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University of East Anglia Climatic Research Unit (CRU)

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  • CRUTEM is a dataset derived from air temperatures near to the land surface recorded at weather stations across all continents of Earth. It has been developed and maintained by the Climatic Research Unit (CRU) since the early 1980s, with funding provided mostly by the US Department of Energy. Since the early 2000s, the Met Office Hadley Centre (MOHC) have also been involved, especially in the regular updating of the operational version of CRUTEM (current version CRUTEM5) and in the development of the CRUTEM uncertainty model. The lead scientist for most of this work was Professor Phil Jones, but for CRUTEM5 it is Professor Tim Osborn. CRUTEM has been combined with the MOHC's dataset of sea surface temperatures to provide a near-global dataset of temperatures across Earth's surface, called HadCRUT. These datasets have been widely used for assessing anthropogenic climate change.

  • The CRU CY datasets consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables in 289 countries. Spatial averages are calculated using area-weighted means. Variables include cloud cover (cld), diurnal temperature range (dtr), frost day frequency (frs), precipitation (pre), daily mean temperature (tmp), monthly average daily maximum (tmx) and minimum (tmn) temperature, vapour pressure (vap), Potential Evapo-transpiration (pet) and wet day frequency (wet). The CRU CY datasets produced by the Climatic Research Unit (CRU) at the University of East Anglia. Spatial averages are calculated using area-weighted means. CRU CY is derived directly from the CRU TS dataset and version numbering is matched between the two datasets. Thus, the first official version of CRU CY is v3.21, as it is based on CRU TS v3.21 (1901-2012) and the latest version of CRU-CY is v4.03, as it is based on CRU TS v4.03. The data are available as text files with the extension '.per' and can be opened by most text editors. To understand the CRU-CY dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014).

  • Time-series (TS) datasets are month-by-month variation in climate over the last century or so as produced by the Climatic Research Unit (CRU) at the University of East Anglia. These are calculated on high-resolution (0.5x0.5 degree) grids, which are based on an archive of monthly mean temperatures provided by more than 4000 weather stations distributed around the world. They allow variations in climate to be studied, and include variables such as cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum temperature, vapour pressure, potential evapo-transpiration and wet day frequency. The CRU TS data are monthly gridded fields based on daily values -hence the ASCII and netcdf files both contain monthly mean values for the various parameters.

  • The CRU CY datasets consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables in 289 countries. Spatial averages are calculated using area-weighted means. Variables include cloud cover (cld), diurnal temperature range (dtr), frost day frequency (frs), precipitation (pre), daily mean temperature (tmp), monthly average daily maximum (tmx) and minimum (tmn) temperature, vapour pressure (vap), Potential Evapo-transpiration (pet) and wet day frequency (wet). The CRU CY datasets produced by the Climatic Research Unit (CRU) at the University of East Anglia. Spatial averages are calculated using area-weighted means. CRU CY is derived directly from the CRU TS dataset and version numbering is matched between the two datasets. Thus, the first official version of CRU CY is v3.21, as it is based on CRU TS v3.21 (1901-2012) and the latest version of CRU-CY is v3.26 based on CRU TS v3.26 (1901-2017) for 289 countries. The data are available as text files with the extension '.per' and can be opened by most text editors. To understand the CRU-CY dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014).

  • Time-series (TS) datasets are month-by-month variation in climate over the last century or so as produced by the Climatic Research Unit (CRU) at the University of East Anglia. These are calculated on high-resolution (0.5x0.5 degree) grids, which are based on an archive of monthly mean temperatures provided by more than 4000 weather stations distributed around the world. They allow variations in climate to be studied, and include variables such as cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum temperature, vapour pressure, Potential Evapo-transpiration and wet day frequency. At present, the BADC holds the latest Time Series data generated by CRU for the period 1901-2017. Those are available as CRU TS 3.26 data. The BADC also holds the preliminary CRU TS3.00 datasets for the period 1901-2006 as well as the subsequent CRU TS 3.10, 3.20, 3.21, 3.22, 3.23, 3.24 and CRU TS 3.25 datasets for the period 1901-2016. The CRU TS data are monthly gridded fields based on daily values -hence the ASCII and netcdf files both contain monthly mean values for the various parameters.

  • The CRU CY3.21 dataset consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables in 289 countries for the period Jan. 1901 to Dec. 2012. It was produced in 2013 by the Climatic Research Unit (CRU) at the University of East Anglia. Spatial averages are calculated using area-weighted means. Variables include cloud cover (cld), diurnal temperature range (dtr), frost day frequency (frs), precipitation (pre), daily mean temperature (tmp), monthly average daily maximum (tmx) and minimum (tmn) temperature, vapour pressure (vap), Potential Evapo-transpiration (pet) and wet day frequency (wet). CRU CY3.21 is derived directly from the CRU TS3.21 dataset. Version numbering is matched between the two datasets. The data are available as text files with the extension '.per' and can be opened by most text editors. To understand the CRU-CY3.21 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS3.21. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014). CRU CY data are available for download to all CEDA users.

  • The CRU CY version 3.22 dataset consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables, including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, Potential Evapo-transpiration and wet day frequency. This dataset was produced in 2014 by the Climatic Research Unit (CRU) at the University of East Anglia. The data are available as text files with the extension '.per' and can be opened by most text editors. Spatial averages are calculated using area-weighted means. CRU CY3.22 is derived directly from the CRU TS3.22 dataset. CRU CY version 3.22 spans the period 1901-2013 for 289 countries. To understand the CRU-CY3.22 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS3.22. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014). CRU CY data are available for download to all CEDA users.

  • CRUTEM4 is a gridded dataset of global historical near-surface air temperature anomalies over land. This specific version is CRUTEM4.2.0.0-2013-03, available for each month from January 1850 to March 2013, on a 5 degree grid. Hemispheric and global anomaly series are provided. The dataset is a collaborative product of the Climatic Research Unit at the University of East Anglia and the Met Office Hadley Centre. The CRUTEM4 dataset is updated on a monthly basis; these updates are available from the institutions listed below (see Links).

  • Blending a sea-surface temperature (SST) dataset with land air temperature makes an implicit assumption that SST anomalies are a good surrogate for marine air temperature anomalies. It has been shown that this is the case, and that marine SST measurements provide more useful data and smaller sampling errors than marine air temperature measurements would. So blending SST anomalies with land air temperature anomalies is a sensible choice. This dataset contains monthly and seasonal blended sea-surface temperatures (MOHSST6) with land air temperature data from the Climatic Research Unit (CRU) at the University of East Anglia (P.D. Jones). Data are represented on 5 deg. grids from 1856 to May 2002. The data were provided by the Met Office.

  • This is a collection of the University of East Anglia Climatic Research Unit (CRU) Japanese Reanalysis (JRA) data. The CRU JRA data are 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The dataset is constructed by combining data from the Japanese Reanalysis data produced by the Japanese Meteorological Agency (JMA) and adjusted where possible to align with the CRU TS data (these 'ten meteorological variables' are not the same ten available from CRU TS). The CRU JRA dataset is intended to be a replacement of the CRUNCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRUNCEP dataset rather than that which is available from UCAR.