From Many to One: Consensus Inference in a MIP
Publication Name
Geophysical Research Letters
Abstract
A Model Intercomparison Project (MIP) consists of teams who estimate the same underlying quantity (e.g., temperature projections to the year 2070). A simple average of the ensemble of the teams' outputs gives a consensus estimate, but it does not recognize that some outputs are more variable than others. Statistical analysis of variance (ANOVA) models offer a way to obtain a weighted frequentist consensus estimate of outputs with a variance that is the smallest possible. Modulo dependence between MIP outputs, the ANOVA approach weights a team's output inversely proportional to its variance, from which optimally weighted estimates follow. ANOVA weights can also provide a prior distribution for Bayesian Model Averaging of the MIP outputs when external evaluation data are available. We use a MIP of carbon-dioxide-flux inversions to illustrate the ANOVA-based weighting and subsequent frequentist consensus inferences.
Open Access Status
This publication may be available as open access
Volume
49
Issue
14
Article Number
e2022GL098277
Funding Number
20‐OCOST20‐0004
Funding Sponsor
National Aeronautics and Space Administration