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Title: Distinct Patterns of Cloud Changes Associated with Decadal Variability and Their Contribution to Observed Cloud Cover Trends

Abstract

With the goal of understanding the relative roles of anthropogenic and natural factors in driving observed cloud trends, this study investigates cloud changes associated with decadal variability including the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). In the preindustrial simulations of CMIP5 global climate models (GCMs), the spatial patterns and the vertical structures of the PDO-related cloud cover changes in the Pacific are consistent among models. Meanwhile, the models show consistent AMO impacts on high cloud cover in the tropical Atlantic, subtropical eastern Pacific, and equatorial central Pacific, and on low cloud cover in the North Atlantic and subtropical northeast Pacific. The cloud cover changes associated with the PDO and the AMO can be understood via the relationships between large-scale meteorological parameters and clouds on interannual time scales. When compared to the satellite records during the period of 1983–2009, the patterns of total and low cloud cover trends associated with decadal variability are significantly correlated with patterns of cloud cover trends in ISCCP observations. On the other hand, the pattern of the estimated greenhouse gas (GHG)-forced trends of total cloud cover differs from that related to decadal variability, and may explain the positive trends in the subtropicalmore » southeast Pacific, negative trends in the midlatitudes, and positive trends poleward of 50°N/S. In most models, the magnitude of the estimated decadal variability contribution to the observed cloud cover trends is larger than that contributed by GHG, suggesting the observed cloud cover trends are more closely related to decadal variability than to GHG-induced warming.« less

Authors:
 [1]; ORCiD logo [1];  [2];  [3]
  1. Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
  2. Cloud Processes Research and Modeling Group, Lawrence Livermore National Laboratory, Livermore, California
  3. Department of Atmospheric Physics, Nanjing University, Nanjing, China
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1567716
Alternate Identifier(s):
OSTI ID: 1570423
Report Number(s):
LLNL-JRNL-752025
Journal ID: ISSN 0894-8755
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Published Article
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Name: Journal of Climate Journal Volume: 32 Journal Issue: 21; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; cloud cover; climate sensitivity; satellite observations; clouds; decadal variability; Pacific decadal oscillation

Citation Formats

Chen, Yong-Jhih, Hwang, Yen-Ting, Zelinka, Mark D., and Zhou, Chen. Distinct Patterns of Cloud Changes Associated with Decadal Variability and Their Contribution to Observed Cloud Cover Trends. United States: N. p., 2019. Web. doi:10.1175/JCLI-D-18-0443.1.
Chen, Yong-Jhih, Hwang, Yen-Ting, Zelinka, Mark D., & Zhou, Chen. Distinct Patterns of Cloud Changes Associated with Decadal Variability and Their Contribution to Observed Cloud Cover Trends. United States. https://doi.org/10.1175/JCLI-D-18-0443.1
Chen, Yong-Jhih, Hwang, Yen-Ting, Zelinka, Mark D., and Zhou, Chen. Fri . "Distinct Patterns of Cloud Changes Associated with Decadal Variability and Their Contribution to Observed Cloud Cover Trends". United States. https://doi.org/10.1175/JCLI-D-18-0443.1.
@article{osti_1567716,
title = {Distinct Patterns of Cloud Changes Associated with Decadal Variability and Their Contribution to Observed Cloud Cover Trends},
author = {Chen, Yong-Jhih and Hwang, Yen-Ting and Zelinka, Mark D. and Zhou, Chen},
abstractNote = {With the goal of understanding the relative roles of anthropogenic and natural factors in driving observed cloud trends, this study investigates cloud changes associated with decadal variability including the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). In the preindustrial simulations of CMIP5 global climate models (GCMs), the spatial patterns and the vertical structures of the PDO-related cloud cover changes in the Pacific are consistent among models. Meanwhile, the models show consistent AMO impacts on high cloud cover in the tropical Atlantic, subtropical eastern Pacific, and equatorial central Pacific, and on low cloud cover in the North Atlantic and subtropical northeast Pacific. The cloud cover changes associated with the PDO and the AMO can be understood via the relationships between large-scale meteorological parameters and clouds on interannual time scales. When compared to the satellite records during the period of 1983–2009, the patterns of total and low cloud cover trends associated with decadal variability are significantly correlated with patterns of cloud cover trends in ISCCP observations. On the other hand, the pattern of the estimated greenhouse gas (GHG)-forced trends of total cloud cover differs from that related to decadal variability, and may explain the positive trends in the subtropical southeast Pacific, negative trends in the midlatitudes, and positive trends poleward of 50°N/S. In most models, the magnitude of the estimated decadal variability contribution to the observed cloud cover trends is larger than that contributed by GHG, suggesting the observed cloud cover trends are more closely related to decadal variability than to GHG-induced warming.},
doi = {10.1175/JCLI-D-18-0443.1},
journal = {Journal of Climate},
number = 21,
volume = 32,
place = {United States},
year = {2019},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1175/JCLI-D-18-0443.1

Figures / Tables:

Table 1 Table 1: Models used for the diagnostics of pre-industrial simulation and 1% CO2 Simulation.

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