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Dataset

 
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HadCRUT.5.0.0.0: Ensemble near-surface temperature anomaly grids and time series

Status: Planned
Online Status: ONLINE
Publication State: Published
Publication Date: 2020-12-21
Download Stats: last 12 months

Abstract

HadCRUT5 (Met Office Hadley Centre/Climatic Research Unit global surface temperature anomalies, version 5) is a gridded dataset of global historical near-surface air temperature anomalies since the year 1850. It has been developed and maintained by the Met Office Hadley Centre and University of East Anglia Climatic Research Unit. Air temperature information over land is derived from CRUTEM5 monthly average meteorological station temperature series, an expanded compilation of station series with revised quality control methods. Temperatures over ocean are derived from the HadSST4 sea-surface temperature dataset, including revised assessments of instrumental biases. Temperature data are presented as monthly average near-surface temperature anomalies, relative to the 1961-1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018, with derived global and hemispheric time series.

Two variants of the dataset are provided. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200‐member ensemble accompanied by additional uncertainty information.

Citable as:  Met Office Hadley Centre; University of East Anglia Climatic Research Unit; Morice, C.P.; Kennedy, J.J.; Rayner, N.A.; Winn, J.P.; Hogan, E.; Killick, R.E.; Dunn, R.J.H.; Osborn, T.; Jones, P.D.; Simpson, I. (2020): HadCRUT.5.0.0.0: Ensemble near-surface temperature anomaly grids and time series. Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/b9698c5ecf754b1d981728c37d3a9f02
Abbreviation: Not defined
Keywords: HadOBS, HadCRUT, HadCRUT5, near-surface air temperature

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

HadCRUT.5.0.0.0. has been processed using the HadSST4 and CRUTEM5.0 inputs. The dataset builds on the legacy of previous HadCRUT datasets.

The data have been produced by the Met Office Hadley Centre and sent to the Centre for Environmental Data Analysis for archival and distribution.

Data Quality:
The data have been quality controlled by the data provider but not by the Centre for Environmental Data Analysis (CEDA), see dataset associated documentation.
File Format:
The data are NetCDF4 formatted.

Process overview

This dataset was generated by the computation detailed below.
Title HadCRUT5 data processing deployed on Met Office computers
Abstract HadCRUT5 has been processed on Met Office computing facilities, using HadSST4 and CRUTEM5 inputs. CRUTEM5 station data are gridded onto a regular latitude-longitude grid using the HadCRUT4 ensemble method. Resulting land air temperature anomaly grids are merged with sea-surface temperature anomalies from the HadSST4 dataset though weighted averaging based on grid cell areal land fractions to produce the non-infilled HadCRUT5 grids. HadCRUT5 analysis grids are computed using a Gaussian process based statistical method to provide improved estimates of surface temperature anomaly fields, extending the data coverage into regions for which the available observations are informative. Analysis uncertainties are presented through an ensemble method. The Gaussian process based method is applied separately for land and marine temperature grids. Global grids are produced using a land-sea area fraction based weighting, in which sea ice regions (as defined by HadISST2 sea-ice concentrations) are treated as land. Regional time series are derived as grid box area weighted averages, with accompanying uncertainties. Global time series are defined as the average of northern and southern hemisphere series. Remaining uncertainty from unrepresented regions is estimated from sub-sampling experiments using the ERA5 reanalysis.
Input Description None
Output Description None
Software Reference None
  • units: K
  • long_name: 2.5% confidence limit for blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • var_id: tas_lower
  • names: 2.5% confidence limit for blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • units: K
  • long_name: 97.5% confidence limit for blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • var_id: tas_upper
  • names: 97.5% confidence limit for blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • units: K
  • var_id: tas_median
  • long_name: blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • names: blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • units: K2
  • var_id: tas_cov
  • long_name: error_covariance for blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • names: error_covariance for blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • units: 1
  • long_name: fraction of total area represented in the analysis
  • var_id: area_fraction
  • names: fraction of total area represented in the analysis
  • var_id: weights
  • units: 1
  • long_name: fractional weight for land data in blending with marine observations
  • names: fractional weight for land data in blending with marine observations
  • var_id: latitude_bnds
  • var_id: latitude_vector_1_bnds
  • var_id: latitude_vector_2_bnds
  • units: 1
  • var_id: location_index_1
  • long_name: location_index_1
  • names: location_index_1
  • var_id: location_index_1_bnds
  • units: 1
  • var_id: location_index_2
  • long_name: location_index_2
  • names: location_index_2
  • var_id: location_index_2_bnds
  • var_id: longitude_bnds
  • var_id: longitude_vector_1_bnds
  • var_id: longitude_vector_2_bnds
  • standard_name: realization
  • var_id: realization
  • units: 1
  • names: realization
  • var_id: realization_bnds
  • units: K
  • long_name: standard_uncertainty computed from error_covariance for blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • var_id: tas_cov
  • names: standard_uncertainty computed from error_covariance for blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • units: K
  • long_name: standard_uncertainty in blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • var_id: tas_unc
  • names: standard_uncertainty in blended air_temperature_anomaly over land with sea_water_temperature_anomaly
  • units: K
  • var_id: tas_total_unc
  • long_name: standard_uncertainty in blended air_temperature_anomaly over land with sea_water_temperature_anomaly: total uncertainty (1 sigma)
  • names: standard_uncertainty in blended air_temperature_anomaly over land with sea_water_temperature_anomaly: total uncertainty (1 sigma)
  • var_id: time_bnds
  • units: K
  • var_id: coverage_unc
  • long_name: uncertainty due to spatial sampling of gridded observations
  • names: uncertainty due to spatial sampling of gridded observations
  • long_name: uncertainty from area not represented in the analysis (1 sigma)
  • units: K
  • var_id: coverage_unc
  • names: uncertainty from area not represented in the analysis (1 sigma)

Co-ordinate Variables

  • standard_name: latitude
  • var_id: latitude
  • long_name: latitude
  • units: degrees_north
  • names: latitude
  • units: degrees_north
  • standard_name: latitude
  • var_id: latitude_vector_1
  • long_name: latitude_vector_1
  • names: latitude, latitude_vector_1
  • units: degrees_north
  • var_id: latitude_vector_2
  • long_name: latitude_vector_2
  • standard_name: latitude
  • names: latitude, latitude_vector_2
  • units: degrees_east
  • standard_name: longitude
  • long_name: longitude
  • var_id: longitude
  • names: longitude
  • var_id: longitude_vector_1
  • units: degrees_east
  • standard_name: longitude
  • long_name: longitude_vector_1
  • names: longitude, longitude_vector_1
  • standard_name: longitude
  • long_name: longitude_vector_2
  • units: degrees_east
  • var_id: longitude_vector_2
  • names: longitude, longitude_vector_2
  • long_name: time
  • standard_name: time
  • var_id: time
  • names: time
Coverage
Temporal Range
Start time:
1850-01-01T00:00:00
End time:
2018-12-31T23:59:59
Geographic Extent

 
90.0000°
 
-180.0000°
 
180.0000°
 
-90.0000°