TERRACLIMATE
What is terraclimate?
TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. The data cover the period from 1958-2019. We plan to update these data periodically (annually).
New: We have also provided future TerraClimate layers commensurate with global mean temperatures +2C and +4C above preindustrial levels. These data are available for pseudo years 1985-2015 and described in more detail below.
New: We have also provided future TerraClimate layers commensurate with global mean temperatures +2C and +4C above preindustrial levels. These data are available for pseudo years 1985-2015 and described in more detail below.
Datasets
|
Primary Climate Variables: Maximum temperature, minimum temperature, vapor pressure, precipitation accumulation, downward surface shortwave radiation, wind-speed
|
Derived variables: Reference evapotranspiration (ASCE Penman-Montieth), Runoff, Actual Evapotranspiration, Climate Water Deficit, Soil Moisture, Snow Water Equivalent, Palmer Drought Severity Index, Vapor pressure deficit
|
Methods
TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser spatial resolution, but time-varying data from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55). Conceptually, the procedure applies interpolated time-varying anomalies from CRU Ts4.0/JRA55 to the high-spatial resolution climatology of WorldClim to create a high-spatial resolution dataset that covers a broader temporal record.
Temporal information is inherited from CRU Ts4.0 for most global land surfaces for temperature, precipitation, and vapor pressure. However, JRA55 data is used for regions where CRU data had zero climate stations contributing (including all of Antarctica, and parts of Africa, South America, and scattered islands).For primary climate variables of temperature, vapor pressure and precipitation, we provide additional data on the number of stations (between 0 and 8) that contributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used exclusively for solar radiation and wind speeds.
TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. We used a modified Thornthwaite-Mather climatic water-balance model and extractable soil water storage capacity data at a 0.5° grid from Wang-Erlandsson et al. (2016).
Temporal information is inherited from CRU Ts4.0 for most global land surfaces for temperature, precipitation, and vapor pressure. However, JRA55 data is used for regions where CRU data had zero climate stations contributing (including all of Antarctica, and parts of Africa, South America, and scattered islands).For primary climate variables of temperature, vapor pressure and precipitation, we provide additional data on the number of stations (between 0 and 8) that contributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used exclusively for solar radiation and wind speeds.
TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. We used a modified Thornthwaite-Mather climatic water-balance model and extractable soil water storage capacity data at a 0.5° grid from Wang-Erlandsson et al. (2016).
accuracy
TerraClimate exhibited strong validation with a number of station-based observations from a variety of networks including the Global Historical Climate Network, SNOTEL, and RAWS. Validation statistics were slightly better than those achieved using the parent CRU Ts 4.0 data, primarily for measures of error due to the improved spatial realism of TerraClimate. In addition, TerraClimate fields of annual reference evapotranspiration were well linked to station-based reference evapotranspiration from FLUXNET stations. Finally, we found that interannual runoff modeled by TerraClimate correlated well to measured streamflow from a number of watersheds globally.
Climate Projections
Future climate projections were developed for two different climate futures: (1) when global mean temperatures are 2C warmer than pre-industrial, and (2) when global mean temperatures are 4C above preindustrial. We use a pattern scaling approach that makes use of monthly projections from 23 CMIP5 global climate models as described in Qin et al., 2020 and provide projections for monthly climate by imposing projected changes in means and variance from the modes scalable to the change in global temperature. This approach is computationally more efficient and provides projections that are mainly agnostic with respect to which model and energy/climate policy pathways we take. We additionally use a simplistic approach for reducing reference evapotranspiration with increasing CO2 concentrations that is applied in our water balance projections. Data are provided for pseudo-years 1985-2015, where we use the observational record and scale changes in individual climate variables. We also provide climatological (30-yr) summaries of the variables of interest.
Data limitations
- Long-term trends in data are inherited from parent datasets. TerraClimate should not be used directly for independent assessments of trends.
- TerraClimate will not capture temporal variability at finer scales than parent datasets and thus is not able to capture variability in orographic precipitation ratios and inversions.
- The water balance model is very simple and does not account for heterogeneity in vegetation types or their physiological response to changing environmental conditions.
- Limited validation in data-sparse regions (e.g., Antarctica);
Visualizations
Netcdf files from THREDDS web server (guide for dataset abbreviations)
Read me first: Best practices for accessing our datasets
- Individual years (1958-present)
- Aggregated years (1958-present)
- Individual years for +2C climate futures
- Individual years for +4C climate futures
- Climatologies (1961-1990 and 1981-2010; and +2C and +4C future scenarios)
Read me first: Best practices for accessing our datasets
- Download individual netCDF files for individual variables and years
- directly from data catalogs
- using wget script tool to batch download files
- Download subsets and point data using THREDDS web services
- Google Earth Engine
- 'Get an account' -> https://earthengine.google.com/new_signup/
- 'code in the playground' -> https://developers.google.com/earth-engine/
- 'data' -> https://code.earthengine.google.com/dataset/IDAHO_EPSCOR/TERRACLIMATE
Updates to data products (recalls, new variables) will be highlighted here:
February 1, 2021
An update to netCDF software may create problems for users accessing netCDF files from the cloud. A workaround involves adding #fillmismatch at the end of the netcdf file name. For example,
http://thredds.northwestknowledge.net/thredds/dodsC/MET/erc/erc_2012.nc#fillmismatch
Subsequent improvements to the netCDF infrastructure will fix this problem.
July 1, 2020
February 1, 2021
An update to netCDF software may create problems for users accessing netCDF files from the cloud. A workaround involves adding #fillmismatch at the end of the netcdf file name. For example,
http://thredds.northwestknowledge.net/thredds/dodsC/MET/erc/erc_2012.nc#fillmismatch
Subsequent improvements to the netCDF infrastructure will fix this problem.
July 1, 2020
- Added +2C and +4C TerraClimate datasets for pseudo-years 1985-2015 and projections from 23 climate models described in Qin et al., 2020
- Data for 2019 added
- File formats have been updated to NETCDF4 format to improve data efficiency. Note that these files will contain a scale_factor and offset that need to be considered upon reading the data in.
- Preliminary data for 2018 is now available.
- Added Palmer Drought Severity Index (PDSI) and Vapor Pressure Deficit (VPD) to the list of variables provided
- Added 2016 and 2017 data. These are considered preliminary data due to the lack of CRU data currently.
- Solar radiation and wind data have been revised due to substantial inhomogeneities in the record. We now use a combination of JRA-55, JRA-55C, and ERA Interim data as follow:
- JRA-55 1958-1972; JRA-55C 1973-1978; ERA-Interim 1979-2017
- We bias correct these three data such that monthly means are equivalent for JRA-55 1963-1972 and JRA-55C 1973-1982 (e.g., January mean solar radiation for these two 10-year periods is assumed to be unchanged), and JRA-55C 1979-1988 and ERA-Interim 1979-1988. This is done to facilitate a single time series of solar radiation and wind speed anomalies that is used in TerraClimate.
- Potential evapotranspiration and water balance variables were rerun to account for this update
Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018, Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data,