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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 2xCO2forcing.
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Bayesian History Matching Applied to the Calibration of a Gravity Wave Parameterization
Abstract Breaking atmospheric gravity waves (GWs) in the tropical stratosphere are essential in driving the roughly 2‐year oscillation of zonal winds in this region known as the Quasi‐Biennial Oscillation (QBO). As Global Climate Models (GCM)s are not typically able to directly resolve the spectrum of waves required to drive the QBO, parameterizations are necessary. Such parameterizations often require knowledge of poorly constrained physical parameters. In the case of the spectral gravity parameterization used in this work, these parameters are the total equatorial GW stress and the half width of phase speed distribution. Radiosonde observations are used to obtain the period and amplitude of the QBO, which are compared against values obtained from a GCM. We utilize two established calibration techniques to obtain estimates of the range of plausible parameter values: History matching & Ensemble Kalman Inversion (EKI). History matching is found to reduce the size of the initial range of plausible parameters by a factor of 98%, requiring only 60 model integrations. EKI cannot natively provide any uncertainty quantification but is able to produce a single best estimate of the calibrated values in 25 integrations. When directly comparing the approaches using the Calibrate, Emulate, Sample method to produce a posterior estimate from EKI, history matching produces more compact posteriors with fewer model integrations at lower ensemble sizes compared to EKI; however, these differences become less apparent at higher ensemble sizes.
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- Award ID(s):
- 2004492
- PAR ID:
- 10500428
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 16
- Issue:
- 4
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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