Maximum-entropy weighting of multiple earth climate models

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 finitetime limit on the minimum cost of modifying a model synthesis framework, at finite rates of change, is also reported.Keywords climate model · maximum entropy method · Boltzmann principle · thermodynamics · cost-benefit analysis · finite-time information limit 1 Introduction A major challenge facing humanity is the possibility of climate change due to human and/or natural forcings, and how best to respond in a rational and informed manner. To this end, detailed global circulation models (GCMs) have been developed to predict the behaviour of the Earth climate system (atmosphere and oceans), involving solution of the continuity, Navier-Stokes, angular momentum and energy equations and constitutive

Maximum Entropy Analysis of Flow Networks with Nonlinear Constraints

“…The method is demonstrated by application to several example systems, including a 327-node urban electrical power distribution system in Campbell, Australian Capital Territory, which contains distributed power sources. This study builds upon previous MaxEnt analyses of the steady state of flow and dissipative systems [8][9][10][11][12][13] and flow networks [14][15][16][17][18][19].…”

Control Volume Analysis, Entropy Balance and the Entropy Production in Flow Systems

“…Consider a container of N interacting molecules, for which it is infeasible to examine the allocation of individual molecules to energetic or other states. We therefore consider the canonical ensemble of all possible configurations of the system [65][66][67][68][69][70], in which replicas of the system are allocated to a coupled bivariate classification scheme according to their energy ǫ ij and volume V ij , where i and j respectively index the discrete energy and volume states of the ensemble. This is illustrated schematically in Figure 2.…”