Maximum-Entropy Weighting of Multi-Component Earth Climate Models

TL;DR

This paper introduces a maximum entropy framework for selecting the most representative Earth climate model from multiple options, optimizing model synthesis under various constraints to improve climate projection accuracy.

Contribution

It develops novel maximum entropy methods for synthesizing climate models, reducing computational effort by identifying the most probable models without exhaustive calculations.

Findings

Framework effectively identifies representative models

Finite-time cost limits for model modifications derived

Approaches accommodate multiple constraints

Abstract

A maximum entropy-based framework is presented for the synthesis of projections from multiple Earth climate models. This identifies the most representative (most probable) model from a set of climate models -- as defined by specified constraints -- eliminating the need to calculate the entire set. Two approaches are developed, based on individual climate models or ensembles of models, subject to a single cost (energy) constraint or competing cost-benefit constraints. A finite-time limit on the minimum cost of modifying a model synthesis framework, at finite rates of change, is also reported.