Sensitivity of of coupled climate and ice sheet of modern Greenland to atmospheric, snow and ice sheet parameters

As part of a project working to improve coupled climate-ice sheet modelling by studying the response of ice sheets to changes in climate across different periods since the Last Glacial Maximum, we present an analysis of an ensemble of coupled climate and ice sheet simulations of the modern Greenland using the FAMOUS-BISICLES model and statistical emulation.

FAMOUS-BISICLES, a variant of FAMOUS-ice (Smith et al., 2021a), is a low resolution (7.5°X5°) global climate model that is two-way coupled to a higher resolution (minimum grid spacing of 1.2 km) adaptive mesh ice sheet model, BISICLES. It uses a system of elevation classes to downscale the lower resolution atmospheric variables onto the ice sheet grid and calculates surface mass balance using a multilayer snow model. FAMOUS-ice is computationally affordable enough to simulate the millennial evolution of the coupled climate-ice sheet system as well as to run large ensembles of simulations. It has also been shown to simulate Greenland well in previous work using the Glimmer shallow ice model (Gregory et al., 2020).

The ice sheet volume and area are sensitive to a number of parametrisations related to atmospheric and snow surface processes and ice sheet dynamics. Based on that, we designed a perturbed parameters ensemble using a Latin Hypercube sampling technique and ran simulations with climate forcings appropriate for the late 20th century.

Gaussian process emulation allows us explore parameter space in a more systematic and faster way than with more complex earth system models and make predictions at input parameter values that are not evaluated in the simulations. We find that the mass balance is most correlated to three parameters:

• n, the exponent in Glen’s flow law, and beta, the coefficient of the basal drag law, both influencing the amount of ice lost through discharge
• rho_threshold, a parameter setting the minimum value the dense firn albedo can possibly reach

Finally, using a history matching approach, we built an implausibility metric (based on surface mass balance, ice volume loss, near-surface and sea-surface temperature) to identify the regions of the parameter space that produce plausible runs.