TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Abstract

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. 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. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

Figure 1. Validation of TerraClimate temperature and precipitation time series using GHCN stations.

(a) Pearsons’ correlation coefficient (ρ) and (b) mean absolute error (MAE, units of °C) between GHCN stations and co-located pixels from TerraClimate for time series of annual mean temperature (TAS) from 1958–2015. (c,d) Show the correlation and relative mean absolute error (units of % of mean annual Pr) for calendar year accumulated precipitation (Pr). Statistics are reported for GHCN stations with at least 30 years of complete data.

Figure 2. Validation of TerraClimate temperature, precipitation, and reference evapotranspiration time series.

Pearsons’ correlation coefficient (ρ) with data from co-located pixels from TerraClimate and (a) annual mean temperature (TAS) from 1980–2015 from homogenized RAWS and SNOTEL stations, (b) annual mean precipitation (PR) from quality controlled SNOTEL stations, and (c) annual mean reference evapotranspiration (ET₀) from FLUXNET stations from 1994–2014. Statistics are only reported for stations that had at least 10 years of complete data.

Figure 3. Validation of TerraClimate runoff data.

Validation statistics of TerraClimate estimated runoff from the WBM showing (a) Pearson’s correlation coefficient, and (b) mean absolute error (mm) for annual water-year (Oct-Sep) runoff (Q) for 587 streamflow from BGDC’s Climate Sensitive Stations Dataset covering at least 30 complete years from 1958–2015. (c) Shows a scatterplot of observed annual mean Q from streamflow stations versus TerraClimate estimated Q and water-year accumulated precipitation (P). The 1:1 line is shown for reference.

Figure 4. Illustration of added value from the TerraClimate dataset.

Top panel shows July 2015 monthly average maximum temperature (Tmax) from TerraClimate with the inset highlighting the region for (a–h). Comparison of (a,b) July 2015 Tmax, (c,d) 2015 calendar year accumulated precipitation, (e,f) 2015 calendar year climatic water deficit, and (g,h) 2015 runoff between the TerraClimate dataset (left) and CRU Ts4.0 dataset (right).

Figure 5. Coverage of CRU Ts4.0 station influence used in TerraClimate anomalies.

Proportion of monthly data with (left) at least 8 and (right) 0 stations from CRU Ts4.0 contributing to anomalies from 1958–2015. (a,b) Show coverage for mean temperature, used in TerraClimate maximum and minimum temperature, (c,d) show coverage for precipitation, and (e,f) show coverage for vapor pressure. TerraClimate uses anomalies from JRA-55 rather than assume climatological averages from CRU for voxels where 0 stations contribute.