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

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 re...

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Bibliographic Details
Published in:PloS one 2017-12, Vol.12 (12), p.e0190115-e0190115
Main Authors: Fuller, Robert William, Wong, Tony E, Keller, Klaus
Format: Article
Language:English
Subjects:
Analysis
Antarctic ice sheet
Assessments
Bayesian analysis
Biology and Life Sciences
Climate change
Conditional probability
Earth Sciences
Environmental policy
Estimates
Floods
Global temperatures
Ice
Ice sheets
Influence
Inversion
Mathematical models
People and Places
Physical Sciences
Polar environments
Probability
Sea level
Sea level changes
Shoreline protection
Statistical analysis
Uncertainty
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0190115