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Keywords:

  • daily temperature;
  • homogenisation;
  • quality control;
  • reconstruction;
  • trends;
  • semivariance;
  • Spain

Abstract

This paper gives the complete details of the protocols applied for developing a spatially and temporarily high-resolution dataset of temperature for northeastern Spain. Our methodologies used data from a large number of observatories (1583) spanning some portions of the period between 1900 and 2006. The raw dataset was first tested for internal and external consistency to check data quality. To improve data completeness, a linear regression model was then utilized to infill gaps in the daily temperature series using the best correlated data from nearby sites. Discontinuities in the reconstructed series were determined by combining the results of three homogeneity-relative tests: the Standard Normal Homogeneity Test (SNHT), the Eastrerling and Peterson two-phased regression method and the Vincent test. To assess the possible impact of data homogenisation on trends and statistical properties of the final series, a set of tests (e.g. semivariance models and L-moment statistics) was applied to the series before and after correction. Semivariance models suggest a significant improvement in the spatial dependence of the corrected dataset on both seasonal and annual timescales. Also, L-moments gave no evidence of significant changes in the probability distribution of daily temperature series after correction. Taken together, the newly compiled dataset seems to be more robust and reveals more coherent spatial and temporal patterns of temperature compared with the original dataset. From the temporal and spatial perspectives, the new dataset comprises the most complete register of temperature in northeast Spain (1900–2006), with a reasonably spatial coverage. Accordingly, this database can provide a more reliable base for studying temperature changes and variability in the region. This dataset can also be of particular relevance to a number of meteorological, ecological, hydrological and agricultural applications on local, regional and continental scales. Copyright © 2011 Royal Meteorological Society