An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)
Item
Title (Dublin Core)
An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)
Description (Dublin Core)
<p>Uncertain parameters in physical parameterizations of
general circulation models (GCMs) greatly impact model performance. In
recent years, automatic parameter optimization has been introduced for
tuning model performance of GCMs, but most of the optimization methods are
unconstrained optimization methods under a given performance indicator.
Therefore, the calibrated model may break through essential constraints that
models have to keep, such as the radiation balance at the top of the model. The
radiation balance is known for its importance in the conservation of model
energy. In this study, an automated and efficient parameter optimization
with the radiation balance constraint is presented and applied in the
Community Atmospheric Model (CAM5) in terms of a synthesized performance
metric using normalized mean square error of radiation, precipitation,
relative humidity, and temperature. The tuned parameters are from the
parameterization schemes of convection and cloud. The radiation constraint
is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"><sup>−2</sup></span>. Results show that the synthesized performance under the optimal
parameters is 6.3 % better than the control run (CNTL) and the
radiation imbalance is as low as 0.1 W m<span class="inline-formula"><sup>−2</sup></span>. The proposed method
provides an insight for physics-guided optimization, and it can be easily
applied to optimization problems with other prerequisite constraints in
GCMs.</p>
general circulation models (GCMs) greatly impact model performance. In
recent years, automatic parameter optimization has been introduced for
tuning model performance of GCMs, but most of the optimization methods are
unconstrained optimization methods under a given performance indicator.
Therefore, the calibrated model may break through essential constraints that
models have to keep, such as the radiation balance at the top of the model. The
radiation balance is known for its importance in the conservation of model
energy. In this study, an automated and efficient parameter optimization
with the radiation balance constraint is presented and applied in the
Community Atmospheric Model (CAM5) in terms of a synthesized performance
metric using normalized mean square error of radiation, precipitation,
relative humidity, and temperature. The tuned parameters are from the
parameterization schemes of convection and cloud. The radiation constraint
is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m<span class="inline-formula"><sup>−2</sup></span>. Results show that the synthesized performance under the optimal
parameters is 6.3 % better than the control run (CNTL) and the
radiation imbalance is as low as 0.1 W m<span class="inline-formula"><sup>−2</sup></span>. The proposed method
provides an insight for physics-guided optimization, and it can be easily
applied to optimization problems with other prerequisite constraints in
GCMs.</p>
Creator (Dublin Core)
L. Wu
T. Zhang
Y. Qin
W. Xue
Subject (Dublin Core)
Geology
QE1-996.5
Publisher (Dublin Core)
Copernicus Publications
Date (Dublin Core)
2020-01-01T00:00:00Z
Type (Dublin Core)
article
Identifier (Dublin Core)
10.5194/gmd-13-41-2020
1991-959X
1991-9603
https://doaj.org/article/1bafd660d67c4a959915e67743c1568c
Source (Dublin Core)
Geoscientific Model Development, Vol 13, Pp 41-53 (2020)
Language (Dublin Core)
EN
Relation (Dublin Core)
https://www.geosci-model-dev.net/13/41/2020/gmd-13-41-2020.pdf
https://doaj.org/toc/1991-959X
https://doaj.org/toc/1991-9603
Provenance (Dublin Core)
Journal Licence: CC BY