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Empirical downscaling implies that large-scale climate information, for instance from a climate model, is downscaled with help from observations. Statistical relationships between the local scale observation and the large-scale model fields are identified for the historical time period. For future climate change the same empirical relationships are assumed to be valid which may not always be the case.
As empirical downscaling relies on observations it is required that such observations exist and that they are representative for the scales that should be addressed. A complication with the empirical downscaling methods is that downscaling is most often done one variable at the time. An implication is that time series of several variables may not be completely consistent.
It is also noted that the assumption that the empirical relationships are the same also in the future climate may not always be true. Examples when this assumption may be violated include areas where non-linear changes are seen. This could for instance involve areas with retreating snow cover where temperature distributions changes differently in their different parts (e.g. Kjellström, 2004). Another example would be areas where strong drying is seen that would exacerbate warming and thereby further increase the response.
It is recommended that users of empirically downscaled information carefully consider if such potential problems may have any implication on their applications.
What are advantages/disadvantages with dynamical/
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statistical downscaling?
A major advantage with empirical downscaling over dynamical downscaling it can be relatively easily used for downscaling large ensembles of climate model data requiring only limited computational capacity. The key drawback is that the empirical methods assumes that future relationship between local and large scales remain the same as in the historical climate. Dynamical downscaling, on the other hand, allows for such changes over time. Furthermore, regional models provide internally consistent climate states implying that several variables from the model can be used simultaneously.