Monte Carlo Drift Correction – Quantifying the Drift Uncertainty of Global Climate Models

Abstract. Global climate models are susceptible to drift, causing spurious trends in output variables. Drift is often corrected using data from a control simulation. However, internal climate variability within the control simulation introduces uncertainty to the drift correction process. To quantify this drift uncertainty, we develop a probabilistic technique: Monte Carlo drift correction (MCDC). MCDC involves random sampling of the control time series. We apply MCDC to an ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that drift correction partially addresses a problem related to drift: energy non-conservation. Nevertheless, the energy balance of several models remains suspect. We quantify the drift uncertainty of global quantities associated with energy balance and thermal expansion of the ocean. When correcting drift in a cumulatively-integrated energy flux, we find that it is preferable to integrate the flux before correcting the trend: an alternative method would be to correct the bias before integrating the flux, but this alternative method amplifies the drift uncertainty by up to an order of magnitude. We find that drift uncertainty is often smaller than other sources of uncertainty: for thermosteric sea-level rise projections for the 2090s, ensemble-mean drift uncertainty (9 mm) is an order of magnitude smaller than scenario uncertainty (138 mm) and model uncertainty (98 mm). However, drift uncertainty may dominate time series that have weak trends: for historical thermosteric sea-level rise since the 1850s, ensemble-mean drift uncertainty is 15 mm, which is of comparable magnitude to the impact of omitting volcanic forcing in control simulations. Therefore, drift uncertainty may influence comparisons between historical simulations and observation-based estimates of thermosteric sea-level rise. When evaluating and analysing global climate model data that are susceptible to drift, researchers should consider drift uncertainty.