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GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L04701, doi:10.1029/2006GL028083, 2007
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Arguments against a physical long-term trend in global ISCCP cloud
amounts
Amato T. Evan,1 Andrew K. Heidinger,2 and Daniel J. Vimont3
Received 11 September 2006; revised 8 January 2007; accepted 23 January 2007; published 17 February 2007.
[1] The International Satellite Cloud Climatology Project
(ISCCP) multi-decadal record of cloudiness exhibits a wellknown global decrease in cloud amounts. This downward
trend has recently been used to suggest widespread
increases in surface solar heating, decreases in planetary
albedo, and deficiencies in global climate models. Here we
show that trends observed in the ISCCP data are satellite
viewing geometry artifacts and are not related to physical
changes in the atmosphere. Our results suggest that in its
current form, the ISCCP data may not be appropriate for
certain long-term global studies, especially those focused on
trends.’’ Citation: Evan, A. T., A. K. Heidinger, and D. J.
Vimont (2007), Arguments against a physical long-term trend in
global ISCCP cloud amounts, Geophys. Res. Lett., 34, L04701,
doi:10.1029/2006GL028083.
[2] The International Satellite Cloud Climatology Project (ISCCP) data set of cloud amounts and other products
[Rossow and Schiffer, 1999] is a more than 20 year
archive of daily global observations. Recently, this record
has been used to study long-term trends in surface solar
radiation [Pinker et al., 2005], with studies concluding that
changes in cloudiness seen in the ISCCP record are
evidence for a widespread increase in surface solar heating
[Hatzianastassiou et al., 2005] and a decrease in planetary
albedo [Pallé et al., 2004; Pallé et al., 2005], having
implications for global climate models [Pallé et al., 2006].
Still others have used the ISCCP multi-decadal trends to
suggest that long-term changes in cloudiness are causing
widespread changes in outgoing longwave radiation [Cess
and Udelhofen, 2003], are evidence of recent global brightening [Wild et al., 2005], and result from feedbacks associated with global warming [Ding et al., 2004]. However,
these trends in total cloudiness have not been observed in
surface [Norris, 2005] and other satellite [Jacobowitz et al.,
2003; Wylie et al., 2005] cloud records. While a lack of
corroboration with other data sets does not imply a deficiency in the ISCCP data, it has been suggested that the
ISCCP cloud amounts may be affected by satellite related
artifacts [Campbell, 2004; Norris, 2000].
[3] The ISCCP data set utilizes radiance information,
with a nominal resolution of 8km at nadir, from a series
of geostationary satellites to create 3-hourly maps of cloud1
Cooperative Institute for Meteorological Satellite Studies, University
of Wisconsin-Madison, Madison, Wisconsin, USA.
2
Office of Research and Applications, National Environmental Satellite,
Data, and Information Service, NOAA, Madison, Wisconsin, USA.
3
Department of Atmospheric Science, University of WisconsinMadison, Madison, Wisconsin, USA.
Copyright 2007 by the American Geophysical Union.
0094-8276/07/2006GL028083$05.00
iness and other associated products. The spatial coverage of
the data is extended by utilizing Advanced Very High
Resolution Radiometer (AVHRR) data from polar orbiting
satellite roughly at latitudes above and below 60N and 60S,
respectively. The exception to this is a segment of the Indian
Ocean where geostationary satellite coverage did not exist
until the late 1990s and data from the AVHRR was
employed. We explore the long term variability of the
ISCCP data using the D2 monthly mean cloud product for
the years of 1983– 2006 [Rossow and Schiffer, 1999], the
most recent release (data from the British Atmospheric Data
Centre, http://badc.nerc.ac.uk, and the Langley Research
Center EOS DAAC, http://eosweb.larc.nasa.gov/). While
we only consider the infrared total clouds product from this
data set, which uses radiance measurements at 11 mm, this
field is directly tied to the other cloud and clear-sky
products available from ISCCP [Rossow and Schiffer,
1999].
[4] In order to minimize the effect of ENSO on the
analysis, we regressed out the monthly Nino 3.4 index from
each 2.5-degree grid cell (index data from the Climate
Prediction Center at http://www.cpc.noaa.gov). In order to
analyze the low frequency variability of the ISCCP data we
created a low pass filtered data set by applying a 13-month
boxcar filter to the time series at each cell in order to
remove any intra-annual signal from the time series. In
addition, we created a deseasonalized data set for analysis
by removing the monthly mean from each month’s data at
every grid cell. Mean time series of the unprocessed, low
frequency, and deseasonalized data was made by averaging
the area weighted pixels from 60S – 60N. This latitude range
was chosen in order to emphasize the contribution to the
global cloudiness time series from geostationary satellites
and not that from the polar orbiting platforms. The dotted
line in Figure 1 is the mean time series of the unprocessed
data, the thin solid line is the deseasonalized series, and the
thick line is the mean series of the low pass filtered
cloudiness. All three time series show low frequency
variability that is characterized by a 1% increase in clouds
from the beginning of the record until about 1987, followed
by a 4% decrease in cloud amounts for the next 13 years,
and again a 1% increase from late 2000 until the end of the
record.
[5] To determine the regions that contribute the most to
the interannual patterns of cloudiness from Figure 1, we
regressed the ISCCP low pass filtered mean time series
(thick line in Figure 1) back onto the low pass filtered
ISCCP cloud data. We used this time series in order to
minimize the effect of any intra-annual variability on the
resultant regression maps. Figure 2 is a map of the coefficients from this linear regression; regions of high values
signify areas that contribute the greatest to the low frequency
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Figure 1. Time series of ISCCP IR total cloud amounts.
The dotted line is the time series of total cloud amounts
from monthly mean infrared ISCCP data, area weighted and
averaged over 60S – 60N. The solid thin black line is the
deseasonalized version of this time series, and the thick
black line is the same data but processed with a low pass
filter.
variability in the ISCCP time series (Figure 1), and regions
with low or negative values contribute the least or negatively
to that same mean pattern. The black contour bounds areas
where the resultant time series from the regression can
explain enough variance in the low pass filtered data to be
statistically significant at the 0.95% level based on a twotailed t-test and 22 degrees of freedom. The most striking
features in Figure 2 are the circular patterns centered in the
Atlantic, Western Pacific and Eastern Pacific Oceans. These
circles correspond to so-called geostationary ‘‘footprints’’
that describe the area observed by each satellite. At the
center of these footprints a satellite sensor’s viewing angle is
perpendicular to the surface, corresponding to a satellite
zenith angle of 0 degrees. At the footprint edges the satellite
zenith angle is much higher, corresponding to a longer path
length through the atmosphere that light must travel before it
is detected by the sensor. The band of negative regression
coefficients over the Indian Ocean corresponds to the area
where geostationary data was not available until 1997, and
AVHRR polar orbiting satellite data was used in the record.
[6] The patterns in Figure 2 reflect different satellite
viewing geometries and how those angles alter the record
of cloudiness on a long term basis, a so-called ‘limb
darkening’ effect, which is well known [Joyce et al.,
2001; Minnis, 1989]. Furthermore, as the equatorial edges
of the geostationary footprints happen to fall over oceanic
regions, the low frequency ISCCP time series in Figure 1
(solid black line) better represents over-water than over-land
cloud patterns. This distinction in the ISCCP long-term
trends between oceanic and land covered regions has been
noted in other studies [Norris, 2005].
[7] We propose that if the ISCCP low frequency time
series was caused by a large-scale signal that is simply
enhanced at the edges of the satellite view, the regression
coefficients and associated time series would be more
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Figure 2. Regression map of mean total cloud variability
back onto ISCCP data. Map of coefficients from the
regression of mean low pass filtered ISCCP cloud time
series back onto the low pass filtered data. The black
contour is the 95% significance level for the percent
variance of the filtered data explained by the time series
resulting from the regression analysis, and the gray contour
signifies one standard deviation above the mean value of the
regression coefficients.
spatially uniform across those footprints. To test this theory
we separated the deseasonalized ISCCP data into regions
with high and low regression coefficients from the map in
Figure 2. Here we use the deseasonalized data since the low
pass filtered one is too much smoothed to reveal sudden
changes in cloudiness, and the unprocessed record masks
those changes in the seasonality. The cutoff for this distinc-
Figure 3. Time series of clouds and 1/mu. Time series of
deseasonalized ISCCP total cloud data for regions with
regression coefficients (Figure 2) one standard deviation
above the mean value (black solid line) and those with
values less that this cutoff (black dashed line). The righthanded axis corresponds to values of 1/mu, where the solid
gray line is the time series of 1/mu for regions with
regression coefficients one standard deviation above the
mean value, and the gray dashed line is averaged over all
other regions.
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Table 1. Important Geostationary Satellite Eventsa
Event
Approximate Date
Longitude
Effect on 1/mu and Retrieved
Cloud Amounts
Launch of GOES 7
Failure of GOES 6
Repositioning of METEOSAT 3
Launch of GOES 8
Repositioning of METEOSAT 5
AVHRR KLM Calibration Error
Feb-87
Jul-89
Aug-91
Apr-94
Jul-98
Jun-01
75 West
136 West
75 West
75 West
63 East
Global
Decrease
Increase
Decrease
Decrease
Decrease
Increase
a
Possibly some of the more significant alterations to the ISCCP geostationary configurations that contribute to the pattern of 1/mu in Figure 3, and
ultimately the downward trend in cloud amounts seen in Figure 1.
tion was one standard deviation above the mean regression
coefficient value (a value of roughly 2). The areas with high
regression coefficients comprised about 20% of the global
surface between 60S– 60N, and is bounded by the gray
contour in Figure 2. We then created two area weighted
mean time series of cloud amounts using this deseasonalized data averaged over 60S – 60N for the areas with
regression coefficients above and below our cutoff. The
resultant time series correspond to areas with high regression coefficients (which we refer to as the HR series), and
low regression coefficients (the LR series).
[8] The black dashed line in Figure 3 is the LR series,
and the black solid line is the HR series. There is an almost
20% reduction in cloud amount for the HR time series from
the beginning of the record through mid 2001. This is very
different from the LR series, which shows a small amount of
long term variability; a decrease in clouds from the late
1980s to the early 1990s. This separation is remarkable
considering we choose a somewhat arbitrary dividing line
between the LR and HR regions. The HR series also shows
several abrupt increases (1984 – 1985, 1989– 1990, 2000 –
2002) and decreases (1983 – 1984, 1987– 1989, 1992 –1993,
1995 – 1996, 1998 – 1999) in cloud amounts, and many
periods of little to no interannual variability (1985 –1987,
1993 – 1995, 1996 –1998, 2002 – 2005).
[9] These observations would be consistent with the
theory that changes in the number of geostationary satellites
are altering the global mean cloud amounts [Campbell,
2004]; as the number of observing satellites increase, as
was the case from the mid 1980s through the 1990s, more
regions that had previously been at the limbs of a satellite’s
field of view would be closer to nadir for a newer imager.
However, the abrupt changes in global cloud amounts that
result from sudden changes in the geostationary viewing
platforms would be somewhat hidden in the annual cycle,
leading to what would appear to be a steady downward
trend in global cloudiness. The increase in cloud amounts
around 1985 and again 1990 could result from failures or
repositioning of some operational instruments. Also, the
increase in cloudiness during 2001 and 2002 may be
physical, but it also corresponds to the switch to the new
generation of AVHRR satellites (KLM series). The impact
of this change in the reference AVHRR satellite on the
ISCCP products are currently under investigation by the
ISCCP processing team (B. Rossow, personal communication, 2006).
[10] To explore how changes in satellite geometries may
be altering cloud amounts in the ISCCP data we created a
monthly time series of 1/mu, the inverse of the cosine of the
satellite zenith angle (ISCCP D1 data from the British
Atmospheric Data Centre, http://badc.nerc.ac.uk, and the
Langley Research Center EOS DAAC, http://eosweb.larc.
nasa.gov/). The satellite zenith angle describes the angle
between the local zenith and the line of sight to the satellite,
assuming a non-curved surface. The cosine of the satellite
zenith angle (mu) varies between 1 (at nadir) and 0 (looking
out tangentially from the satellite). 1/mu is therefore a
relative measure of the distance a photon must travel from
a plane surface to be detected by the satellite sensor. A value
of 2 for 1/mu corresponds to a path length that is double
than that at nadir.
[ 11 ] We then separated the 1/mu data by regions
corresponding to high and low regression coefficients from
the map in Figure 2, similar to what was done for the
deseasonalized cloud data. The solid gray line in Figure 3 is
a time series of 1/mu that is area weighted, averaged over
the HR regions, and smoothed using 2 recursive 5-month
boxcar filters. The sudden drops in 1/mu likely result from
the introduction of new geostationary satellites into the
ISCCP data, or repositioning of existing satellites to fill in
gaps in the global coverage. Table 1 lists a few key changes
in the ISCCP satellite constellation that may have resulted
in the pattern of 1/mu for the solid gray line in Figure 3,
including the introduction, failure, or movement of various
viewing platforms. This list is not exhaustive and only
highlights a few changes in the ISCCP observational platforms.
[12] The correlation between the HR and 1/mu time series
is remarkable. Clearly there is a deterministic relationship
between cloudiness at the tropical limbs of the geostationary
satellites, and the satellite zenith angle, which is supported
by the theory that limb darkening causes observations of
increase cloudiness at the edges (especially the tropical
ones) of the geostationary satellites [Joyce et al., 2001;
Minnis, 1989]. It is also evident that were this not the case,
the ISCCP data would exhibit no significant long term
upward (during the early 80s) and downward (over much
of the 1980s and 1990s) trends in cloudiness. If artifacts at
the equatorial limbs did not exist, it is likely that the low
frequency variability in mean global cloud amounts would
be more like that in the LR time series (black dashed line,
Figure 3), which is an average of cloudiness over 80% of
the global surface between 60S –60N. Again, the LR series
will show some variability that is similar to the HR series
since we choose a somewhat arbitrary dividing line between
the two regions, but this choice is sufficient for our
illustrative purposes.
[13] To contrast the time series of 1/mu for the regions of
high regression coefficients, we also created a time series of
1/mu (also area weighted and 5-month recursively boxcar
filtered) for the LR regions as specified in Figure 2. The
gray dashed line in Figure 3 is the LR time series of 1/mu,
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Figure 4. Comparison of ISCCP cloud amounts over the
Indian Ocean. Two time series of low pass filtered infrared
cloud amounts for a region of the equatorial Indian Ocean
where the regression coefficients in Figure 2 are strongly
positive (20S – 20N and 55W – 65W, black line) and
negative (20S – 20N and 65W– 75W, gray line).
and shows much less variance than that for the HR values of
1/mu. This is not surprising since 1/mu is more sensitive to
change at larger satellite zenith angles.
[14] To again emphasize this effect of viewing geometry
on the long term trends from ISCCP, we have also created
two time series of cloud amounts for a region of the
equatorial Indian Ocean where the regression coefficients
are strongly positive (20S– 20N and 55W – 65W) and negative (20S – 20N and 65W – 75W). Until a geostationary
satellite was moved to over this area in the late 1990s, the
region that is further west corresponded to an area that was
at the edges of a satellite footprint, and the region further
east was one covered by AVHRR data. Figure 4 is a time
series of those regions, which have been smoothed using
two recursive 13 month boxcar filters. The black line
represents the area further west and shows roughly a 20%
drop in cloud amounts when the new geostationary instrument was introduced. The gray line is of the region that used
the AVHRR data for the first 15 years of the record, and
shows a steady time series.
[15] While polar orbiting platforms also suffer from
artifacts due to viewing geometry, these are averaged out
over several days as these instruments view the same
location with the same satellite geometry roughly once a
week. In contrast, the effect on the geostationary platforms
is cumulative, and therefore can lead to a completely nonphysical signal, like that of the black line in Figure 4. This
cumulative effect of the viewing geometry likely explains
why none of the regions in Figure 2 that are covered by the
AVHRR data are well characterized by the low frequency
ISCCP series in Figure 1, despite the fact that the same
processing algorithm is applied to both the geostationary
and polar orbiting instruments.
[16] Although the ISCCP data is very appropriate for
many applications, clearly its use in global multi-decadal
studies is troubling. Especially as we find similar results, to
varying degrees, by repeating this analysis for ISCCP high,
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mid, and low cloud amounts. However, it may be possible
that new methodologies can be developed to process the
ISCCP data and account for these types of artificial variations in cloudiness. One simple post-processing method
would be to simply remove those areas at the tropical
geostationary limbs from the calculation of global mean
cloud amounts. Another may be to regress out the time
series of 1/mu from the cloud data. However, clearly the
best way to deal with the effects of the satellite viewing
geometry on global cloud amounts would at the data
processing stage, especially since the ‘limb darkening’
effect is non-linear and therefore a regression technique
will not be able to completely account for the effect of
changing viewing geometry on the cloud data.
[17] We have demonstrated that the long-term global
trends in cloudiness from the ISCCP record are influenced
by artifacts associated with satellite viewing geometry.
Results from earlier studies based on these trends may be
influenced by these non-physical artifacts, and we therefore
suggest that development of a correction for the data is
warranted. As the number of publications on the subject of
climate change continues to grow [Stanhill, 2001], this
paper highlights the need to critically explore the source
of any trends in global, multi-decadal satellite data sets.
[18] Acknowledgments. The Authors would like to thank Joel
Norris, Steven Ackerman, Bryan Baum, Dee Wade, Bill Rossow, and
Michael Pavolonis for their help in preparing this work. We also are
grateful to two anonymous reviewers for their helpful comments. Funding
for this research was provided by the NOAA/NESDIS Polar Program and
the NOAA/NESDIS/ORA AVHRR Reprocessing Program. The views,
opinions, and findings contained in this report are those of the authors
and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision.
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A. T. Evan, Cooperative Institute for Meteorological Satellite Studies,
University of Wisconsin-Madison, 1215 West Dayton Street, Madison, WI
53706, USA. (atevan@wisc.edu)
A. K. Heidinger, Office of Research and Applications, National
Environmental Satellite, Data, and Information Service, NOAA, 1225
West Dayton Street, Madison, WI 53706, USA.
D. J. Vimont, Department of Atmospheric Science, University of
Wisconsin-Madison, 1225 West Dayton Street, Madison, WI 53706, USA.
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