Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses

Robert William Fuller, Tony E. Wong, Klaus Keller

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections.

Original languageEnglish (US)
Article numbere0190115
JournalPloS one
Volume12
Issue number12
DOIs
StatePublished - Dec 1 2017

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Ice Cover
Ice
Uncertainty
ice
Oceans and Seas
Sea level
Probability distributions
Statistical Models
uncertainty
probability distribution
sea level
Electric fuses
Temperature
stems

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

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abstract = "The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections.",
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Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses. / Fuller, Robert William; Wong, Tony E.; Keller, Klaus.

In: PloS one, Vol. 12, No. 12, e0190115, 01.12.2017.

Research output: Contribution to journalArticle

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AU - Wong, Tony E.

AU - Keller, Klaus

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