TerraClimate

Overview

TerraClimate is a global climate and climatic water balance dataset developed by the Climatology Lab at University of California, Merced. This package provides access to TerraClimate data for Brazil and the Amazon region.

This dataset provides:

TerraClimate is essential for understanding climate variability, water availability, drought risk, agricultural potential, and climate change impacts across Brazil and the Amazon.

Data Source and Methodology

TerraClimate data is compiled by: - University of California Climatology Lab - Integration of satellite and ground-based observations - Validated against station networks - Downscaled to ~4km global resolution - Monthly temporal resolution with daily and subdaily estimates available

For more information, visit TerraClimate Project.

Available Climate Variables

TerraClimate provides 13 main climate and water balance variables:

Dataset Code Description Units
max_temperature tmax Maximum 2-m Temperature °C
min_temperature tmin Minimum 2-m Temperature °C
wind_speed ws Wind Speed at 10-m m/s
vapor_pressure_deficit vpd Vapor Pressure Deficit kPa
vapor_pressure vap 2-m Vapor Pressure kPa
snow_water_equivalent swe Snow Water Equivalent at End of Month mm
shortwave_radiation_flux srad Downward Shortwave Radiation Flux W/m²
soil_moisture soil Soil Moisture at End of Month mm
runoff q Runoff mm
precipitation ppt Accumulated Precipitation mm
potential_evaporation pet Reference Evapotranspiration mm
climatic_water_deficit def Climatic Water Deficit mm
water_evaporation aet Actual Evapotranspiration mm
palmer_drought_severity_index PDSI Palmer Drought Severity Index unitless

Data Format and Coverage

Spatial Resolution

Temporal Resolution

Data Type

Function Parameters

1. dataset

Selects which climate variable to download.

# Temperature and radiation
dataset = "max_temperature"         # tmax
dataset = "min_temperature"         # tmin
dataset = "shortwave_radiation_flux" # srad

# Water and moisture
dataset = "precipitation"           # ppt
dataset = "potential_evaporation"   # pet
dataset = "water_evaporation"       # aet (actual evapotranspiration)
dataset = "soil_moisture"           # soil
dataset = "runoff"                  # q

# Atmospheric variables
dataset = "wind_speed"              # ws
dataset = "vapor_pressure"          # vap
dataset = "vapor_pressure_deficit"  # vpd

# Drought and composite indices
dataset = "climatic_water_deficit"  # def
dataset = "palmer_drought_severity_index" # PDSI
dataset = "snow_water_equivalent"   # swe

2. raw_data

Controls data format returned.

raw_data = FALSE  # logical

3. time_period

Specifies which year(s) to download.

Available range: 1958 to present (most recent months have 2-3 month lag)

time_period = 2020              # single year
time_period = c(2010, 2020)     # specific years
time_period = 2010:2020         # range of years

5. language

Output language for variable names and documentation.

language = "eng"  # character string

Important Download Considerations

Data Size: TerraClimate raster data is substantial. Consider:

Recommendations: - Use legal_amazon_only = TRUE to reduce size by ~95% - Download single or 2-3 year periods rather than decades at once - Use high-speed internet connection - Have at least 10-50 GB free disk space for multi-year downloads

Examples

# download precipitation data for the Legal Amazon (2020)
precip <- load_climate(
  dataset = "precipitation",
  time_period = 2020,
  legal_amazon_only = TRUE,
  language = "eng"
)
# download maximum temperature for multiple years, all of Brazil
max_temp <- load_climate(
  dataset = "max_temperature",
  time_period = 2010:2012,
  language = "eng"
)

Data Notes

Variable Definitions

Data Quality

Important Limitations

  1. Raster data large: Multi-year downloads can be hundreds of MB to GBs
  2. 4km resolution: Suitable for regional analysis; may miss local variation
  3. Monthly aggregation: Daily and sub-daily variation not captured
  4. Recent data lag: Most recent 2-3 months not yet available
  5. Interpolation uncertainty: Some regions have lower data density
  6. Snow/ice areas: Less accurate in high mountains or glaciated regions (not major issue for Amazon)