Objective Climate Model Predictions Using Jeffreys' Prior: the General Multivariate Normal Case

TL;DR

This paper derives formulas for objective climate predictions using Jeffreys' Prior under a general multivariate normal model, accounting for complex covariance structures to improve probabilistic climate forecasts.

Contribution

It extends previous work by providing explicit expressions for Jeffreys' Prior in the general multivariate normal case with non-constant, non-diagonal covariance matrices.

Findings

Derived complex matrix expressions for Jeffreys' Prior in the general case.

Simplified calculations for climate model output assuming normal distribution.

Enhanced framework for objective probabilistic climate forecasting.

Abstract

Objective probabilistic forecasts of future climate that include parameter uncertainty can be made by using the Bayesian prediction integral with the prior set to Jeffreys' Prior. The calculations involved in determining the prior can then be simplified by making parametric assumptions about the distribution of the output from the climate model. The most obvious assumption to make is that the climate model output is normally distributed, in which case evaluating the prior becomes a question of evaluating gradients in the parameters of the normal distribution. In previous work we have considered the special cases of diagonal (but not constant) covariance matrix, and constant (but not diagonal) covariance matrix. We now derive expressions for the general multivariate normal distribution, with non-constant non-diagonal covariance matrix. The algebraic manipulation required is more complex than for the special cases, and involves some slightly esoteric matrix operations including taking the expectation of a vector quadratic form and differentiating the determinants, traces and inverses of matrices.