Representing twentieth-century space-time climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate

The authors describe the construction of a 0.5 ° latitude/longitude gridded dataset of monthly terrestrial surface climate over for the period 1901-1996. The dataset comprises a suite of 7 climate elements: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressur...

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Bibliographic Details
Main Authors: Mark New, Mike Hulme, Phil Jones
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 2000
Subjects:
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.556.4822
http://badc.nerc.ac.uk/data/cru/cru05_doc.pdf
Description
Summary:The authors describe the construction of a 0.5 ° latitude/longitude gridded dataset of monthly terrestrial surface climate over for the period 1901-1996. The dataset comprises a suite of 7 climate elements: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover and ground-frost frequency. The spatial coverage extends over all land areas, excluding Antarctica. Fields of monthly climate anomalies, relative the 1961-1990 mean, were interpolated from surface climate data. The anomaly grids were then added to a 1961-1990 mean monthly climatology (described in Part I) to arrive at grids of monthly climate. The primary variables, precipitation, mean temperature and diurnal temperature range, were interpolated directly from station observations. The resulting time-series are compared with other, coarser resolution, datasets of similar temporal extent. The remaining climatic elements, termed secondary variables, were interpolated from merged datasets, comprising station observations and, in regions where there were no station data, synthetic data estimated using predictive relationships with the primary variables, which are described and evaluated. It is argued that this new dataset represents an advance other products because (i) it has higher spatial resolution than other datasets of similar temporal extent, (ii) it has longer temporal coverage than other products of similar spatial resolution; (iii) it encompasses a more extensive suite of surface climate variables than available elsewhere and (iv) the construction method ensures that strict temporal fidelity is maintained. The dataset is available from the Climatic Research Unit.