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Updated high‐resolution grids of monthly climatic observations – the CRU TS3 .10 Dataset

Ian Charles Harris, Philip Douglas Jones, Timothy J. Osborn, David Lister

International Journal of Climatology · 2013 · 6,376 citationsOpen access

doi.org/10.1002/joc.3711Free full text

Abstract

ABSTRACT This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3 .10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet‐day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well‐known formulae. Time series for hemispheric averages and 20 large sub‐continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3 .10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series ( http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/ ). © 2013 Royal Meteorological Society

Cite this paper

Harris, I. C., Jones, P. D., Osborn, T. J., & Lister, D. (2013). Updated high‐resolution grids of monthly climatic observations – the CRU TS3 .10 dataset. International Journal of Climatology, 34(3), 623–642. https://doi.org/10.1002/joc.3711

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