Historical surface observed climate data is an important component of climate knowledge. Within Australia the Bureau of Meteorology is the custodian of a vast quantity of surface observed climate data collected from a variety of instruments and locations (Rayner et al.
2004). Spatial modelling applications require gridded climate datasets, which are estimates of climate variables over large areas rather than observed climate records. Gridded climate data sets are constructed by spatial interpolation of climate observations. Time series from such gridded data sources can be utilised at locations where there are no observed climate records, or to fill in gaps of incomplete records. In some instances synthetic time series are preferable over observed climate records as the spatial interpolation algorithm smoothes out local site specific environmental effects. A complete and accurate climate data source is a prerequisite for the efficient modelling of a wide variety of environmental processes (Jeffrey et al. 2001; Rayner et al. 2004).
The use of biophysical and hydrological models is driving an increasing demand for spatially interpolated climate data. However Daly (2006) concludes that many users do not have a substantial background in geospatial climatology and as a result are not in a position to critically assess the suitability of spatial climate data sets. Errors associated with and between spatial climate data sets are commonly assumed to be negligible. This assumption may effect the interpretation of results, conclusions and decisions made from these results. There is a growing awareness that spatially interpolated climate data sets contain a degree of error (Willmott & Johnson 2005).
The complexity of interpolating observed climate variables onto gridded cells is highlighted by irregular topography and under representation of high elevation areas (Beesley et al.
2009). The complexity is reflected in rainfall and potential evaporation, where these metrics can be highly variable over area and time, making them difficult to reliably interpolate from surrounding station observations (Jeffrey et al. 2001). There are currently three sources of historical, continuous, daily climate data within Australia;
SILO Patched Point data
SILO Data Drill – 0.05°
Climate Futures for Tasmania (CFT) created a fourth daily climate data source, AWAP – 0.1°. This data source was created to compare directly to regional climate model outputs at 0.1°C resolution in validation exercises, and is not available commercially as a formal gridded climate data product. This data source is included in this analysis to illustrate effects of interpolating to larger grid scales.
The SILO data sources (www.longpaddock.qld.gov.au/silo/; Jeffrey et al. 2001) are presently the most easily accessible and are frequently used in agricultural models, although the AWAP data source is increasingly being used. The historical daily climate data sources are not however, fully consistent with one another due to the scale and interpolation methods (Beesley et al. 2009).
Jeffrey et al. (2001) undertook a comprehensive archiving of Australian rainfall and climate data constructed from ground based observational data. Continual daily time step records were constructed using spatial interpolation algorithms to estimate missing data. The product of this was the SILO Patched Point Data set which was constructed for daily rainfall, maximum and minimum temperature, evaporation, solar radiation and vapour pressure for approximately 4600 locations across Australia. Locations without observational data, (e.g. maximum temperature) contain interpolated estimates that are computed for those locations, or in some instances are taken directly from gridded surfaces.
Jeffrey et al. (2001) also produced SILO Data Drill – 0.05° which is a high resolution gridded surface generated by spatial interpolation calculated by splining and kriging techniques of observed daily data. All climate variables (except mean sea level pressure) were generated by spatial interpolation using a trivariate thin plate smoothing spline with latitude, longitude and elevation as independent variables. Elevation was expressed in kilometers to minimise the validated root mean square interpolation error, latitude and longitude were in units of degrees. All surfaces were fitted by minimising the generalised cross validation error with constraint of first order smoothness imposed. The daily climate data in the SILO data drill sets are all synthetic with no original meteorological station data left in the calculated grid fields. SILO Data drill data has been produced on a regular grid 0.05° extending from latitude 10°S to 44°S and longitude 112°E to 154°E.
The Bureau of Meteorology and CSIRO also generated a meteorological analysis and
remotely sensed datasets for Australia as a contribution to AWAP
(www.bom.gov.au/jsp/awap). The AWAP product provides interpolated data sets from 1900
been developed and applied to meteorological observations of rainfall, maximum and minimum temperature, potential and total evaporation, solar radiation and vapour pressure to produce analyses at a resolution of 0.05° by 0.05°. The analyses (grids) are computer generated using a sophisticated technique which incorporates an optimised Barnes successive correction technique that applies a weighted averaging process to the station data (Jones et al.
2007). Topographical information is included by the use of temperature ratio (actual temperature divided by monthly average) in the analysis process. The AWAP climate data is interpolated from observations catalogued by the Bureau of Meteorology‟s climate databank (Australian Data Archive for Meteorology).
The objectives of this Chapter were to:
1. Compare the monthly and annual means between the climate variables maximum
and minimum temperature, rainfall and potential evaporation of the four currently available data sources at six sites, for the period 1971 to 2007.
2. Compare monthly and annual means of simulated perennial ryegrass growth from the biophysical model DairyMod between the four currently available data sources at six sites, for the period 1971 to 2007.