tsgettoolbox.tsgettoolbox.terraclimate4C

tsgettoolbox.tsgettoolbox.terraclimate4C(lat, lon, variables=None, start_date=None, end_date=None)

global:1/24deg::M:Monthly normals from Terraclimate with 4deg C hotter climate.

method: These layers from TerraClimate were derived from the essential climate variables of TerraClimate. Water balance variables, actual evapotranspiration, climatic water deficit, runoff, soil moisture, and snow water equivalent were calculated using a water balance model and plant extractable soil water capacity derived from Wang-Erlandsson et al (2016).

title: TerraClimate: monthly climate and climatic water balance for global land surfaces

summary: This archive contains a dataset of high-spatial resolution (1/24deg, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. These data were created by using climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim version 1.4 and version 2 datasets, with coarser resolution time varying (i.e. monthly) data from CRU Ts4.0 and JRA-55 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.

method: These layers from TerraClimate were creating using climatically aided interpolation of monthly anomalies from the CRU Ts4.0 and Japanese 55-year Reanalysis (JRA-55) datasets with WorldClim v2.0 climatologies.

keywords: WORLDCLIM,global,monthly, temperature,precipitation,wind,radiation,vapor pressure,evapotranspiration,water balance,soil water capacity,snow water equivalent,runoff

history: Created by John Abatzoglou, University of California Merced

creator_url: climate.nkn.uidaho.edu/TerraClimate

creator_email: jabatzoglou at ucmerced.edu

institution: University of California Merced

project: Global Dataset of Monthly Climate and Climatic Water Balance (1958-2015)

acknowledgment: Please cite the references included herein. We also acknowledge the WorldClim datasets (Fick and Hijmans, 2017; Hijmans et al., 2005) and the CRU Ts4.0 (Harris et al., 2014) and JRA-55 (Kobayashi et al., 2015) datasets.

geospatial_lat_min: -89.979164

geospatial_lat_max: 89.979164

geospatial_lon_min: -179.97917

geospatial_lon_max: 179.97917

time_coverage_start: 1958-01-01T00:0

time_coverage_end: present

time_coverage_resolution: P1M

standard_nam_vocabulary: CF-1.0

license: No restrictions

geospatial_lat_units: decimal degrees north

geospatial_lat_resolution: -0.041666668

geospatial_lon_units: decimal degrees east

geospatial_lon_resolution: 0.041666668

references: Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, and K.C. Hegewisch, 2017, High-resolution global dataset of monthly climate and climatic water balance from 1958-2015, submitted to Scientific Data.

source: WorldClim v2.0 (2.5m), CRU Ts4.0, JRA-55

version: v1.0

Conventions: CF-1.6

Parameters:

• lat (float) – The latitude of the point. North hemisphere is positive from 0 to 90. South hemisphere is negative from 0 to -90.

• lon (float) – The longitude of the point. Western hemisphere (west of Greenwich Prime Meridian) is negative 0 to -180. The eastern hemisphere (east of the Greenwich Prime Meridian) is positive 0 to 180.

• variables (str) –

  At the command line can supply a comma separated list of variable names. Using the Python API needs to be a Python list of strings.

  The current list of available variables are in the following table.

  Short	Long	Units
  aet	Actual ET	mm
  def	Climate water deficit	mm
  pet	Reference ET	mm
  q	Runoff	mm
  soil	Soil moisture	mm
  swe	Snow water equivalence	mm
  tmax	maximum temperature	degC
  tmin	minimum temperature	degC
  vap	Vapor pressure	kPa
  vpd	Vapor pressure deficit	kPa
  ws	wind_speed	m/s

• start_date (str) –

  [optional, defaults to first date in time-series, input filter]

  The start_date of the series in ISOdatetime format, or ‘None’ for beginning.

• end_date (str) –

  [optional, defaults to last date in time-series, input filter]

  The end_date of the series in ISOdatetime format, or ‘None’ for end.