Parameter uncertainty quantification in an idealized GCM with a seasonal cycle

TL;DR

This paper introduces a Bayesian calibration method leveraging the seasonal cycle for uncertainty quantification in an idealized GCM, significantly improving parameter estimates and reducing prediction uncertainty.

Contribution

It develops a novel seasonal cycle-based calibration and UQ approach using the calibrate-emulate-sample method in an idealized GCM setting.

Findings

Seasonally averaged statistics reduce calibration error by up to ten times.

Posterior distributions become narrower by factors of two to five.

Uncertainty in climate predictions decreases with seasonal cycle-based calibration.

Abstract

Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude, compared with anthropogenic climate change, in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof-of-concept with an idealized general circulation model (GCM). Uncertainty quantification is performed using the calibrate-emulate-sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect-model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the size of the parameter posterior distributions leads to a reduction in the uncertainty of climate model predictions.