JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110, D21110, doi:10.1029/2005JD006183, 2005
Trends in upper-level cloud cover and surface divergence over the
tropical Indo-Pacific Ocean between 1952 and 1997
Joel R. Norris
Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA
Received 6 May 2005; revised 5 July 2005; accepted 29 August 2005; published 12 November 2005.
[1] This study investigates the spatial pattern of linear trends in surface-observed upper-
level (combined midlevel and high-level) cloud cover, precipitation, and surface
divergence over the tropical Indo-Pacific Ocean during 1952–1997. Cloud values were
obtained from the Extended Edited Cloud Report Archive (EECRA), precipitation
values were obtained from the Hulme/Climate Research Unit data set, and surface
divergence was alternatively calculated from wind reported by Comprehensive OceanAtmosphere Data Set and from wind derived from Smith and Reynolds Extended
Reconstructed sea level pressure data. Between 1952 and 1997, upper-level cloud cover
increased by about 4%-sky-cover over the central equatorial South Pacific and decreased
by about 4–6%-sky-cover over the adjacent subtropics, the western Pacific, and the
equatorial Indian Ocean. Trends in precipitation and surface convergence are usually
positive where upper-level cover trends are positive and negative where they are
negative. Consistency between time series of upper-level cloud cover reported by
EECRA and the International Satellite Cloud Climatology Project (ISCCP) during
1984–1997 for various subregions provides further confirmation that the surfaceobserved upper cloud trends are real. Estimated radiative effects of surface-observed
cloud cover anomalies are also well correlated with all-sky radiation flux anomalies
reported by the Earth Radiation Budget Satellite in most areas. Contrastingly, EECRA
and ISCCP low-level cloud cover regional time series exhibit little correspondence, and
EECRA low-level cloud cover trends are uniformly positive across the tropical IndoPacific with no apparent relationship to changes in surface divergence. Although the
spatial pattern of upper-level cloud trends resembles that associated with El Niño, the
trends are approximately three times larger than those predicted by a linear relationship
to sea surface temperature in the Niño3.4 region.
Citation: Norris, J. R. (2005), Trends in upper-level cloud cover and surface divergence over the tropical Indo-Pacific Ocean
between 1952 and 1997, J. Geophys. Res., 110, D21110, doi:10.1029/2005JD006183.
1. Introduction
[2] Clouds play a key role in the climate system by
generally enhancing the reflection of solar or shortwave
(SW) radiation back to space and by generally restricting
the emission of thermal infrared or longwave (LW) radiation
to space. SW reflection acts to cool the climate system,
primarily at the surface, and lesser LW emission acts to
warm the climate system, primarily in the atmosphere. The
influence of clouds on radiation flux may be conveniently
quantified by ‘‘cloud radiative forcing’’ (CRF), defined as
the difference between actual radiative flux and what it
would be were clouds absent. Under most conditions, topof-atmosphere (TOA) SW CRF becomes more negative for
greater cloud cover by optically thick clouds and TOA LW
CRF becomes more positive for greater cloud cover by nonIR-transmissive clouds with tops high in the troposphere.
Copyright 2005 by the American Geophysical Union.
0148-0227/05/2005JD006183$09.00
Although satellite measurements indicate that SW CRF
cooling currently dominates LW CRF warming in the global
average [Ramanathan et al., 1989], it is presently not
known whether the net cooling effect of clouds will
strengthen or weaken in response to global warming caused
by increased greenhouse gas concentrations [Moore et al.,
2001]. In fact, one reason that global climate models do not
agree on the future magnitude of global warming is that they
have difficulty in correctly and consistently simulating
clouds. These shortcomings motivate observational investigations of cloud and radiation variability during the past
several decades that will elucidate relationships between
changes in cloudiness and changes in other parameters of
the climate system.
[3] Recent satellite studies have documented substantial
decreases in tropical mean cloud cover and reflected SW
radiation (RSW) and a substantial increase in tropical mean
outgoing LW radiation (OLR) between 1985 and 1999 [e.g.,
Wielicki et al., 2002a; Chen et al., 2002; Cess and
Udelhofen, 2003]. The increase in OLR is consistent with
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a reduction in cloud cover at higher levels, as reported by
Wang et al. [2002]. Surface synoptic cloud reports made by
ships and land stations corroborate the decline in tropical
mean upper-level cloud cover after 1985 and moreover
provide the opportunity to investigate cloud changes in
the presatellite era [Norris, 2005]. Specifically, the latter
study found an overall decline in upper-level cloud cover
between 1952 and 1997 when averaged over the tropical
ocean in general and the Indo-Pacific region in particular.
Previous research has documented significant decadal and
multidecadal changes in OLR, surface-observed total cloud
cover, precipitation, atmospheric circulation, and sea surface temperature over the tropical Indo-Pacific during the
later decades of the 20th century [e.g., Nitta and Yamada,
1989; Graham, 1994; Nitta and Kachi, 1994; Morrissey and
Graham, 1996; Chu and Wang, 1997; Waliser and Zhou,
1997; Zhang et al., 1997; Curtis and Hastenrath, 1999;
Deser et al., 2004], and an investigation of the relationships
between meteorological and cloud trends would aid attribution of the regional mean decrease in upper-level cloud
cover noted by Norris [2005].
[4] The present study uses surface synoptic cloud observations obtained from the Extended Edited Cloud Report
Archive (EECRA) [Hahn and Warren, 1999] and surface
meteorological observations from several data sets [Hulme
et al., 1998; Kaplan et al., 1998; Smith and Reynolds, 2004;
Woodruff et al., 1987; Xie and Arkin, 1997] to calculate
trends in upper-level cloud cover, precipitation, and nearsurface atmospheric circulation during 1952 – 1997 over the
tropical Indo-Pacific Ocean. Spatial patterns of trends are
examined for physical consistency and for resemblance to
those associated with El Niño/Southern Oscillation (ENSO),
the dominant climate process in this area of the globe. Time
series of cloud and meteorological parameters are presented
for selected subregions, and time series from surface cloud
observations are compared to those from the International
Satellite Cloud Climatology Project (ISCCP) [Rossow and
Schiffer, 1999] during 1984– 1997 to assess the consistency
between the two independent data sets. Time series of LW
and SW radiation anomalies associated with surfaceobserved cloud cover anomalies are furthermore empirically
estimated to partially quantify radiative impacts of cloud
variability, particularly in the presatellite era. These are
compared to all-sky LW and SW flux anomalies from the
Earth Radiation Budget Satellite (ERBS) [Wielicki et al.,
2002a] during 1985 – 1997. The documentation of multidecadal cloud cover and meteorological trends over the
tropical Indo-Pacific Ocean will increase our understanding
of low-frequency cloud cover variations, their radiative
impact, and associated changes in the atmosphere and ocean
during a period of increasing anthropogenic forcing on the
climate system.
2. Data and Processing
2.1. Cloud and Radiation Parameters
[5] Surface-observed upper-level (combined midlevel and
high-level) cloud cover (NU) values over the ocean during
1952 – 1997 were inferred from individual EECRA total
cloud cover (N) and low-level cloud cover (Nh) reports
[Hahn and Warren, 1999] by assuming random overlap, i.e.,
NU = (N Nh)/(1 Nh). In order to directly compare
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surface-observed low-level cloud cover with satellite-observed low-level cloud cover, EECRA low-level cloud
cover values were adjusted to represent the ‘‘satellite view’’
(NL) by removing the portion of low-level cloud cover
overlapped by higher clouds, i.e., NL = Nh (1 N)/(1 Nh). Individual cloud values were then averaged into
seasonal means and 5 10 grid boxes using only daytime
observations since human observers have difficulty identifying cloudiness on nights with little or no moonlight [Hahn
et al., 1995]. Radiation flux anomalies that would result
from anomalies in cloud cover with all other cloud properties remaining the same are called ‘‘cloud cover radiative
forcing (CCRF) anomalies.’’ LW CCRF anomalies for each
5 10 grid box were estimated by multiplying upperlevel cloud cover anomalies by climatological seasonal LW
CRF per unit cloud cover. SW CCRF anomalies were
similarly determined by multiplying upper-level and ‘‘satellite view’’ low-level cloud cover anomalies by SW CRF
per unit cloud cover. For further discussion of the EECRA
data, overlap assumptions, averaging methods, and CCRF
estimation, see Norris [2005].
[6] Daytime upper-level and low-level cloud fractions
from ISCCP [Rossow et al., 1996; Rossow and Schiffer,
1999] were obtained during 1984– 1999 by summing over
all cloud types with tops above and below the 680 hPa level,
respectively. Monthly 2.5 2.5 values were then averaged to seasonal data in 5 10 grid boxes with weighting
according to grid box area, ocean fraction, and the number
of days in each month. Monthly 2.5 2.5 ERBS scanner
LW and SW CRF values during 1985 to 1989 [Barkstrom et
al., 1989] were similarly averaged to seasonal data in 5 10 grid boxes. ERBS nonscanner OLR and RSW fluxes
during 1985 – 1999 [Wielicki et al., 2002a] were obtained as
36-day rather than monthly data to avoid aliasing of the
diurnal cycle due to satellite precession [Trenberth, 2002;
Wielicki et al., 2002b]. These were averaged to seasonal
means with weighting according to the number of days in
each 36-day interval contributing to a season and then
bilinearly interpolated from 10 10 to 5 10 grid
spacing. For further discussion of the processing of these
satellite data sets, see Norris [2005].
2.2. Other Meteorological Parameters
[7] In addition to cloud and radiation flux, this study
examines multidecadal changes in sea surface temperature
(SST), precipitation, sea level pressure (SLP), zonal and
meridional wind components, and surface divergence. SST
values during 1952– 1999 were obtained from the Kaplan
Extended SST data set, which removes data gaps and
diminishes sampling error by applying optimal estimation
in the space of 80 empirical orthogonal functions to ship
observations [Kaplan et al., 1998]. The primary source of
precipitation data, acquired for 1952 – 1998, was the gridded
product constructed from land and island station rain gauge
measurements by M. Hulme at the Climate Research Unit
(CRU), University of East Anglia [Hulme, 1992; Hulme,
1994; Hulme et al., 1998]. Since the Hulme/CRU data set
lacks information on precipitation over open ocean, it was
supplemented in this study by the Climate Prediction Center
(CPC) Merged Analysis of Precipitation (CMAP) data set
[Xie and Arkin, 1997] during 1979 –1999. The combination
of satellite retrievals and reanalysis output with rain gauge
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observations in CMAP enables full global coverage, yet
with some apparently spurious trends in tropical oceanic
precipitation [Yin et al., 2004]. Monthly mean SST and
precipitation data in 2.5 2.5 or 5 5 grid boxes were
averaged to seasonal means in 5 10 grid boxes with
weighting according to grid box area and the number of
days in each month.
[8] Although reanalyses provide information on atmospheric circulation that is internally consistent and globally
available at all levels in the troposphere, they are not useful
for investigating multidecadal tropical variability due to the
presence of large artifacts presumably generated by changes
in the observing system and/or data assimilation procedures
[Wu and Xie, 2003; Kinter et al., 2004]. For this reason, the
present study uses zonal and meridional wind components
contained in the Comprehensive Ocean-Atmosphere Data
Set (COADS) [Woodruff et al., 1987] to examine trends in
surface atmospheric circulation over the ocean between
1952 and 1997. These data, averaged primarily from ship
observations, unfortunately suffer from a spurious increase
in scalar wind speed since 1950 that is inconsistent with
observed SLP gradients [Ward and Hoskins, 1996]. The
increase in wind speed appears to be quasi-linear and has
been attributed to increasing anemometer height and more
frequent use of anemometers in place of Beaufort estimates
[Ramage, 1987; Cardone et al., 1990]. A linear fit indicates
wind speed averaged over the tropical Indo-Pacific Ocean
(30S – 30N, 50E– 100W) has been increasing at a rate of
4.7% per decade, and following the practice of previous
studies [e.g., Wu and Xie, 2003], zonal and meridional wind
components in each grid box were adjusted to remove a
4.7% per decade trend (i.e., enhancing wind speeds early in
the record and reducing wind speeds late in the record).
COADS monthly mean wind component data in 2 2
grid boxes were averaged to seasonal means in 5 10
grid boxes with weighting by the area in each 2 2 grid
box contributing to a 5 10 grid box and by the number
of days in each month. Sampling noise is still present in
many of the seasonal 5 10 values, so 1-2-1 smoothing
in latitude and longitude was applied twice to zonal and
meridional wind anomalies after assigning values of zero to
missing data and land grid boxes. Surface divergence was
calculated from the smoothed wind components using
centered finite differences in spherical coordinates.
[9] Although the artificial increase in scalar wind speed is
generally spatially uniform, it is possible that use of a
globally constant linear correction factor introduces errors.
Therefore zonal and meridional wind components were
independently calculated from SLP by iterating the following approximate steady-state horizontal momentum equations using the method of Murphree and van den Dool
[1988],
@u
1 @p
@u
@u
¼
þ fv ex u þ Dr2 u u þ v
@t
r @x
@x
@y
@v
1 @p
@v
@v
;
0¼
¼
fu ey v þ Dr2 v u þ v
@t
r @y
@x
@y
0¼
where notation is conventional, except that all quantities are
seasonal means. These equations were solved in spherical
coordinates without the curvature terms. Air density at sea
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level, r, was specified as a constant 1.2 kg m3, and the
diffusion coefficient, D, was specified as a constant 5 106 m2 s1. Linear damping coefficients representing the
effects of surface friction were separately specified for
zonal and meridional directions (ex and ey), as suggested by
Deser [1993], and their values were chosen to minimize the
RMS difference between climatological seasonal mean
wind components reported by COADS and those derived
from SLP. For the Indo-Pacific region, the best ex is 1.4 105 s1 and the best ey is 3.0 105 s1. Values of u and
v at the boundaries of the domain (coastlines, 40S, and
40N) were calculated from a three-way balance between
the pressure gradient, Coriolis acceleration, and linear drag
(advection and diffusion were ignored). Pressure gradients
at 2 2 grid spacing were obtained from the Smith and
Reynolds [2004] Extended Reconstructed SLP data set,
which uses land station SLP measurements and statistical
information from a reanalysis to supplement observations
and reduce noise in COADS SLP. Winds estimated from
SLP were averaged to 5 10 grid boxes, and surface
divergence was subsequently calculated in the same manner
as for winds from COADS.
3. Results
3.1. Spatial Patterns of Trends
[10] Figure 1 displays the climatological distributions of
annual mean surface-observed upper-level cloud cover,
‘‘surface view’’ low-level cloud cover, COADS wind, and
surface divergence over the tropical Indo-Pacific Ocean.
The far eastern South Pacific is not included in the domain
since ship sampling is very sparse in that area, and magenta
rectangles designate subregions that will subsequently be
discussed in greater detail. As expected, climatological
surface convergence occurs broadly over the tropical western Pacific Ocean and the Indian Ocean and more narrowly
along the Intertropical Convergence Zone (ITCZ) and South
Pacific Convergence Zone (SPCZ). Upper-level cloud cover
is most prevalent in these regions, indicating that it is
predominantly associated with deep convection (Figure 1a)
[e.g., Fu et al., 1994; Norris, 1998]. ISCCP reports more
upper-level cloud cover in convective areas than does
EECRA (not shown), probably because satellites detect
more thin cirrus than do surface observers. Less upper-level
cloud cover occurs in regions of surface divergence where
mean downward motion reduces the relative humidity of the
middle and upper troposphere (Figure 1a). Low-level cloudiness has relative maxima not only in locations of subtropical stratocumulus west of Baja California, Australia, and
Peru [Klein and Hartmann, 1993; Norris, 1998] but also
occurs frequently in areas of deep convection, at least as
seen from the surface (Figure 1b).
[11] The spatial pattern of upper-level cloud cover change
between 1952 and 1997 accounted for by linear trends is
displayed in Figure 2. Note that linear trends are used as a
convenient way to summarize variability, not because the
trends are necessarily expected to continue in the same
direction in the future. Trends were calculated using the
robust technique of determining the median of pairwise
slopes [e.g., Lanzante, 1996], but the least-squares method
produced very similar results. COADS wind corrected for
the intensification of scalar wind speed (Figure 2a) and
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Figure 1. Annual mean Comprehensive Ocean-Atmosphere Data Set (COADS) wind and surface
divergence with surface-observed (a) upper-level cloud cover and (b) ‘‘surface view’’ low-level cloud
cover, all averaged over 1952 –1997. The divergence contour interval is 106 s1, negative contours are
dashed, and the zero contour is thickened. Magenta rectangles designate subregions discussed in greater
detail later in the paper.
wind derived from SLP (Figure 2b) provide two independent assessments of changes in surface divergence between
1952 and 1997. The fact that the two data sets agree on the
sign of the trends over much of the tropical Indo-Pacific
Ocean is encouraging, considering the sampling uncertainty
of the COADS winds, the limitations of the method for
removing the wind speed artifact, and the approximations
involved in deriving wind from seasonal mean SLP. Sensitivity analysis indicates that the use of different linear
damping coefficients modifies the magnitude but not the
sign of the SLP-derived divergence field; this is also the
case if advection and diffusion terms are excluded (not
shown). Greater differences occur between divergence patterns for COADS winds corrected and uncorrected for the
spurious intensification of wind speed, and the secular
increase in wind speed has the effect of producing more
convergence in regions of climatological convergence and
more divergence in regions of climatological divergence.
Were COADS winds not corrected, the sign of trends in
Figure 2a would change from positive to negative over the
equatorial Indian Ocean, the western tropical Pacific, and
along the ITCZ, and from negative to positive over the
eastern subtropical South Indian Ocean and eastern subtropical North Pacific Ocean (not shown).
[12] The most robust signal seen in Figure 2 is the
enhancement of upper-level cloud cover and surface convergence anomalies over the central equatorial South Pacific
(Region A) during the latter half of the 20th century.
Figure 3 shows that island precipitation rates generally
increased during the same period, providing further confirmation that deep convection in this area became more
frequent and/or intense, as has been noted by previous
studies [e.g., Graham, 1994; Nitta and Kachi, 1994;
Morrissey and Graham, 1996; Deser et al., 2004]. Contrastingly, upper-level cloud cover and precipitation decreased and surface divergence anomalies increased over
most of the tropical western Pacific (Region B) between
1952 and 1997. Despite the direct link to deep convection,
this study does not examine trends in surface-observed
cumulonimbus cloudiness since that cloud type exhibits
dubiously large changes in zonal mean cloud amount
[London et al., 1991; Bajuk and Leovy, 1998a]. The reduction
in surface-observed upper-level cloud cover is consistent with
the decreasing trend in satellite-observed high-level cloud
cover reported by Nitta and Kachi [1994] for 1978 –1994, but
not with the decreasing trends in OLR documented by Chu
and Wang [1997] for 1974 – 1992 and by Waliser and Zhou
[1997] for 1974 – 1996. This disagreement remains even if
upper-level cloud trends are calculated for the same time
period as the OLR trends, implying that either the reported
OLR decrease over the tropical western Pacific is due to
changes in factors besides cloud cover (e.g., emissivity and
temperature) or that trends in one of the data sets are spurious.
It is suspicious that trends in OLR from the latter studies are
uniformly negative over the central equatorial Pacific, the
western equatorial Pacific, and the equatorial Indian Ocean,
Figure 2. Linear trends between 1952 and 1997 in surface-observed upper-level cloud cover with
(a) COADS wind and surface divergence and (b) sea level pressure (SLP)-derived wind and surface
divergence. Trend coefficients were multiplied by 46 years to convert them to absolute change during
1952 –1997, and divergence contours are as in Figure 1, except the interval is 0.2 106 s1.
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Figure 3. As in Figure 2, except for Hulme/Climate
Research Unit (CRU) precipitation rate with COADS wind
and surface divergence.
unlike the case for trends in upper-level cloud cover, precipitation, and surface convergence.
[13] The reduction of mean convergence in the western
Pacific and trend toward more convergence in the central
Pacific are consistent with the weakening of equatorial trade
winds associated with rising SLP in the western Pacific and
falling SLP in the eastern Pacific, as illustrated in Figure 4
[e.g., Curtis and Hastenrath, 1999; Deser et al., 2004]. This
weakening of the Pacific Ocean cell of the Walker circulation has also reduced equatorial upwelling and meridional
overturning in the upper ocean, thus leading to increased
SST in central and eastern equatorial Pacific, as seen in
Figure 4 [Liu and Huang, 2000; McPhaden and Zhang,
2002]. The spatial pattern of Pacific Ocean SST trends
resembles the interdecadal ‘‘ENSO-like’’ mode identified
by Zhang et al. [1997]. In contrast to the situation during El
Niño episodes, the long-term decrease in the strength of the
Walker circulation is not associated with an unambiguous
long-term increase in the strength of the Hadley circulation
over the central Pacific Ocean. The COADS wind trends
displayed in Figure 2a exhibit a large southward crossequatorial component instead of a simple increase in equatorward flow in both hemispheres that would correspond to
a stronger Hadley circulation.
[14] Figure 2 exhibits decreasing trends in upper-level
cloud cover over the subtropical North Pacific (Region C),
the subtropical South Pacific (Region D), and the equatorial
Indian Ocean (Region E) that are largely consistent with the
reduction in precipitation seen in the available island
precipitation records (Figure 3) and reported by previous
studies [e.g., Morrissey and Graham, 1996; Deser et al.,
2004]. COADS and SLP-derived winds within Region C
agree that mean convergence has weakened along the
climatological ITCZ (i.e., increasing divergence anomalies)
but disagree on the sign of trends in the climatologically
divergent area north of the ITCZ. Given the infrequency of
convection in such climatologically divergent regions, variations in upper-level cloudiness are probably more closely
related to variations in advected tropospheric humidity
rather than variations in local surface divergence. The
mixed signs of COADS surface divergence trends in Region
D may result from inadequate sampling, but SLP-derived
winds show increasing surface divergence over most of the
area. The trend patterns of upper-level cloud cover and
surface divergence over the central Pacific correspond to a
weakening of the ITCZ and a northward broadening of
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Figure 4. As in Figure 2, except for sea surface
temperature (SST), SLP, and SLP-derived winds. The SLP
contour interval is 0.2 hPa.
SPCZ. Interpretation of atmospheric circulation changes in
Region E is more difficult, since Figures 2a and 2b exhibit
opposite trends in surface wind direction in this area, with
Figure 2a indicating a weakening and Figure 2b indicating a
strengthening of the Indian Ocean cell of the Walker
circulation. Figure 4, however, does show increasing SLP
along the equatorial Indian Ocean and decreasing SLP in the
adjacent subtropics, which is consistent with a weakening of
mean surface convergence (i.e., increasing divergence
anomalies) in Region E.
[15] Unlike the case for upper-level cloud cover, the
spatial pattern of ‘‘surface view’’ low-level cloud cover
trends does not bear any resemblance to the spatial pattern
of surface divergence trends (Figure 5). Low-level cloud
cover, primarily cumulus, is instead rising almost everywhere over the tropical Indo-Pacific Ocean. ‘‘Satellite
view’’ low-level cloud cover trends exhibit an even more
geographically uniform enhancement, aside from an area of
reduced cloud cover west of Australia (not shown). The
ubiquitous low-level cloud cover increase occurs over the
entire tropical ocean [Norris, 1999; Norris, 2005] and is
clearly not sensitive to long-term changes in atmospheric
circulation. If an artifact is present in the data, though, it
must be subtle, since upper-level cloud cover trends appear
quite realistic, despite being partially calculated from ‘‘surface view’’ low-level cover in the random overlap equation.
In fact, the connection between upper-level, low-level, and
total cloud cover through the random overlap equation
implies that total cloud cover trends must be partially
spurious if low-level trends are spurious but upper-level
trends are not. The occurrence of an artificial increase in
Figure 5. As in Figure 2, except for ‘‘surface view’’ lowlevel cloud cover with COADS wind and surface
divergence.
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Figure 6. Trends in COADS wind and surface divergence with (a) surface-observed upper-level cloud
cover and (b) ‘‘surface view’’ low-level cloud cover that result from regression on the Niño3.4 index
(SST averaged over 5S – 5N, 120 – 170W). Trends were obtained by multiplying regression
coefficients by the absolute change in Niño3.4 SST between 1952 and 1997 determined by a linear
trend. The divergence contours are as in Figure 1, except the interval is 0.1 106 s1.
total cloud cover in addition to real changes would explain
why connections between trends in atmospheric circulation
and trends in total cloud cover documented by Curtis and
Hastenrath [1999] are less clear than those presented in this
study. Detrending the total cloud cover data prior to
analysis, as was done by Deser et al. [2004], mitigates the
effect of the questionable cloud cover increase, and the
resulting spatial pattern of interdecadal total cloud cover
change reported in that study is very similar to that shown in
Figure 2.
[16] The increase in upper-level cloud cover over the
central equatorial Pacific and the corresponding decreases
over the adjacent subtropics, the tropical western Pacific,
and the equatorial Indian Ocean appear to be the dominant
mode of variability in the Indo-Pacific region. This pattern
occurs in all seasons of the year and resembles the interannual cloud response to the warm phase of the ENSO
phenomenon (El Niño) [e.g., Deser and Wallace, 1990;
Fu et al., 1996; Bajuk and Leovy, 1998b; Park and Leovy,
2004]. The tendency for more warm events and fewer cold
events since the late 1970s [Trenberth and Hoar, 1996]
raises the question as to whether changes in the frequency
and intensity of El Niño are responsible for the long-term
cloud trends. This hypothesis can be tested by regressing
cloud and meteorological data onto the Niño3.4 index (SST
averaged over 5S – 5N, 120 –170W) and then multiplying
the resulting coefficients by the change in Niño3.4 SST
between 1952 and 1997 accounted for by a linear trend, as
was done in Figure 6. Note that values were detrended prior
to the regression calculation so that the predicted trend
would be independent from the input data.
[17] Trends in upper-level cloud cover and surface convergence over the central equatorial Pacific in response to El
Niño as seen in Figure 6 are similar to those seen in Figure 2
but are more equatorially symmetric and are centered 20
farther west. Parallel offsets occur in the horseshoe-shaped
band of reduced upper-level cloud cover and surface divergence anomalies that surround the area of enhanced convection in the central equatorial Pacific. ‘‘Surface view’’
low-level cloud cover changes (Figure 6b) generally have
the same sign as upper-level cloud cover changes
(Figure 6a), indicating that for the central and western
tropical Pacific, low-level cloud variations due to El Niño
are primarily associated with changes in the occurrence of
deep convection. Although the meteorological factors gov-
erning the fractional area of cumulus produced by intermittent shallow convection are not well understood, the fact
that the spatial pattern of low-level cloud cover is related to
circulation changes on El Niño timescales (Figure 6b) but
not on multidecadal timescales (Figure 5) provides further
evidence that the low-level cloud trends displayed in
Figure 5 are unrealistic.
[18] Comparison of contour intervals in Figures 2 and 6
demonstrates that a linear relationship to Niño3.4 SST
explains only a third of the observed trend in upper-level
cloud cover and half of the observed trend in surface
divergence. Although observational uncertainties will reduce the magnitude of the regression coefficients to some
extent, such reductions do not explain the dissimilar upperlevel cloud trend magnitudes in Figures 2 and 6. Rather, the
differences stem from the fact that the upper-level cloud
trend amplitude normalized by the interannual standard
deviation is two or three times larger than the normalized
Niño3.4 SST trend amplitude. This is not the case for
precipitation, which has a normalized trend amplitude
comparable to the normalized Niño3.4 SST trend amplitude
(not shown).
3.2. Regional Time Series
[19] In order to examine temporal variability in cloudiness and meteorological parameters in greater detail, time
series for each of the designated subregions were created by
averaging seasonal 5 10 anomalies with weighting
according to grid box area and, except for Hulme/CRU
precipitation, ocean fraction. Missing data other than
Hulme/CRU precipitation were assigned a value of zero
and averaged with the rest of the 5 10 values, but this
generated negligible bias since very few data are missing.
Figures 7– 11 display the resulting time series, and Table 1
lists linear correlations between selected time series for each
subregion. Despite the differing methods of observation, the
presence of sampling uncertainty, and retrieval errors due to
Mount Pinatubo volcanic aerosols [Luo et al., 2002], Table 1
shows that EECRA and ISCCP upper-level cloud cover are
well correlated (r = 0.65 – 0.87). With the exception of
Region D, similarly high correlations occur between estimated –LW CCRF and ERBS OLR (r = 0.59 – 0.89) and
between estimated – SW CCRF and ERBS RSW (r = 0.57–
0.88), even though many factors besides cloud cover affect
ERBS all-sky radiation flux, such as Pinatubo volcanic
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and therefore more representative of area-wide oceanic
precipitation (Regions A, B, and D). Note that the Hulme/
CRU data set underestimates the large 1997 – 1998 precipitation anomaly in Figure 7 because many central equatorial
Pacific island records do not extend into the 1990s. Time
series of surface divergence calculated from COADS winds
and derived from SLP have best correspondence where the
ENSO signal is strongest (Figures 7, 9, and 10). The
differing COADS and SLP-derived divergence trends in
Figure 8 may result from inaccurate correction of the secular
increase in COADS wind speed or limitations of the
derivation from SLP.
[20] The most prominent signals in Figures 7 and 8 are
positive anomalies in upper-level cloud cover, precipitation,
and surface convergence over the central equatorial South
Pacific (Region A) and negative anomalies in the same over
the western tropical Pacific (Region B) during El Niño
events. Opposite and smaller variations occur during cold
La Niña events. Estimated CCRF anomalies agree remarkably well with ERBS all-sky radiation anomalies in Figure 7,
implying that variability in all-sky radiation flux over the
central equatorial South Pacific is predominantly due to
variability in cloud cover. ISCCP upper-level cloud anomalies have greater amplitude than EECRA anomalies, probably because the satellites detect thin cirrus clouds that go
unreported by surface observers. Figures 9 – 11 exhibit
ENSO-related cloud and meteorological variations as well,
but with less amplitude and in the presence of substantial
Figure 7. Time series of seasonal values of cloud and
meteorological parameters averaged over Region A (15S–
5N, 170E –120W) with weighting by grid box area and
ocean fraction (except for Hulme/CRU precipitation). Thin
blue lines designate Extended Edited Cloud Report Archive
(EECRA) upper-level and ‘‘satellite-view’’ low-level cloud
cover (%-sky-cover), estimated longwave (LW) and shortwave (SW) cloud cover radiative forcing (CCRF) (W m2),
Hulme/CRU precipitation (cm month1), SST (C), and
COADS surface divergence (107 s1). Thick red lines
designate International Satellite Cloud Climatology Project
(ISCCP) upper-level and low-level cloud cover (%), Earth
Radiation Budget Satellite (ERBS) outgoing LW radiation
(OLR) and reflected SW radiation (RSW) (W m2), Climate
Prediction Center (CPC) Merged Analysis of Precipitation
(CMAP) precipitation (cm month1), and SLP-derived
surface divergence (107 s1). Note that the signs of the
OLR, SW CCRF, and divergence time series have been
reversed for better comparability. Anomalies are referenced
from the 1985 – 1989 mean, and 1-2-1 smoothing was
applied.
aerosols during 1991– 1993 [Wielicki et al., 2002a]. Contrastingly, EECRA and ISCCP ‘‘satellite view’’ low-level
cloud cover exhibit substantial disagreement (r < 0.29) due
to likely artifacts in one or both cloud data sets [Campbell,
2004; Norris, 2005]. Since CMAP data include rain gauge
measurements when available, it is not surprising that
Hulme/CRU and CMAP precipitation time series are better
correlated in subregions where islands are widely distributed
Figure 8. As in Figure 7, except for Region B (10S–
15N, 100– 160E).
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using the least squares method since it offers a straightforward procedure for determining confidence intervals [von
Storch and Zwiers, 2001], but the method of pairwise slopes
produced similar results. The confidence intervals are based
on the independent combination of the uncertainty of trend
fitting and the uncertainties of the individual time series
values. Uncertainties of individual values were determined
by assessing the amount of difference between EECRA and
ISCCP upper-level cloud cover, estimated CCRF and ERBS
all-sky flux, Hulme/CRU and CMAP precipitation, and
COADS and SLP-derived divergence. Specifically, the
uncertainty of an arbitrary time series value was obtained
from the standard error calculated by treating the time series
variable (e.g., EECRA cloud) as the independent parameter
and the analogous data set (e.g., ISCCP cloud) as the
predicted parameter while assuming a 1:1 slope between
them. All computations assumed that the effective sample
size was one half of the nominal sample size, a ratio that
leads to a minor overestimation of the effective sample size
in Region A and underestimation elsewhere, according to
autocorrelation calculations loosely based upon Zwiers and
von Storch [1995].
[22] As seen in Table 2, statistically significant downward trends in upper-level cloud cover occur in Regions
B, C, D, and E, and a nearly significant upward trend
occurs in Region A. Although trends in precipitation and
surface divergence are mostly not statistically different
from zero, they are nevertheless physically consistent
Figure 9. As in Figure 7, except for Region C (5– 25N,
120 –180W).
interannual variability. Table 1 correspondingly shows that
correlations between EECRA upper-level cloud cover and
Niño 3.4 SST are much stronger for the equatorial Pacific
(Regions A and B) than for the subtropical Pacific and
equatorial Indian Oceans (Regions C, D, and E). The
relatively high correlations between upper-level cloud cover
and CMAP precipitation in the latter subregions (r = 0.53 –
0.70) demonstrate that surface observers are reporting real
interannual cloud variations in these areas. Despite the weak
ENSO signal, upper-level cloud cover and surface divergence anomalies have substantial consistency over the
subtropical North Pacific (Region C). This is not the case
for the subtropical South Pacific and equatorial Indian
Ocean (Regions D and E), presumably because the smaller
number of surface observations in those areas leads to
greater sampling uncertainty, something to which divergence calculations are particularly susceptible.
[21] In addition to ENSO and interannual variability,
long-term trends are apparent in many of the cloud and
meteorological time series, and the overall absence of
substantial decadal variability in Figures 7 – 11 provides a
posteriori justification that the grid box trends displayed in
Figures 2 – 6 were generally good statistical models for
summarizing cloud and meteorological changes between
1952 and 1997. Table 2 lists 46-year trend values along with
95% confidence intervals for some of the time series
displayed in Figures 7 – 11. The trends were calculated
Figure 10. As in Figure 7, except for Region D (20 – 30S,
130– 180W).
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NORRIS: TROPICAL INDO-PACIFIC UPPER CLOUD TRENDS
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Figure 11. As in Figure 7, except for Region E (10S–
10N, 60–100E).
with the direction of the upper-level cloud cover trends.
LW and SW CCRF trends are statistically significant in
all subregions, with changes in the absolute magnitude of
CCRF having the same sign as changes in upper-level
cloud cover. All else being equal, the trends toward more
positive LW CCRF and more negative SW CCRF over
the central equatorial South Pacific (Region A) have
largely acted to reduce atmospheric cooling and surface
heating. Conversely, less positive LW CCRF and less
negative SW CCRF over the equatorial Indian Ocean and
subtropical and western regions of the Pacific Ocean
have acted to enhance atmospheric cooling and surface
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heating. When averaging over the tropical entire IndoPacific Ocean, the weakening of LW and SW CCRF
dominates, as demonstrated by Figure 6c of Norris
[2005]. Note that the true trends in SW CCRF may
actually be 2 – 4 W m2 more positive than those
reported in Table 2, since the possibly spurious increase
in surface-observed low-level cloud cover would introduce
a spurious negative trend in SW CCRF (with no effect on
LW CCRF). It should also be kept in mind that CCRF
time series and trends do not represent multidecadal
variations in all-sky radiation flux, which are affected by
changes in greenhouse gas concentrations, aerosol characteristics, temperature, humidity, and other factors in addition to cloud cover.
[23] Previous researchers have identified interdecadal
‘‘regime shifts’’ in time series of Pacific SST and other
meteorological parameters [e.g., Graham, 1994; Zhang et
al., 1997], and several time series in Figure 7 can be
interpreted as exhibiting an abrupt change during the mid1970s. Deser et al. [2004] examined multidecadal variability in a tropical Pacific cloud index constructed by
subtracting surface-observed total cloud cover anomalies
averaged over 0 – 18S, 130 – 176W (approximately
corresponding to Region A) from anomalies averaged
over 0 – 24N, 140– 176W (approximately corresponding
to Region C). The resulting time series indicated that total
cloud cover south of the Equator was relatively larger
during 1925 – 1946 and 1977 – 1997 and relatively smaller
during 1906 – 1924 and 1947 – 1976. Additionally, the
spatial patterns of differences in total cloud cover between the various epochs exhibit relatively more cloud
cover over the central equatorial South Pacific and less
cloud cover in the surrounding areas alternating with
relatively less cloud cover over the central equatorial
South Pacific and more cloud cover in the surrounding
areas. The resemblance of the spatial patterns of Deser et
al. to that in Figure 2 of the present study implies that
the 1952 – 1997 trends in upper-level cloud cover are
actually part of a longer sequence of fluctuations that
persist in regimes lasting 20– 30 years at a time. This,
however, does not exclude the possibility of nonstationary
trends in upper-level cloud cover, since Deser et al.
detrended the cloud data prior to carrying out interdecadal
differencing. The tropical mean increase in OLR and
decrease in RSW from 1985 to 1997 reported by ERBS
[Wielicki et al., 2002a] are consistent with the declining
trend in Indo-Pacific mean upper-level cloud cover, but
Table 1. Linear Correlations Between Regional Time Series Displayed in Figures 7 – 11a
Correlation Parameters
Region A
Region B
Region C
Region D
Region E
EECRA upper - ISCCP upper
EECRA low - ISCCP low
Hulme precipitation - CMAP precipitation
COADS divergence – Derived divergence
– LW CCRF- ERBS OLR
– SW CCRF- ERBS RSW
EECRA upper - Hulme precipitation
EECRA upper - CMAP precipitation
EECRA upper - COADS divergence
EECRA upper - Derived divergence
EECRA upper - Niño3.4 index
+0.78
0.31
+0.68
+0.80
+0.83
+0.81
+0.68
+0.82
0.77
0.67
+0.63
+0.87
+0.17
+0.80
+0.28
+0.89
+0.88
+0.63
+0.87
0.53
0.39
0.58
+0.65
0.07
+0.54
+0.70
+0.59
+0.57
+0.47
+0.67
0.43
0.56
0.16
+0.66
+0.29
+0.67
+0.30
+0.36
+0.13
+0.40
+0.53
0.27
0.30
0.35
+0.69
+0.13
+0.42
+0.17
+0.67
+0.62
+0.17
+0.70
0.27
0.02
0.32
a
Note that the signs of the CCRF time series have been reversed for better comparability to OLR and RSW.
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Table 2. Changes From 1952 to 1997 for Regional Time Series Displayed in Figures 7 – 11, With 95% Confidence Intervalsa
Parameters
Region A
Region B
Region C
Region D
Region E
EECRA upper, %-sky-cover
LW CCRF, W m2
SW CCRF, W m2
Hulme precipitation, cm month1
COADS divergence, 107 s1
Derived divergence, 107 s1
+4.4 ± 4.5
+4.9 ± 4.6
7.0 ± 4.9
+3.8 ± 3.9
3.5 ± 3.3
2.4 ± 2.0
5.8 ± 3.7
9.2 ± 4.5
+7.8 ± 4.5
1.4 ± 1.8
+0.8 ± 1.4
+2.3 ± 1.3
5.7 ± 4.0
7.2 ± 4.3
+3.5 ± 3.4
2.0 ± 3.1
+1.1 ± 2.4
+1.4 ± 1.6
3.8 ± 3.4
4.6 ± 4.2
+2.5 ± 4.3
1.6 ± 1.6
0.3 ± 3.2
+0.7 ± 3.1
4.0 ± 3.2
5.6 ± 4.1
+4.8 ± 4.3
0.5 ± 1.6
+1.1 ± 1.4
+0.5 ± 1.0
a
Changes were calculated by fitting least-squares linear trends to the time series and multiplying by 46 years. Confidence intervals were calculated from
uncertainties of individual time series values combined with the uncertainty in trend fitting. Uncertainties in individual values were determined by
comparison to values from a parallel data set. The effective sample size was assumed to be one-half the nominal sample size.
their lack of ENSO-like patterns [Allan and Slingo, 2002]
suggests that they cannot be explained by the trends in
atmospheric circulation described in this study.
4. Summary
[24] This study examined spatial trend patterns and regional time series of surface-observed upper-level cloud
cover and related meteorological observations over the
tropical Indo-Pacific Ocean during 1952– 1997. Surfaceobserved upper-level cloud cover was calculated from
EECRA synoptic reports of total and low-level cloud cover
reports assuming random overlap, and ‘‘satellite view’’ lowlevel cloud cover was determined as the difference between
total and upper-level cloud cover. The radiative effects of
surface-observed cloud cover anomalies, defined as LW and
SW CCRF, were also estimated. Regional time series of
EECRA and ISCCP upper-level cloud cover are well
correlated, thus providing confidence that surface-observed
upper-level cloud variations are real. Contrastingly, EECRA
and ISCCP ‘‘satellite view’’ low-level cloud cover time
series largely disagree, probably due to artifacts in one or
both data sets. Despite the questionable low-level cloud
variations, time series of estimated LW and SW CCRF
exhibit good correspondence to time series of ERBS OLR
and RSW in most regions. This implies that cloud cover
anomalies are the largest contributors to variability in allsky radiation flux over the tropical Indo-Pacific Ocean.
Upper-level cloud cover anomalies are also positively
correlated with precipitation and surface convergence
anomalies. Generally similar results were obtained for
surface convergence calculated from COADS winds corrected for an artificial increase in scalar wind speed and for
winds alternatively derived from SLP. El Niño is the biggest
signal in the cloud and meteorological time series, especially
in the western and central equatorial Pacific, but physically
consistent long-term trends occur in upper-level cloud
cover, precipitation, and surface divergence as well.
[25] Between 1952 and 1997, upper-level cloud cover
increased by about 4%-sky-cover over the central equatorial
South Pacific, decreased by about 6%-sky-cover over the
western tropical and northern subtropical Pacific, and decreased by about 4%-sky-cover over the southern subtropical Pacific and equatorial Indian Ocean. All else being
equal, trends in estimated LW CCRF show that radiative
cooling, primarily occurring in the atmosphere, has declined
by about 5 W m2 over the central equatorial South Pacific
and risen by 5– 9 W m2 elsewhere. Corresponding trends
in estimated SW CCRF show that radiative heating,
primarily occurring at the surface, has declined by about
7 W m2 over the central equatorial South Pacific and risen
by 3 –8 W m2 elsewhere. Assessment of uncertainties in
trend fitting and individual time series values indicates that
nearly all of these changes are statistically different from
zero. Trends in low-level cloud cover are positive over
almost all of the tropical Indo-Pacific Ocean and may be
spurious since they evidently have no relationship to
changes in atmospheric circulation. If there were no lowlevel cloud cover increase, then trends in SW CCRF would
lead to 2– 4 W m2 more heating than those described
above. Setting aside the effects of other influential processes, the direct radiative impact of the cloud cover trends
would act to decrease SST in the central equatorial Pacific
and increase SST elsewhere. Thus the long-term warming
of the tropical western Pacific and Indian Oceans may result
in part from the reduction in cloud cover, as occurs on
interannual timescales in response to El Niño [Klein et al.,
1999]. Owing to the dominance of atmosphere-ocean
dynamics in the central Pacific, however, it is difficult to
determine how much the enhancement of cloud cover in
that area has acted as a negative feedback on SST or
affected the occurrence of El Niño.
[26] The geographical distribution of upper-level cloud
can largely be explained by changes in atmospheric circulation as seen through precipitation and surface divergence.
Island precipitation trends, although sparse in many places,
are mostly positive where upper-level cloud trends are
positive and negative where they are negative. Weakening
of equatorial trade winds has reduced convergence, convection, and upper-level cloudiness in the western Pacific and
enhanced convergence, convection, and upper-level cloudiness in the central Pacific. Surface convergence has also
decreased in the central Pacific ITCZ and the equatorial
Indian Ocean, which is consistent with diminishing upperlevel cloud cover in those areas. Because the area with
decreasing cloud cover is larger than the area with increasing cloud cover, upper-level cloud cover averaged over the
entire tropical Indo-Pacific region has experienced a net
decline. The spatial pattern of upper-level cloud trends
generally resembles the upper-level cloud response to El
Niño, but linear regression on the Niño3.4 SST index
demonstrates that the trend magnitudes are three times
larger than what would be expected from the historical
increase in the frequency and intensity of El Niño between
1952 and 1997. The upper-level cloud changes instead
appear to be, at least in part, a manifestation of multidecadal
‘‘ENSO-like’’ variations that change every 20 –30 years.
The possibility remains, however, that nonstationary trends
are also present, and more research is needed to determine
whether the observed changes in tropical Indo-Pacific
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upper-level cloud cover between 1952 and 1997 are solely
low-frequency natural variability or also a response to
global climate change.
[27] Acknowledgments. An NSF CAREER award, ATM02-38527,
and a NASA GWEC grant, NAG5-11731, supported this work. T. Wong
graciously provided 36-day ERBS data very similar to those used by
Wielicki et al. [2002b]. Gridded land and island rain gauge data
(g55wld0098.dat version 1.0) were constructed and supplied by Mike
Hulme at the Climatic Research Unit, University of East Anglia, who
was supported by the UK Department of the Environment, Transport and
the Regions (Contract EPG 1/1/85). ERBE CRF were obtained from the
NASA Langley Research Center Atmospheric Sciences Data Center (eosweb.larc.nasa.gov), ISCCP cloud from the NASA Goddard Institute for
Space Studies (isccp.giss.nasa.gov), Kaplan SST from the Lamont-Doherty
Earth Observatory Data Library (ingrid.ldeo.columbia.edu), CMAP precipitation from the Climate Analysis Section at the National Center for
Atmospheric Research (www.cgd.ucar.edu/cas/guide/), Smith and Reynolds
Extended Reconstructed SLP from the National Climatic Data Center
(ftp.ncdc.noaa.gov/pub/data/erslp/), and COADS wind from the Climate
Diagnostics Center (www.cdc.noaa.gov/coads/). The author thanks B. D.
Norris for editing the manuscript.
References
Allan, R. P., and A. Slingo (2002), Can current climate model forcings
explain the spatial and temporal signatures of decadal OLR variations?,
Geophys. Res. Lett., 29(7), 1141, doi:10.1029/2001GL014620.
Bajuk, L. J., and C. B. Leovy (1998a), Are there real interdecadal variations
in marine low clouds?, J. Clim., 11, 2910 – 2921.
Bajuk, L. J., and C. B. Leovy (1998b), Seasonal and interannual variations
in stratiform and convective clouds over the tropical Pacific and Indian
Oceans from ship observations, J. Clim., 11, 2922 – 2941.
Barkstrom, B., et al. (1989), Earth Radiation Budget Experiment (ERBE)
archival and April 1985 results, Bull. Am. Meteorol. Soc., 70, 1254 –
1262.
Campbell, G. G. (2004), View angle dependence of cloudiness and the
trend in ISCCP cloudiness, paper presented at 13th Conference on
Satellite Meteorology and Oceanography, Am. Meteorol. Soc., Norfolk,
Va. (Available at http://ams.confex.com/ams/pdfpapers/79,041.pdf)
Cardone, V. J., J. G. Greenwood, and M. A. Cane (1990), On trends in
historical marine wind data, J. Clim., 3, 113 – 127.
Cess, R. D., and P. M. Udelhofen (2003), Climate change during 1985 –
1999: Cloud interactions determined from satellite measurements, Geophys. Res. Lett., 30(1), 1019, doi:10.1029/2002GL016128.
Chen, J., B. E. Carlson, and A. D. Del Genio (2002), Evidence for strengthening of the tropical general circulation in the 1990s, Science, 295, 838 –
841.
Chu, P.-S., and J.-B. Wang (1997), Recent climate change in the tropical
western Pacific and Indian Ocean regions as detected by outgoing longwave radiation records, J. Clim., 10, 636 – 646.
Curtis, S., and S. Hastenrath (1999), Long-term trends and forcing mechanisms of circulation and climate in the equatorial Pacific, J. Clim., 12,
1134 – 1144.
Deser, C. (1993), Diagnosis of the surface momentum balance over the
tropical Pacific Ocean, J. Clim., 6, 64 – 74.
Deser, C., and J. M. Wallace (1990), Large-scale atmospheric circulation
features of warm and cold episodes in the tropical Pacific, J. Clim., 3,
1254 – 1281.
Deser, C., A. S. Phillips, and J. W. Hurrell (2004), Pacific interdecadal
climate variability: Linkages between the Tropics and North Pacific
during boreal winter since 1900, J. Clim., 17, 3109 – 3124.
Fu, R., A. D. Del Genio, and W. B. Rossow (1994), Influence of ocean
surface conditions on atmospheric vertical thermodynamic structure and
deep convection, J. Clim., 7, 1092 – 1108.
Fu, R., W. T. Liu, and R. E. Dickinson (1996), Response of tropical clouds
to the interannual variation of sea surface temperature, J. Clim., 9, 616 –
634.
Graham, N. E. (1994), Decadal-scale climate variability in the tropical and
North Pacific during the 1970s and 1980: Observations and model results, Clim. Dyn., 10, 135 – 162.
Hahn, C. J., and S. G. Warren (1999), Extended edited synoptic cloud
reports from ships and land stations over the globe, 1952 – 1996, Rep.
NDP026C, 71 pp., Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl.
Lab., Oak Ridge, Tenn. (Available at http://cdiac.esd.ornl.gov/epubs/ndp/
ndp026c/ndp026c.html)
Hahn, C. J., S. G. Warren, and J. London (1995), The effect of moonlight
on observation of cloud cover at night, and application to cloud climatology, J. Clim., 8, 1429 – 1446.
D21110
Hulme, M. (1992), A 1951 – 80 global land precipitation climatology for the
evaluation of general circulation models, Clim. Dyn., 7, 57 – 72.
Hulme, M. (1994), Validation of large-scale precipitation fields in general
circulation models, in Global Precipitation and Climate Change, NATO
ASI Ser., edited by M. Desbois and F. Desalmand, 466 pp., Springer, New
York.
Hulme, M., T. J. Osborn, and T. C. Johns (1998), Precipitation sensitivity to
global warming: comparison of observations with HadCM2 simulations,
Geophys. Res. Lett., 25, 3379 – 3382.
Kaplan, A., M. Cane, Y. Kushnir, A. Clement, M. Blumenthal, and
B. Rajagopalan (1998), Analyses of global sea surface temperature
1856 – 1991, J. Geophys. Res., 103, 18,567 – 18,589.
Kinter, J. L., M. J. Fennessy, V. Krishnamurthy, and L. Marx (2004), An
evaluation of the apparent interdecadal shift in the tropical divergent
circulation in the NCEP-NCAR reanalysis, J. Clim., 17, 349 – 361.
Klein, S. A., and D. L. Hartmann (1993), The seasonal cycle of low stratiform clouds, J. Clim., 6, 1587 – 1606.
Klein, S. A., B. J. Soden, and N.-C. Lau (1999), Remote sea surface
temperature variations during ENSO: Evidence for a tropical atmospheric
bridge, J. Clim., 12, 917 – 932.
Lanzante, J. R. (1996), Resistant, robust and non-parametric techniques for
the analysis of climate data: theory and examples including applications
to historical radiosonde station data, Int. J. Climatol., 16, 1197 – 1226.
Liu, Z., and B. Huang (2000), Cause of tropical Pacific warming trend,
Geophys. Res. Lett., 27, 1935 – 1938.
London, J., S. G. Warren, and C. J. Hahn (1991), Thirty-year trend of
observed greenhouse clouds over the tropical oceans, Adv. Space Res.,
11, 45 – 49.
Luo, Z., W. B. Rossow, T. Inoue, and C. J. Stubenrauch (2002), Did the
eruption of Mt. Pinatubo volcano affect cirrus properties?, J. Clim., 15,
2806 – 2820.
McPhaden, M. J., and D. Zhang (2002), Slowdown of the meridional overturning circulation in the upper Pacific Ocean, Nature, 415, 603 – 608.
Moore, B., W. L. Gates, L. J. Mata, A. Underdal, R. J. Stouffer, B. Bolin,
and A. Ramirez Rojas (2001), Advancing our understanding, in Climate
Change 2001: The Scientific Basis, edited by J.T. Houghton et al.,
pp. 769 – 786, Cambridge Univ. Press, New York.
Morrissey, M. L., and N. E. Graham (1996), Recent trends in rain gauge
precipitation measurements from the Tropical Pacific: Evidence for an
enhanced hydrologic cycle, Bull. Am. Meteorol. Soc., 77, 1207 – 1219.
Murphree, T., and H. van den Dool (1988), Calculating tropical winds from
the time mean sea level pressure fields, J. Atmos. Sci., 45, 3269 – 3282.
Nitta, T., and M. Kachi (1994), Interdecadal variations of precipitation over
the tropical Pacific and Indian Oceans, J. Meteorol. Soc. Jpn., 72, 823 –
831.
Nitta, T., and S. Yamada (1989), Recent warming of tropical sea surface
temperature and its relationship to the Northern Hemisphere circulation,
J. Meteorol. Soc. Jpn., 67, 375 – 383.
Norris, J. R. (1998), Low cloud type over the ocean from surface observations. part II: geographical and seasonal variations, J. Clim., 11, 383 –
403.
Norris, J. R. (1999), On trends and possible artifacts in global ocean cloud
cover between 1952 and 1995, J. Clim., 12, 1864 – 1870.
Norris, J. R. (2005), Multidecadal changes in near-global cloud cover and
estimated cloud cover radiative forcing, J. Geophys. Res., 110, D08206,
doi:10.1029/2004JD005600.
Park, S., and C. B. Leovy (2004), Marine low-cloud anomalies associated
with ENSO, J. Clim., 17, 3448 – 3469.
Ramage, C. S. (1987), Secular change in reported surface wind speeds over
the ocean, J. Clim. Appl. Meteorol., 26, 525 – 528.
Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom,
E. Ahmad, and D. Hartmann (1989), Cloud-radiative forcing and climate:
Results from the Earth Radiation Budget Experiment, Science, 243, 57 –
63.
Rossow, W. B., and R. A. Schiffer (1999), Advances in understanding
clouds from ISCCP, Bull. Am. Meteorol. Soc., 80, 2261 – 2287.
Rossow, W. B., A. W. Walker, D. E. Beuschel, and M. D. Roiter (1996),
International Satellite Cloud Climatology Project (ISCCP) Documentation of New Cloud Datasets, Rep. WMO/TD-No. 737, 115 pp., World
Meteorol. Org., Geneva. (Available at http://isccp.giss.nasa.gov/docs/
documents.html)
Smith, T. M., and R. W. Reynolds (2004), Reconstruction of monthly mean
oceanic sea level pressure based on COADS and station data, J. Atmos.
Ocean. Technol., 21, 1272 – 1282.
Trenberth, K. E. (2002), Changes in tropical clouds and radiation, Science,
296, 2095a.
Trenberth, K. E., and T. J. Hoar (1996), The 1990 – 1995 El Niño-Southern
Oscillation event: Longest on record, Geophys. Res. Lett., 23, 57 – 60.
von Storch, H., and F. W. Zwiers (2001), Statistical Analysis in Climate
Research, 484 pp., Cambridge Univ. Press, New York.
11 of 12
D21110
NORRIS: TROPICAL INDO-PACIFIC UPPER CLOUD TRENDS
Waliser, D. E., and W. Zhou (1997), Removing satellite equatorial crossing
time biases from the OLR and HRC datasets, J. Clim., 10, 2125 – 2146.
Wang, P.-H., P. Minnis, B. A. Wielicki, T. Wong, and L. B. Vann (2002),
Satellite observations of long-term changes in tropical cloud and outgoing longwave radiation from 1985 to 1998, Geophys. Res. Lett.,
29(10), 1397, doi:10.1029/2001GL014264.
Ward, M. N., and B. J. Hoskins (1996), Near-surface wind over the global
ocean 1949 – 1988, J. Clim., 9, 1877 – 1895.
Wielicki, B. A., et al. (2002a), Evidence for large decadal variability in the
tropical mean radiative energy budget, Science, 295, 841 – 844.
Wielicki, B. A., et al. (2002b), Science, 296, 2095a.
Woodruff, S. D., R. J. Slutz, R. L. Jenne, and P. M. Steurer (1987), A
comprehensive ocean-atmosphere data set, Bull. Am. Meteorol. Soc.,
68, 1239 – 1250.
Wu, R., and S.-P. Xie (2003), On equatorial Pacific surface wind changes
around 1977: NCEP-NCAR Reanalysis versus COADS observations,
J. Clim., 16, 167 – 173.
D21110
Xie, P., and P. A. Arkin (1997), Global precipitation: A 17-year monthly
analysis based on gauge observations, satellite estimates, and numerical
model outputs, Bull. Am. Meteorol. Soc., 78, 2539 – 2558.
Yin, X., A. Gruber, and P. Arkin (2004), Comparison of the GPCP and
CMAP merged gauge-satellite monthly precipitation products for the
period 1979 – 2001, J. Hydrometeor., 5, 1207 – 1222.
Zhang, Y., J. M. Wallace, and D. S. Battisti (1997), ENSO-like interdecadal
variability: 1900 – 93, J. Clim., 10, 1004 – 1020.
Zwiers, F. W., and H. von Storch (1995), Taking serial correlation into
account in tests of the mean, J. Clim., 8, 336 – 351.
J. R. Norris, Scripps Institution of Oceanography, University of
California, San Diego, 9500 Gilman Drive, Dept. 0224, La Jolla, CA
92093-0224, USA. (jnorris@ucsd.edu)
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