Abstract
Emergent constraints reduce uncertainties in future climate projections by the comparison with current climate and observations. However, previous methods for emergent constraints are limited to variables following normal or multivariate normal distributions. Here we devise a copula-based emergent constraint (CEC) framework that enables the flexible selection of marginal distribution functions and the combination of multiple constraints. The Markov chain Monte Carlo (MCMC) algorithm is applied to numerically estimate the posterior distribution derived from Bayes’ theorem. This new framework achieves narrower uncertainties in the projections of future global warming than previous approaches that assume normal distributions. Combining two constraints in northern and southern hemispheres further reduces uncertainties after the integration of different information. Due to the flexibility in distribution functions and constraint size, the CEC framework is applicable to more variables and interactions across various spheres of Earth’s system.
© 2025 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
