Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers

Abstract

Variability in oceanic conditions directly impacts ice loss from marine outlet glaciers in Greenland, and thus influences 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 output, we apply: a bias correction using ocean reanalysis, an extrapolation accounting for on-shelf ocean dynamics, and stochastic time series models to generate realizations of natural variability. The bias-correction reduces model errors by $\sim$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, the characteristic timescale, and the amplitude of natural variability of bias-corrected AOGCMs, but at negligible computational expense. We demonstrate the efficiency of this method by processing $>$6000 time series of ocean forcing for $>$200 marine-terminating glacier locations in Greenland 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.

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

“…All code (python scripts) to reproduce the method described in this study are available as a Zenodo repository (Verjans, 2023): https://doi.org/10.5281/zenodo.7808874. The repository includes all intermediary and final output files for member r1 of MIROC-ES2L under scenario ssp585 as an example.…”

“…In addition, Figures 15 and 16 were replaced to include small changes because the correlation between the fit residuals is very close to the correlation between the TF' time series themselves. Finally, the code and datasets on the Zenodo repository were updated (Verjans et al, 2023). The updated Zenodo repository is a new version (Version 2.0) of the original one (Version 1.0), and is directly accessible both from the original DOI as well as from the new DOI: https://doi.org/10.5281/ zenodo.7931326.…”

Biases in ice sheet models from missing noise-induced drift

“…We run ensembles of ten member simulations each, applying stochastic variability separately in SMB, calving rate, and subglacial water pressure, and we quantify the role of each forcing in setting the ice sheet mean state. Realistic stochastic parameterizations for SMB and ocean thermal forcing (which determines frontal melt) have been described in previous studies (Ultee et al, 2023;Verjans et al, 2023). In this study, for ease of interpretability, we apply simple white noise to different forcing variables with mean that remains constant in time and equal to deterministic steady-state values.…”