Is there an upper extent to systematic bias correction of climate model simulations? Application to low-frequency variability within the Niño3.4 region

Most systematic bias correction approaches which are developed based on the bias of the statistical properties of interest perform well to bias correct the current climate simulations with respect to observations. However, the significance of the application of systematic bias correction approaches on the raw output of climate model simulations remains a debate due to the unavailability of future climate observation to validate the approach.

The output of a recent ultra-high resolution climate model simulation, UHR-CESM, demonstrates the best performance to simulate variability of sea surface temperature (SST) in the tropical Pacific with an exception of a small bias in mean. This knowledge encouraged us to use the outputs of the model to represent the truth both in current and future climates. We use the output of the model in response to the current climate CO₂ concentration as the representative of the current climate. While the outputs of model simulation in response to doubling and quadrupling CO₂ concentrations are used as the representative of the truth of future climates.

We bias correct monthly SST simulations for 8 (eight) Coupled Model Intercomparison Project 6 (CMIP6) over the Niño 3.4 region having the same CO₂ concentration as our reference model using a novel time-frequency continuous wavelet-based bias correction (CWBC). The results show a nearly perfect correction of distributional, trend, and spectral attributes biases in the 8 (eight) climate model simulations in the current climate and a consistent reduction of the biases in the model simulation in response to doubled CO₂ concentration. Although the overall quality of the statistical attributes is improved after the application of bias correction in response to the more extreme change of quadrupled CO₂ concentration, a degradation in the spectral attributes is observed. It shows that a systematic bias correction approach has its upper limit. Therefore, while the application of bias correction approaches is recommended prior to the further use of raw climate model simulations, up to what extent future climate simulations are reliably bias corrected should be handled carefully.