Application of Multiple Analysis of Series for Homogenization to Beijing daily temperature series (1960–2006)

Homogenization of climate observations remains a challenge to climate change researchers, especially in cases where metadata (e.g., probable dates of break points) are not always available. To examine the influence of metadata on homogenizing climate data, the authors applied the recently developed Multiple Analysis of Series for Homogenization (MASH) method to the Beijing (BJ) daily temperature series for 1960–2006 in three cases with different references: (1) 13M—considering metadata at BJ and 12 nearby stations; (2) 13NOM—considering the same 13 stations without metadata; and (3) 21NOM—considering 20 further stations and BJ without metadata. The estimated mean annual, seasonal, and monthly inhomogeneities are similar between the 13M and 13NOM cases, while those in the 21NOM case are slightly different. The detected biases in the BJ series corresponding to the documented relocation dates are as low as −0.71°C, −0.79°C, and −0.5°C for the annual mean in the 3 cases, respectively. Other biases, including those undocumented in metadata, are minor. The results suggest that any major inhomogeneity could be detected via MASH, albeit with minor differences in estimating inhomogeneities based on the different references. The adjusted annual series showed a warming trend of 0.337, 0.316, and 0.365°C (10 yr)−1 for the three cases, respectively, smaller than the estimate of 0.453°C (10 yr)−1 in the original series, mainly due to the relocation-induced biases. The impact of the MASH-type homogenization on estimates of climate extremes in the daily temperature series is also discussed.