Bias correction of General Circulation Model (GCM) is now an essential part of climate change studies. However, the climate change trend has been overlooked in majority of bias correction approaches. Here, a novel signal processing‐based approach for correcting systematic biases in the time‐varying trend of GCM simulations is proposed. The approach corrects for systematic deviations in spectral attributes of raw GCM simulations using discrete wavelet transforms. The order one and two moments of the underlying trend represented by the lowest frequency of wavelet component are corrected to ensure continuity in the corrected time series from the current to the future simulation period. The approach is applied to correct two data sets that exhibit opposite time‐varying trends representing the global mean sea level (GMSL) and the Arctic sea‐ice extent. Results indicate that bias in trend is corrected, while continuity in time and observed variability at all frequencies in current climate simulations are maintained.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.