Homogenization of daily temperature series in the European Climate Assessment & Dataset.

The daily maximum and minimum temperature series of the European Climate Assessment & Dataset are homogenized using the quantile matching approach. As the dataset is large and the detail of metadata is generally missing, an automated method locates breaks in the series based on a comparison with surrounding series and applies adjustments which are estimated using homogeneous segments of surrounding series as reference. A total of 6,500 series have been processed and after removing duplicates and short series, about 2,100 series have been adjusted. Finally, the effect of the homogenization of daily maximum and minimum temperature on trend estimation is shown to produce a much more spatially homogeneous and then plausible picture. Changes in measurement routines affect the temperature records, inserting biases in the series and compromising the climatological analyses. A homogenization procedure, based on statistical approaches such as the quantile matching, is developed and applied to the European Climate Assessment & Dataset, which collects data for all over Europe. Effect are shown first focusing on representative case studies and then on the geographical consistency of the trends in temperatures in the entire dataset.Example of quantile matching for the month of May on the break of 1952 in the station of Bamberg, Germany. Shaded curves represent the original probability density function, solid lines represent the adjusted ones. [ABSTRACT FROM AUTHOR]