TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho (IDAHO_EPSCOR/TERRACLIMATE)

TerraClimate is a dataset of monthly climate and climatic water balance for
global terrestrial surfaces. It 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, the University of Idaho
provides 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. A modified Thornthwaite-Mather climatic water-balance model and
extractable soil water storage capacity data was used at a 0.5° grid from
Wang-Erlandsson et al. (2016).

Data Limitations:

1. Long-term trends in data are inherited from parent datasets.
   TerraClimate should not be used directly for independent assessments of
   trends.

2. 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.

3. The water balance model is very simple and does not account for
   heterogeneity in vegetation types or their physiological response to
   changing environmental conditions.

4. Limited validation in data-sparse regions (e.g., Antarctica).