Uncertainty in homogenized daily temperatures and derived indices of extremes illustrated using parallel observations in Canada

Abstract

Parallel daily temperature observations at site pairs over a 5-year period at 88 locations across Canada were used to derive and validate adjustments required during homogenization process. The data was first ‘aligned’ for compatible observing times at 12 locations (other locations do not have this problem). Then the homogenization adjustments were obtained using three procedures (Seasonal Bias, Monthly Interpolation and Quantile Matching) and two approaches (using parallel and neighbours observations). The root mean squared error (RMSE) between the daily temperatures of site 1 and site 2, and the percentage of days within 0.5 °C (PD05) between site 1 and site 2 were used to assess the uncertainty in the mean and extreme values, respectively. The instruments were not necessarily collocated as the distance between the two observing sites varied from 0 to 30 km. The results confirm that it is necessary to apply adjustments for known issues first, such as a different observing time. They also show that when a shift between site 1 and site 2 (defined by the annual mean of the daily temperature differences) is small [<0.25 standard deviation (SD)], the adjustments do not reduce the error between site 1 and site 2. When the shift size is between 0.25 and 0.5 SD, the adjustments derived from parallel observations help to reduce the uncertainty. When the shift is large (>0.5 SD), both approaches reduce the error, although the adjustments derived from parallel observations provide better results as compared to those computed from neighbour observations. The results also indicate that Quantile Matching adjustments can provide a better estimate of the adjustments than the other methods evaluated to indices of extreme temperature computed from the adjusted daily values; however, highly correlated neighbours are needed when the adjustments are based on neighbours observations.