"'gu23wld0096.dat' (Version 1.0) constructed and supplied by Dr Mike Hulme at the Climatic Research Unit, University of East Anglia, Norwich, UK. This work has been supported by the UK Department of the Environment, Transport and the Regions (Contract EPG 1/1/48)."
The appropriate scientific papers to reference are as follows:
Hulme,M. (1992) A 1951-80 global land precipitation climatology for the evaluation of General Circulation Models Climate Dynamics, 7, 57-72
Hulme,M. (1994) Validation of large-scale precipitation fields in General Circulation Models pp.387-406 in, Global precipitations and climate change (eds.) Desbois,M. and Desalmand,F., NATO ASI Series, Springer-Verlag, Berlin, 466pp.
The station dataset from which this gridded dataset has been
constructed is an extension of the original CRU/US DoE data
described in Eischeid et al. (1991). Substantial additional work in
extending these station time series and increasing the network has
been undertaken by the Climatic Research Unit in recent years. A
total of over 11,800 station time series now exist. For access to
these station data one should approach Russ Vose working on the
Global Historical Climatology Network (GHCN Version 2) at Arizona State University, USA (Email: rvose@smtpl.asu.edu).
Two gridded datasets were initially calculated based on two different reference periods: 1931-70 and 1951-90. Stations could only contribute to these climatologies if they possessed 75% or more valid monthly measurements in the reference period. For 1931-70, 5986 stations were used resulting in historical gridded time series for 1276 2.5° by 3.75° gridboxes. The maximum number of stations per gridbox was 51. For 1951-90, 6647 stations were included generating time series for 1419 2.5° by 3.75° gridboxes. The maximum number of stations per gridbox was 45. These two datasets were then combined using the common 1951-70 period to blend the data on the basis of mean monthly values and their variance. When merged, a total of 1520 gridboxes possessed time series of which 1019 had complete data between 1900 and 1996. The period of most complete coverage was from 1952-70; the year 1959 possessed no missing data in any of the 813 gridboxes.
No corrections for gauge undercatch have been applied to the station data (cf. Sevruk, 1982; Legates and Willmott, 1990). A spatially varying, but temporally constant, correction could be applied to the estimates derived from Legates and Willmott (1990), although this would not alter the trends in the data. Applying time-dependent corrections to gauge time series on a global scale is a gigantic undertaking which may well not be either feasible or justifiable.
A number of Northern Hemisphere high latitude time series contain inhomogeneities due to varying sensitivities of different gauge designs and mountings to snowcatch. These have been well documented for certain countries (e.g. Russia; Groisman et al., 1991) and work is underway to "clean" other country datasets (e.g. Canada and Scandinavian countries). Groisman's adjusted data have now been added to the master station dataset used here, but further improvements in the reliability of this gridded dataset over high latitudes will follow. For the present, the user should be cautious about the precise interpretation of high latitude precipitation trends outside Russia, especially in winter.
No topographic weighting has been applied to the interpolation scheme. A number of different methods exist for incorporating the effects of topography on precipitation (e.g. the PRISM and AURELHY methods and the spline algorithms of Hutchinson, 1995). However, the dependence of precipitation anomalies on elevation is much smaller and more ambiguous. Since the method used here only interpolates anomalies, and not precipitation values themselves, excluding the effects of elevation is reasonable. There are, however, other problems associated with using precipitation anomalies in a gridding algorithm like this and these are discussed by Hulme and New (1997). Further discussion and applications of these, and other gridded, precipitation datasets can be found in the following publications:
Hulme,M. (1991) An intercomparison of model and observed global precipitation climatologies Geophys. Res. Lett., 18, 1715-1718
Hulme,M. (1992) A 1951-80 global land precipitation climatology for the evaluation of General Circulation Models Climate Dynamics 7, 57-72.
Hulme,M., Marsh,R. and Jones,P.D. (1992) Global changes in a humidity index between 1931-60 and 1961-90 Climate Research, 2, 1-22.
Hulme,M. (1992) Rainfall changes in Africa: 1931-60 to 1961-90 Int. J. Climatol., 12, 685-699.
Hulme,M. and Jones,P.D. (1993) A historical monthly precipitation dataset for global land areas: applications for climate monitoring and climate model evaluation pp. A/14-A/17 in, Analysis methods of precipitation on a global scale Report of a GEWEX Workshop, 14-17 September 1992, Koblenz, Germany, WMO/TD-No.558, Geneva
Hulme,M. (1994) Validation of large-scale precipitation fields in General Circulation Models pp.387-406 in, Global precipitations and climate change (eds.) Desbois,M. and Desalmand,F., NATO ASI Series, Springer-Verlag, Berlin, 466pp.
Hulme,M. (1995) Estimating global changes in precipitation Weather, 50, 34-42.
Hulme,M. (1996) Recent climate change in the world's drylands Geophys. Res. Letts., 23, 61-64 Jones,P.D. and Hulme,M. (1996) Calculating regional climatic time series for temperature and precipitation: methods and illustrations Int. J. Climatol., 16, 361-377 Hulme,M. and New,M. (1997) The dependence of large-scale precipitation climatologies on temporal and spatial gauge sampling J.Climate 10, 1099-1113. see also:
Legates,D.R. (1995) Global and terrestrial precipitation: a comparative assessment of existing climatologies Int. J. Climatol., 15, 236-258.
Hutchinson,M.F. (1995) Interpolating mean rainfall using thin-plate smoothing splines Int. J. Geographical Inf. Systems, 9, 385-403. Legates,D.R. and Willmott,C.J. (1990) Mean seasonal and spatial variability in gauge-corrected, global precipitation Int. J. Climatol., 10, 111-128.
Shepherd,D. (1984) Computer mapping: the SYMAP interpolation algorithm in, Spatial statistics and models (eds.) Gaile,G.L. and Willmott,C.J., D.Reidel Publishing, Dordrecht, 133pp.
*** No liability is accepted for errors in the dataset ***
For further information about these gridded datasets contact:
Dr Mike Hulme
Climatic Research Unit
School of Environmental Sciences
University of East Anglia
Norwich NR4 7TJ, UK
tel: +1603 593162; fax: +1603 507784
email m.hulme@uea.ac.uk
web site: http://www.cru.uea.ac.uk/~mikeh