Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data

Abstract

Abstract. Probabilistic predictions of the sea level contribution from Antarctica often have large uncertainty intervals. Calibration of model simulations with observations can reduce uncertainties and improve confidence in projections, particularly if this exploits as much of the available information as possible (such as spatial characteristics), but the necessary statistical treatment is often challenging and can be computationally prohibitive. Ice sheet models with sufficient spatial resolution to resolve grounding line evolution are also computationally expensive. Here we address these challenges by adopting and comparing dimension-reduced calibration approaches based on a principal component decomposition of the adaptive mesh model BISICLES. The effects model parameters have on these principal components are then gathered in statistical emulators to allow for smooth probability density estimates. With the help of a published perturbed parameter ice sheet model ensemble of the Amundsen Sea Embayment (ASE), we show how the use of principal components in combination with spatially resolved observations can improve probabilistic calibrations. In synthetic model experiments (calibrating the model with altered model results) we can identify the correct basal traction and ice viscosity scaling parameters as well as the bedrock map with spatial calibrations. In comparison a simpler calibration against an aggregated observation, the net sea level contribution, imposes only weaker constraints by allowing a wide range of basal traction and viscosity scaling factors. Uncertainties in sea level rise contribution of 50-year simulations from the current state of the ASE can be reduced with satellite observations of recent ice thickness change by nearly 90 %; median and 90 % confidence intervals are 18.9 [13.9, 24.8] mm SLE (sea level equivalent) for the proposed spatial calibration approach, 16.8 [7.7, 25.6] mm SLE for the net sea level calibration and 23.1 [−8.4, 94.5] mm SLE for the uncalibrated ensemble. The spatial model behaviour is much more consistent with observations if, instead of Bedmap2, a modified bedrock topography is used that most notably removes a topographic rise near the initial grounding line of Pine Island Glacier. The ASE dominates the current Antarctic sea level contribution, but other regions have the potential to become more important on centennial scales. These larger spatial and temporal scales would benefit even more from methods of fast but exhaustive model calibration. Applied to projections of the whole Antarctic ice sheet, our approach has therefore the potential to efficiently improve our understanding of model behaviour, as well as substantiating and reducing projection uncertainties.

Quantifying the potential future contribution to global mean sea level from the Filchner–Ronne basin, Antarctica

“…Early estimates of uncertainties in projections of future sea level change from the Antarctic Ice Sheet were derived from sensitivity studies that evaluated a small sample of a parameter space directly in individual ice sheet models (e.g. De-Conto and Pollard, 2016;Winkelmann et al, 2012;Golledge et al, 2015;Ritz et al, 2015). Model intercomparison experiments have since been used to quantify uncertainties associated with differences in the implementation of physical processes between models, beginning with idealized set-ups (e.g.…”

“…MISMIP and MISMIP+; Pattyn et al, 2012;Cornford et al, 2015), and more recently on an ice sheet scale as part of the ISMIP6 project (Seroussi et al, 2020). Recently, the use of uncertainty quantification techniques has become more common for estimating uncertainties in projections of, for example, sea level rise, based on the current knowledge of uncertainties associated with model parameters or forcing functions (parametric uncertainty) (Edwards et al, 2019;Schlegel et al, 2018Schlegel et al, , 2015Bulthuis et al, 2019;Aschwanden et al, 2019;Nias et al, 2019;Wernecke et al, 2020). This includes techniques that weight model parameters and outputs according to some performance measures, to provide a probabilistic assessment of sea level change (Pollard et al, 2016;Ritz et al, 2015).…”

Global warming levels control time of emergence of Antarctic ice loss

“…Statistical emulation has been used in a number of studies investigating ice sheet sea level contribution and regional sea level change 6,9,18,19,58 . In this case, the statistical emulator is a regression model that is based on Gaussian Processes (GP).…”

“…The process-based ice sheet model simulations using the standard RCP2.6 and RCP8.5 forcings are then used to train a statistical emulator that employs Gaussian Process (GP) Regression (Fig 1). While previous studies have employed similar statistical techniques for determining the probabilistic sea level contribution of the ice sheet or ice sheet catchment for a given time period 9,18,19 , here we also consider the temporal evolution of the sea level contribution. In particular, the GP regression uses the 4 ice sheet model parameters, and a combination of i) the direct effect, ii) the cumulative effect, and iii) the committed effect of global warming as independent variables.…”

Deep Gaussian processes for biogeophysical parameter retrieval and model inversion

“…Gaussian Processes have provided very good results for retrieval in all Earth science domains, be it land and vegetation parameter retrieval [20,21,22,23], ocean and water bodies modeling [24,25], cryosphere ice sheet modeling and process emulation [26], or atmospheric parameter retrieval [27].…”