Forum Replies Created
October 9, 2017 at 8:53 am in reply to: Downscaling data/indicators to higher resolution for test cases. #2376
the bias adjustment method can indeed affect the climate change signal, e.g. if there is a bias in the variance. Methods that adjust for this will also affect the climate change signal, which is why the results can differ between different methods.
It sounds like you are approaching this topic well by switching to “wet” pdfs, and by trying to remove trends. Perfectly retaining the change signal is probably difficult, but to significantly reduce the effect (as you state you have) might be enough, given any other improvement you might get by the full pdf adjustment.
PeterMay 11, 2016 at 12:48 pm in reply to: Downscaling data/indicators to higher resolution for test cases. #1226
I have used the qmap library before, and it works well (if you have timeseries and not just indicators). You can chose between fitting a function to your distribution, or using and empirical distribution.
The downscalingR package was new to me, but the methods seem straight forward. The “delta” and “scaling” methods should work well observational timeseries together with indicators as provided on the SWICCA portal.
PeterMay 5, 2016 at 3:19 am in reply to: Downscaling data/indicators to higher resolution for test cases. #1218
Yes, scaling of a local observed timeseries with some climate change factor is what is often called the delta change method. This is a kind of donwscaling of climate information, and would be suitable in this case.April 28, 2016 at 1:14 pm in reply to: Downscaling data/indicators to higher resolution for test cases. #1199
I wasn’t at the workshop, so I don’t know the practical examples you mention, so perhaps Ronald can address that. However, I’m a bit confused on whether you are discussing bias correction or downscaling? Bias correction would be to remove bias from a modeled timeseries, where a similar reference data exists. Statistical downscaling can use similar methods as used for bias correction, but then has the purpose of adjusting the statistical properties to mimic a higher resolution.
In SWICCA, the use is primarily of indicators, such as the average change in a variable. Thus, there are mostly no modeled timeseries that would require bias correction. Note also that the precipitation and temperature data used to force the hydrological models were already bias corrected within the IMPACT2C project.
Taking a local timeseries and scaling it with a climate change signal from the indicators can be considered a sort of downscaling. Is that what is discussed here? That could be done using e.g. the change in the mean, or the change in different moments, or percentiles of a distribution. Can you please clarify what you want to do? Perhaps with a practical example?