• homogeneity;
  • climate data;
  • data adjustment techniques;
  • metadata


Long-term in situ observations are widely used in a variety of climate analyses. Unfortunately, most decade- to century-scale time series of atmospheric data have been adversely impacted by inhomogeneities caused by, for example, changes in instrumentation, station moves, changes in the local environment such as urbanization, or the introduction of different observing practices like a new formula for calculating mean daily temperature or different observation times. If these inhomogeneities are not accounted for properly, the results of climate analyses using these data can be erroneous. Over the last decade, many climatologists have put a great deal of effort into developing techniques to identify inhomogeneities and adjust climatic time series to compensate for the biases produced by the inhomogeneities. It is important for users of homogeneity-adjusted data to understand how the data were adjusted and what impacts these adjustments are likely to make on their analyses. And it is important for developers of homogeneity-adjusted data sets to compare readily the different techniques most commonly used today. Therefore, this paper reviews the methods and techniques developed for homogeneity adjustments and describes many different approaches and philosophies involved in adjusting in situ climate data. © 1998 Royal Meteorological Society