Temperature in Russia increased to -2.82 celsius in 2023 from -2.91 celsius in 2022. This dataset includes a chart with historical data for Russia Average Temperature.

The Far Eastern Federal District had the coldest average temperature in Russia in January 2023, at over 31 degrees Celsius below zero. In the Siberian Federal District, the average January temperature was 2.2 degrees Celsius below zero. The highest mean monthly temperature in July of the same year was observed in the Southern Federal District at 24.2 degrees Celsius above zero.

The mean surface temperature change across Russia relative to the baseline from 1951 to 1980 took only positive values since 1999. The highest deviation was recorded in 2020 at 3.7 degrees Celsius. In 2023, the temperature change reached around 2.5 degrees Celsius.

From January to December 2018, the North Caucasian federal district of Russia was the warmest region with an average temperature of 10.2 degrees Celsius. The Far Eastern federal district was the coldest, with 5.54 degrees Celsius below zero on average.

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.

It was estimated that by the year 2050, the mean annual temperature in Samara in the Volga federal district of Russia would increase by three degrees Celsius compared to 2019 levels due to climate change. The country's capital Moscow would see a slighly lower increase in its average annual temperature by 2.9 degrees Celsius, compared to a change by 5.5 degrees Celsius which was forecasted for the temperature in the warmest month of the year.

Global Temperature: Daily Temperature Departure from Normal: Russian Federation: Utta data was reported at 1.450 Degrees Celsius in 22 Mar 2025. This records an increase from the previous number of 0.950 Degrees Celsius for 21 Mar 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 22 Mar 2025, with 541 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.

The mean temperature in the winter of 2023 in Russia was 0.6 degrees Celsius higher than the long-term mean from 1991 to 2020. The average summer temperature increased in all regions of the country.

Russia Cooling Degree Days data was reported at 164.160 Degrees Celsius in 2020. This records an increase from the previous number of 137.450 Degrees Celsius for 2019. Russia Cooling Degree Days data is updated yearly, averaging 126.900 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 227.280 Degrees Celsius in 2010 and a record low of 71.310 Degrees Celsius in 1978. Russia Cooling Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Russian Federation – Table RU.World Bank.WDI: Environmental: Climate Risk. A cooling degree day (CDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is above 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

In January and July 2023, the average monthly temperature was higher than the norm in most federal districts of Russia. The highest deviation was recorded in the Ural Federal District in January 2023, when the average monthly temperature was 1.6 degrees Celsius higher than the norm. In the Southern Federal District, which had the warmest temperature nationwide in July 2023, the deviation from the norm in that month was almost one degree Celsius.

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

The average parameters of cold-season temperature are presented for all stations in the northern permafrost zone of Russia for different periods in 1930-2017. Ratio of the temperature parameters (sum of winter Temperature, winter Temperature mean, Temperature January) to δ¹⁸O values is given for all stations on average (the coefficient Y in the equation Ti=Y⋅δ¹⁸O).

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).

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.

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.

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.

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.

The MMAAT data set is archived at the World Data Center-B Research Institute of Hydrometeorological Information (RIHMI), Kaluga, Russia. The parameters include mean monthly and annual air temperature for the Northern Hemisphere since 1981.

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.

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.