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Impacts of ENSO events on cloud radiative effects in
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preindustrial conditions: Changes in cloud fraction and
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their dependence on interactive aerosol emissions,
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deposition and transport
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Yang Yang1, Lynn M. Russell1, Li Xu1, Sijia Lou1, Maryam A. Lamjiri1, Richard
C. J. Somerville1, Arthur J. Miller1, Daniel R. Cayan1, Michael J. DeFlorio1,
Steven J. Ghan2, Ying Liu2, Balwinder Singh2, Hailong Wang2, Jin-Ho Yoon2,
and Philip J. Rasch2
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Scripps Institution of Oceanography, University of California, San Diego, La
Jolla, California, USA
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Atmospheric Science and Global Change Division, Pacific Northwest National
Laboratory, Richland, Washington, USA
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Key points:
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Interannual variability in cloud radiative effects is driven by mid-level and high
clouds.
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Wind-related feedbacks on natural aerosol emissions enhance this variability by 3–
4%.
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Wet scavenging dampens interannual variability by 4-6% over the tropics.
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Abstract
We use three 150-year pre-industrial simulations of the Community Earth
System Model (CESM) to quantify the impacts of El Niño–Southern Oscillation
(ENSO) events on shortwave and longwave cloud radiative effects (CRESW
and CRELW). Compared to recent observations from the Clouds and the
Earth’s Radiant Energy System dataset, the model simulation successfully
reproduced larger variations of CRESW and CRELW over the tropics. The ENSO
cycle is found to dominate interannual variations of cloud radiative effects.
Simulated cooling (warming) effects from CRESW (CRELW) are strongest over
the tropical western and central Pacific Ocean during warm ENSO events, with
the largest difference exceeding 40 Wm–2 (30 Wm–2), with weaker effects of
10–30 Wm–2 over Indonesian regions and the subtropical Pacific Ocean.
Sensitivity tests show that variations of cloud radiative effects are mainly
driven by ENSO-related changes in cloud fraction. The variations in mid-level
and high cloud fractions each account for approximately 20–50% of the
interannual variations of CRESW over the tropics and almost all of the
variations of CRELW between 60°S and 60°N. The variation of low cloud
fraction contributes to most of the variations of CRESW over the mid-latitude
oceans. Variations in natural aerosol concentrations explained 10–30% of the
variations of both CRESW and CRELW over the tropical Pacific, Indonesian
regions and the tropical Indian Ocean. Changes in wet scavenging of natural
aerosols dampen more than 4–6% of the variations of cloud radiative effects
averaged over the tropics. In contrast, changes in sea spray emissions
enhance the variations by 3–4%.
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1. Introduction
Clouds strongly influence the Earth’s radiation balance. They reflect
incoming solar radiation back to space (shortwave albedo effect), which
enhances the reflected solar flux by 47.5±3 Wm–2 globally, and absorb
outgoing infrared radiation (longwave greenhouse effect), which reduces the
outgoing longwave flux relative to clear sky by approximately 26.4±4 Wm–2.
Overall, clouds exert a net cooling effect of about –21.1±5 Wm–2 at the top of
atmosphere (TOA) (Stephens et al., 2012), which is 6 times larger than that
from doubling CO2 concentration (Ramanathan et al. 1989; Loeb et al. 2009).
Any changes in cloud properties such as cloud fraction, cloud top height, and
microphysical features would perturb cloud radiative forcing and greatly
modulate the radiative balance of the Earth system (Slingo, 1990; Wielicki et
al. 1998; Curry et al. 2000; Stephens, 2005). The Intergovernmental Panel on
Climate Change (IPCC) reported that simulations of clouds and their radiative
feedbacks are still one of the largest uncertainties in the fifth-generation
climate models (Boucher et al., 2013).
On interannual time scales, many regional changes in the global climate
system are associated with the El Niño–Southern Oscillation (ENSO)
phenomena. ENSO is characterized by anomalous sea surface temperatures
(SST) in the equatorial Pacific and has far reaching impacts on global and
regional temperature, precipitation, and circulation. Using cloud data from the
Extended Edited Cloud Reports Archive (EECRA) from year 1954 to recent
years, Park and Leovy (2004) and Eastman et al. (2011) both showed that
interannual variations of cloud cover in the tropics have strong correlations to
the ENSO index. For example, warmer central and eastern tropical Pacific
SST (warm ENSO phase, i.e. El Niño) is associated with increased cloud
cover in the tropical central Pacific Ocean and reduced cloud cover over the
Indonesian and eastern Pacific regions, and vice versa for cool ENSO phase
(La Niña) events.
ENSO’s influence on cloud fraction typically varies with height. Zhu et al.
(2007) showed that thick low cloud fraction decreased more than 20% in the
tropical eastern Pacific during the 1997–1998 El Niño event, which was
associated with an upward large-scale motion and a weak atmospheric
stability. However, thin high cloud fraction was found to increase over the
tropical Pacific and decrease over Indonesian regions during El Niño events
compared to those during La Niña events (Norris, 2005; Marchand, 2013). The
mean vertical distribution of clouds is also sensitive to ENSO events. Zelinka
and Hartmann (2011) and Lelli et al. (2014) reported that during warm ENSO
events, the vertical distribution of tropical clouds shifts upward.
Due to anomalous tropical atmospheric conditions during ENSO events,
cloud radiative effects exhibit year-to-year differences. Moore and Vonder
Haar (2001) examined the interannual variability of net cloud radiative effects
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from 1984 to 1990 using radiative flux data from the Earth Radiation Budget
Experiment (ERBE). They found that during the El Niño season of 1986/87,
Pacific basin and global net cloud cooling effects were reduced by 0.90 Wm–2
and 1.29 Wm–2, respectively, compared to those during the La Niña season of
1988/89. Kato (2009) examined the interannual variation of the global radiation
budget using the Clouds and the Earth’s Radiant Energy System (CERES)
dataset from 2000 to 2004. He found that large anomalies in TOA shortwave
and longwave fluxes over the tropical western and central Pacific were
associated with changes in ENSO phase, and that clouds are mostly
responsible for the observed year-to-year variations. Loeb et al. (2007) also
reported that most of the variability in CERES TOA flux is driven by variations
in global cloud fraction, as observed using coincident CERES and the
Moderate Resolution Imaging Spectrometer (MODIS) data between 2000 and
2005. With CERES data from 2000 to 2010, Dessler (2010) showed that the
short-term shortwave and longwave cloud feedback are 0.12 ± 0.78 and 0.43 ±
0.45Wm–2K–1, respectively. The TOA radiation balance is sensitive to cloud
heights, since low clouds exert cooling effects on the TOA, while high clouds
exert warming effects. However, previous studies have not evaluated the
contributions of clouds at different heights to interannual variations of cloud
radiative effects.
Aerosols are another component of the atmosphere that substantially
perturbs the Earth’s radiative balance. Aerosol particles affect the climate
system directly by scattering and absorbing solar radiation and indirectly by
altering cloud microphysical properties (Twomey 1977; Albrecht 1989;
Lohmann and Feichter 2005). Climate variability, such as changes in ENSO
phase, can influence the concentrations and distribution of aerosols and trace
gases through changing their emissions, deposition and transport (Chandra et
al., 1998, 2009; Prospero and Lamb, 2003; Ziemke and Chandra, 2003; Logan
et al., 2008; Mitchell et al., 2010; Li et al., 2011; Hsu et al., 2012; Wu et al.,
2013; Yang et al., 2014, 2015; DeFlorio et al., 2015; Lou et al., 2015; Xu et al.,
2015). Using Sea-viewing Wide Field-of-view Sensor (SeaWiFS)
measurements from 1997 to 2010, Hsu et al. (2012) found a high correlation
between ENSO Index and monthly AOD anomalies over the Indonesian
regions. With MODIS and Global Precipitation Climatology Project (GPCP)
data from 2000–2010, Wu et al. (2013) reported that warm ENSO events are
associated with aerosol increases over the Indonesian regions resulting from
suppressed precipitation, which reduces the wet scavenging of aerosols and
promotes dry conditions favorable for fire burning.
The influence of ENSO on aerosols is not confined to local tropical Pacific
regions. Prospero and Lamb (2003) suggested that increased remote
concentrations of Barbados summer dust might be related to warm ENSO
events in the previous winter, which promote remote zonal teleconnection
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patterns in atmospheric circulation and precipitation over Africa which favor
increased dust emission and subsequent westward transport. Mitchell et al.
(2010) also found that Australian dust emission may be enhanced by El
Niño-induced dry conditions.
Li et al. (2011) retrieved the column-average Ångström exponent from
MODIS, the Multiangle Imaging Spectroradiometer (MISR) and SeaWiFS for
the period 2000–2011. The Ångström exponent is often used as a qualitative
indicator of aerosol particle size. They showed that increases in Ångström
exponent over the tropical western Pacific during El Niño events resulted from
reductions of sea salt emissions and aerosol transport associated with surface
wind anomalies. van der Werf et al. [2006] and Logan et al. [2008] showed that
a significant increase in biomass burning emissions occurred because of the
enhanced fires in Indonesia during El Niño events.
These ENSO-related variations of natural aerosol emissions and
concentrations also have potential impacts on the interannual variations of
cloud radiative effects. However, few previous studies have considered the
role of variations in natural aerosols when investigating interannual variations
of cloud radiative effects. Studies which have addressed interannual variations
of cloud radiative effects have been constrained by relatively short 10-year
satellite data observations, which only contain a few cycles of interannual
variability. Consequently, the conclusions drawn from using these datasets are
subject to sampling error. Extensive model simulations with realistic ENSO
physics can be used to estimating the interannual variations of cloud radiative
effects. In addition, the roles of ENSO-related variations in cloud fraction at
different heights and variations in natural aerosols due to interactive
emissions, deposition and transport can be explored, and can potentially
provide a better understanding of climate variability and contribute to improved
predictions of tropical climate.
We present here a systematic investigation of the interannual variations of
cloud radiative effects based on a set of three 150-year simulations in
preindustrial conditions using the Community Earth System Model (CESM).
We quantify: (1) the observed and simulated interannual variations of cloud
radiative effects; (2) their relationship with ENSO events; and (3) the
contributions of natural aerosol variability and cloud fraction at various vertical
levels to interannual variations in cloud radiative effects.
The CESM model and numerical experiments are described in Section 2.
Section 3 provides an overview of the simulated interannual variations of cloud
radiative effects and comparisons to observations. Section 4 investigates
ENSO-related interannual variations of cloud radiative effects by comparing
simulations with and without interactive emissions and concentrations of
natural aerosols. Section 5 shows the contributions of cloud fractions and
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natural aerosols to the interannual variations of cloud radiative effects. Section
6 summarizes these results.
2. Model description and experimental design
Simulations in preindustrial conditions were performed using CESM,
which includes components from the Earth’s atmosphere, ocean, land,
land-ice, and sea-ice (Hurrell et al., 2013). The atmospheric model resolution
is 1.9° latitude by 2.5° longitude with 30 vertical layers ranging from the
surface to 3.6 hPa. The CESM treats the properties and processes of major
aerosol species (sea salt, mineral dust, sulfate, black carbon, primary organic
matter and secondary organic aerosol) in the modal aerosol module (MAM3)
(Liu et al., 2012). Aerosol size distributions are represented by three lognormal
modes: Aitken, accumulation and coarse modes. Mass mixing ratios of
different aerosol species and the number mixing ratio are predicted for each
mode. Aerosol optical properties are parameterized according to Ghan and
Zaveri (2007). The model physically treats the aerosol effects on stratiform
cloud microphysics using an up-to-date prognostic method, through droplet
activation parameterized in terms of sub-grid updraft velocity, size distribution
and volume-mean hygroscopicity of aerosol modes (Abdul-Razzak and Ghan,
2000). The simulated droplet number concentration affects conversion of cloud
water to rain through the dependence of the droplet collision and coalescence
process on droplet number (Khairoutdinov and Kogan, 2000). A more detailed
description of the model aerosol representation can be found in Liu et al.
(2012). CESM simulations which include aerosol indirect effects more
accurately reproduce the observed time evolution of globally averaged surface
air temperatures (Meehl et al., 2013). Stratiform cloud microphysics predicts
number and mass mixing ratios of droplets and ice crystals and diagnoses
number and mass mixing ratios of rain and snow as described by Liu et al.
(2007), Morrison and Gettelman (2008), and Gettelman et al. (2010). The
treatment of deep and shallow convective cloud parameterizations is
described in Zhang and McFarlane (1995) and Park and Bretherton (2009),
respectively. These parameterization schemes convert condensate to
precipitation as a function of the local convective mass flux and temperature,
without explicit aerosol effects. Cloud macrophysical processes are
represented according to Park et al. (2014).
To identify the relative roles of variations in cloud fractions and interactive
natural aerosol emissions, deposition and transport in interannual variations of
cloud radiative effects, the following 150-year simulations are performed:
1. IRUN: the standard simulation of preindustrial conditions using
interactive (“I”) emissions, deposition and transport. Emissions,
deposition and transport of natural aerosols are allowed to interact with
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meteorological fields.
2. ERUN: the sensitivity simulation of preindustrial conditions using
prescribed emissions (“E”) of natural aerosols. The emissions of sea
salt aerosol used in the simulation are interpolated in time between the
12 monthly mean values derived from the 150-year IRUN simulation,
and they are not changed by the actual yearly values of wind and
temperature. Hence, this simulation contains no interannual variability
of sea salt emissions. However, the emissions of dust aerosol are
interactive in this simulation. The model setup conditions are otherwise
the same as those in IRUN.
3. CRUN: the sensitivity simulation of preindustrial conditions using
prescribed aerosol concentrations. The concentrations of all aerosol
components are interpolated in time between the 12 monthly mean
values derived from the 150-year IRUN simulation, and they are not
changed by interactive processes such as emission, deposition or
transport. Hence, this simulation contains no interannual variability of
aerosol concentration for all aerosol species. The model setup
conditions are otherwise the same as those in IRUN.
In this work, we focus on the interannual variations of cloud radiative
effects in preindustrial conditions (for the year 1850 emissions), because much
of the uncertainty in aerosol indirect forcing arises from uncertainties in natural
aerosols (Carslaw et al., 2013). The fire emissions have no interannual
variations in any of the simulations. Only regions between 60°S–60°N are
analyzed in this study because the cloud radiative effects over the polar
regions are highly affected by sea ice and snow (Kato, 2009; Dong et al.,
2010). The cloud radiative effect (CRE) is defined as the difference of net
downward radiative flux between all sky and clear sky at the TOA, with positive
(negative) cloud radiative effect indicating that the cloud warms (cools) the
underlying atmosphere and surface (Ramanathan et al. 1989).
3. Simulated interannual variations of cloud radiative effects
The observed and simulated annual shortwave and longwave cloud
radiative effects (CRESW and CRELW) are shown in Fig. 1. CESM successfully
reproduces the observed maximum value of annual CRESW and CRELW over
the tropics, as well as large CRESW over the mid-latitude oceans and the
subtropical eastern Pacific Ocean. The simulated global climatological mean
CRESW and CRELW from IRUN is –51.1 and 24.1 Wm–2, respectively. These
values are in agreement with satellite observations (–47.1 and 26.5 Wm–2 from
CERES Energy Balanced and Filled (CERES-EBAF; Loeb et al. 2009) and –
54.2 and 30.4 Wm–2 from ERBE (Harrison et al. 1990) for CRESW and CRELW
respectively) and multi-model mean values (–51.4 and 26.0 Wm–2 from The
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Coupled Model Intercomparison Project Phase 5; Dolinar et al., 2015). For the
ERUN and CRUN sensitivity simulations, the spatial distribution and
magnitudes of CRESW and CRELW are almost the same as those in the IRUN
simulation, with global mean CRESW (CRELW) of –51.5 (24.1) and –50.6 (24.1)
Wm–2 from ERUN and CRUN, respectively (Fig. S1). Detailed evaluation of
simulated cloud radiative effects from CESM can be found in Park et al.
(2014).
Standard deviations of observed CRESW and CRELW from CERES-EBAF
satellite observations for years 2001–2014 as well as those from model
simulations are plotted in Fig. 2. In this work, the standard deviation is used to
represent the magnitude of variation, which is calculated with monthly
anomalies of CRESW and CRELW for each grid over the 150-yr model record.
The standard deviation of simulated CRESW is largest over the tropical western
and central Pacific Ocean, the eastern Pacific Ocean, the tropical Indian
Ocean, and parts of Europe, eastern Asia, Africa, Australia, North and South
America (Fig. 2c), which is consistent with observations (Fig. 2a), with
maximum variations exceeding 18 Wm–2. The global (60°S–60°N, hereafter)
area-weighted mean value of the simulated CRESW standard deviation
computed from monthly anomalies over each grid is 13.2 Wm–2 for
preindustrial conditions in IRUN, which is larger than 10.5 Wm–2 from
present-day observation. For simulated CRELW, large standard deviation
values are located mainly within the tropics, with maximum values over the
tropical western and central Pacific and the tropical Indian Ocean (Fig. 2d),
which also have a positive bias over the tropics compared to observations (Fig.
2a). The global area-weighted mean value of the simulated CRELW standard
deviation in IRUN is 8.4 Wm–2, larger than observed value of 6.8 Wm–2.
Overall, the model realistically captures the spatial distribution of observed
interannual variations of CRESW and CRELW, with global spatial pattern
correlation coefficients of 0.76 and 0.84, respectively. However, the model
overestimates the observed variations both in CRESW and CRELW by 23–36%
globally. For fixed emissions of sea salt, the global area-weighted mean of
standard deviation is reduced by 2–3% for ERUN (Fig. 2e and f) relative to
those in IRUN. With fixed aerosol concentrations, the model produces more
variations of cloud radiative effects over the tropics, with global area-weighted
mean values of simulated standard deviations for CRESW and CRELW of 13.6
and 8.6 Wm–2 (Fig. 2g and h).
In order to examine the interannual variations of cloud radiative effects
from both observations and simulations for different latitudinal bands, the zonal
mean of the standard deviation of simulated and observed CRESW and CRELW
are presented in Fig. 3. The model reproduces the observed spatial variability
of CRESW and CRELW, with the largest standard deviation over the tropics and
decreasing values as the latitude increases. However, the model
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overestimates the observed variations in all simulations, especially over the
tropics. The largest variations of simulated CRESW are in the range of 16–17
Wm–2 in IRUN and ERUN and about 19 Wm–2 in CRUN, larger than the
observed value of 13–14 Wm–2 from CERES-EBAF. The maximum simulated
CRELW variability ranges from 14 to 16 Wm–2, also larger than observed values
of 9–10 Wm–2. Notably, the standard deviations of CRESW and CRELW over the
tropics from IRUN are reduced compared to CRUN, and the standard
deviation simulated from IRUN is more realistic than CRUN, suggesting that
variations of natural aerosols play an important role in modulating interannual
variations of CRESW and CRELW. Averaged over the tropics (23°S–23°N), the
mean standard deviation of CRESW (CRELW) are 15.2 (11.5), 14.8 (11.1), and
16.1 (12.0) Wm–2 in IRUN, ERUN, and CRUN, respectively. With interactive
sea salt emissions, the model simulated 3–4% higher variations over the
tropics, whereas deposition- and transport-related feedbacks decreased
variations of cloud radiative effects by 4–6% relative to the IRUN simulation.
4. ENSO-related interannual variations of cloud radiative effects
To explore the interannual variations of cloud radiative effects, we
calculated empirical orthogonal functions (EOFs) of yearly anomalies of the
simulated CRESW and CRELW. The leading EOF of yearly CRESW and CRELW
anomalies for the 150-year IRUN simulation are shown in Fig. 4. The leading
EOFs explain 22.5% and 31.4% of the interannual variations of simulated
CRESW and CRELW, respectively. The variability is generally largest over
regions in the 30°S to 30°N latitudinal band for both CRESW and CRELW. In this
region, most of the observed climate variability is associated with ENSO
phenomena. The EOF of CRESW shows a negative pattern over the tropical
western and central Pacific Ocean, whereas it displays a positive pattern over
the Indonesian regions, the eastern Indian Ocean, the tropical eastern Pacific
Ocean, the subtropical Pacific Ocean, and the subtropical west coast of North
America. The EOF of CRELW shows a similar pattern with opposite sign as that
of CRESW.
The leading principal components (PCs) that correspond to the leading
EOF mode of CRESW and CRELW (shown in Fig. 4) and the time series of the
Niño 3.4 Index calculated from the simulated SST are shown in Figure 5a. The
Niño 3.4 Index is the averaged SST anomaly over Niño 3.4 region (5°S–5°N,
170–120°W), which is one of several ENSO indicators and is used to
characterize the intensity of an ENSO event. The leading PCs are strongly
correlated with the Niño 3.4 Index, with correlation coefficients of 0.89 (0.93),
which are statistically significant at the 95th percentile. These strong
correlations demonstrate that the ENSO cycle has strong impacts on
interannual variations of cloud radiative effects.
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To further investigate the relationship between the ENSO cycle and
interannual variations of cloud radiative effects, we show the power spectrum
of the modeled Niño 3.4 Index as well as the leading PCs of yearly CRESW and
CRELW anomalies in Fig. 5b. The Niño 3.4 Index shows a significant peak at a
frequency of 0.23 cycles per year, suggesting that the simulated ENSO occurs
on a time scale of about 4.3 years. It is in the range of the observed ENSO
period, which ranges from 2 to 7 years with an average value of approximately
4 years (MacMynowski and Tziperman, 2008). The PCs of the leading EOF of
CRESW and CRELW display the same peak of 0.23 cycles per year with the
Niño 3.4 Index. This similarity in the cycle frequency indicates that ENSO has
a strong effect on the interannual variations of cloud radiative effects between
60°S and 60°N. Spatial patterns of the correlation coefficients between
monthly anomalies of CRESW (CRELW) and the Niño 3.4 index are almost the
same as the patterns of the EOF, with statistically significant values mainly
between 30°S and 30°N, as shown in Fig. 6. Spatial patterns of the correlation
coefficients between the PCs of the leading EOF mode of CRESW (CRELW) and
SST monthly anomaly also show significant ENSO signal (Fig. S2). These
further imply a close relationship between the ENSO cycle and the interannual
variations of cloud radiative effects.
To quantify the impacts of ENSO events on cloud radiative effects, we
show the composite differences in observed and simulated CRESW and CRELW
between El Niño events and La Niña events in Fig. 7, in addition to the
changes in CRESW and CRELW caused by variations of natural aerosol
emissions and concentrations. An El Niño (La Niña) event is characterized by
a three month running mean of the monthly Niño 3.4 Index that is above
(below) the threshold of +0.4°C (–0.4°C) (Trenberth, 1997). Based on this
method, 506 (720) of the 1800 months from the IRUN simulation and 35 (37) of
the 168 months from CERES-EREB observations are identified as occurring El
Niño (La Niña) events. Relative to those during La Niña events, simulated
cooling (warming) effects from CRESW (CRELW) during El Niño events from
IRUN are stronger over the tropical western and central Pacific Ocean, with
the largest difference exceeding 40 Wm–2 (30 Wm–2) (Fig. 7c and d).
Simulated CRESW (CRELW) during El Niño events have weaker cooling
(warming) effects than those during La Niña events by 10–30 Wm–2 over the
Indonesian regions and the subtropical Pacific Ocean. The pattern of the
composite differences in cloud radiative effects agrees with the leading EOF
mode pattern (Fig. 4).
Compared to the composite differences in observed CRESW and CRELW
between El Niño and La Niña events from the CERES-EBAF satellite data
(Fig. 7a and b), the model realistically simulates the observed spatial
distribution of these differences, although some values over the tropics are
over-predicted. Variations in sea salt emissions between El Niño and La Niña
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events slightly enhance the cooling (warming) effect of CRESW (CRELW) over
the tropical Pacific Ocean and weaken this effect over the Indonesian regions,
with magnitudes less than 10 Wm–2 (Fig. 7e and f). The changes of CRESW
and CRELW resulting from variations in natural aerosol concentrations (IRUN–
CRUN, considering interactive emissions, deposition and transport processes)
(Fig. 7g and h) are larger than those due to variations in emissions (IRUN–
ERUN). Over eastern Indonesian regions and the tropical western Pacific
Ocean, changes in natural aerosol concentrations increase the cooling
(warming) effect of CRESW (CRELW) by 10–30 (0–20) Wm–2, whereas the
CRESW (CRELW) effect is weaker over the tropical central Pacific Ocean.
Observed and simulated composite differences of CRESW and CRELW
averaged over the Indonesian regions (90–150°E, 20°S–20°N) and the tropical
western and central Pacific Ocean (150°E –120°W, 20°S–20°N) are presented
in Fig. 8. Over the Indonesian regions, the composite difference of simulated
CRESW (CRELW) between El Niño and La Niña events in IRUN is 7.1 (–7.4)
Wm–2. With fixed sea salt emissions, the magnitude of this difference
decreases to 4.3 (–5.2) Wm–2 in ERUN. With fixed natural aerosol
concentrations, this difference increases to 11.2 (–8.9) Wm–2 in CRUN. Over
the western and central Pacific Ocean, the composite difference of simulated
CRESW (CRELW) is –6.0 (5.6) Wm–2 in IRUN. The magnitude of this difference
also decreases in ERUN and increases in CRUN. These results indicate that
variability in natural aerosol emissions is associated with larger differences in
cloud radiative effects over the tropics between El Niño and La Niña events,
and variations in natural aerosol concentrations dampen these differences.
5. Contributions of ENSO-related cloud fractions and natural aerosols
Variations in cloud radiative effects are largely affected by variations in
cloud fraction (Loeb et al., 2007; Kato, 2009), which correlate strongly with
ENSO patterns in the tropics (Park and Leovy, 2004; Eastman et al., 2011;
Zelinka and Hartmann, 2011). The composite differences in observed total
cloud fraction between El Niño and La Niña events from MODIS for years
2001–2014, as well as simulated values from the IRUN simulation are shown
in Fig. 9a and b. The model realistically reproduces the pattern of larger cloud
fraction over the tropical western and central Pacific Ocean and lower cloud
fraction over the Indonesian regions during El Niño events relative to those
during La Niña events. Over the tropics, a positive ENSO event is associated
with larger values of cloud fraction in the tropical western and central Pacific
Ocean, likely resulting from the stronger and deeper convection during El Niño
events driven by warmer SST. Reduced cloud fraction is found over the
Indonesian regions and the tropical eastern Indian Ocean because of
increased subsidence likely caused by the shifting of convection to the central
Pacific. We also find that the cloud cover over the tropical eastern Pacific
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Ocean decreases slightly, which is driven by changes in low stratiform clouds
associated with an upward large-scale motion and a weak atmospheric
stability (Zhu et al., 2007; Eastman et al., 2011). During a positive ENSO
event, total cloud fraction decreases over the southwest coast of North
America between 10°N and 30°N, because the warmer sea surface can cause
a reduction in marine stratiform clouds (Norris and Leovy 1994; Wyant et al.
1997; Clement et al. 2009). Over the southern Pacific Ocean, the cloud
fraction also decreases, possibly as a result of weakened convection
associated with decreasing SST over this region.
Composite differences between El Niño and La Niña events from the IRUN
simulation in simulated low, mid-level, and high cloud fractions, respectively,
are presented in Fig. 9c, d, and e. Relative to those in La Niña events, the low
cloud fraction during El Niño events decreases over the tropical eastern and
central Pacific Ocean, whereas mid-level and high cloud fractions increase
over this region, consistent with observations (Norris, 2005; Zhu et al., 2007;
Marchand, 2013). Differences in mid-level and high cloud fractions between El
Niño and La Niña events are largest between 30°S and 30°N and are similar to
differences in total cloud fraction. This suggests that changes in mid-level and
high cloud fractions, which dominate the variation of total cloud fraction, have
large influences on the interannual variations of cloud radiative effects
between 30°S and 30°N (see Section 6). Differences in total, low, mid-level
and high cloud fractions between El Niño and La Niña events from the ERUN
simulation are similar to those from the IRUN simulation in both spatial
distribution and magnitudes (Fig. S3). However, variations in mid-level and
high cloud fractions are smaller in CRUN than those in IRUN. This result
indicates that although changes in cloud fraction are driven by changes in
meteorology, changes in natural aerosol emissions and concentrations also
influence cloud fraction and can further perturb cloud radiative effects.
Previous findings have identified changes in aerosols as the main drivers
of global dimming and brightening (Streets et al., 2009; Ruckstuhl et al., 2008;
Cermak et al., 2010). After evaluating the role of changes in SST on variations
of marine cloud, Eastman et al. (2011) suggested that changes in aerosol
concentrations in the atmosphere could also be a factor influencing cloud
fraction. Composite differences of observed and simulated wind fields at 850
hPa between El Niño and La Niña events from the NCEP/NCAR (National
Centers for Environmental Prediction/National Center for Atmospheric
Research) reanalysis data for years 2001–2014 (Kalnay et al., 1996) and the
IRUN simulation are plotted in Fig. 10a and c. During El Niño events, easterly
winds are reduced over the tropical Pacific. The sea salt emissions show a
similar pattern and decrease over the tropical central Pacific Ocean and
increase over the subtropical Pacific Ocean (Fig. 10e), similar to the findings of
Xu et al. (2015). Reduced sea salt emissions also cause increased SST
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through the reduction in scattered light (Fig. 10g), which in turn decreases
zonal wind (Martensson et al., 2003). The changes in SST further result in the
corresponding changes in the cloud fraction (Fig. 10i) and cloud radiative
effects (Fig. 7e and f).
In addition to emissions, changes in natural aerosol concentrations are
primarily driven by wet scavenging and transport processes, which are
manifested in the precipitation and wind fields, respectively. The spatial
distribution of correlation coefficients between monthly anomalies of sea salt
concentrations and monthly anomalies of precipitation rate and zonal winds
from the ERUN simulation are compared in Fig. 11. There are significant
negative correlations over most ocean regions between sea salt
concentrations and precipitation, whereas weaker correlations are found
between sea salt concentrations and zonal winds. This difference in
correlations could indicate that wet scavenging has a stronger effect on sea
salt concentrations than transport. Although dry deposition also plays an
important role in determining sea salt aerosol concentrations (Liu et al., 2012),
the changes between El Niño and La Niña events in sea salt dry deposition are
much smaller than those of wet deposition (Fig. S4). The composite
differences in observed and simulated precipitation rate between El Niño and
La Niña events from the CMAP (CPC Merged Analysis of Precipitation)
dataset for years 2001–2014 (Xie and Arkin, 1997) and the IRUN simulation
are shown in Fig. 10b and d. The model adeptly reproduces the observed
pattern of increasing precipitation over the tropical Pacific Ocean, with the
largest difference of 6-8 mm day-1 over the tropical western Pacific Ocean and
decreasing precipitation over the Indonesian regions. These changes in
precipitation lead to increases in wet scavenging of sea salt aerosols over the
tropical Pacific Ocean and decreases over the Indonesian regions between El
Niño and La Niña events (Fig. 10f). Changes in aerosol concentrations
associated with wet scavenging process also increase SST through the
reduction of scattered light and increased cloud fraction over the tropical
western Pacific Ocean, as well as decreased SST and cloud fraction over the
Indonesian regions (Fig. 10h and j). These effects result in increases and
decreases, respectively, of cloud radiative effects over the tropical Pacific
Ocean and over the Indonesian regions (Fig. 7g and h).
To estimate the contribution of ENSO-related variations in cloud fractions
and natural aerosols to the interannual variations of cloud radiative effects, we
used a multivariate regression for both CRESW and CRELW in the IRUN
simulation, similar to the approach of Xu et al. (2015). The variations in low,
mid-level and high cloud fractions illustrate the contributions of clouds at
different heights to cloud radiative effects. The variations in sea salt
concentrations are chosen as a proxy for variations in natural aerosols due to
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their large role as CCN over the oceans in preindustrial conditions. A general
model relating these quantities can be framed in the following form.
(
)( )
=
+
( )+
( )+
( )
( )+
+
where CRESW(t), CRELW(t), CLDLOW(t), CLDMED(t), CLDHGH(t), and
SSLT(t) are the annual anomalies of shortwave and longwave cloud radiative
effects, low, mid-level and high cloud fractions, and column burden
concentration of sea salt. is an error term. The regression coefficients
,
, , ...,
are determined by a least squares fit. The total variance explained
by the regressions (R2) are presented in Fig. S5. The regressions account for
80% and 82% of CRESW and CRELW variability, respectively, averaged over
regions between 60°S and 60°N, as well as 90% and 86% averaged between
30°S and 30°N. One sensitivity test of the regression model using total cloud
fraction instead of low, mid-level and high cloud fractions shows large
decreases in R2 from 80% and 82% to 63% and 56% for CRESW and CRELW,
respectively. This suggests that the interannual variations of cloud radiative
effects depend not only on cloud fraction, but also on cloud height, especially
in the longwave bands. Although the changes in liquid water path can also
influence the variations of cloud radiative effects (Stanfield et al., 2015), the
liquid water path is a function of cloud fraction in the model and it is also
perturbed by aerosols. When we included the liquid water path term in the
regression model, R2 values are almost the same as those without the liquid
water path term. Therefore, we only consider the low, mid-level, and high cloud
fractions and the sea salt concentration in the linear regression model.
In order to evaluate the contributions of predictors in linear models, we
applied the LMG (Lindeman, Merenda and Gold) method (Lindeman et al.,
1980; Grömping, 2006). This method provides a decomposition of the model
explained variance into non-negative contributions. The estimated fraction of
annual variance in CRESW and CRELW explained by the low, mid-level, and
high cloud fractions, as well as sea salt concentration using the LMG method,
are shown in Fig. 12. Over the tropics, the variations in mid-level and high
cloud fraction each account for about 20–50% of the interannual variations of
CRESW (Fig. 12c and e). This results from large variations in mid-level and high
cloud fractions during ENSO events. In the mid-latitude oceans, the
interannual variations of CRESW are primarily driven by low cloud fraction
variability (Fig. 12a) due to the important role of marine stratocumulus over
these regions. For CRELW, almost all variations between 60°S and 60°N are
contributed by the mid-level and high cloud fraction variability (Fig. 12d and f),
whereas the low cloud fraction variability exerts little effect (Fig. 12b) since
CRELW are primarily driven by mid-level and high clouds. The variation in sea
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salt concentration (including interactive emissions, deposition and transport
processes) explains 10–30% of the interannual variations of both CRESW and
CRELW over the tropical Pacific Ocean, the Indonesian regions, and the
tropical Indian Ocean (Fig. 12g and h).
Although wet scavenging of natural aerosols dampens interannual
variations in cloud radiative effects, simulated variations are larger than those
from the present-day observations, especially over the tropics (Fig. 3). He et
al. (2015) found CESM overestimated observed precipitation over 30°S–30°N
compared to the data from GPCP, possibly leading to under-prediction of
natural aerosols over the tropics. By comparing to MODIS-derived CCN data,
Gantt et al. (2014) also found CESM under-predicted 66.7% of column CCN
globally. Due to this underestimation of aerosol concentrations, the dampening
role of variations in natural aerosols may be underestimated as well, which
might explain why the model overestimates the variations of cloud radiative
effects over the tropics. On the other hand, it is worth noting that observations
are at the present-day conditions for only 14 years from 2001 to 2014, which is
a constraint on studying interannual atmospheric variations in a robust way.
Furthermore, present-day observations may not fully represent the variability
in cloud radiative effects at preindustrial conditions.
6. Conclusions
150-year simulations in preindustrial conditions with the CESM model
showed interannual variations of cloud radiative effects with features that are
characteristic of the El Niño–Southern Oscillation (ENSO) events. The
interannual variations of shortwave and longwave cloud radiative effects at the
top of atmosphere (CRESW and CRELW) are quantified using standard
deviation values. The model produces large variations in CRESW over the
tropical western and central Pacific, the eastern Pacific, the tropical Indian
Ocean, as well as parts of Europe, eastern Asia, Africa, Australia, North and
South America, similar to what is observed in CERES observations, with
maximum variations exceeding 18 Wm–2. The large variations of CRELW are
located mainly within the tropics. The global area-weighted mean standard
deviations of CRESW and CRELW are 13.2 and 8.4 Wm–2, respectively. Both of
these values are larger than the observed values of 10.5 and 6.8 Wm–2 in
present-day conditions.
Using EOF analysis, the leading PCs of simulated CRESW (CRELW) are
strongly correlated with the ENSO index. Correlation coefficients between the
Niño 3.4 Index and the leading PCs of CRESW (CRELW) are 0.89 (0.93). Using
spectral analysis, the power spectrum of the leading PCs of CRESW (CRELW)
showed the same peak with the Niño 3.4 Index at the frequency of 0.23 cycles
per year, about 4.3 years per cycle. These results demonstrate that the ENSO
is the only detectable cycle in the interannual variations of cloud radiative
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effects in the preindustrial atmosphere simulated by CESM. Relative to those
during La Niña events, simulated cooling (warming) effects from CRESW
(CRELW) during El Niño events are stronger over the tropical western and
central Pacific Ocean, with the largest difference exceeding 40 Wm–2 (30 Wm–
2
). In contrast, the cooling (warming) effects are weaker by 10–30 Wm–2 over
the Indonesian regions and the subtropical Pacific Ocean.
The changes in cloud fraction are found to be mainly responsible for the
variations of cloud radiative effects between El Niño and La Niña events. In a
positive ENSO event (El Niño), the cloud fraction increases in the tropical
western and central Pacific Ocean and decreases over the Indonesian
regions, the tropical eastern Indian Ocean, the eastern Pacific Ocean, the
southwest coast of North America and the southern Pacific Ocean. Relative to
those in La Niña events, the low cloud fraction during El Niño events
decreases over the tropical eastern and central Pacific Ocean, whereas
mid-level and high cloud fraction increases over this region, consistent with
observations.
Wind-related changes in sea salt emissions, transport and
precipitation-related changes in wet scavenging are found to contribute to the
variations in sea surface temperature and cloud fraction. Variations in sea salt
emissions between El Niño and La Niña events slightly enhance the cooling
(warming) effect of CRESW (CRELW) over the tropical Pacific Ocean and
weaken this effect over the Indonesian regions, with magnitudes less than 10
Wm–2. The cooling (warming) effects of CRESW (CRELW) are also enhanced by
10–30 (0–20) Wm–2 over the eastern Indonesian regions and the tropical
western Pacific Ocean, whereas the CRESW (CRELW) are weaker over the
tropical central Pacific Ocean, mainly resulting from variations in wet
scavenging of natural aerosols. These indicate that the variations in natural
aerosol emissions enhance 3–4% of variations in cloud radiative effects over
the tropics, and the variations in natural aerosol concentrations resulting from
wet scavenging process dampen these variations of CRESW (CRELW) by 4–
6%.
The LMG method for the linear model used here suggests that the
variability in mid-level and high cloud fractions each account for about 20–50%
of the interannual variations of CRESW over the tropics and almost all of the
interannual variations of CRELW between 60°S and 60°N. In the mid-latitude
oceans, the interannual variations of CRESW are primarily driven by low cloud
fraction variability. The variations in sea salt concentrations caused by
interactive emissions, deposition and transport processes explain 10–30% of
the interannual variations of both CRESW and CRELW over the tropical Pacific
Ocean, the Indonesian regions, and the tropical Indian Ocean.
The combination of simulations with fully interactive aerosol, climatological
mean emissions, and climatological mean concentrations could also be used
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to investigate the role of feedbacks of other aerosol sources with climate
variability, such as dust, dimethyl sulfide and carbonaceous aerosol from wild
fires.
Acknowledgments. This research was supported by NSF AGS1048995 and by
DOE DE-SC0006679 as part of the U.S. Department of Energy, Office of
Science, Biological and Environmental Research, Decadal and Regional
Climate Prediction using Earth System Models (EaSM) program. The Pacific
Northwest National Laboratory is operated for the DOE by Battelle Memorial
Institute under contract DE-AC05-76RLO 1830. CMAP Precipitation data and
NCEP Reanalysis data are provided by the NOAA/OAR/ESRL PSD, Boulder,
Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. The
National Energy Research Scientific Computing Center (NERSC) provided
computational resources. The data and codes for these results are posted at:
http://portal.nersc.gov/project/m1374/CRE_EaSM.
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Figure 1. Observed mean of (a) shortwave and (b) longwave cloud radiative
effects from Clouds and the Earth’s Radiant Energy System Energy Balanced
and Filled dataset (CERES-EBAF) for years 2001–2014, and simulated
climatological mean (c) shortwave and (d) longwave cloud radiative effects
from IRUN simulation of CESM model. Global area-weighted mean values of
cloud radiative effects are presented at upper right corner of each panel.
Figure 2. Standard deviation of observed (a) shortwave and (b) longwave
cloud radiative effects from Clouds and the Earth’s Radiant Energy System
Energy Balanced and Filled dataset (CERES-EBAF) for years 2001–2014, and
standard deviation of simulated (c, e, g) shortwave and (d, f, h) longwave cloud
radiative effects from IRUN, ERUN and CRUN using CESM model. Global
(60°S–60°N) area-weighted mean of standard deviations computed from
monthly anomalies for each grid boxes are given at the upper right corner of
each panel.
Figure 3. Observed and simulated zonal mean of standard deviation of (a)
shortwave and (b) longwave cloud radiative effects. Black line represents
values from CERES-EBAF observations. Red, green and blue lines represent
values from IRUN, ERUN and CRUN of CESM model simulations.
Figure 4. The leading EOF mode of simulated yearly (a) CRESW and (b)
CRELW from IRUN simulation. The variance explained by the leading mode
EOF is given at the upper right corner of each panel.
Figure 5. (a) The time series of principal components (PCs) of leading mode
EOF (corresponding to EOF in figure 3) of simulated CRESW (blue line) and
CRELW (green line) and Niño 3.4 Index (red line). (b) The power spectrum of
PCs of EOF of simulated CRESW and CRELW and power spectrum of Niño 3.4
Index. Correlation coefficient between Niño 3.4 Index and PCs of EOF of
CRESW/CRELW is shown in the top-right corner of the each left panel. The
corresponding dashed lines in the right panel indicate the 95% significance
levels. CRESW, CRELW and Niño 3.4 Index are computed from IRUN
simulation.
Figure 6. Spatial distribution of the correlation coefficients between month
anomalies of (a) shortwave and (b) longwave cloud radiative effects and
monthly Niño 3.4 Index. Cloud radiative effects and Niño 3.4 Index are
computed from IRUN simulation. The dotted areas indicate statistical
significance with 95% confidence from a two-tailed Student’s t test.
Figure 7. Composite differences in (a, b) observed and (c, d) simulated
CRESW (left column) and CRELW (right column) from CERES-EBAF satellite
data and IRUN simulation, as well as the effects of changes of these
differences due to changes in (e, f) natural aerosol emissions (IRUN–ERUN)
and (g, h) concentrations (IRUN–CRUN) on CRESW and CRELW between El
Niño and La Niña events. Observed monthly Niño 3.4 Index are calculated
using surface air temperature from NCEP/NCAR (National Centers for
Environmental Prediction/National Center for Atmospheric Research)
reanalysis data. The location boxed (90–150°E, 20°S–20°N and 150°E –
120°W, 20°S–20°N) selected to capture the features of changes in CRESW and
CRELW in Indonesian Regions and the tropical eastern and central Pacific
between El Niño and La Niña events from different simulations is also shown in
(a).
Figure 8. Composite differences in simulated CRESW (top) and CRELW (bottom)
over Indonesian region (IR, 90–150°E, 20°S–20°N) and the tropical eastern
and central Pacific (TP, 150°E –120°W, 20°S–20°N) between El Niño and La
Niña events from CERES-EBAF dataset, and IRUN, ERUN, and CRUN
simulations.
Figure 9. Composite differences in (a) observed and (b) simulated total cloud
fraction and simulated (c) low (d) mid-level and (e) high cloud fraction between
El Niño and La Niña events. Low, mid-level and high cloud are cloud in
pressure range of 1200–700, 700–400 and 400–50 hPa, respectively.
Observed cloud fraction are from the Moderate Resolution Imaging
Spectrometer (MODIS) for years 2001 to 2014. Simulated cloud fraction are
from 150-year IRUN simulation.
Figure 10. Composite differences in wind field at 850 hPa from (a)
NCEP/NACR reanalysis dataset and (c) IRUN simulation, as well as
differences in (b) observed and (d) simulated precipitation rate from CMAP
data and IRUN simulation between El Niño and La Niña events. The
corresponding composite differences in sea salt (e) emissions and (f) wet
scavenging between El Niño and La Niña events in IRUN, and the associated
changes in changes in (g, h) sea surface temperature (SST) and (i, j) total
cloud fraction (CLDTOT) caused by changes in emissions and concentrations
between El Niño and La Niña events. CMAP and NCEP/NACR data are used
for years 2001–2014.
Figure 11. Correlation coefficients between monthly anomalies of sea salt
concentrations and (a) monthly anomalies of precipitation rate and (b) zonal
winds from ERUN simulation. The dotted areas indicate statistical significance
with 95% confidence from a two-tailed Student’s t test.
Figure 12. The LMG method for estimated fraction of annual variance in
CRESW (left column) and CRELW (right column) explained by (a, b) low, (c, d)
mid-level, and (e, f) high cloud fraction, and (g, h) sea salt concentrations from
IRUN simulation.