Robinson, E.L.; Blyth, E.; Clark, D.B.; Comyn-Platt, E.; Finch, J.; Rudd, A.C.

Climate hydrology and ecology research support system meteorology dataset for Great Britain (1961-2015) [CHESS-met] v1.2

https://doi.org/10.5285/b745e7b1-626c-4ccc-ac27-56582e77b900 Cite this dataset

THIS DATASET HAS BEEN SUPERSEDED.

The new release extends the dataset to cover the years 2016-17. The file metadata has been improved, including the projection information.

If you need access to this archived version, please contact the CMP

1km resolution gridded meteorological variables over Great Britain for the years 1961-2015. This dataset contains time series of daily mean values of air temperature (K), specific humidity (kg kg-1), wind speed (m s-1), downward longwave radiation (W m-2), downward shortwave radiation (W m-2), precipitation (kg m-2 s-2) and air pressure (Pa), plus daily temperature range (K). These are the variables required to run the JULES land surface model [1] with daily disaggregation.

The precipitation data were obtained by scaling the Gridded estimates of daily and monthly areal rainfall (CEH-GEAR) daily rainfall estimates [2,3] to the units required for JULES input. Other variables were interpolated from coarser resolution datasets, taking into account topographic information.

This release supersedes the previous version [4], doi:10.5285/10874370-bc58-4d23-a118-ea07df8a07f2, as it corrects errors in the air pressure and daily temperature range files for 2013-2015.

[1] Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes, Geoscientific Model Development, 4, 677-699. https://doi.org/10.5194/gmd-4-677-2011, 2011.
[2] Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D. G.; Keller, V. D. J. (2016). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2015) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/33604ea0-c238-4488-813d-0ad9ab7c51ca
[3] Keller,V. D. J., Tanguy, M. , Prosdocimi, I. , Terry, J. A. , Hitt, O., Cole, S. J. , Fry, M., Morris, D. G., Dixon, H. (2015) CEH-GEAR: 1km resolution daily and monthly areal rainfall estimates for the UK for hydrological use. Earth Syst. Sci. Data Discuss., 8, 83-112. https://doi.org/10.5194/essdd-8-83-2015.
[4] Robinson, E.L., Blyth, E., Clark, D.B., Comyn-Platt, E., Finch, J. , Rudd, A.C. (2016). Climate hydrology and ecology research support system meteorology dataset for Great Britain (1961-2015) [CHESS-met]. NERC Environmental Information Data Centre. https://doi.org/10.5285/10874370-bc58-4d23-a118-ea07df8a07f2

Publication date: 2017-02-07

These data are held and managed by the Environmental Information Data Centre

For more details and to access the data, go to https://catalogue.ceh.ac.uk/id/b745e7b1-626c-4ccc-ac27-56582e77b900

Format

netCDF

Where/When

Study area
Temporal extent: 1961-01-01 to 2015-12-31

Provenance & quality

The precipitation was obtained by scaling the daily rainfall estimates from the CEH-Gridded Estimates of Areal Rainfall (CEH-GEAR) dataset [1,2] to units of kg m-2 s-1. The Met Office Rainfall Evaporation Calculation System (MORECS) [3,4] air temperature and vapour pressure, and hours of bright sunshine were interpolated to 1km resolution, then corrected for the local topography using the Integrated Hydrological Digital Terrain Model (IHDTM) [5] to find the CHESS-met air temperature (K), specific humidity (kg kg-1) and downward long- and shortwave radiation (W m-2). The wind speed (m s-1) was found by interpolating the MORECS wind speed, then correcting for topography using the UK Energy Technology Support Unit (ETSU) average wind speed dataset [6,7]. The daily temperature range (K) was reprojected from the CRU TS 3.21 (1961-2012) and CRU TS 3.24 (2013-2015) monthly data [8,9]. The air pressure (Pa) was found by calculating a climatology of the WATCH Forcing Data (WFD) air pressure [10], then correcting for topography using the IHDTM. The data were created using software developed in Fortran 95 and python.

This version of the CHESS-met dataset corrects errors in the previous version [11], which were:
a) surface air pressure files for 2013-2015 had incorrect time coordinate variable
b) daily temperature range values for 2013-2015 were erroneous due to a binary conversion error

These errors have been corrected in this release. All other files remain unchanged.

[1] Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D. G.; Keller, V. D. J. (2016). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2015) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/33604ea0-c238-4488-813d-0ad9ab7c51ca
[2] Keller,V. D. J., Tanguy, M. , Prosdocimi, I. , Terry, J. A. , Hitt, O., Cole, S. J. , Fry, M., Morris, D. G., Dixon, H. (2015) CEH-GEAR: 1km resolution daily and monthly areal rainfall estimates for the UK for hydrological use. Earth Syst. Sci. Data Discuss., 8, 83-112. https://doi.org/10.5194/essdd-8-83-2015.
[3] Thompson, N., Barrie, I. A., and Ayles, M.: The Meteorological Office rainfall and evaporation calculation system: MORECS, Meteorological Office, Bracknell, 1981.
[4] Hough, M. N., and Jones, R. J. A.: The United Kingdom Meteorological Office rainfall and evaporation calculation system: MORECS version 2.0-an overview, Hydrology and Earth System Sciences, 1, 227-239, 10.5194/hess-1-227-1997, 1997.
[5] Morris, D. G., and Flavin, R. W.: A digital terrain model for hydrology., Proceedings of the 4th International Symposium on Spatial Data Handling, 1, 250-262, 1990.
[6] Newton, K., and Burch, S. F.: Estimation of the UK wind energy resource using computer modelling techniques and map data, Energy Technology Support Unit, 50, 1985.
[7] Burch, S. F., and Ravenscroft, F.: Computer modelling of the UK wind energy resource: Final overview report., AEA Industrial Technology, 1992.
[8] CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2012). 2013.
[9] Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset, International Journal of Climatology, 34, 623-642, https://doi.org/10.1002/Joc.3711, 2014.
[10] Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E., Osterle, H., Adam, J. C., Bellouin, N., Boucher, O., and Best, M.: Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century, Journal of Hydrometeorology, 12, 823-848, https://doi.org/10.1175/2011jhm1369.1, 2011.
[11] Robinson, E.L., Blyth, E., Clark, D.B., Comyn-Platt, E., Finch, J. , Rudd, A.C. (2016). Climate hydrology and ecology research support system meteorology dataset for Great Britain (1961-2015) [CHESS-met]. NERC Environmental Information Data Centre. https://doi.org/10.5285/10874370-bc58-4d23-a118-ea07df8a07f2

Citations

Tanguy, M., Prudhomme, C., Smith, K., & Hannaford, J. (2018). Historical gridded reconstruction of potential evapotranspiration for the UK. Earth System Science Data, 10(2), 951–968. https://doi.org/10.5194/essd-10-951-2018

Lloyd, C.E.M., Johnes, P. J., Freer, J.E., Carswell, A.M., Jones, J.I., Stirling, M.W., … Collins, A. L. (2019). Determining the sources of nutrient flux to water in headwater catchments: Examining the speciation balance to inform the targeting of mitigation measures. Science of The Total Environment, 648, 1179–1200. https://doi.org/10.1016/j.scitotenv.2018.08.190

Martínez-de la Torre, A., Blyth, E.M., & Weedon, G.P. (2019). Using observed river flow data to improve the hydrological functioning of the JULES land surface model (vn4.3) used for regional coupled modelling in Great Britain (UKC2). Geoscientific Model Development, 12(2), 765–784. https://doi.org/10.5194/gmd-12-765-2019

Myrgiotis, V., Williams, M., Rees, R.M., & Topp, C.F.E. (2019). Estimating the soil N2O emission intensity of croplands in northwest Europe. Biogeosciences, 16(8), 1641–1655. https://doi.org/10.5194/bg-16-1641-2019

Coxon, G., Addor, N., Bloomfield, J.P., Freer, J., Fry, M., Hannaford, J., Howden, N.J.K., Lane, R., Lewis, M., Robinson, E.L., Wagener, T., & Woods, R. (2020). CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth System Science Data, 12(4), 2459–2483. https://doi.org/10.5194/essd-12-2459-2020

Ridding, L.E., Newton, A. C., Redhead, J.W., Watson, S.C.L., Rowland, C.S., & Bullock, J.M. (2020). Modelling historical landscape changes. In Landscape Ecology (Vol. 35, Issue 12, pp. 2695–2712). Springer Science and Business Media LLC. https://doi.org/10.1007/s10980-020-01059-9

Redhead, J. W., Powney, G. D., Woodcock, B. A., & Pywell, R. F. (2020). Effects of future agricultural change scenarios on beneficial insects. In Journal of Environmental Management (Vol. 265, p. 110550). Elsevier BV. https://doi.org/10.1016/j.jenvman.2020.110550

Revill, A., Myrgiotis, V., Florence, A., Hoad, S., Rees, R., MacArthur, A., & Williams, M. (2021). Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield. In Agronomy (Vol. 11, Issue 2, p. 314). MDPI AG. https://doi.org/10.3390/agronomy11020314

Comber, A., Collins, A. L., Haro-Monteagudo, D., Hess, T., Zhang, Y., Smith, A., & Turner, A. (2019). A Generic Approach for Live Prediction of the Risk of Agricultural Field Runoff and Delivery to Watercourses: Linking Parsimonious Soil-Water-Connectivity Models With Live Weather Data Apis in Decision Tools. In Frontiers in Sustainable Food Systems (Vol. 3). Frontiers Media SA. https://doi.org/10.3389/fsufs.2019.00042

Krivtsov, V., Forbes, H., Birkinshaw, S., Olive, V., Chamberlain, D., Buckman, J., Yahr, R., Arthur, S., Christie, D., Monteiro, Y., & Diekonigin, C. (2022). Ecosystem services provided by urban ponds and green spaces: a detailed study of a semi-natural site with global importance for research. In Blue-Green Systems (Vol. 4, Issue 1, pp. 1–23). IWA Publishing. https://doi.org/10.2166/bgs.2022.021

Chen, S., & Ruan, X. (2023). A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes. In Journal of Hydrology (Vol. 618, p. 129118). Elsevier BV. https://doi.org/10.1016/j.jhydrol.2023.129118

Brown, C., Seo, B., Alexander, P., Burton, V., Chacón‐Montalván, E.A., Dunford, R., Merkle, M., Harrison, P.A., Prestele, R., Robinson, E. L., & Rounsevell, M. (2022). Agent‐Based Modeling of Alternative Futures in the British Land Use System. In Earth’s Future (Vol. 10, Issue 11) https://doi.org/10.1029/2022ef002905

Baker, E., Harper, A.B., Williamson, D., & Challenor, P. (2022). Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES.Geoscientific Model Development, 15(5), 1913-1929. https://doi.org/10.5194/gmd-15-1913-2022

Murgatroyd, A., Gavin, H., Becher, O., Coxon, G., Hunt, D., Fallon, E., Wilson, J., Cuceloglu, G., & Hall, J.W. (2022). Strategic analysis of the drought resilience of water supply systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2238). https://doi.org/10.1098/rsta.2021.0292

Browning, E., Freeman, R., Boughey, K.L., Isaac, N.J.B., & Jones, K.E. (2022). Accounting for spatial autocorrelation and environment are important to derive robust bat population trends from citizen science data. Ecological Indicators, 136, 108719. https://doi.org/10.1016/j.ecolind.2022.108719

Stański, K., Lycett, S., Porphyre, T., & Bronsvoort, B.M.de C. (2021). Using machine learning improves predictions of herd-level bovine tuberculosis breakdowns in Great Britain. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-81716-4

Buechel, M., Slater, L., & Dadson, S. (2022). Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios. Communications Earth & Environment, 3(1). https://doi.org/10.1038/s43247-021-00334-0

Goodwin, C.E.D., Bütikofer, L., Hatfield, J.H., Evans, P.M., Bullock, J.M., Storkey, J., Mead, A., Richter, G.M., Henrys, P.A., Pywell, R.F., & Redhead, J.W. (2022). Multi-tier archetypes to characterise British landscapes, farmland and farming practices. Environmental Research Letters, 17(9), 095002. https://doi.org/10.1088/1748-9326/ac810e

Rose, N., & Dolega, L. (2021). It’s the Weather: Quantifying the Impact of Weather on Retail Sales. Applied Spatial Analysis and Policy, 15(1), 189-214. https://doi.org/10.1007/s12061-021-09397-0

Supplemental information

CHESS Explorer

The CHESS Explorer application provides the ability for users to preview the data, visualise maps of the different meteorological variables, and understand how they vary across the country and through time. Notice that this application displays data up to 2012 for now.

MAJIC (Managing Access to Jules in the Cloud)

A web application that allows users to drive the JULES land-surface model with the CHESS-met dataset within a high performance computing environment over the web.

Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2015) [CEH-GEAR]

1 km gridded estimates of daily and monthly rainfall for Great-Britain and Northern Ireland from 1890 to 2015, used for the CHESS-met precipitation variable.

Correspondence/contact details

Emma Robinson

UK Centre for Ecology & Hydrology

Maclean Building, Benson Lane, Crowmarsh Gifford
Wallingford
Oxfordshire
OX10 8BB
UNITED KINGDOM

enquiries@ceh.ac.uk