A Signal Processing Approach to Correct Systematic Bias in Trend and Variability in Climate Model Simulations

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. Plain Language Summary GCMs are an essential tool to assess climate change impacts. However, they exhibit systematic biases which restrict their direct use. A wide range of alternatives for correcting biases has been proposed. Most such approaches are unable to correct biases in trend and variability attributes together. When a variability correction is applied, it reduces biases in trend, however, introduces a discontinuity in trend between current and future climate bias‐corrected simulations. We present an approach for correcting systematic biases in trend and variability to ensure continuity of GCM simulations from the current to the future. The application of our approach to two data sets exhibiting opposite trends ‐ the GMSL and the Arctic sea ice extent ‐ shows a remarkable improvement that maintains continuity overtime, corrects bias in trends, and preserves observed variability across the frequency spectrum in current period GCM simulations. Key Points Tradition alternatives for bias correction distort continuity in corrected series A discrete wavelet transform based signal processing alternative for correcting trend and variability bias is proposed The approach is demonstrated to correct trends in global mean sea level and Arctic sea ice extent simulations for the future