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Title: Investigation of the Residual in Column-Integrated Atmospheric Energy Balance Using Cloud Objects

Abstract

Observationally based atmospheric energy balance is analyzed using Clouds and the Earth’s Radiant Energy System (CERES)-derived TOA and surface irradiance, Global Precipitation Climatology Project (GPCP)-derived precipitation, dry static and kinetic energy tendency and divergence estimated from ERA-Interim, and surface sensible heat flux from SeaFlux. The residual tends to be negative over the tropics and positive over midlatitudes. A negative residual implies that the precipitation rate is too small, divergence is too large, or radiative cooling is too large. The residual of atmospheric energy is spatially and temporally correlated with cloud objects to identify cloud types associated with the residual. Spatially, shallow cumulus, cirrostratus, and deep convective cloud-object occurrence are positively correlated with the absolute value of the residual. The temporal correlation coefficient between the number of deep convective cloud objects and individual energy components, net atmospheric irradiance, precipitation rate, and the sum of dry static and kinetic energy divergence and their tendency over the western Pacific are 0.84, 0.95, and 0.93, respectively. However, when all energy components are added, the atmospheric energy residual over the tropical Pacific is temporally correlated well with the number of shallow cumulus cloud objects over tropical Pacific. Because shallow cumulus alters not enough atmospheric energymore » compared to the residual, this suggests the following: 1) if retrieval errors associated with deep convective clouds are causing the column-integrated atmospheric energy residual, the errors vary among individual deep convective clouds, and 2) it is possible that the residual is associated with processes in which shallow cumulus clouds affect deep convective clouds and hence atmospheric energy budget over the tropical western Pacific.« less

Authors:
 [1];  [1];  [1];  [1];  [2];  [3];  [1]
  1. NASA Langley Research Center, Hampton, VA (United States)
  2. Science Systems and Applications, Inc., Hampton, VA (United States)
  3. National Center for Atmospheric Research, Boulder, CO (United States)
Publication Date:
Research Org.:
University Corporation for Atmospheric Research, Boulder, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1537000
Grant/Contract Number:  
SC0012711
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 29; Journal Issue: 20; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences

Citation Formats

Kato, Seiji, Xu, Kuan-Man, Wong, Takmeng, Loeb, Norman G., Rose, Fred G., Trenberth, Kevin E., and Thorsen, Tyler J. Investigation of the Residual in Column-Integrated Atmospheric Energy Balance Using Cloud Objects. United States: N. p., 2016. Web. doi:10.1175/jcli-d-15-0782.1.
Kato, Seiji, Xu, Kuan-Man, Wong, Takmeng, Loeb, Norman G., Rose, Fred G., Trenberth, Kevin E., & Thorsen, Tyler J. Investigation of the Residual in Column-Integrated Atmospheric Energy Balance Using Cloud Objects. United States. doi:10.1175/jcli-d-15-0782.1.
Kato, Seiji, Xu, Kuan-Man, Wong, Takmeng, Loeb, Norman G., Rose, Fred G., Trenberth, Kevin E., and Thorsen, Tyler J. Wed . "Investigation of the Residual in Column-Integrated Atmospheric Energy Balance Using Cloud Objects". United States. doi:10.1175/jcli-d-15-0782.1. https://www.osti.gov/servlets/purl/1537000.
@article{osti_1537000,
title = {Investigation of the Residual in Column-Integrated Atmospheric Energy Balance Using Cloud Objects},
author = {Kato, Seiji and Xu, Kuan-Man and Wong, Takmeng and Loeb, Norman G. and Rose, Fred G. and Trenberth, Kevin E. and Thorsen, Tyler J.},
abstractNote = {Observationally based atmospheric energy balance is analyzed using Clouds and the Earth’s Radiant Energy System (CERES)-derived TOA and surface irradiance, Global Precipitation Climatology Project (GPCP)-derived precipitation, dry static and kinetic energy tendency and divergence estimated from ERA-Interim, and surface sensible heat flux from SeaFlux. The residual tends to be negative over the tropics and positive over midlatitudes. A negative residual implies that the precipitation rate is too small, divergence is too large, or radiative cooling is too large. The residual of atmospheric energy is spatially and temporally correlated with cloud objects to identify cloud types associated with the residual. Spatially, shallow cumulus, cirrostratus, and deep convective cloud-object occurrence are positively correlated with the absolute value of the residual. The temporal correlation coefficient between the number of deep convective cloud objects and individual energy components, net atmospheric irradiance, precipitation rate, and the sum of dry static and kinetic energy divergence and their tendency over the western Pacific are 0.84, 0.95, and 0.93, respectively. However, when all energy components are added, the atmospheric energy residual over the tropical Pacific is temporally correlated well with the number of shallow cumulus cloud objects over tropical Pacific. Because shallow cumulus alters not enough atmospheric energy compared to the residual, this suggests the following: 1) if retrieval errors associated with deep convective clouds are causing the column-integrated atmospheric energy residual, the errors vary among individual deep convective clouds, and 2) it is possible that the residual is associated with processes in which shallow cumulus clouds affect deep convective clouds and hence atmospheric energy budget over the tropical western Pacific.},
doi = {10.1175/jcli-d-15-0782.1},
journal = {Journal of Climate},
number = 20,
volume = 29,
place = {United States},
year = {2016},
month = {9}
}

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Works referenced in this record:

Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)
dataset, January 2018

  • Fumihiko, Akazawa; Turki, Alraddadi; Pascual, Ananda
  • DOI: 10.17882/42182

    Works referencing / citing this record:

    Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)
    dataset, January 2018

    • Fumihiko, Akazawa; Turki, Alraddadi; Pascual, Ananda
    • DOI: 10.17882/42182