Separating radiative forcing by aerosol-cloud interactions and rapid cloud adjustments in the ECHAM-HAMMOZ aerosol-climate model using the method of partial radiative perturbations
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Using the method of offline radiative transfer modeling within the partial radiative perturbation (PRP) approach, the effective radiative forcing by aerosol–cloud interactions (ERFaci) in the ECHAM–HAMMOZ aerosol climate model is decomposed into a radiative forcing by anthropogenic cloud droplet number change and adjustments of the liquid water path and cloud fraction. The simulated radiative forcing by anthropogenic cloud droplet number change and liquid water path adjustment are of approximately equal magnitude at −0.52 and −0.53 W m−2, respectively, while the cloud-fraction adjustment is somewhat weaker at −0.31 W m−2 (constituting 38 %, 39 %, and 23 % of the total ERFaci, respectively); geographically, all three ERFaci components in the simulation peak over China, the subtropical eastern ocean boundaries, the northern Atlantic and Pacific oceans, Europe, and eastern North America (in order of prominence). Spatial correlations indicate that the temporal-mean liquid water path adjustment is proportional to the temporal-mean radiative forcing, while the relationship between cloud-fraction adjustment and radiative forcing is less direct. While the estimate of warm-cloud ERFaci is relatively insensitive to the treatment of ice and mixed-phase cloud overlying warm cloud, there are indications that more restrictive treatments of ice in the column result in a low bias in the estimated magnitude of the liquid water path adjustment and a high bias in the estimated magnitude of the droplet number forcing. Since the present work is the first PRP decomposition of the aerosol effective radiative forcing into radiative forcing and rapid cloud adjustments, idealized experiments are conducted to provide evidence that the PRP results are accurate. The experiments show that using low-frequency (daily or monthly) time-averaged model output of the cloud property fields underestimates the ERF, but 3-hourly mean output is sufficiently frequent.
Date Issued
2019-12-17
Date Acceptance
2019-09-23
Citation
Atmospheric Chemistry and Physics, 2019, 19 (24), pp.15415-15429
ISSN
1680-7316
Publisher
Copernicus Publications
Start Page
15415
End Page
15429
Journal / Book Title
Atmospheric Chemistry and Physics
Volume
19
Issue
24
Copyright Statement
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/ ).
the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/ ).
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000503444400004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Environmental Sciences
Meteorology & Atmospheric Sciences
Environmental Sciences & Ecology
TECHNICAL NOTE
MICROPHYSICS
SENSITIVITY
PARAMETERIZATION
PRECIPITATION
PERFORMANCE
FEEDBACKS
IMPACT
ALBEDO
GCM
Publication Status
Published