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.