Bayesian Functional Emulation of CO2 Emissions on Future Climate Change Scenarios

TL;DR

This paper introduces a Bayesian functional emulator for climate-economy models, enabling continuous-time evaluation of CO2 emissions projections in future climate scenarios.

Contribution

It develops a novel Bayesian hierarchical functional regression framework with autoregressive covariance modeling for climate model emulation.

Findings

Effective Bayesian inference on emulator parameters.

Functional framework allows continuous-time analysis.

Improved modeling of temporal dependencies in climate simulations.

Abstract

We propose a statistical emulator for a climate-economy deterministic integrated assessment model ensemble, based on a functional regression framework. Inference on the unknown parameters is carried out through a mixed effects hierarchical model using a fully Bayesian framework with a prior distribution on the vector of all parameters. We also suggest an autoregressive parameterization of the covariance matrix of the error, with matching marginal prior. In this way, we allow for a functional framework for the discretized output of the simulators that allows their time continuous evaluation.