This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (NClimDiv) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. As of November 2018, NClimDiv includes county data and additional inventory files.

SILO is a Queensland Government database containing continuous daily climate data for Australia from 1889 to present, in a number of ready-to-use formats, suitable for modelling and research applications. The SILO database contains two major classes of data: point (station) time series and spatial grids, both based on observed data from the Bureau of Meteorology ADAM (Australian Data Archive for Meteorology) database. For point data, interpolated or derived values are used where observations are missing. Gridded data are spatially interpolated from observations.

The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. On an annual basis, approximately one year of "final" nClimGrid will be submitted to replace the initially supplied "preliminary" data for the same time period. Users should be sure to ascertain which level of data is required for their research.

We present a continuous land climate reconstruction dataset extending from 60 kyr before present to the pre-industrial period at 0.5deg resolution on a monthly timestep for 0degN to 90degN. It has been generated from 42 discrete snapshot simulations using the HadCM3B-M2.1 coupled general circulation model. We incorporate Dansgaard-Oeschger (DO) and Heinrich events to represent millennial scale variability, based on a temperature reconstruction from Greenland ice-cores, with a spatial fingerprint based on a freshwater hosing simulation with HadCM3B-M2.1. Interannual variability is also added and derived from the initial snapshot simulations. Model output has been downscaled to 0.5deg resolution (using simple bilinear interpolation) and bias corrected using either the University of East Anglia, Climate Research Unit observational data (for temperature, precipitation, windchill, and minimum monthly temperature), or the EWEMBI dataset (for incoming shortwave energy). Here we provide datasets for; surface air temperature, precipitation, incoming shortwave energy, wind-chill, snow depth (as snow water equivalent), number of rainy days per month, minimum monthly temperature, and the land-sea mask and ice fractions used in the simulations. The datasets are in the form of NetCDF files. The variables are represented by a set of 24 files that have been compressed into nine folders: temp, precip, down_sw, wind_chill, snow, rainy_days, tempmonmin, landmask and icefrac. Each file represents 2500 years. The landmask and ice fraction are provided annually, whereas the climate variables are given as monthly files equivalent to 30000 months, between the latitudes 0deg to 90degN at 0.5deg resolution. Each of the climate files therefore have the dimensions 180 (lat) x 720 (lon) x 30000 (month). We also provide an example subset of the temperature dataset, which gives decadal averages for each month for 0-2500 years.

NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).

Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.

Terrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together with ancillary calibration data.

This data has been superseded by a newer version of the dataset. Please refer to NOAA's Climate Divisional Database for more information. The U.S. Climate Divisional Dataset provides data access to current U.S. temperature, precipitation and drought indeces. Divisional indices included are: Precipitation Index, Palmer Drought Severity Index, Palmer Hydrological Drought Index, Modified Palmer Drought Severity Index, Temperature, Palmer Z Index, Cooling Degree Days, Heating Degree Days, 1-Month Standardized Precipitation Index (SPI), 2-Month (SPI), 3-Month (SPI), 6-Month (SPI),12-Month (SPI) and the 24-Month (SPI). All of these Indices, except for the SPI, are available for Regional, State and National views as well. There are 344 climate divisions in the CONUS. For each climate division, monthly station temperature and precipitation values are computed from the daily observations. The divisional values are weighted by area to compute statewide values and the statewide values are weighted by area to compute regional values. The indices were computed using daily station data from 1895 to present.

The Provincial Climate Data Set (PCDS) Portal contains observations of weather and climate variables (such as temperature and rainfall amounts) for British Columbia. Locations of observation stations are shown on an interactive map of the province which enables a user to zoom and pan to a region of interest, learn about the stations that are there, filter the displayed stations based on observation date, weather element, observing agency, region and more.

The CRU JRA V2.5 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 degree latitude x 0.5 degree longitude grid, the grid is near global but excludes Antarctica (this is the same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2023.

The dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.08 data (see the Process section and the ReadMe file for full details).

The CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).

The CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. This version of CRUJRA, v2.5 (1901-2023) is, where possible, adjusted to align with CRU TS monthly means or totals. A consequence of this is that, if CRU TS changes, then CRUJRA changes.

For this version, and version 4.07 of CRU TS, the CLD (cloud cover, %) variable is now actualised (converted from gridded anomalies) using the original CLD climatology and not the revised climatology introduced last year. This change/reversion is summarised here: https://crudata.uea.ac.uk/cru/data/hrg/cru_cl_1.1/Read_Me_CRU_CL_CLD_Reversion.txt

Since CLD is used to align DSWRF, CRUJRA Downward Short Wave Radiation Flux (DSWRF) will now be 'closer to' version 2.2 and earlier and should be used in preference to v2.3.

If this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:

Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3

Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology 34, 623-642.

Kobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and Basic Characteristics. J. Met. Soc. Jap., 93(1), 5-48 https://dx.doi.org/10.2151/jmsj.2015-001

While high-resolution future climate data are increasingly available for the contiguous United States, there has been limited focus on Alaska and Hawaii. Our study provides high temporal and spatial resolution climate data (daily precipitation and temperature on spatial grids of 10 km for Alaska and 1 km for Hawaii) based on 23 climate models from the Coupled Model Intercomparison Project Phase 6 and four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). These datasets include different statistical downscaling and bias correction techniques (seven for precipitation, six for temperature). While one method (i.e., empirical quantile mapping) generally outperforms the others, its performance can vary by climate quantity, region, or climate model, motivating our aim to provide an ensemble dataset of bias-corrected outputs. This community dataset will permit investigations into the sources of climate uncertainty and climate change uncertainty at the local scale. They may also be used for climate risk assessment across a range of societal sectors. A primary reason for their development by the authors is to drive hydrologic models and explore questions of hydrologic change in these underexplored regions. These data are described in a paper under review in the journal Scientific Data.

Diaries and Journals containing weather information in a non-tabular format. Records date from 1735 through the early 20th century. Much of the weather and climate data recorded by the founding fathers of this country (Washington, Jefferson and Franklin to name a few) were archived in original manuscripts, then microfilmed and stored at the National Archive and Records Administration (NARA). Those records available from NARA on microfilm have been imaged and placed on the EV2 system. To date, there are more than 42 million of those images on-line. These colonial diaries and data are a treasure trove to the climatologist seeking data on climate of the 19th century.

WorldClim version 1 has average monthly global climate data for minimum, mean, and maximum temperature and for precipitation. WorldClim version 1 was developed by Robert J. Hijmans, Susan Cameron, and Juan Parra, at the Museum of Vertebrate Zoology, University of California, Berkeley, in collaboration with Peter Jones and Andrew Jarvis (CIAT), and with Karen Richardson (Rainforest CRC).

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products. ERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5. ERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions.

Climate change in the future will continue to bring about unprecedented climate and climate extremes, and buildings and infrastructure will be exposed to them. To ensure that new and existing buildings deliver satisfactory performance over their design lives, their performance under current and future projected climates needs to be assessed by undertaking building simulations. Reference years are one year (or a few years) prepared from the climate time series to capture aspects of interest from the long-term climate datasets. This database provides access to the following building simulation reference year files for 564 locations in Canada. 1. Typical Meteorological Year data for building energy applications are prepared using Sandia method (Hall et al. 1978; NREL 2008) by concatenating twelve typical meteorological months selected based on Finkelstein‐Schafer (FS) statistics. 2. Temperature reference years: Typical Downscaled Year, Extreme Cold Year, and Extreme Warm Year data are prepared following Nik (2016; 2017) by concatenating twelve typical, extreme cold, and extreme warm months respectively to capture the variability within the ensemble of climate model simulations. 3. Moisture Reference year data are prepared for hygrothermal applications. The median ranked year in terms of MI is selected as the conditioning year and the 10% level year is selected as the extreme year for hygrothermal applications. The data are provided for a historical time-period: 1991-2021 and seven future time-periods coinciding with 0.5ºC, 1ºC, 1.5ºC, 2ºC, 2.5ºC, 3ºC, 3.5ºC of global warming.

The U.S. Monthly Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters for thousands of U.S. stations located across the 50 states, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station in Canada. NOAA Climate Normals are a large suite of data products that provide users with many tools to understand typical climate conditions for thousands of locations across the United States. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). In addition to the standard set of normals, users also can find "agricultural normals", which are used in many industries, including but not limited to construction, architecture, pest control, etc. These supplemental "agricultural normals" include frost-freeze date probabilities, growing degree day normals, probabilities of reaching minimum temperature thresholds, and growing season length normals. Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data. All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.

The U.S. Daily Climate Normals for 2006 to 2020 are 15-year averages of meteorological parameters that provide users supplemental normals for specialized applications for thousands of locations across the United States, as well as U.S. Territories and Commonwealths, and the Compact of Free Association nations. The stations used include those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the U.S. Climate Reference Network (USCRN) and other automated observation stations. In addition, precipitation normals for stations from the U.S. Snow Telemetry (SNOTEL) Network and the citizen-science Community Collaborative Rain, Hail and Snow (CoCoRaHS) Network are also available. The Daily Climate Normals dataset includes various derived products such as air temperature normals (including maximum and minimum temperature normals, heating and cooling degree day normals, and others), precipitation normals (including precipitation and snowfall totals, and percentiles, frequencies and other statistics of precipitation, snowfall, and snow depth), and agricultural normals (growing degree days (GDDs)). All data utilized in the computation of the 2006-2020 Climate Normals were taken from the Global Historical Climatology Network-Daily, but the Daily Normals are adjusted so that they are consistent with the Monthly Normals. The source datasets (including intermediate datasets used in the computation of products) are also archived at NOAA NCEI. A comparatively small number of station normals sets (~50) have been added as Version 1.0.1 to correct quality issues or because additional historical data during the 1991-2020 period has been ingested.

AbstractPredicting effects of climate change on plant disease is critical for protecting ecosystems and food production. Here, we show how disease pressure responds to short-term weather, historical climate, and weather anomalies by compiling a global database (4339 plant–disease populations) of disease prevalence in both agricultural and wild plant systems. We hypothesized that weather and climate would play a larger role in disease in wild versus agricultural plant populations, which the results supported. In wild systems, disease prevalence peaked when temperature was 2.7°C warmer than the historical average for the same time of year. We also found evidence of a negative interactive effect between weather anomalies and climate in wild systems, consistent with the idea that climate maladaptation can be an important driver of disease outbreaks. Temperature and precipitation had relatively little explanatory power in agricultural systems, though we observed a significant positive effect of current temperature. These results indicate that disease pressure in wild plants is sensitive to nonlinear effects of weather, weather anomalies, and their interaction with historical climate. In contrast, warmer temperatures drove risks for agricultural plant disease outbreaks within the temperature range examined regardless of historical climate, suggesting vulnerability to ongoing climate change.

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. This Climate Data Record (CDR) includes lower tropospheric, mid-tropospheric, and lower stratospheric temperatures over land and ocean derived from microwave radiometers on NOAA and NASA polar orbiting satellites. The temperatures are from measurements produced by Microwave Sounding Units (MSU) since 1978 and Advanced Microwave Sounding Unit-A (AMSU-A) since 1998 flying on NOAA polar orbiting satellites, on NASA Aqua satellite (operating since mid-1998) and on the European MetOp satellite (operating since late 2006). The instruments are cross-track through-nadir scanning externally-calibrated passive microwave radiometers. Brightness temperature measurements are derived at microwave frequencies within the 50-60 GHz oxygen absorption complex, and (in the case of AMSU-A) at a few microwave frequencies above and below that absorption complex. There are three atmospheric layers for which intermediate products are processed: (1) lower-tropospheric (TLT) deep-layer average temperature, computed as a weighted difference between view angles of AMSU-A channel 5, whose heritage comes from MSU channel 2, (2) mid-tropospheric (TMT) deep-layer temperature, computed as an average of the central portion of the scan of AMSU-A channel 5, whose heritage also comes from MSU channel 2, and (3) lower-stratospheric (TLS) deep layer temperatures, computed from the central portion of the scan of AMSU channel 9, whose heritage comes from MSU channel 4. This CDR includes several products. The global monthly anomaly data data are averaged onto a 2.5 x 2.5 degree latitude-longitude grid for each of the three atmospheric layers. Monthly anomalies are averaged for each of the three atmospheric layers over multiple regions, including Global, hemispheric, tropic, extratropic, polar and contiguous U.S. A mean annual cycle of monthly mean layer temperatures is also included. Anomalies are deviations from 1981-2010 mean. The datasets have been converted from the native ASCII format to CF-compliant netCDF-4 format.

The U.S. Climate Risk Projections by County, 2040-2049 data set contains a projection for 2040-2049 risk for the entire contiguous U.S. at the county level with a novel climate risk index integrating multiple hazards, exposures and vulnerabilities. Multiple hazards such as weather and climate are characterized as a frequency of heat wave, cold spells, drought, and heavy precipitation events along with anomalies of temperature and precipitation using high resolution (4 km) downscaled climate projections. Exposure is characterized by projections of population, infrastructure, and built surfaces prone to multiple hazards including sea level rise and storm surges. Vulnerability is characterized by projections of demographic groups most sensitive to climate hazards. This approach can guide planners in targeting counties at most risk and where adaptation strategies to reduce exposure or protect vulnerable populations might be best applied.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

Time series of monthly minimum and maximum temperature, precipitation, and potential evapotranspiration were derived for 1,469 watersheds in the conterminous United States for which stream flow measurements were also available from the national streamflow database, termed the Hydro-Climatic Data Network (HCDN), developed by Slack et al. (1993a,b). Monthly climate estimates were derived for the years 1951-1990.The climate characteristic estimates of temperature and precipitation were estimated using the PRISM (Daly et al. 1994, 1997) climate analysis system as described in Vogel, et al. 1999.Estimates of monthly potential evaporation were obtained using a method introduced by Hargreaves and Samani (1982) which is based on monthly time series of average minimum and maximum temperature data along with extraterrestrial solar radiation. Extraterrestrial solar radiation was estimated for each basin by computing the solar radiation over 0.1 degree grids using the method introduced by Duffie and Beckman (1980) and then summing those estimates for each river basin. This process is described in Sankarasubramanian, et al. (2001). Revision Notes: This data set has been revised to update the number of watersheds included in the data set and to updated the units for the potential evapotranspiration variable. Please see the Data Set Revisions section of this document for detailed information.