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.

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

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.

The highest average precipitation volume across Russia in 2018 was observed in the Central federal district at 570 millimeters. The area with the lowest volume was the Far Eastern federal district with 415 millimeters of precipitation.

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.

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 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 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.

In 2021, the volume of precipitation in Russia was nearly 2.6 percent above the average from 1991 to 2020. The annual anomaly marked a slight increase from the previous year. From 1991 to 1999, the figure continued a mainly declining trend, with the highest negative anomaly recorded in 1996, at 5.6 percent below the mean rainfall.

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.

Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals

Authors: Xi Guo; Puying Zhang; Yaojie Yue

This is the outcome data of our research which is under submission.

Though the impact of climate change on potential crop distributions has been extensively explored, there are few studies on potential wheat distributions at specific global warming levels (GWLs), e.g., 1.5 °C and 2 °C.

Here, a grided (0.5 degree × 0.5 degree) dataset of global potential wheat distribution under the 1.5 °C and 2 °C GWLs is proposed. This dataset is produced using the MaxEnt model with support of multi-model data(GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M).

The predictive accuracy of the proposed dataset was carefully validated between the predicted global wheat distribution and multiple known datasets. For more details of the approach used to predict the global wheat distribution please refer to: Yue, Y., Zhang, P., Shang, Y., 2019. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci Total Environ 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153.

The results indicate the regional differences in the potential suitability of wheat cultivation under different GWLs. Eastern Europe, Pakistan, Northern India, Russia, and Canada witnessed a significant increase in wheat planting suitability. In contrast, Central Eastern Africa, Southeastern Australia, Southeastern China, Southern Brazil, France, Spain, and Italy demonstrated a significant decrease in wheat suitability. Compared with 1.5 °C GWLs, wheat planting suitability decreases more evidently in 2 °C GWLs in Central and Eastern Africa, Central and Southern India, Southeastern China, Australia, Mexico, Southern Brazil, and Argentina. Simultaneously, regions such as Russia, Pakistan, Canada, and the Great Lakes area of the United States observed further increases in wheat planting suitability. To ensure favorable conditions for the cultivation of wheat, it is crucial to limit the global average temperature increase to less than 2 °C.

Our findings demonstrate the influence of different GWLs on potential global wheat distribution, highlighting the regional differences in the potential suitability of wheat cultivation under different GWLs.

We argue that the potential global wheat distribution datasets under different GWLs are a valuable complement to currently available products. This potential global wheat distribution is one of the few products to take into account 1.5 °C and 2 °C GWLs based on multi-modal data. We believe that it can provide more valuable information for policymakers to make decisions for the warming world.

The data of the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals is stored in a zip package, that is Global Planting Suitability of Wheat.zip. This package consists of 1 folder, i.e., SR1.5&2.0.

This subfolder contains GeoTIFF files for the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals. Correspondingly Wheat_SR15.tif and Wheat_SR20.tif. The grid value of each file ranges from 0 to 1, indicating the possibility of wheat planting in each grid, and the higher the value, the higher the possibility that wheat exists.

Reference:

Yue, Y., Zhang, P., Shang, Y., 2019. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci Total Environ 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153.