Climate model data often show deviations from reference data, e.g. gridded observational or reanalysis data when assessed over a longer period of time, typically 30 years. For climate impact indicators that are sensitive to a threshold or for impact models calibrated on certain reference data, it is therefore important to bias adjust the climate model data. A commonly used family of bias adjustment methods is called quantile mapping that aim to adjust the quantile distribution of climate model data in a way that it becomes very similar to the quantile distribution of the reference data. In other words, they do not only adjust the mean of the climate model data but the full distribution and are therefore assumed to be better applicable for analysis of, e.g., extreme events.
The MultI-scale bias AdjuStment tool (MIdAS, Berg et al. 2022) is a semi-parametric quantile-mapping method. In contrast to the fully parametric methods, it does not pre-assume a certain statistical distribution for the data but uses an empirical spline-fit to describe the distribution of the data.
MIdAS works as follows:
- For every day of the year throughout the annual cycle, a quantile-quantile relation is estimated between model and reference data that falls between the day of the year plus/minus 15 days throughout the reference period. For e.g., for day of year 16, all January 1 to January 31 throughout the reference period are used to construct the quantile-quantile relation.
- The quantile-quantile relation is smoothed by a linear spline-fit. This is meant to reduce the impact of variability in the data, especially on the tails of the data.
- The quantile-quantile relation is applied to all data of the same day of the year throughout the whole climate projection time series.
Daily mean temperature
Daily minimum and maximum temperature
Berg, P., Bosshard, T., Yang, W., & Zimmermann, K. (2022). MIdASv0.2.1 – MultI-scale bias AdjuStment. Geoscientific Model Development, 15(15), 6165–6180. https://doi.org/10.5194/gmd-15-6165-2022
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geoscientific Model Development, 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019
Vrac, M., Noël, T., & Vautard, R. (2016). Bias correction of precipitation through singularity stochastic removal: Because occurrences matter. Journal of Geophysical Research, 121(10), 5237–5258. https://doi.org/10.1002/2015JD024511