A potential disintegration of the West Antarctic Ice Sheet: Implications for economic analyses of climate policy

D. Diaz and K. Keller

The American Economic Review (2016)

DOI: 10.1257/aer.p20161103

Key objectives of international greenhouse gas (GHG) policy are to “prevent dangerous anthropogenic interference with the climate system” and “enable economic development to proceed in a sustainable manner” (UNFCCC, 1992). One common interpretation of “dangerous anthropogenic interference” is to trigger a climate threshold or “tipping point” response – a nonlinear shift in the Earth system with the potential for abrupt, irreversible, or hysteresis effects (e.g., Alley et al., 2003). Examples of possible threshold responses include a disruption of the oceanic thermohaline circulation, sudden methane releases from the oceans or permafrost, or disintegrations of the Greenland or Antarctic ice sheets. The geological record shows that the Earth system can show such threshold responses, but the mechanisms, dynamics, and sensitivities are deeply uncertain (Alley et al., 2003). A sound representation of these potential climate threshold responses and their consequences in integrated assessment models (IAMs) is important, for example, given the salience to agreed-upon policy objectives. IAMs are simplified representations of the coupled natural and human systems used to evaluate climate change scenarios and to inform policy decisions (e.g., by computing the US government’s social cost of carbon (SCC) estimate). Because IAMs analyses face severe challenges (discussed below) in representing these complex and uncertain thresholds, decision-relevant metrics like the SCC may be biased (Stern, 2013). Here we explore one such threshold response, a potential disintegration of the West Antarctic Ice Sheet (WAIS) and consequent sea-level rise (SLR). We review current analytical approaches and the scientific understanding of WAIS, identify key methodological and conceptual issues, and demonstrate avenues to address some of them through a stochastic hazard IAM framework that combines emulation, expert knowledge, and learning. We conclude with a discussion of challenges and research needs.

model output and code archive: https://github.com/delavane/DICEWAIS

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