m s-1 ).

18. wd_10m - Wind direction at 10 m ( deg ).

19. ws_2m - Wind speed at 2 m ( m s-1 ).

20. wd_2m - Wind direction at 2 m ( deg ).

21. wU_10m - U component of wind at 10 m ( m s-1 ).

22. wV_10m - V component of wind at 10 m ( m s-1 ).

23. wU_2m - U component of wind at 2 m ( m s-1 ).

24. wV_2m - V component of wind at 2 m ( m s-1 ).

25. prs - Atmospheric pressure ( hPa ).

26. sdp - Snow depth ( cm ).

27. prc - Liquid precipitations ( mm ).

Meteorological forcing dataset for Arctic River Basins includes five elements: daily maximum, minimum and average temperature, daily precipitation and daily average wind speed. The data is in NetCDF format with a horizontal spatial resolution of 0.083°, covering Yenisy, Lena, ob, Yukon and Mackenzie catchments. The data can be used to dirve hydrolodical model (VIC model) for hydrological process simulation of the Arctic River Basins. The further quality control were made for daily observation data from Global Historical Climatology Network Daily database(GHCN-D), Global Summary of the Day (GSPD),The U.S. Historical Climatology Network (USHCN),Adjusted and homogenized Canadian climate data (AHCCD) and USSR / Russia climate data set (USSR / Russia). The thin plate spline interpolating method, which similar to the method used in PNWNAmet datasets (Werner et al., 2019), was employed to interpolate daily station data to 5min spatial resolution daily gridded forcing data using WorldClim and ClimateNA monthly climate normal data as a predictor.