Calibration and Uncertainty Quantification of a Gravity Wave Parameterization: A Case Study of the Quasi‐Biennial Oscillation in an Intermediate Complexity Climate Model

Laura Mansfield, Aditi Sheshadri

Journal of Advances in Modeling Earth Systems · 27 October 2022 · Stanford University

In one sentence. Calibrate-emulate-sample with a Gaussian process emulator quantifies gravity wave parameterization uncertainty, showing QBO parametric uncertainty comparable to internal variability under 2xCO2.

Abstract

Abstract The drag due to breaking atmospheric gravity waves plays a leading order role in driving the middle atmosphere circulation, but as their horizontal wavelength range from tens to thousands of kilometers, part of their spectrum must be parameterized in climate models. Gravity wave parameterizations prescribe a source spectrum of waves in the lower atmosphere and allow these to propagate upwards until they either dissipate or break, where they deposit drag on the large‐scale flow. These parameterizations are a source of uncertainty in climate modeling which is generally not quantified. Here, we explore the uncertainty associated with a non‐orographic gravity wave parameterization given an assumed parameterization structure within a global climate model of intermediate complexity, using the Calibrate, Emulate and Sample (CES) method. We first calibrate the uncertain parameters that define the gravity wave source spectrum in the tropics, to obtain climate model settings that are consistent with properties of the primary mode of tropical stratospheric variability, the Quasi‐Biennial Oscillation (QBO). Then we use a Gaussian process emulator to sample the calibrated distribution of parameters and quantify the uncertainty of these parameter choices. We find that the resulting parametric uncertainties on the QBO period and amplitude are of a similar magnitude to the internal variability under a 2xCO2 forcing.

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