Introduction
About
This Python module and the provided data structures are designed to help minimize discrepancies between modeled and observed climate data of different time periods. Data from past periods are used to adjust variables from current and future time series so that their distributional properties approximate possible actual values.
Fig 1: Schematic representation of a bias adjustment procedure
In this way, for example, modeled data, which on average represent values that are too cold, can be bias-corrected by applying an adjustment procedure. The following figure shows the observed, the modeled, and the bias-corrected values. It is directly visible that the delta adjusted time series (\(T^{*DM}_{sim,p}\)) are much more similar to the observed data (\(T_{obs,p}\)) than the raw modeled data (\(T_{sim,p}\)).
Fig 2: Temperature per day of year in modeled, observed and bias-adjusted climate data
Available Methods
python-cmethods provides the following bias correction techniques:
Linear Scaling
Variance Scaling
Delta Method
Quantile Mapping
Detrended Quantile Mapping
Quantile Delta Mapping
Please refer to the official documentation for more information about these methods as well as sample scripts: https://python-cmethods.readthedocs.io/en/stable/
Except for the variance scaling, all methods can be applied on stochastic and non-stochastic climate variables. Variance scaling can only be applied on non-stochastic climate variables.
Non-stochastic climate variables are those that can be predicted with relative certainty based on factors such as location, elevation, and season. Examples of non-stochastic climate variables include air temperature, air pressure, and solar radiation.
Stochastic climate variables, on the other hand, are those that exhibit a high degree of variability and unpredictability, making them difficult to forecast accurately. Precipitation is an example of a stochastic climate variable because it can vary greatly in timing, intensity, and location due to complex atmospheric and meteorological processes.
Except for the detrended quantile mapping (DQM) technique, all methods can be applied to single and multidimensional data sets. The implementation of DQM to 3-dimensional data is still in progress.
For any questions – please open an issue at https://github.com/btschwertfeger/python-cmethods/issues. Examples can be found in the python-cmethods repository and of course within this documentation.
References
Schwertfeger, Benjamin Thomas and Lohmann, Gerrit and Lipskoch, Henrik (2023) “Introduction of the BiasAdjustCXX command-line tool for the application of fast and efficient bias corrections in climatic research”, SoftwareX, Volume 22, 101379, ISSN 2352-7110, (https://doi.org/10.1016/j.softx.2023.101379)
Schwertfeger, Benjamin Thomas (2022) “The influence of bias corrections on variability, distribution, and correlation of temperatures in comparison to observed and modeled climate data in Europe” (https://epic.awi.de/id/eprint/56689/)
Linear Scaling and Variance Scaling based on: Teutschbein, Claudia and Seibert, Jan (2012) “Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods” (https://doi.org/10.1016/j.jhydrol.2012.05.052)
Delta Method based on: Beyer, R. and Krapp, M. and Manica, A.: “An empirical evaluation of bias correction methods for palaeoclimate simulations” (https://doi.org/10.5194/cp-16-1493-2020)
Quantile and Detrended Quantile Mapping based on: Alex J. Cannon and Stephen R. Sobie and Trevor Q. Murdock “Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?” (https://doi.org/10.1175/JCLI-D-14-00754.1)
Quantile Delta Mapping based on: Tong, Y., Gao, X., Han, Z. et al. “Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods”. Clim Dyn 57, 1425–1443 (2021). (https://doi.org/10.1007/s00382-020-05447-4)