From the Strüngmann Forum Report, Clouds in the Perturbed Climate System:
Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation
Edited by Jost Heintzenberg and Robert J. Charlson. 2009.
© MIT Press ISBN 978-0-262-01287-4
2
Trends in Observed Cloudiness
and Earth’s Radiation Budget
What Do We Not Know and
What Do We Need to Know?
Joel R. Norris1 and Anthony Slingo2
1Scripps
Institution of Oceanography, San Diego, La Jolla, CA, U.S.A.
Systems Science Centre, University of Reading, Reading, U.K.
2Environmental
Abstract
The response of clouds and their radiative effects to global warming represents a longstanding and considerable area of uncertainty in our understanding of climate change.
At present, it is not known whether changes in cloudiness will exacerbate, mitigate,
or have little effect on the increasing global surface temperature caused by anthropogenic greenhouse radiative forcing. Another substantial uncertainty is the magnitude of
radiative forcing resulting from the modification of cloud properties by anthropogenic
aerosols. Global climate models provide scant reliable insight regarding these issues because of their inability to parameterize correctly or otherwise represent the small-scale
convective, turbulent, and microphysical processes that control cloud properties. It is
therefore crucial to document and assess global and regional low-frequency variations
in clouds and radiation flux that have occurred over the past several decades, a period
marked by rapidly rising temperature and changes in anthropogenic aerosol emissions.
This will enable us to estimate from observations how clouds and their impacts on the
radiation budget are responding to global warming and aerosol changes. Moreover,
a trustworthy observational record will provide a good constraint on global climate
model simulations.
Previous investigators have documented multidecadal variations in various cloud
and radiation parameters, but no conclusive results are yet available. Problems include
the lack of global and quantitative surface measurements, the shortness of the available
satellite record, the inability to determine correctly cloud and aerosol properties from
satellite data, many different kinds of inhomogeneities in the data, and insufficient precision to measure the small changes in cloudiness and radiation that nevertheless can
have large impacts on the Earth’s climate. Many of these deficiencies emerge from the
18
J. R. Norris and A. Slingo
absence of an observing system designed to monitor variations in clouds and radiation
on timescales relevant to climate; to compensate, observations must be assembled from
a system originally designed for purposes of weather forecasting. Although we cannot
go back in time to make more and better observations, we need to improve our processing of the available historical measurements to mitigate inhomogeneities, provide better
retrievals of cloud and aerosol properties, and extend the record farther back in time.
Furthermore, it is essential to construct an observation system with sufficient stability
and longevity to measure long-term variations in cloudiness and the radiation budget
with improved precision and accuracy. Our present observing system has unfortunately
little prospect of enhancement at this time and, moreover, is in danger of future deterioration since there are no definite commitments to replace several critical instruments
when current satellite missions end.
Introduction
Clouds and Earth’s Radiation Budget
Clouds greatly impact the Earth’s radiation budget (ERB) because they reflect solar or shortwave (SW) radiation back to space and restrict the emission
of thermal or longwave (LW) radiation to space. One conventional measure
of this radiative effect is “cloud radiative forcing” (CRF); that is, the difference between actual radiation flux and what it would be if clouds were absent
(Ramanathan et al. 1989). A more proper term might be “cloud radiative effect,”
since “forcing” is usually reserved for external perturbations to the climate system rather than internal components. Regardless of etymology, we will follow
common usage and employ CRF in this chapter. Shortwave cloud radiative
forcing (SWCRF) is almost always negative, since reflection by clouds usually
causes a loss of energy from the climate system, and LWCRF is almost always
positive, since clouds usually reduce the amount of radiation emitted to space
and thus cause a gain of energy to the climate system.
Several factors influence SWCRF, including the incident solar flux, the horizontal extent of clouds, the vertically integrated amount of cloud condensate
(cloud water path), and cloud particle size, phase, and shape. Cloud water path
and cloud particle characteristics contribute both to cloud optical thickness
and cloud albedo, with the water path being the dominant factor. Variations in
cloud cover generally affect SWCRF much more than do variations in cloud
albedo. The magnitude of SWCRF varies linearly with insolation such that
cloud changes occurring during daytime, the summer season, and at low latitude have the greatest impact on ERB. The dominance of clouds in the SW portion of the spectrum is illustrated by the fact that if clouds were instantaneously
removed, the reflectivity of the planet would be approximately halved.
In addition, cloud condensate and cloud particle characteristics affect cloud
emissivity in the LW part of the spectrum, and little upwelling radiation passes
through clouds with high emissivity (low transmissivity). Usually, clouds emit
Trends in Observed Cloudiness and Earth’s Radiation Budget
19
less radiation than does the surface due to their colder temperatures, and extensive coverage by clouds with tops high in the atmosphere reduces substantially
the amount of LW flux escaping to space. Variability in the horizontal extent
of high-level clouds generally has a greater influence on LWCRF than does
variability in the specific height or emissivity of high-level clouds. Outgoing
LW radiation would increase by about 15% if clouds were instantaneously removed. The loss of radiation by SWCRF is greater than the retention of radiation by LWCRF in the global average, and clouds consequently produce a net
cooling effect in the current climate.
The basic geographical distributions and seasonal variations of reflected
SW and outgoing LW radiation are well observed and well understood. Information is also available on interannual variability, particularly the large perturbations to ERB resulting from the El Niño/Southern Oscillation (ENSO) phenomenon (Figure 2.1). These perturbations are closely related to shifts in the
spatial distribution of optically thick clouds with high tops, which are primarily
generated by deep convection. One interesting feature, which may have farreaching consequences for climate stability, is the tendency for a cancellation
between the SWCRF and LWCRF over tropical deep convection regions. It
CERES minus ERBS radiative anomalies
January 1998 minus January 1985–1989
Shortwave
30°N
0
30°S
90°E
180°
90°W
Longwave
30°N
0
30°S
90°E
180°
–100 –80 –60 –40 –20
0
90°W
20
Radiative flux anomaly
40
60
80
100
(Wm–2)
Figure 2.1 Differences in outgoing shortwave (top) and longwave (bottom) radiation
fluxes between CERES data for January 1998 (the peak of a very strong ENSO event)
and the January average of ERBE data for 1985–1989.
20
J. R. Norris and A. Slingo
has been argued that this is a fundamental property of the tropical atmosphere,
which ensures that the net cloud radiative feedback from deep tropical convection during climate change must be close to zero (Hartmann et al. 2001; cf. also
Bretherton and Hartmann, this volume).
Importance of Small Cloud and ERB Changes
and the Difficulties in Measuring Them
Global climate is very sensitive to even slight changes in clouds and radiation,
hence requiring very precise measurements of variations in these parameters.
For example, an instantaneous doubling of CO2 (100% increase) would produce about a 4 W m–2 reduction in outgoing radiation flux, which is less than
2% of the 235 W m–2 in global mean outgoing radiation. Since the trend in CO2
concentration over the span of a few decades is much smaller than a doubling,
the trend in ERB is also commensurately much smaller and thus even more
difficult to detect. Such seemingly minor departures from radiative balance,
when sustained over decades, could nevertheless produce changes in global
temperature and climate sufficient to have substantial effects on human society
and natural ecosystems. A second measurement obstacle is that radiation flux,
unlike long-lived greenhouse gases, has little spatial coherence and must be
measured from space at many locations to obtain a reasonable global average
for ERB. This must be done by satellites, which suffer the additional disadvantage of being able to make observations of upwelling radiation intensity from
only a single direction and spot on the Earth. Radiative modeling is required to
convert this into a hemispheric flux.
Small cloud changes are important because they can exert more leverage over ERB than equivalent changes in greenhouse gases. For example, a
15–20% relative increase in low-level cloud amount is presumed to counteract the radiative forcing caused by a doubling of CO2 (Slingo 1990). Lowlevel marine stratocumulus and summertime midlatitude frontal clouds have
an especially large negative net CRF and impact on ERB because, unlike the
case for tropical deep convective clouds, their SWCRF is much greater than
their LWCRF (Ramanathan et al. 1989). In contrast, optically thin high-level
clouds have a positive net CRF because they strongly reduce LW emission
while transmitting SW radiation. Since various cloud types have strikingly different radiative effects, it is not a simple matter to determine the overall global
impact of cloud changes; each cloud type and climate regime must be examined in particular. Moreover, alterations of cloud albedo, cloud emissivity, and
cloud height can affect ERB even when cloud amount remains the same. Since
insolation strongly effects the magnitude of SWCRF, shifts in the latitude or
seasonal cycle of clouds can change ERB even if mean cloud amount and
cloud properties do not change. To detect long-term variations in their properties and radiative effects, clouds must be monitored with precision everywhere
around the globe.
Trends in Observed Cloudiness and Earth’s Radiation Budget
21
Cloud Simulations and Climate Sensitivity in Global Climate Models
Over the past two decades, generations of global climate models (GCMs) have
been tuned to ERB, CRF, and cloud data to ensure that they represent at least
the current effects of clouds as well as possible. Despite this empirical contribution, many studies comparing simulated clouds with observed clouds have
found that GCMs poorly represent clouds when evaluated on terms for which
they were not explicitly tuned. In fact, GCMs frequently reproduce the observed time-averaged ERB only by compensating for errors (e.g., cloud fraction that is too small and cloud optical thickness that is too large). Observed
relationships between clouds and dynamical parameters are especially poorly
simulated by GCMs (e.g., Norris and Weaver 2001; Tselioudis and Jakob 2002)
even though these are more likely to be relevant to climate change questions
than is the production of realistic cloud and radiation climatologies. One of the
main reasons for such incorrect and inconsistent cloud and radiation simulations is that GCMs lack sufficient spatial resolution to represent properly the
nonlinear small-scale convective, turbulent, and microphysical processes that
control cloud properties, which instead must be crudely parameterized.
Climate sensitivity has been conventionally defined as the equilibrium
change in global mean surface temperature in response to the radiative forcing
caused by a doubling of CO2 in the atmosphere. Recent GCM intercomparisons
definitively indicate that low-level marine clouds provide the greatest contribution to the spread in climate sensitivity between models (Bony and Dufresne
2005). This was confirmed in the findings of the recent IPCC Fourth Assessment Report (Randall et al. 2007). Some GCMs suggest that the horizontal
extent of low-level clouds over low-latitude oceans will increase with higher
global temperature, whereas other GCMs suggest that low-level cloud amount
will decrease. Changes in trade cumulus (small cloud fraction and weak negative net CRF) may be more important than changes in marine stratocumulus
(large cloud fraction and strong negative net CRF) because the former occur
over a much larger area of the ocean. The amount of coverage by trade cumulus or stratocumulus is notoriously difficult to predict in large-scale models
because the dynamical forcing is weak and small changes in this forcing and
in the boundary layer properties can have a large impact on the mean relative
humidity and hence on the cloud cover. Note that the importance of marine
boundary layer clouds to the climate sensitivity of GCMs does not necessarily imply that such clouds are equally important to the climate sensitivity of
the Earth, nor is it necessarily the case that changes in CRF will be greatest
in regions where climatologic CRF is currently largest. Nevertheless, marine
boundary layer clouds can potentially exert greater radiative leverage on the
climate system than any other cloud type.
22
J. R. Norris and A. Slingo
Two Key Questions
How Are Clouds Responding to Global Warming?
One long-standing and critical issue for our understanding of climate change is
to determine how clouds have changed in response to global warming and how
they will change in the future, and whether the cloud radiative response will
exacerbate, mitigate, or have little effect on the increase in Earth’s temperature
caused by anthropogenic greenhouse gases (Randall et al. 2007). The enduring
problems with GCMs motivate an investigation of the observational record
made over the past several decades. Despite the presence of natural variability
in the historical cloud and ERB time series, a climate change signal may be
apparent, especially when we consider that the last forty years have been a time
period of substantially increasing anthropogenic radiative forcing and global
temperature. Relating multidecadal changes in cloud properties and ERB to
changes in temperature and atmospheric circulation will provide insight into
cloud feedbacks on the climate system and a useful constraint on GCM climate
simulations. Our ability to document and interpret the observational record is
unfortunately hampered by limitations and weaknesses in the global observational system, which was developed to monitor weather rather than climate.
How Are Clouds Responding to Anthropogenic Aerosols?
A second key question is how clouds and CRF have been altered in response
to changes in the concentration and composition of anthropogenic aerosol
particles. Without a dynamic feedback on cloud macrophysics, an increase in
the number of hygroscopic particles will produce a larger number of smaller
cloud droplets and hence increase cloud albedo. Changes in cloud particle size,
phase, and number concentration have furthermore been hypothesized to have
a substantial influence on cloud microphysical and precipitation processes,
thus potentially affecting cloud condensate, cloud lifetime, and cloud fraction.
Although local changes in cloud albedo and regional changes in cloud droplet
radius have been observed, it has been difficult to demonstrate unambiguously
a significant large-scale influence of anthropogenic aerosol on cloud albedo
and cloud fraction due to the very large confounding impact of meteorology.
Since aerosol transport is highly correlated with atmospheric dynamics, it is no
simple matter to distinguish the effects of anthropogenic aerosol from natural
meteorological influences on cloud properties. Another complication is that
strong atmospheric radiative heating and surface radiative cooling by aerosols
may possibly change regional circulation patterns and thus affect cloudiness
through a non-microphysical mechanism (e.g., Menon et al. 2002). Investigations of long-term trends in cloud properties in regions that have experienced
trends in aerosols may reveal the true aerosol influence on cloudiness and
provide constraints for GCM simulations. It is necessary, however, to control
Trends in Observed Cloudiness and Earth’s Radiation Budget
23
carefully for changes in meteorological conditions and to determine whether
aerosol radiative heating may have modified regional circulation4. The specific
radiative impacts of aerosols, cloudiness, or aerosol–cloud interactions must
also be correctly identified and distinguished in long-term satellite measurements, which is difficult because thin cloudiness appears optically similar to
aerosol haze (e.g., Charlson et al. 2007; Koren et al. 2007).
The Observational Record
Visual Cloud Observations
The longest record of cloudiness comes from synoptic reports made by human
observers at weather stations over land and on ships over the ocean. These
include the fraction of sky dome covered by all clouds and clouds in the lowest
layer as well as morphological cloud types at low, mid, and high levels. The
largest archive of synoptic cloud observations is the Extended Edited Cloud
Report Archive (EECRA) (Hahn and Warren 1999). Several investigations
have found that interannual variability and trends in surface-observed cloudiness are physically consistent with trends and variability in related meteorological parameters in some regions of the world (e.g., Norris 2000a, 2005b;
Park and Leovy 2000; Sun et al. 2001). Although visual cloud observations
lack quantitative radiative information, the application of locally derived linear
empirical coefficients can nonetheless provide useful (but incomplete) information on changes in ERB. The reason for this is that monthly anomalies in
cloud cover, the most quantitative parameter in synoptic cloud reports, are the
largest contributors to monthly anomalies in ERB. The smaller radiative impacts of variations in cloud albedo, cloud emissivity, and cloud height (aside
from differing changes in low- and upper-level cloud cover) cannot be derived
from visual cloud observations.
Short- and longwave anomalies estimated from gridded surface cloud cover
observations correspond substantially to satellite measurements of SWCRF
and LWCRF on interannual and decadal timescales for many locations of the
world (Norris 2005a). In regions with inadequate sampling, such as some tropical land areas and most tropical and southern hemisphere ocean areas, there
is much less agreement. The limited information obtained from surface cloud
observations is nonetheless valuable because it provides the only near-global
record of cloud cover and related ERB changes during the pre-satellite era. Satellite measurements of upper-level cloud cover and LW radiation are consistent
with the surface-observed record during the period of overlap, suggesting that
it may also be reliable in the pre-satellite era (Norris 2005a). For example, the
time series of upper-level cloud cover anomalies obtained from the EECRA and
from the International Satellite Cloud Climatology Project (ISCCP) (Rossow
and Schiffer 1999) coincide well (top of Figure 2.2), apart from the 1991–1993
24
J. R. Norris and A. Slingo
Cloud cover anomaly (%)
Near-global land (60ºS–60°N)
3
0 Upper
–3
3
0 Total
–3
3
Low
0
–3
3
Cu
0
–3 gray = ISCCP
black = EECRA
1950 1960 1970 1980 1990 2000
Near-global ocean (60ºS–60°N)
3
0
–3
3
0
–3
3
0
–3
3
0
–3
Upper
Total
Low
Cu
gray = ISCCP
black = EECRA
1950 1960 1970 1980 1990 2000
Figure 2.2 Time series of anomalies in total, upper-level, low-level, and cumulus
cloud cover from ISCCP satellite (gray) and EECRA surface (black) observations averaged between 60°S and 60°N over land and ocean.
time period when aerosols resulting from the Mt. Pinatubo volcanic eruption
caused upper-level cloudiness to be misidentified as low-level cloudiness in
ISCCP (Luo et al. 2002).
Surface-observed, upper-level cloud cover has declined globally over the
past several decades (Figure 2.2), and this has weakened LWCRF over time.
Although it appears that this long-term decrease in upper-level cloud cover
was abruptly reversed in October 2001, the increase reported by ISCCP is actually an artifact. Use of an incorrect calibration coefficient for the NOAA16 polar orbiter produced an underestimate of cloud-top temperature (Knapp
2008), which correspondingly caused an overestimate of upper-level cloud
cover. Surface and satellite records disagree on trends in total cloud cover,
low-level cloud cover (Figure 2.2), and reflected SW radiation. The surface
data indicate that in recent decades there has been a large increase in low-level
cloud amount, especially from cumulus clouds over tropical oceans. Such an
enhancement of cloud cover would lead to a physically implausible reduction
in absorbed SW radiation unless there was a large commensurate decrease in
cloud albedo (a parameter that cannot be quantified in visual cloud reports)
(Norris 2005a). The origin of this apparent artifact has not yet been identified.
Surface Radiation Measurements
The Global Energy Balance Archive (GEBA) contains measurements of
downwelling solar radiation at the surface that were made at various locations
around the world during the past several decades, primarily in North America
and Eurasia and only over land (Gilgen and Ohmura 1999). Not all stations are
trustworthy; many report unrealistically large interannual and decadal variations in solar radiation flux, particularly in the United States and in developing
Trends in Observed Cloudiness and Earth’s Radiation Budget
25
countries. Furthermore, sensor degradation may lead to a spurious reduction
in observed solar radiation (M. Wild, pers. comm.). Nonetheless, several studies have made use of high-quality stations and documented a geographically
widespread decrease in surface solar radiation from the 1960s until the mid1980s, followed by an increase from the 1990s onward (Wild et al. 2005).
These trends are popularly known as “global dimming” and “global brightening,” even though they do not necessarily occur around the entire world.
The reported trends probably represent regional rather than global mean
changes in downwelling solar radiation, and may suffer from an urban bias
(Alpert et al. 2005).
Several explanations for the observed “global dimming” and “global brightening” have been offered, including trends in cloud cover and cloud optical
thickness. The most plausible general cause, however, is a rise followed by
a decline in anthropogenic aerosol burden. According to this scenario, haze
from fossil fuel and biomass burning has increased since the middle of the
20th century and has, as a result, enhanced atmospheric reflection and absorption of solar radiation (“global dimming”). Air pollution control laws enacted
during the 1980s and later in certain countries reduced this haze, thus allowing
more solar radiation to reach the surface (“global brightening”). Another likely
factor contributing to “brightening” in Europe was the shutdown of fossil fuel
combustion sources following the collapse of communism in Eastern Europe.
In certain developing countries such as India, “global dimming” continues and
there has been no reversal to “brightening” (Wild et al. 2005).
It has been difficult to distinguish impacts of cloud changes from aerosol
changes using GEBA data because the values typically exist only as monthly
averages of the direct plus diffuse flux. Thus, cloudy intervals cannot be separated from cloudless intervals in the time series so as to distinguish cloud
radiative effects and potential aerosol impacts on clear-sky solar radiation. This
shortcoming is alleviated in the Baseline Surface Radiation Network (BSRN)
dataset, which provides data on downwelling SW and LW radiation every minute at a small number of stations as of 1990 (Ohmura et al. 1998). For the larger
and lengthier GEBA record, the radiative effects of cloud cover variations on
surface solar radiation can be reliably estimated from surface synoptic cloud
reports. The recent study of Norris and Wild (2007) demonstrated that cloud
cover variability, associated with weather and atmospheric circulation regimes,
was the dominant cause of interannual anomalies in surface solar radiation
over Europe but not of long-term trends. Removal of the cloud cover contribution from the solar radiation time series revealed more distinct “dimming” and
“brightening” trends than were apparent in previous studies.
Retrievals of the components of the surface radiation budget are available
from several satellite programs (e.g., Gupta et al. 1999, Zhang et al. 2004);
however, care must be taken in using them because a significant amount of
modeling is used to create such products. A score or so of well-instrumented
surface sites (e.g., BSRN) provide a reference both for satellite retrievals and
26
J. R. Norris and A. Slingo
for the much larger number of surface sites with limited instrumentation. It
must also be remembered that the signals of an enhanced greenhouse effect are
much larger at the surface than at the top of the atmosphere, so direct surface
measurements should play an important part in the detection and attribution of
global warming.
One intriguing, new potential method of monitoring changes in the Earth’s
global albedo is that of “earthshine” observations (Pallé et al. 2004). This
approach would use a network of surface telescopes to measure (a) variations
in the brightness of the non-sunlit part of the Moon and thus (b) how much
solar radiation is reflected by the Earth to the Moon and back again.
Satellite Measurements of the Earth’s Radiation Budget
The arrival of the satellite era provided the first global direct measurements of
clouds and ERB, starting with the basic instruments mounted on Explorer 7 in
1959 and TIROS 1 in 1960. The Nimbus series of experimental satellites, culminating in the important scanning radiometer observations from Nimbus 7,
laid the foundation for the comprehensive Earth Radiation Budget Experiment
(ERBE) from 1985–1990. ERBE placed broadband scanning instruments on
two operational weather satellites and on the dedicated satellite, ERBS, which
flew in a precessing low-inclination orbit to sample all times of day through
its 72-day orbital repeat cycle. After ERBE, further scanning radiometers flew
on the ScaRaB missions from 1994–1995 and 1998–1999. The CERES instruments on the TRMM, Terra, and Aqua satellites of the NASA Earth Observing
System set the highest standards for accuracy (Wielicki et al. 1996).
Many of the satellites mentioned above were, or are, in sun-synchronous
orbits with limited diurnal sampling. CERES overcomes this problem by incorporating additional data from the geostationary satellites used also by ISCCP.
The ERBS and TRMM satellites, as well as the Russian satellites that carried
the ScaRaB instruments, are in precessing orbits, providing coverage of all
local times over the orbital repeat period; however, at any one location, the
temporal sampling is still limited. To date, the only broadband radiometers in
geostationary orbit are the GERB instruments on Meteosat-8 and Meteosat-9,
which provide 15-minute data for the whole visible disk, albeit over a limited,
although climatically important, geographical region (Harries et al. 2005). The
broadband data from all of these instruments provide a wealth of information on the geographical distributions and the diurnal, seasonal, and interannual variability of the ERB, including the effect of clouds. This is possible
because the signals are large (typically tens of W m–2 or more, as is clear from
Figure 2.1) and thus are well observed relative to the instrument noise and
absolute calibration.
Whereas multiyear changes in ERB due to El Niño/La Niña and the 1991
Mt. Pinatubo volcanic eruption are clearly apparent in the satellite tropical
mean time series of outgoing LW radiation (Figure 2.3), it is much more
Trends in Observed Cloudiness and Earth’s Radiation Budget
27
challenging to study decadal and longer period variability and to search for
trends associated with climate change. The latter topics place much more stringent demands on the measurements, which were seldom designed for such
work. Some evidence has been put forward for the existence of decadal variations in the tropical radiation budget, particularly from the ERBS wide-fieldof-view (WFOV) instrument; however, detection of such changes is hampered
by various observational artifacts. Several of these were discovered after the
original publication by Wielicki et al. (2002), including the existence of a spurious semiannual cycle due to aliasing of the diurnal cycle by the precessing
satellite orbit. The first correction to the ERBS data adjusted for a spurious
change in radiation flux that resulted from a downward trend in satellite altitude; the second correction adjusted for a previously unidentified degradation
of solar transmission through the instrument dome. These two small corrections to the ERBS data reduced the change in LW flux by a factor of four and
reversed the sign of the change in the net radiation, compared with the original study (Wong et al. 2006). The most recent results indicate a slight trend
towards more LW emission (consistent with a weakening of LWCRF) but a
larger trend towards less SW reflection so as to produce a net gain of energy to
the Earth. Measurements of ocean heat content are consistent with a net gain of
energy during the past two decades (Wong et al. 2006). It is interesting to note
Longwave anomaly (Wm–2)
10
5
CERES/TRMM SC
ScaRaB/Meteor SC
ScaRaB/Resurs SC
Nimbus 7 NS
ERBE/ERBS NS
ERBE/ERBS SC
CERES/Terra SC
97/98 El Niño
82/83 El Niño
86/87 El Niño
91/92 El Niño
0
98/01 La Niña
Pinatubo recovery
El Chichon
eruption
88/90 La Niña Pinatubo eruption
–5
1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001
Time (year)
Figure 2.3 Time series of longwave radiation anomalies averaged over 20°N to
20°S based on the new ERBS Nonscanner WFOV Edition3_Rev1 (solid black line),
Nimbus 7 Nonscanner (gray dashed line), ERBS Scanner (solid gray line), CERES/
Terra FM1 Scanner ES4 Edition2_Rev1 (dotted line), CERES/TRMM Scanner
Edition2 (gray circle), ScaRaB/Meteor Scanner (triangle), and ScaRaB/Resurs Scanner (black circle) dataset. Anomalies are defined with respect to the 1985–1989 period
(from Wong et al. 2006).
28
J. R. Norris and A. Slingo
that the GCMs, rightly or wrongly, do not reproduce the magnitude of decadal
change in SW and net radiation flux between the 1985–1990 and 1994–1999
periods that are reported by ERBS (Wong et al. 2006). Investigation of changes
in ERB over longer periods of time than that covered by ERBS WFOV must
concatenate multiple satellite records (Figure 2.3).
Further evidence of the acute observational challenges presented by climate
change is illustrated by the accuracy requirements for climate datasets listed by
Ohring et al. (2005) and the error analysis of the current CERES sensors performed by Loeb et al. (2007). The stability requirement for measurements of
the net SW flux at the top of the atmosphere necessary to identify the feedbacks
from low clouds during climate change is listed by Ohring et al. (2005) as 0.3
Wm–2 per decade. Loeb et al. (2007) show that from the current CERES instrumentation, 10–15 years of data would be needed before a trend of this magnitude could be detected against the background of natural variability. Clearly,
this is only achievable if such well-characterized sensors continue to be flown
to provide an unbroken time series from which the cloud feedback could be inferred. The main problem is identifying such a small trend in the face of natural
interannual and interdecadal variability, which Figure 2.3 shows can be much
larger in magnitude than 0.3 Wm–2, at least regionally. Greater natural variability extends the time period needed to detect a trend of a given magnitude.
The strength of ERB measurements (i.e., that they provide information on
the net effect of all the radiatively active constituents on the heating and cooling of the planet) can also be viewed as a weakness. Without additional information, it is difficult to unravel these effects. Retrievals of cloud properties
have been combined with ERBE and later data to study the radiative effects of
different cloud types and how they contribute to the changes in ERB that take
place (e.g., during ENSO events). It is clearly important that complementary
information should be available for aerosols.
Satellite Cloud Measurements
International Satellite Cloud Climatology Project
The most commonly used cloud dataset in climate studies is the ISCCP
(Rossow and Schiffer 1999). Originally intended to provide a cloud climatology and data for studying cloud processes at synoptic scales, ISCCP uses
narrowband radiance data obtained from weather satellites to retrieve cloud
fraction and other cloud properties in 280 km grid boxes around the globe
every three hours. A radiative transfer model has been applied to these cloud
data along with observed stratospheric aerosol and GCM-derived tropospheric
aerosol to produce broadband SW and LW fluxes at the top of the atmosphere,
the surface, and several levels in between (Zhang et al. 2004). It is interesting
to note that interannual and decadal variations in the ISCCP flux dataset have
Trends in Observed Cloudiness and Earth’s Radiation Budget
29
some resemblance to those reported by ERBS (Figure 2.4), considering that
they are independently derived datasets.
ISCCP faces much greater difficulty in maintaining homogeneous and
stable data over time than do the ERB investigations since it makes use of a
long series of weather satellites that were not designed to maintain onboard
calibration or interface with other satellites. The ISCCP processing intercalibrates geostationary satellites (the main source of data) using a polar orbiter
that flies below each of them, which itself must be calibrated over time. One
problem involves calibrating successive polar orbiters so that artificial changes
in global cloud properties do not appear at the transition between satellites,
as was the case in the original ISCCP C-series dataset (Klein and Hartmann
1993). Since the surface of the Earth happens to be more radiatively stable than
any current satellite-observing system, the processing of the ISCCP D-series
6
3
0
Radiative anomalies (Wm–2)
-3
-6
ISCCP
AVHRR
HIRS
ERBS/NS
LW
14
7
0
14
-7
ISCCP
AVHRR
ERBS/NS
SW
ISCCP
AVHRR
ERBS/NS
NET
-14
7
0
-7
-14
1985
1987
1989
1991
1993
1995
1997
1999
Time (year)
Figure 2.4 Time series of longwave, shortwave, and net radiation anomalies averaged
over 20°N to 20°S from the ERBS Nonscanner WFOV Edition3_Rev1 (red), ISCCP FD
(blue), HIRS Pathfinder OLR (pink), and AVHRR Pathfinder ERB (green) data records.
Anomalies are defined with respect to the 1985–1989 period (from Wong et al. 2006).
30
J. R. Norris and A. Slingo
dataset included the adjustment of satellite measurements such that global
mean surface temperature and reflectance (including the effects of tropospheric
aerosol) did not change over time (Brest et al. 1997). Although this method
substantially reduced inhomogeneities in the ISCCP time series, it obviously
precludes the use of the data to investigate changes in the global surface properties. Moreover, the method will introduce an artifact into the cloud data to
the extent that the Earth’s surface has changed in the global mean. Nonetheless, it is nominally possible to examine changes in global cloud properties,
and ISCCP reports that a general decrease in global mean total cloud amount
has occurred over the past couple of decades (Figure 2.5), due primarily to a
decrease in low-level cloud amount at low latitudes.
Despite the multiple layers of calibration and adjustment in ISCCP, this
trend and other variations in global mean cloud amount appear to be entirely
spurious. The pattern of correlation between the global mean time series and
the time series in each grid box resembles the circular geostationary satellite
fields of view rather than any geophysical pattern, and the spatial pattern of
local trends becomes strongly negative near the edges of the geostationary satellite fields of view. Evan et al. (2007) attribute this to a systematic change in
the average satellite view angle over time. A geostationary satellite sees pixels
near the outside boundary of its view area with a greater slant path than it sees
pixels in the center of its view area (i.e., near-nadir). At visible wavelengths,
thin low-level clouds appear optically thicker for greater slant path and thus are
easier to detect by the threshold methods employed by ISCCP. Since only three
geostationary satellites were available at the beginning of the ISCCP record
and five geostationary satellites were available at the end, locations that were
once seen at high satellite view angle now were seen at low satellite view
angle. This means that very optically thin clouds that were once detected were
no longer detected, and reported cloud amount consequently decreased.
The view angle artifact has less impact on the calculated ISCCP short- and
longwave radiative fluxes than it does on cloud amount because the artificial
reduction in cloud amount is compensated by a corresponding artificial enhancement of cloud optical thickness. Moreover, the clouds that are no longer
identified are those that are closest to the threshold of detection and thus least
radiatively important.
Even when view angle artifacts associated with changes in the number and
position of geostationary satellites are statistically removed, other apparently
spurious variability remains. For example, coincident variations in cloud
amount are seen across the entire view area of a geostationary satellite (Norris
2000b). There is no reason why cloud anomalies in opposite hemispheres and
seasons (and often land vs. ocean) should be so closely correlated unless there
were some unidentified artifacts in the satellite measurements or application of
incorrect calibration (e.g., Norris and Wild 2007).
Trends in Observed Cloudiness and Earth’s Radiation Budget
31
Total (day) anomaly (%-amount)
Total (day) cloud global mean time series (original)
6
4
2
0
–2
–4
–6
1985
1990
1995
Year
2000
2005
Local correlation with global mean time series
90°N
0.3
60°N
0.2
30°N
0.1
0
0
–0.1
30°S
–0.2
60°S
90°S
180°
–0.3
120°W
60°W
0
60°E
120°E
180°
Local linear trend (%-amount per decade)
90°N
3
60°N
2
30°N
1
0
0
–1
30°S
–2
60°S
90°S
180°
–3
120°W
60°W
0
60°E
120°E
180°
Figure 2.5 Top: time series of ISCCP global mean total cloud amount. Middle: correlation between the global mean time series and the time series at each grid box. Bottom:
local linear trend in total cloud amount for each grid box.
Additional Cloud Datasets
Another cloud dataset with a multidecadal record is the AVHRR pathfinder
atmosphere (PATMOS) dataset (Jacobowitz et al. 2003). PATMOS cloud and
radiation properties are derived from the AVHRR instrument on polar-orbiter
weather satellites. Although they do not suffer from shifts in geostationary
32
J. R. Norris and A. Slingo
satellite view angle, as does ISCCP, the nominally sun-synchronous polar orbiters contributing to PATMOS experience substantial orbital drift during their
tenure, causing the local equatorial crossing time to occur ever later in the day.
This means that the diurnal cycle of cloud properties gets aliased into a longterm trend (i.e., if more clouds occur in the morning than in the afternoon, it
will look like cloud amount has declined over time). PATMOS corrects for
diurnal drift by statistically regressing out the long-term trend, which must
be repeated for each successive satellite in the series. Removing trends in this
manner limits the use of these data for long-term climate studies since any real
cloud trend may get removed as well. A better method would be to observe
the diurnal cycle from a geostationary satellite and then adjust the diurnal drift
based on that. PATMOS time series also exhibit extremely large variations that
appear to be related to transitions between satellites (Figure 2.4).
A third multidecadal cloud dataset is based on the high resolution infrared
radiometer sounder (HIRS) on polar-orbiting weather satellites (Wylie et al.
2005). The HIRS dataset operates by comparing radiances from several infrared channels that view different layers of the atmosphere, and it is especially
good at detecting high-level optically thin clouds. Like PATMOS, HIRS suffers from orbital drift and inhomogeneities at satellite transitions. The time
series of tropical outgoing LW radiation derived from HIRS (Mehta and Susskind 1999), however, does appear to agree with the ISCCP flux dataset and
ERBS time series (Figure 2.4).
Challenges for the Future
It is crucial to avoid treating cloud, radiation, and aerosol observations in isolation. Instead, we must study them in their meteorological context (i.e., temperature, humidity, and winds) because clouds are formed by dynamic and
thermodynamic processes, and aerosols provide a perturbation to the formation and dissipation processes as well as to the physical and radiative properties of clouds once they have formed. Numerical weather prediction reanalyses
provide the most consistent source of such data, and now they include output
for the ERB and cloud fields as well. Reanalyses are thus an important resource for climate studies; however, although the representation of the clearsky ERB can be good, there are still significant errors in the simulation of
clouds and of the all-sky ERB (Allan et al. 2004). A climate-quality reanalysis
using a coupled ocean–atmosphere model, paying attention to the radiative
and hydrological cycles, would be a major step forward and should be a high
priority (Bengtsson et al. 2007).
Several independent cloud and ERB datasets suggest that there might have
been a decrease in high-level cloud cover during the past several decades that
has reduced LWCRF over time. ERBS, ISCCP flux dataset, and ocean heat
content measurements indicate that this slight cooling effect has recently been
Trends in Observed Cloudiness and Earth’s Radiation Budget
33
opposed by a stronger warming effect caused by a weakening of SWCRF.
Considering all the corrections that have been applied to the data, can we confidently conclude that these trends are real? If they truly exist, are they related
to anthropogenic climate change or are they merely produced by some natural
decadal cycle? What implications do the cloud and ERB trends have on our
understanding of climate sensitivity?
Answering these questions with confidence will require a thorough reexamination and reprocessing of the different types of available data to reconstruct
our best understanding of past variations in cloudiness and ERB and how they
are associated with other parameters of the climate system. We should especially use knowledge gained from more recent detailed and comprehensive satellite measurements to improve older records. Better known quantities should
be employed to constrain less known quantities when empirical calibration is
unavoidable, and improved physical models should be developed to address
issues like the perceived variation of cloud optical thickness with satellite view
angle. Since the magnitude of systematic artifacts relative to real cloud and
radiation variability is typically greatest at the largest scales, patterns of regional changes may be more robust than global changes. In particular, investigations should search for regional trends in cloudiness and ERB that are likely
to be associated with anthropogenic global warming or aerosol.
Considering the many inadequacies in the historical data record, it may
seem preferable to forget about it and instead to devote our efforts solely to
making more precise, comprehensive, and stable measurements in the future.
This would be a mistake, we believe, since many years of observation will be
required to distinguish a climate change signal from natural variability. We do
not have the luxury of waiting for those years to come, but rather must make
better use of the record that we do have.
Since there have been damaging gaps in the past record of ERB measurements, which have made it difficult to quantify decadal variability or to identify climate trends, it is essential that we maintain continuity in the future.
Any trends in ERB are expected to be small and thus require careful crosscalibration of the various instruments flown at different periods, and without
overlaps. Such cross-calibration is very difficult, if not impossible, to achieve.
Independent parameters, such as ocean heat content, should also be monitored
to provide additional constraints on ERB. As global warming signals begin to
emerge clearly above the level of natural variability, one of the greatest challenges facing attempts to ensure continuity of ERB and other measurements is
the difficulty of obtaining adequate funding for climate monitoring missions,
despite their obvious importance. It is relatively straightforward to justify the
continuity of missions to support numerical weather prediction, because of the
requirements of operational agencies. However, no satellite agency has yet
been able to commit to continuous funding of climate monitoring. Thus, there
is a real danger of losing the continuous record of ERB measurements because of future funding constraints. The numbers presented above suggest that
34
J. R. Norris and A. Slingo
the minimum requirement for monitoring changes in the ERB associated with
global warming would be continuation of the coverage from the CERES or
similar instruments at the current level. Other complementary measurements
that might be useful include radiation budget measurements of the sunlit portion of the globe from a satellite at the Lagrange point L1. A particularly valuable mission that has been proposed is a climate calibration observatory, which
would be used to cross-calibrate instruments on other satellites to make better
use of their data by bringing them together to a common reference scale that is
compatible with the CERES data.
Acknowledgments
We are grateful to Richard Allan for providing Figure 2.1, the data for which were retrieved from the NASA Langley DAAC. The data for Figures 2.2 and 2.5 were obtained
from the ONRL Carbon Dioxide Information Analysis Center and the NASA Goddard
Institute for Space Studies. We thank Takmeng Wong for providing Figures 2.3 and 2.4.
An NSF CAREER award, ATM02-38527, supported the work by Joel Norris.
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