Assessing the Impact of Retreat Mechanisms in a Simple Antarctic Ice Sheet Model Using Bayesian Calibration

The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver of sea-level changes. Anthropogenic climate change may drive a sizeable AIS tipping point response with subsequent increases in coastal flooding risks. Many studies analyzing flood risks use simple models to project the future responses of AIS and its sea-level contributions. These analyses have provided important new insights, but they are often silent on the effects of potentially important processes such as Marine Ice Sheet Instability (MISI) or Marine Ice Cliff Instability (MICI). These approximations can be well justified and result in more parsimonious and transparent model structures. This raises the question of how this approximation impacts hindcasts and projections. Here, we calibrate a previously published and relatively simple AIS model, which neglects the effects of MICI and regional characteristics, using a combination of observational constraints and a Bayesian inversion method. Specifically, we approximate the effects of missing MICI by comparing our results to those from expert assessments with more realistic models and quantify the bias during the last interglacial when MICI may have been triggered. Our results suggest that the model can approximate the process of MISI and reproduce the projected median melt from some previous expert assessments in the year 2100. Yet, our mean hindcast is roughly 3/4 of the observed data during the last interglacial period and our mean projection is roughly 1/6 and 1/10 of the mean from a model accounting for MICI in the year 2100. These results suggest that missing MICI and/or regional characteristics can lead to a low-bias during warming period AIS melting and hence a potential low-bias in projected sea levels and flood risks.Comment: v1: 16 pages, 4 figures, 7 supplementary files; v2: 15 pages, 4 figures, 7 supplementary files, corrected typos, revised title, updated according to revisions made through publication proces

A tighter constraint on Earth-system sensitivity from long-term temperature and carbon-cycle observations

Developing sound strategies to manage climate risks hinges critically on Earth-system properties, including the Earth-system sensitivity (ESS). Current ESS estimates are subject to large and deep uncertainties. Long-term carbon cycle models can provide a useful avenue to constrain ESS, but previous efforts either lack a formal data assimilation framework, or focus on discrete paleoevents. Here, we improve on ESS estimates by using a Bayesian approach to fuse deep-time paleoclimate CO2 and temperature data over the last 420 Myrs with a long-term carbon cycle model. Our best sensitivity estimate of 3.4 deg C (2.6-4.7 deg C; 5-95% range) shows a narrower range than previous assessments, implying increased learning. Our sensitivity analyses indicate that during the Cretaceous, a much weaker chemical weathering efficiency of gymnosperms and shift in the timing of gymnosperm- to angiosperm-dominated vegetation yield better agreement with temperature records. Research into improving the understanding about these plant-assisted weathering mechanisms hence provides potentially powerful avenues to further constrain this fundamental Earth-system property