and adjustment of the data, is very liberal in that it cannot distinguish between artificial and natural variability. The other, which incorporates station history metadata, is very conservative in that it adjusts only for artificial changes which are identified with a high degree of confidence. These experiments demonstrate that adjustments for change-points can yield very different time series and trends, depending on the scheme used to make adjustment and the manner in which it is implemented. This is illustrated in Figure 8.1, which shows mid-tropospheric (500 hPa) temperature trends from twelve stations operated by the Australian Bureau of Meteorology (Gaffen et al., 2000). The trends in the original data for the period 195995 show warming of between 0.05 and 0.71 °C/decade. The data from seven stations were adjusted due to a step-like warming of approximately 0.75 °C associated with a 1979 change in radiosonde type. The effect of the adjustment is to substantially reduce the trends and in some cases to change the warming to a cooling.
Model-based reanalyses (see the previous discussion on gridding radiosonde data) offer a further potential means of radiosonde temperature bias detection and removal through comparisons with first-guess fields.
Each of these strategies for radiosonde data adjustment, except the last one, depends to some degree on metadatainformation about the history of instruments and observing practices at each station. Despite recent efforts to compile and digitize global radiosonde metadata (Gaffen, 1993, 1996), there are gaps and uncertainties in the historical information. Current efforts to collect and maintain metadata archives are minimal and should be enhanced.break