Dataset
CRU JRA v1.1: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2017.
Abstract
The CRU JRA V1.1 dataset is a 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 variables are provided on a 0.5 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is same as the CRU TS grid, though the set of variables is different) . The data are available at a 6 hourly time-step from January 1901 to December 2017.
The dataset is constructed by combining data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA) and adjusted where possible to align with the CRU TS 3.26 data (see the Process section and the ReadMe file for full details).
The CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).
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. A link to the CRU NCEP documentation for comparison is provided in the documentation section.
If this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:
Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated
high-resolution grids of monthly climatic observations - the CRU TS3.10
Dataset. International Journal of Climatology 34, 623-642.
Kobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and
Basic Characteristics. J. Met. Soc. Jap., 93(1), 5-48
https://dx.doi.org/10.2151/jmsj.2015-001
Details
Previous Info: |
No news update for this record
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Previously used record identifiers: |
No related previous identifiers.
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Access rules: |
Access to these data is available to any registered CEDA user. Please Login or Register for a CEDA account to gain access.
Use of these data is covered by the following licence(s): 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: |
The CRU JRA data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. This is the first formal release and was provided to CEDA in December 2018. |
Data Quality: |
The data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harries et al. 2014 and the release notes, links to both can be found in the documentation.
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File Format: |
The data are provided as gzipped NetCDF files, with one file per variable, per year. Each file is approximately 330MB when compressed.
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Related Documents
Citations: 17
The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.
Caen, A., Smallman, T.L., de Castro, A.A., Robertson, E., von Randow, C., Cardoso, M. & Williams, M. (2021) Evaluating two land surface models for Brazil using a full carbon cycle benchmark with uncertainties. Climate Resilience and Sustainability 1. https://doi.org/10.1002/cli2.10 https://doi.org/10.1002/cli2.10 |
Hardouin, L., Delire, C., Decharme, B., et al. (2022) Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing. Environmental Research Letters 17, 094033. https://doi.org/10.1088/1748-9326/ac888d https://doi.org/10.1088/1748-9326/ac888d |
Harris, B.L., Quaife, T., Taylor, C.M. & Harris, P.P. (2024) Contrasting responses of vegetation productivity to intraseasonal rainfall in Earth system models. Earth System Dynamics 15, 1019–1035. https://doi.org/10.5194/esd-15-1019-2024 https://doi.org/10.5194/esd-15-1019-2024 |
Huntingford, C., Sitch, S.A. & O’Sullivan, M. (2021) Impact of merging of historical and future climate data sets on land carbon cycle projections for South America. Climate Resilience and Sustainability 1. https://doi.org/10.1002/cli2.24 https://doi.org/10.1002/cli2.24 |
Jones, S., Rowland, L., Cox, P., et al. (2019) The Impact of a Simple Representation of Non-Structural Carbohydrates on the Simulated Response of Tropical Forests to Drought. https://doi.org/10.5194/bg-2019-452 https://doi.org/10.5194/bg-2019-452 |
not a doi https://doi.org/1874/417964 |
not a doi https://doi.org/1871.1/359583d7-c6cd-4a5d-b7db-663386609ff6 |
not a doi https://doi.org/1871.1/432ccd0d-2471-45c1-9e00-698057cb286e |
Petrescu, A.M.R., Qiu, C., Ciais, P., et al. (2021) The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017. Earth System Science Data 13, 2307–2362. https://doi.org/10.5194/essd-13-2307-2021 https://doi.org/10.5194/essd-13-2307-2021 |
Petrescu, A.M.R., Qiu, C., McGrath, M.J., et al. (2023) The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019. Earth System Science Data 15, 1197–1268. https://doi.org/10.5194/essd-15-1197-2023 https://doi.org/10.5194/essd-15-1197-2023 |
Saunois, M., Stavert, A.R., Poulter, B., et al. (2020) The Global Methane Budget 2000–2017. Earth System Science Data 12, 1561–1623. https://doi.org/10.5194/essd-12-1561-2020 https://doi.org/10.5194/essd-12-1561-2020 |
Smallman, T.L., Milodowski, D.T., Neto, E.S., Koren, G., Ometto, J. & Williams, M. (2021) Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century. Earth System Dynamics 12, 1191–1237. https://doi.org/10.5194/esd-12-1191-2021 https://doi.org/10.5194/esd-12-1191-2021 |
Stratmann, T.S.M., Forrest, M., Traylor, W., Dejid, N., Olson, K.A., Mueller, T. & Hickler, T. (2023) Movement drives population dynamics of one of the most mobile ungulates on <scp>E</scp>arth: Insights from a mechanistic model. Ecology 104. https://doi.org/10.1002/ecy.4071 https://doi.org/10.1002/ecy.4071 |
Tao, J., Zhu, Q., Riley, W.J. & Neumann, R.B. (2021) Improved ELMv1-ECA simulations of zero-curtain periods and cold-season CH&lt;sub&gt;4&lt;/sub&gt; and CO&lt;sub&gt;2&lt;/sub&gt; emissions at Alaskan Arctic tundra sites. The Cryosphere 15, 5281–5307. https://doi.org/10.5194/tc-15-5281-2021 https://doi.org/10.5194/tc-15-5281-2021 |
Tao, J., Zhu, Q., Riley, W.J. & Neumann, R.B. (2021) Warm-season net CO2 uptake outweighs cold-season emissions over Alaskan North Slope tundra under current and RCP8.5 climate. Environmental Research Letters 16, 055012. https://doi.org/10.1088/1748-9326/abf6f5 https://doi.org/10.1088/1748-9326/abf6f5 |
Wang, J., Li, W., Ciais, P., Ballantyne, A., Goll, D., Huang, X., Zhao, Z. & Zhu, L. (2021) Changes in Biomass Turnover Times in Tropical Forests and Their Environmental Drivers From 2001 to 2012. Earth’s Future 9. https://doi.org/10.1029/2020ef001655 https://doi.org/10.1029/2020ef001655 |
Zhang, Y., Boucher, O., Ciais, P., Li, L. & Bellouin, N. (2021) How to reconstruct aerosol-induced diffuse radiation scenario for simulating GPP in land surface models? An evaluation of reconstruction methods with ORCHIDEE_DFv1.0_DFforc. Geoscientific Model Development 14, 2029–2039. https://doi.org/10.5194/gmd-14-2029-2021 https://doi.org/10.5194/gmd-14-2029-2021 |
Process overview
Title | Climatic Research Unit (CRU) procedure to produce the CRU JRA data. |
Abstract | The CRU JRA (Japanese reanalysis) data is a replacement to the CRU NCEP dataset, CRU JRA data follows the style of Nicolas Viovy's original dataset rather than that which is available from UCAR. The CRU JRA dataset is based on the JRA-55 reanalysis dataset and aligned where appropriate with the CRU TS dataset version 3.26 (1901-2017). All JRA variables are regridded from their native TL319 Gaussian grid to the CRU regular 0.5° x 0.5° grid, using the g2fsh spherical harmonics routine from NCL (NCAR Command Language), based on the 'Spherepack' code. The exception is precipitation, which is regridded using ESMF 'nearest neighbour': all other algorithms tried exhibited unwanted artifacts. The JRA-55 reanalysis dataset starts in 1958. The years 1901-1957 are constructed using randomly-selected years between 1958 and 1967. Where alignment with CRU TS occurs, the relevant CRU TS data is used. Of the ten variables listed above, the last four do not have analogs in the CRU TS dataset. These are simply regridded, masked for land only, and output as CRUJRA. The other six are aligned with CRU TS as follows: TMP is aligned with CRU TS TMP. A monthly mean for the JRA data is --- TMAX and TMIN are aligned with CRUJRA TMP and CRU TS DTR. Firstly, at --- PRE is aligned with CRU TS PRE and WET (rain day counts). Firstly, the --- SPFH is aligned with CRU TS VAP. VAP is converted to SPFH, and JRA mean --- DSWRF is aligned with CRU TS CLD. CLD is converted to shortwave --- Where appropriate, CRUJRA values are kept within physically-appropriate |
Input Description | None |
Output Description | None |
Software Reference | None |
- long_name: Air Temperature
- gcmd_url: http://vocab.ndg.nerc.ac.uk/term/P141/4/GVAR0027
- gcmd_keyword: Air Temperature
- names: Air Temperature
Co-ordinate Variables
Temporal Range
1901-01-01T00:00:00
2017-12-31T23:59:59
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-60.0000° |