In this section
@inproceedings{Nowack:2019:10.5065/y82j-f154,
author = {Nowack, P and Ong, QYE and Braesicke, P and Haigh, J and Abraham, L and Pyle, J and Voulgarakis, A},
doi = {10.5065/y82j-f154},
pages = {263--268},
publisher = {UCAR},
title = {Machine learning parameterizations for ozone: climate model transferability},
url = {http://dx.doi.org/10.5065/y82j-f154},
year = {2019}
}
TY - CPAPER
AB - Many climate modeling studies have demon-strated the importance of two-way interactions betweenozone and atmospheric dynamics. However, atmosphericchemistry models needed for calculating changes in ozoneare computationally expensive. Nowack et al. [1] high-lighted the potential of machine learning-based ozoneparameterizations in constant climate forcing simulations,with ozone being predicted as a function of the atmo-spheric temperature state. Here we investigate the roleof additional time-lagged temperature information underpreindustrial forcing conditions. In particular, we testif the use of Long Short-Term Memory (LSTM) neuralnetworks can significantly improve the predictive skill ofthe parameterization. We then introduce a novel workflowto transfer the regression model to the new UK EarthSystem Model (UKESM). For this, we show for the firsttime how machine learning parameterizations could betransferred between climate models, a pivotal step tomaking any such parameterization widely applicable inclimate science. Our results imply that ozone parame-terizations could have much-extended scope as they arenot bound to individual climate models but, once trained,could be used in a number of different models. We hope tostimulate similar transferability tests regarding machinelearning parameterizations developed for other Earthsystem model components such as ocean eddy modeling,convection, clouds, or carbon cycle schemes.
AU - Nowack,P
AU - Ong,QYE
AU - Braesicke,P
AU - Haigh,J
AU - Abraham,L
AU - Pyle,J
AU - Voulgarakis,A
DO - 10.5065/y82j-f154
EP - 268
PB - UCAR
PY - 2019///
SP - 263
TI - Machine learning parameterizations for ozone: climate model transferability
UR - http://dx.doi.org/10.5065/y82j-f154
UR - http://hdl.handle.net/10044/1/75531
ER -