Known Issues

• Since the scaling methods implemented so far scale by default over the mean values of the respective months, unrealistic long-term mean values may occur at the month transitions. This can be prevented either by selecting group='time.dayofyear'. Alternatively, it is possible not to scale using long-term mean values, but using a 31-day interval, which takes the 31 surrounding values over all years as the basis for calculating the mean values. This is not yet implemented in this module, but is available in the command-line tool BiasAdjustCXX.

• Using this module or especially Python to apply bias correction techniques on large data sets can be a very time-consuming task. So this module is more about showing how to apply different methods on climate data and maybe even to bias-correct small data sets. When it comes to large ensembles it is preferred to use the way more efficient tool BiasAdjustCXX. A speed comparison between python-cmethods, BiasAdjustCXX, and xclim was made this tool comparison. Since the development of python-cmethods is continuing, speed improvements have been done since the last bench.