TERRACLIMATE
What is terraclimate?
TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. 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 1958-2015. We plan to update these data periodically (annually).
Datasets
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Primary Climate Variables: Maximum temperature, minimum temperature, vapor pressure, precipitation accumulation, downward surface shortwave radiation, wind-speed
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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
*derived using a one-dimensional soil water balance model |
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.
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
There are several options for acquiring TerraClimate data. We provide a list of current options below:
- Download individual netCDF files for individual variable and years
- Use wget script to batch download files
- Google Earth Engine
- Download the entire immutable copy of TerraClimate 1958-2015: Compressed file ~800GB containing individual netCDF files. Note that this copy will not contain any fixes or updates to the data (see Updates).
- Coming soon: OpenDap
Updates to data products (recalls, new variables) will be highlighted here:
January 23, 2018
January 23, 2018
- 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,
