The climate of the Earth is determined by its balance of radiation. The incoming and outgoing radiation fluxes are strongly modulated by clouds, which are not well understood. The Earth Radiation Budget Experiment (Barkstrom and Smith, 1986) provided data from which the effects of clouds on radiation at the top of the atmosphere (TOA) could be computed (Ramanathan, 1987). At TOA, clouds increase the reflected solar radiation, tending to cool the planet, and decrease the OLR, causing the planet to retain its heat (Ramanathan et al., 1989; Harrison et al., 1990). The effects of clouds on radiation fluxes are denoted cloud forcing. These shortwave and longwave forcings counter each other to various degrees, so that in the tropics the result is a near balance. Over mid and polar latitude oceans, cloud forcing at TOA results in large net loss of radiation. Here, there are large areas of stratus clouds and cloud systems associated with storms. These systems are sensitive to surface temperatures and vary strongly with the annual cycle. During winter, anticyclones form over the continents and move to the oceans during summer. This movement of major cloud systems causes large changes of surface radiation, which in turn drives the surface temperature and sensible and latent heat released to the atmosphere

Gupta, Shashi K.

Smith, G. Louis

Stackhouse, Paul W., Jr.

Wilber, Anne C.

NASA Technical Reports Server

1P3.21           Annual Cycle of Cloud Forcing of Surface Radiation Budget
Anne C. Wilber1, G. Louis Smith2, Paul W. Stackhouse Jr.3 and Shashi K. Gupta1
1. Analytical Services and Materials, Hampton, Virginia
2. National Institute of Aerospace, Hampton, Virginia
3. Langley Research Center, Hampton, Virginia
1. Introduction
The climate of the Earth is determined
by its balance of radiation. The incoming
and outgoing radiation fluxes are strongly
modulated by clouds, which are not well
understood. The Earth Radiation Budget
Experiment (Barkstrom and Smith, 1986)
provided data from which the effects of
clouds on radiation at the top of the
atmosphere (TOA) could be computed
(Ramanathan, 1987). At TOA, clouds
increase the reflected solar radiation,
tending to cool the planet, and decrease the
OLR, causing the planet to retain its heat
(Ramanathan et al., 1989; Harrison et al.,
1990). The effects of clouds on radiation
fluxes are denoted cloud forcing. These
shortwave and longwave forcings counter
each other to various degrees, so that in the
tropics the result is a near balance. Over
mid and polar latitude oceans, cloud forcing
at TOA results in large net loss of radiation.
Here, there are large areas of stratus clouds
and cloud systems associated with storms.
These systems are sensitive to surface
temperatures and vary strongly with the
annual cycle. During winter, anticyclones
form over the continents and move to the
oceans during summer. This movement of
major cloud systems causes large changes
of surface radiation, which in turn drives the
surface temperature and sensible and latent
heat released to the atmosphere. Cloud
forcing of surface radiation is thus an
important feedback mechanism in
atmospheric and oceanic processes.
The Surface Radiation Budget (SRB)
Data Set (Whitlock et al., 1995) permits the
investigation of the effects of clouds on the
radiation budget at the surface of the Earth.
This data set was developed in support of
the Global Energy and Water Experiment
and is based on data from the International
Satellite Cloud Climatology Project ISCCP
(Rossow and Schiffer, 1991; Schiffer and
Rossow, 1983). It includes upward and
downward shortwave and longwave
radiation fluxes and the total radiation fluxes
at the surface. The initial edition used a 2.5o
latitude quasi-equal size grid over the globe
and covered the period July 1983 through
June 1991. The SRB data set has been
upgraded by improvements of the algorithms
used to compute the various components of
radiation and refinement of the resolution
(Stackhouse et al., 2004; Gupta et al.,
2004). The SRB data set has recently been
further upgraded to Release 2.5 (Cox et al.,
2006) and covers a twenty-one-year period.
Darnell et al. (1992) used the earlier
version of the SRB data set to study the
seasonal variation of surface radiation
budget. They presented maps of surface
radiation components and latitudinal plots of
zonal averages of the components of
surface radiation. Gupta et al. (1993)
investigated the cloud forcing for upward
and downward shortwave, longwave and
total radiation fluxes for July 1985 and
January 1986. They demonstrated that the
effect of clouds is to reduce downward
shortwave (DSW) radiation and increase
downward longwave (DLW) radiation.
Clouds cool the surface in the summer
hemisphere, where the reduction of DSW
dominates, and warm the surface in the
winter hemisphere, where the longwave
radiation effect is greater. The global
average total effect of cloud is to cool the
Corresponding author address:
Anne C. Wilber, NASA-Langley Research
Center, MS 936, Hampton, VA 23681.
email: a.c.wilber@larc.nasa.gov
https://ntrs.nasa.gov/search.jsp?R=20060028190 2019-08-29T22:12:47+00:00Z
2surface. Gupta et al. (1999) compared the
SRB data set to results from general
circulation models and found that the
models computed shortwave and longwave
radiation which were 10 to 20 Wm-2 greater
than the SRB data set for global averages.
As the major cloud systems move
during the year with the annual cycle of
insolation, the effects of clouds on the
downward and upward shortwave and
longwave radiation fluxes at the surface vary
also. There are a number of questions which
arise concerning the annual cycle of surface
radiation fluxes. The present paper uses the
Release 2.5 of the GEWEX Surface
Radiation Budget Data Set (Cox et al 2006)
to investigate the annual cycles of cloud
forcing of surface radiation components. In
order to describe these annual cycles, a
principal component analysis is used
whereby the major cyclic effects are
computed as time variations with maps
revealing their geographical distributions.
The advantage of this approach is that it
represents the time and space variations
with the minimum number of terms, which
are determined by the data. The principal
components are statistical descriptors rather
than physical, but often have simple physical
interpretations. Also, the principal
components from the analysis of data can
be compared with those from circulation
model results as an objective technique for
establishing the similarities and differences
between the two in regard to time and space
structure.
2. Data Set
The Release 2.5 Surface Radiation
Budget data set includes the downward and
upward reflected solar radiation flux at the
surface, the upward longwave radiation flux
at the surface and the longwave radiation
flux from the atmosphere to the surface.
These fluxes are provided on a 1o grid for
daily and monthly means for July 1983
through December 2004. These fluxes are
computed by use of a number of data
products. Cloud properties are derived from
ISCCP pixel level (DX) data. Temperature
and humidity profiles come from the
Goddard Earth Observing System (GEOS-4)
reanalysis product of Goddard Space Flight
Center. This most recent release uses
MATCH aerosols and a higher resolution
coastline. Although the algorithms have
undergone several improvements, the
discrimination of cloud over snow and ice
remains a problem with observations
currently available.
3. Analysis Method
Cloud forcing is defined as the radiation
flux for the observed conditions of the sky
minus the flux for clear-sky conditions. The
SRB data set includes the clear sky flux
components computed for each 1o region as
well as the fluxes with the observed clouds,
so that the cloud forcing is simple to retrieve
from the data set. The hypothetical surface
temperature that would exist in the absence
of clouds is not considered, so that the cloud
forcing of upward longwave radiation is
taken to be zero.
Monthly mean fluxes were each
averaged over the twenty-one-year period of
the SRB data set for each calendar month to
form the flux components for a climatological
average month, and the cloud forcing for
each component was computed. The cloud
forcing for each component R is then written
as
CFR(x,t) = CFRAV(x) +  PCi(t) EOFi(x)
where  t denotes the month and x the
latitude and longitude of the region, CFRAV(x)
is the annual average of R for region x,
PCi(t) is the i-th principal component and
EOFi(x) is the i-th empirical orthogonal
function (EOF). The principal component
thus describes a time history and the EOF is
the corresponding geographical distribution
4. Results
The annual-mean cloud forcings are
considered first and then the annual cycles
of the cloud forcing.
The global-average annual-mean of
DSW is 184 Wm-2, so that the cloud forcing
of DSW is –59 Wm-2, i.e. the effect of clouds
3is to reduce surface DSW. Figure 1a is a
map of annual mean downward shortwave
DSW cloud forcing.
The downward longwave radiation flux
for all-sky conditions global-average annual-
mean radiation flux is 349 Wm-2, so that the
cloud forcing of DLW is 34 Wm-2. Figure 1b
shows the annual-mean downward
longwave DLW cloud forcing. The map of
annual-mean net total cloud forcing is shown
by fig. 1c and is very similar to that for DSW
in fig. 1a.
The root-mean-square (RMS) of
shortwave cloud forcing at the surface is
listed in table 1 and is 24.7 Wm-2. This may
be compared with the RMS of the annual
cycle of downward shortwave radiative flux
at the surface for all sky conditions (Wilber
et al., 2006), which is 60.6 Wm-2. The
eigenvalues are normalized so as to sum to
one and are listed in table 1 also.
SW CF LW CF Total CF
RMS,Wm-2 24.7 3.87 25.0
1 0.880 0.710 0.901
2 0.063 0.154 0.048
3 0.028 0.067 0.027
4 0.015 0.029 0.004
Sum of first
4 e-values
0.986 0.960 0.980
 Figure 2 shows the first three principal
components of shortwave cloud forcing. The
first principal component is very nearly a
sine and is an annual cycle with amplitude of
35 Wm-2. The maximum is in June and the
minimum is in December, so that it is in
phase with the insolation. The first principal
component for all-sky DSW has amplitude of
80 Wm-2.
Table 1: RMS and eigenvalues for cloud forcing.
Figure 1: (a) Map of the annual mean of cloud
forcing of downward surface shortwave
flux (b) Same for downward longwave
flux. (c) Same for downward total flux.
(Wm-2)
Figure 2: Principal components for downward
shortwave flux at surface, Wm-2.
Figure 1: (a) Map of the annual mean of cloud forcing of
downward surface shortwave flux (b) Same for
downward longwave flux. (c) Same for downward
total flux. (Wm-2)
4Figure 3a shows EOF-1, which is the
geographical distribution of the DSW cloud
forcing corresponding to the first principal
component. The EOFs are normalized with
a RMS of unity and are measured as
standard deviations.
Figure 4 shows the zonal mean of EOF-1
for DSW cloud forcing as a function of
latitude. The zonal mean has extrema at 60o
north and south latitudes. The maximum in
the Southern Hemisphere is 2 standard
deviations, whereas in the Northern
Hemisphere the extreme value is only -1
because of the land-sea differences.
Figure 4 shows that the zonal mean of
EOF-2 for DSW cloud forcing is small except
for the local maximum and minimum beside
the Equator due to the ITCZ movements and
the maximum near 40o N.
The third principal component describes
2.8% of the variance and is a semiannual
cycle of about 4 W-m-2 with maxima in June
and December.  Figure 3c shows EOF-3 for
DSW cloud forcing  is largest near the poles
due to the semi-annual cycle of insolation.
Figure 4 shows that the zonal mean of
EOF-3 is small except for the near-polar
extrema.
The RMS for downward longwave DLW
cloud forcing is 3.87 Wm -2, smaller than the
DSW cloud forcing by a factor of 6. The first
eigenvalue, 0.710, is smaller and the
remaining eigenvalues are larger than for
DSW cloud forcing, indicating greater variety
of the DLW than the DSW case.
Figure 5 shows that the first principal
component for DLW cloud forcing is an
Figure 3: Maps of empirical orthogonal functions for
downward shortwave flux at surface,
dimensionless. a. EOF-1, b. EOF-2, c. EOF-3.
Figure 4: Zonal means of empirical
orthogonal functions for downward
shortwave flux at surface as functions of
latitude, dimensionless.
5annual cycle with a maximum in August and
amplitude of 4 to 5 Wm-2. The shape is close
to a sine, but has a flatter decrease from
September to December than a sine wave.
Whereas the first principal component of
DSW cloud forcing is in phase with the
insolation, the DLW cloud forcing lags
insolation by two months. This variation of
DLW cloud forcing could be due to changes
of cloud amount or of cloud base height.
Figure 6a is the map of EOF-1 for DLW
cloud forcing.
Figure 7 shows the zonal means of the
first three EOFs of DLW cloud forcing as a
function of latitude. The zonal mean of these
two bands are 1.3 standard deviations at
30oS and -2 standard deviations at 35oN, or
4.2 and -6.5 Wm-2 respectively.
Figure 6b shows EOF-2 for DLW cloud
forcing.
Table 1 shows that the RMS for total
downward radiative cloud forcing is
25.0Wm-2, slightly greater than for DSW
cloud forcing. The first four eigenvalues for
net total cloud forcing are close to those for
DSW cloud forcing. Plots of the first three
Figure 7: Zonal means of empirical
orthogonal functions for downward
longwave flux at surface as functions of
latitude, dimensionless.
Figure 5: Principal components for downward
longwave flux at surface, Wm-2.
Figure 6: Maps of empirical orthogonal functions for
downward longwave f lux at surface,
dimensionless. a. EOF-1, b. EOF-2, c. EOF-3.
6principal components are indistinguishable
from those for DSW cloud forcing and are
not shown. Likewise, the maps of the first
two EOFs for net total cloud forcing are
indistinguishable from those for DSW cloud
forcing and the EOF-3 differ only in small
details. The close similarity of the net total
cloud forcing with the DSW cloud forcing is
due to the small RMS of DLW cloud forcing
relative to that of DSW cloud forcing. In
order to get the energetics of the surface
accurately in  a circulation model, it is more
important to get the downward shortwave
calculation accurate than the longwave.
5. Conclusions
This paper has quantitatively described
the annual cycles of surface radiation
components. The next step is to investigate
the interactions of these radiation fluxes with
the other components of the surface-
atmosphere system in order to establish the
causes and effects of these variations and
thus to increase our understanding of
weather and climate processes. Another
application of these results is comparison
with the output of circulation models, so as
to validate or improve the ability of these
models to simulate weather and climate
processes.
 Averaged over the Earth for one year,
clouds reduce the insolation at the surface
by 59 Wm-2 and increase the downward
longwave radiation flux by 34 Wm-2. In order
to describe the annual cycles, a principal
component analysis is used. The root-mean-
square of the annual cycle of cloud forcing
of downward shortwave radiation is 25 W-m-
2 and of downward longwave radiation is
3.9W-m-2. Most of the cloud forcing of
downward shortwave radiation is in phase
with insolation, but the downward longwave
radiation lags by two months.
A c k n o w l e d g e m e n t s :  This work was
supported by the Clouds and Earth Radiant
Energy System (CERES) program and the
Surface Radiation Budget Program of the
Earth Science Enterprise of NASA through
Langley Research Center by contract to
Analytical Sciences and Materials, Inc. and
the National Institute of Aerospace. Data
were provided by the Langley Atmospheric
Sciences Data Center.
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Annual Cycle of Cloud Forcing of Surface Radiation Budget

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