Bias Correction and Statistical Modeling of Variable Oceanic Forcing of Greenland Outlet Glaciers.

Variability in oceanic conditions directly impacts ice loss from marine outlet glaciers in Greenland, influencing the ice sheet mass balance. Oceanic conditions are available from Atmosphere‐Ocean Global Climate Model (AOGCM) output, but these models require extensive computational resources and lack the fine resolution needed to simulate ocean dynamics on the Greenland continental shelf and close to glacier marine termini. Here, we develop a statistical approach to generate ocean forcing for ice sheet model simulations, which incorporates natural spatiotemporal variability and anthropogenic changes. Starting from raw AOGCM ocean heat content, we apply: (a) a bias‐correction using ocean reanalysis, (b) an extrapolation accounting for on‐shelf ocean dynamics, and (c) stochastic time series models to generate realizations of natural variability. The bias‐correction reduces model errors by ∼25% when compared to independent in‐situ measurements. The bias‐corrected time series are subsequently extrapolated to fjord mouth locations using relations constrained from available high‐resolution regional ocean model results. The stochastic time series models reproduce the spatial correlation, characteristic timescales, and the amplitude of natural variability of bias‐corrected AOGCMs, but at negligible computational expense. We demonstrate the efficiency of this method by generating >6,000 time series of ocean forcing for >200 Greenland marine‐terminating glacier locations until 2100. As our method is computationally efficient and adaptable to any ocean model output and reanalysis product, it provides flexibility in exploring sensitivity to ocean conditions in Greenland ice sheet model simulations. We provide the output and workflow in an open‐source repository, and discuss advantages and future developments for our method. Plain Language Summary: Model simulations of the Greenland ice sheet (GrIS) require knowledge of ocean conditions. The evolution of ocean conditions has a strong impact on ice sheet model predictions, as there are more than 200 glaciers in Greenland flowing directly into the ocean. However, modeling oceanic forcing is difficult. The state‐of‐the‐art approach is to use output from Atmosphere‐Ocean Global Climate Models (AOGCMs). But these models cannot accurately capture the ocean dynamics on the Greenland shelf, and they can show strong biases compared to observations. Furthermore, AOGCMs are computationally expensive, meaning that it is impossible to thoroughly characterize the uncertainty associated with the chaotic nature of climate. Here, we propose a procedure to bias‐correct and extrapolate oceanic output from AOGCMs. Our method exploits observational datasets, as well as available high‐resolution ocean model results. Using statistical models, we reproduce patterns of spatiotemporal ocean variability at low computational expense, and represent internal climate variability and global warming trends. The goal is to provide a scalable procedure to generate ocean forcing for long‐term GrIS model predictions. Key Points: We develop a statistical method to generate ocean forcing boundary conditions for Greenland ice sheet model simulationsThe method bias‐corrects and extrapolates global climate model output using reanalysis products and high‐resolution model resultsStochastic time series models reproduce the spatiotemporal variability of ocean conditions at negligible computational expense [ABSTRACT FROM AUTHOR]