An assessment of key model parametric uncertainties in projections of Greenland Ice Sheet behavior

Abstract

Lack of knowledge about the values of ice sheet model input parameters introduces substantial uncertainty into projections of Greenland Ice Sheet contributions to future sea level rise. Computer models of ice sheet behavior provide one of several means of estimating future sea level rise due to mass loss from ice sheets. Such models have many input parameters whose values are not well known. Recent studies have investigated the effects of these parameters on model output, but the range of potential future sea level increases due to model parametric uncertainty has not been characterized. Here, we demonstrate that this range is large, using a 100-member perturbed-physics ensemble with the SICOPOLIS ice sheet model. Each model run is spun up over 125000 yr using geological forcings and subsequently driven into the future using an asymptotically increasing air temperature anomaly curve. All modeled ice sheets lose mass after 2005 AD. Parameters controlling surface melt dominate the model response to temperature change. After culling the ensemble to include only members that give reasonable ice volumes in 2005 AD, the range of projected sea level rise values in 2100 AD is ～40% or more of the median. Data on past ice sheet behavior can help reduce this uncertainty, but none of our ensemble members produces a reasonable ice volume change during the mid-Holocene, relative to the present. This problem suggests that the model's exponential relation between temperature and precipitation does not hold during the Holocene, or that the central-Greenland temperature forcing curve used to drive the model is not representative of conditions around the ice margin at this time (among other possibilities). Our simulations also lack certain observed physical processes that may tend to enhance the real ice sheet's response. Regardless, this work has implications for other studies that use ice sheet models to project or hindcast the behavior of the Greenland Ice Sheet

Inferred basal friction and surface mass balance of the Northeast Greenland Ice Stream using data assimilation of ICESat (Ice Cloud and land Elevation Satellite) surface altimetry and ISSM (Ice Sheet System Model)

“…Most attempts at assimilating surface altimetry so far have relied on indirect approaches. The first one is ensemble methods, where sub-sets of model runs that are compatible with present-day conditions of the ice sheet are down-selected (Applegate et al, 2012). This type of method does improve spin-ups and inform about the level of uncertainty inherent in the model runs, but does not yield information on the underlying boundary conditions, and potential corrections that have to be applied (within specific measurement error margins) for the model to converge to presentday conditions.…”

Oxygen isotope ratios in the shell of <i>Mytilus edulis</i>: archives of glacier meltwater in Greenland?

“…Uncertainties in model projections are still considerable (Applegate et al, 2012;IPCC, 2007). In order to reduce these uncertainties and validate models, it is vital to collect proxy data on past ice sheet behaviour, such as surface mass balance and runoff (Alley et al, 2010;Applegate et al, 2012;Hanna et al, 2011).…”

“…The natural variability in GrIS mass balance over time is reconstructed by means of modelling studies, using instrumental data (covering the most recent decades) and proxy data (Alley et al, 2010;Israelson et al, 1994). Uncertainties in model projections are still considerable (Applegate et al, 2012;IPCC, 2007). In order to reduce these uncertainties and validate models, it is vital to collect proxy data on past ice sheet behaviour, such as surface mass balance and runoff (Alley et al, 2010;Applegate et al, 2012;Hanna et al, 2011).…”

BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections

“…For example, they are used for uncertainty quantification (Bakker et al, 2017;Grinsted et al, 2010;Urban et al, 2014;Urban and Keller, 2010) and complex model emulation (Applegate et al, 2012;Bakker et al, 2016;Hartin et al, 2015;Meinshausen et al, 2011a), and are incorporated into integrated assessment models (Hartin et al, 2015;Meinshausen et al, 2011a).…”