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:

  • High-resolution climate data: Monthly climate variables at ~4km resolution globally
  • Comprehensive variables: Temperature, precipitation, wind, radiation, soil moisture, drought indices
  • Water balance data: Evapotranspiration, runoff, water deficit calculations
  • Temporal coverage: Monthly data from 1958 to present (continuously updated)
  • Global coverage: Available worldwide, subset here for Brazil
  • Satellite-derived data: Combines satellite observations with ground station networks
  • Research quality: Peer-reviewed, widely used in climate and ecological research

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

  • Resolution: Approximately 4 km (0.04° at equator)
  • Coverage: Global; subset available for Brazil and Legal Amazon
  • Coordinates: WGS84 latitude/longitude

Temporal Resolution

  • Frequency: Monthly
  • Time span: 1958 to present (continuously updated)
  • Data lag: Recent months added as they become available (typically 2-3 months delay)

Data Type

  • Format: NetCDF files (raster/grid data)
  • Size: Large for multi-year, multi-variable downloads
  • Access: Downloaded from THREDDS server

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.

  • TRUE: Returns raw raster data (NetCDF, SpatRaster format)
  • FALSE: Returns aggregated data (specific format depends on configuration)
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.

  • "pt": Portuguese
  • "eng": English
language = "eng"  # character string

Important Download Considerations

Data Size: TerraClimate raster data is substantial. Consider:

  • File size: Multi-year downloads can be very large (hundreds of MB to GBs)
  • Internet: High-speed connection recommended; THREDDS downloads can be slow
  • Storage: Ensure sufficient disk space (multiple years of global data is large)
  • Time: Downloads may take considerable time
  • Memory: Raster processing requires sufficient RAM

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

  • Tmax/Tmin: Monthly average maximum and minimum 2-meter air temperatures
  • Precipitation: Accumulated monthly precipitation
  • Evapotranspiration (AET): Actual water loss from soil + plants through evaporation/transpiration
  • Potential Evaporation (PET): Theoretical maximum evapotranspiration if unlimited water
  • Runoff: Water flowing overland/through soil to streams
  • Soil Moisture: Water stored in root zone at month end
  • Vapor Pressure Deficit (VPD): Difference between saturated and actual vapor pressure (indicator of atmospheric dryness)
  • Water Deficit (DEF): Accumulated water stress (PET - AET)
  • PDSI: Standardized drought index (-4 to +4 scale)

Data Quality

  • Validation: Validated against independent station networks

  • Uncertainty: Varies by region; higher in data-sparse areas

  • Interpolation: Satellite and station data combined and downscaled to 4km

  • Reliability: Generally excellent for temperature and precipitation; water balance variables have higher uncertainty

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)