The CRU Global Climate Dataset available through the IPCC DDC consists of a multi-variate 0.5º latitude by 0.5º longitude resolution mean monthly climatology for global land areas, excluding Antarctica, strictly constrained to the period 1961-1990, together with monthly time series at the same resolution for the period 1901-1995. The mean 1961-1990 climatology comprises a suite of eleven surface variables: precipitation (PRE) and wet-day frequency (WET); mean, maximum and minimum temperature (TMP, TMX, TMN); diurnal temperature range (DTR); vapour pressure (VAP;) global radiation (RAD;) cloud cover (CLD); frost frequency (FRS); and wind speed (WND).
The mean climate surfaces have been constructed from a new dataset of station 1961-1990 climatological normals, numbering between 19,800 (precipitation) and 3615 (windspeed). The station data were interpolated as a function of latitude, longitude and elevation using thin-plate splines. The accuracy of the interpolations are assessed using cross-validation and by comparison with other climatologies. this data-set has been decsribed in: 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.
To calculate monthly time series, grids of monthly anomalies relative to 1961-90 were calculated for each variable and applied to their respective 1961-90 climatology. The anomaly approach was adopted because the network of station normals was much more comprehensive than the network of station time series. The spatial variability in mean climate was best captured by the denser network of station normals, while the more sparse network of primary variable time series captured as much temporal variability as possible. This dataset has been described in: New M.G., Hulme M., and Jones P.D. 2000 representing twentieth-century space-time climate variability. Part II: Development of 1901-1996 monthly grids of terrestrial surface climate. J. Climate 13: 2217-2238.
Undertaking a climate change impacts assessment not only requires climate and non-climatic scenarios for the future, but also good quality observed data describing present-day climate. Often the observed climate datasets required may be extensive (e.g. global coverage) or comprehensive (e.g. many climate variables). Data may be required as monthly means, as monthly time series, or as daily time series and the length of record would ideally be at least 30 years, perhaps longer.
The data is only aggregated if at least 75 % of the observations are available (i.e. % of population or % of area or % of countries) on an annual basis.
The value "-9999" corresponds to "No Data"
Calculated pre 1991-1992 relative country share
Former Yugoslavia SFR:
The Global totals do not include values for Antarctica.
Precipitation is the annual average (within 1961-90) of water falling on the country.
The original data took the form of a value for each month and each box on a 0.5 degree latitude /
longitude grid. The seasonal and annual values are the means of their constituent months.
Original Data Station observations were first collected by national meteorological, hydrological and
related services, and were acquired through the free and unrestricted exchange of meteorological and
related data. These observations were gridded by collaborators at the Climatic Research Unit
(www.cru.uea.ac.uk). The gridded data-set is publicly available, and has been published in a peer-
reviewed scientific journal:
New, M., Hulme, M., and Jones, P., 1999: Representing twentieth-century space-time climate
variability. Part I: Development of a 1961-1990 mean monthly terrestrial climatology. Journal of
Climate 12: 829-856.
New, M.G., Hulme,M., and Jones,P.D., 2000: Representing twentieth-century space-time climate
variability. Part II: Development of 1901-1996 monthly grids of terrestrial surface climate. Journal of
Copyright c 2002 (Aggregations) United Nations Environment Programme/DEWA/GRID-Geneva.
Data aggregation made by Andrea DeBono and Ola Nordbeck (UNEP/DEWA/GRID-Geneva).