The Dataset

'gu23wld0098.dat', Version 1.0, March 1999

An historical monthly precipitation dataset for global land areas from 1900 to 1998, gridded at 2.5° latitude by 3.75° longitude resolution (a 5° latitude/longitude resolution version is also available: 'g55wld0098.dat'), has been constructed and is available for use in scientific research. This work has been supported by the UK Department of the Environment, Transport and the Regions (Contract EPG 1/1/48). The following credit should be used in reports or publications, etc.:

"'gu23wld0098.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.

Hulme,M., Osborn,T.J. and T.C.Johns (1998) Precipitation sensitivity to global warming: Comparison of observations with HadCM2 simulations Geophys. Res. Letts., 25, 3379-3382.

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 11,880 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).

Gridding Method

Thiessen polygon weights were used to average gauge data within each gridbox. Where a monthly station value was missing an estimate was obtained by calculating the mean anomaly for that location derived from surrounding stations. This anomaly interpolation method required the station values to be converted into percentage anomalies from some reference period. These standard anomalies were then interpolated onto the missing station location using an inverse distance (with spherical adjustment), angular weighted method similar to that described in Shepherd (1984) and Legates and Willmott (1990). For this interpolation, a maximum percent anomaly value of 500 per cent was imposed. The interpolated percent anomaly was then converted back into a station mm estimate using that station's mean monthly precipitation total for the reference period. This mean anomaly interpolation was only performed for a missing station value where two stations within a 600km radius possessed valid data (for 1997 and 1998 this search radius was reduced to 400km to minimise instabilities in the resulting gridbox estimates). Otherwise the station value remained missing and hence the gridbox average could not be calculated for that month. A maximum of the 50 nearest stations could contribute to the interpolation.

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 1277 2.5° by 3.75° gridboxes. The maximum number of stations per gridbox was 51. For 1951-90, 6655 stations were included generating time series for 1418 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 726 had complete data between 1900 and 1998. The period of most complete coverage was from 1952-75; the year 1959 possessed no missing data in any of the 1520 gridboxes.

Some Notes About Reliability

All station data have been screened for gross outliers and typographical errors using a number of semi-automated techniques. Owing to the large spatial variability of precipitation these methods, however, are not foolproof. The GHCN has considered and implemented further improvements to these screening methods (Easterling and Peterson, 1995; Easterling et al., 1996).

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 to snowcatch of different gauge designs and mountings. These have been well documented for certain countries (e.g. Russia; Groisman et al., 1991; Scandinavian countries - the North Atlantic Climatological Dataset) and work is underway to "clean" other country datasets (e.g. Canada). Groisman's 'adjusted' data have now been added to the master station dataset used here, as has the NACD archive, 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 and Scandinavia, 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. (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.

Hulme,M., Osborn,T.J. and T.C.Johns (1998) Precipitation sensitivity to global warming: Comparison of observations with HadCM2 simulations Geophys. Res. Letts., 25, 3379-3382.

Doherty,R.M., Hulme,M. and Jones,C.G. (1999) A gridded reconstruction of land and ocean precipitation for the extended Tropics from 1974-1994 Int. J. Climatol., 19, 119-142.

New,M., Hulme,M. and Jones,P.D. (1999) Representing twentieth century space- time climate variability. Part 1: development of a 1961-90 mean monthly terrestrial climatology J.Climate, 12, 829-856.

References

Easterling,D.R. and Peterson,T.C. (1995) A new method for detecting undocumented discontinuities in climatological time series Int. J. Climatol., 15, 369-378

Easterling,D.R., Peterson,T.C. and Karl,T.R. (1996) On the development and use of homogenized climate data sets J.Climate, 9, 1429-1434.

Groisman,P.Ya., Koknaeva,V.V., Belokrylova,T.A. and Karl,T.R. (1991) Overcoming biases of precipitation measurement: a history of the USSR experience Bull. Amer. Met. Soc., 72, 1725-1733.

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  
web site:  http://www.cru.uea.ac.uk/~mikeh

The Dataset

Gridding Method

Some Notes About Reliability

References

*** No liability is accepted for errors in the dataset ***

* No liability is accepted for errors in the dataset *