Correcting Systematic Bias in Climate Model Simulations in the Time-Frequency Domain

Abstract

Systematic biases in General Circulation Model (GCM) simulations require some adjustment before their use in change assessment and adaptation management studies. GCM simulations of the Coupled Model Intercomparison Project 6, although outperform the previous generations of GCMs, exhibit persistent biases in magnitude, variability, and frequency across a range of variables of interest. Here, we propose a novel continuous wavelet-based bias correction (CWBC) approach to address such biases in the time-frequency domain. The correction focuses on the magnitude and frequency of the modeled time series, as interpreted via the time-varying spectrum ascertained using the continuous wavelet transform. The approach is applied to correct systematic biases in the time series of Niño 3.4 sea surface temperature and Arctic sea-ice extent. The application of CWBC successfully reproduces observed attributes in the bias-corrected time series of both climate variables for the current climate simulation along with providing a sensible projection for the future.