ATMOSPHERIC SCIENCE LETTERS
Atmos. Sci. Let. 17: 548–555 (2016)
Published online 31 August 2016 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/asl.691

Changes in marine fog in a warmer climate
Hideaki Kawai,1* Tsuyoshi Koshiro,1 Hirokazu Endo,1 Osamu Arakawa2 and Yuichiro Hagihara3
1 Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
2 Faculty of Life and Environmental Sciences, Universitiy of Tsukuba, Tsukuba, Japan
3 Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Japan*Correspondence to:
H. Kawai, MeteorologicalResearch Institute, Japan
Meteorological Agency, 1-1
Nagamine, Tsukuba, Ibaraki
305-0052, Japan.
E-mail: h-kawai@mri-jma.go.jp

Received: 15 April 2016
Revised: 30 June 2016
Accepted: 1 August 2016

Abstract
Changes in marine fog in a warmer climate are investigated through simulations using theatmospheric component of a global climate model, with both observed and perturbed sea
surface temperature forcing. Global changes in marine fog occurrence in different seasons arecompared. We show that the changes in marine fog occurrence correspond well to changes inhorizontal temperature advection near the surface in a warmer climate. Therefore, the changes
in marine fog can be well explained by large-scale circulation changes. Regarding changes in
the characteristics of marine fog, we show that the in-cloud liquid water content of marine
fog is consistently increased in a warmer climate, for a given horizontal surface temperatureadvection. It is also confirmed that the contribution of changes in marine fog to cloud feedback
is not negligible, but is small.
Keywords:

marine fog; climate change; large-scale circulation; low cloud feedback

1. Introduction
Marine fog (Koračin et al., 2014), especially over the
mid-latitude ocean, is an important target in climate
simulation because it affects maritime traffic andbecause sky-obscuring marine fog is an important
contributor to the Earth’s radiation budget due to its
widespread coverage. The purpose of this study is toreveal global-scale changes in marine fog in a warmer
climate.
Many studies have investigated the global distribution of marine fog and the basic meteorological factors that primarily determine its distribution. Klein
and Hartmann (1993) reported a global distribution of
sky-obscuring marine fog obtained by shipboard observations (Warren et al., 1988), and noted the importanceof surface warm air advection in determining the distribution. Several studies followed, including Norris(1998a, 1998b) and Norris and Klein (2000), who compared and analyzed the meteorological fields related tofog and other cloud regimes, and Koshiro and Shiotani(2014) proposed a stability index related to fog distribution. In addition, Kawai et al. (2015) showed that theoccurrence of clouds below 240 m globally, estimatedfrom satellite lidar, has a high correlation with the temperature difference between a height of 2 m and thesea surface, which is controlled primarily by warm air
advection.
Recently, changes in low cloud in a warmer climate
and low cloud feedback have been studied extensively,
especially with respect to tropical and subtropical low
cloud (e.g., Kawai, 2012; Blossey et al., 2013; Brientand Bony, 2013; Webb and Lock, 2013; Zhang et al.,2013; Bretherton and Blossey, 2014). However, there
have been few studies related to changes and cloud

feedback in mid-latitude low cloud, and even fewer suchstudies of mid-latitude marine fog.
The objective of our study is to discuss the change
in marine fog in a warmer climate. In order to simplify
the discussion of the mechanism and isolate the cloud
feedback of marine fog, we use simulation data from
AMIP [atmospheric models, forced by observed sea
surface temperature (SST)] and AMIP with increased
SST experiments. We comprehensively discuss changes
in marine fog in a warmer climate, changes in meteorological fields that cause the marine fog changes, changes
in the characteristics of marine fog, and the impact ofchanges in marine fog on cloud feedback.

2. Data
2.1. Model data
Under the fifth phase of the Climate Model Intercomparison Project (CMIP5) (Taylor et al., 2012) andthe Cloud Feedback Model Intercomparison Project
Phase-2 (CFMIP2) (Bony et al., 2011), we performed
simulations of type AMIP (labeled as amip), and AMIP
with SST increased by 4 K with uniform perturbations(amip4K) and with a composite pattern obtained from
CMIP3 models (amipFuture). The model runs are 31
years in length (1979–2009). These AMIP series simulations are often used for studies related to cloud feedback; e.g., Webb and Lock (2013) and Webb et al.
(2014).
We ran these simulations using MRI-CGCM3 (Yukimoto et al., 2011, 2012), which has 48 vertical levels in
total, and 5 levels below 910 hPa with a lowest layer ofthickness 10 hPa. The model level data are used, and© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction inany medium, provided the original work is properly cited.

Changes in marine fog in a warmer climate

both monthly and daily average data are used in theanalysis. In addition, CMIP5 multi-model data for sea
level pressure (SLP) are used (the models used are listed
in Table S1, Supporting information) to examine therobustness of circulation changes in MRI-CGCM3.

2.2. Observation data
Shipboard observation data for 1954–2008 from the
Extended Edited Cloud Report Archive (EECRA)(Hahn and Warren, 2009) are used to examine the representation of marine fog in the model simulation. In
addition, we used fog or near-surface cloud frequency
data, obtained from C2 data of the Kyushu University(KU) cloud mask product (Hagihara et al., 2010),
retrieved from Cloud–Aerosol Lidar and Infrared
Pathfinder Satellite Observation (CALIPSO) (Winker
et al., 2009) data for 2007–2009.

3. Basic features of model marine fog
3.1. Marine fog in the model and observationsFirst, to examine the representation of marine fog inthe model, the simulation data are compared with the
shipboard and satellite observations (see Kawai et al.
(2015) for details). Regarding the satellite cloud maskdata, fog is defined as cloud at heights lower than
240 m, and the fog-occurrence frequency derived fromthe cloud mask data is a lower limit of the estimate,because CALIPSO cannot observe clouds that occur
below thick cloud (Kawai et al., 2015). Teixeira (1999)
calculated fog occurrence in a model as the occurrenceof cloud water content greater than 0.016 g kg−1 at thelowest model level. However, in this study we simply
define fog frequency as the cloud fraction at the lowest
model level because in-cloud liquid water content(LWC) typically exceeds 0.1 g kg−1 (shown later inFigure 5), which is much larger than the thresholdof 0.016 g kg−1 , and it is confirmed in a preliminary
comparison that fog frequency calculated from cloud
fraction and the method using a cloud water content
threshold gives similar results. In addition, the cloud
fraction output calculated in the model is temporally
averaged for each model timestep. Therefore, the data
provide an appropriate climatology of fog occurrence
even if the diurnal variation is large.
Figure 1 shows that these three climatologies of
fog-occurrence frequency (MRI-CGCM3 simulation,
shipboard and satellite observations), which are totally
independent, are consistent. In July, marine fog is frequent around Kamchatka Peninsula, near Newfoundland, north of Iceland, and in the Arctic Ocean northof Eurasia, in all three climatologies. Although thenumbers of shipboard observations are insufficient over
the Southern Ocean, fog frequencies in model dataand satellite observations are similar in this region. In
January, the common fog areas are the Northeastern
Pacific and the Southern Ocean in all three climatologies, and both the model simulation and the satellite

549

data show fog occurrence over the Arctic Ocean. Spatial anomaly correlation and root mean square error
against shipboard climatology were calculated monthly
for 10 CMIP5 multi-model data. It turned out that
MRI-CGCM3 has the highest annual mean score of spatial anomaly correlation (0.70) and the third least root
mean square error. In addition, about half of models
failed to capture the observed fog distribution except
for boreal summer. Based on the comparison and thescores, we conclude that MRI-CGCM3 represents fog
relatively well and that the model data are suitable foruse in studies of the future changes, at least in a qualitative sense.

3.2. Vertical cloud structures in the model
Figure 2 shows composites of the vertical structuresof clouds and relative humidity in the model over
the North Pacific in July based on daily data forsoutherly (V > 2 m s−1 ) and northerly (V < −2 m s−1 )surface wind. When the surface wind is southerly(Figure 2(a) and (c)), the contours of potential temperature are tilted near the surface, which denotes a stable boundary layer, relative humidity is high, and theclouds touch the sea surface around 40∘ –50∘ N. This
clearly shows that the model represents warm air advection fog, where warm surface air is cooled by cold SST
and the water vapor is condensed, as the mechanism is
discussed by many studies (e.g., Klein and Hartmann,
1993). On the other hand, when the surface wind isnortherly (Figure 2(b) and (d)), the contours of potential temperature are upright near the surface, which
denotes a well-mixed boundary layer, relative humidity is lower, and the clouds do not touch the sea surface
around 40∘ –50∘ N. This means that fog rarely appears
when the wind is northerly in the model and floating
low clouds instead tend to appear. These features are
consistent with shipboard cross-section observations in
the North Pacific, including those reported by Tanimoto
et al. (2009).

4. Results
4.1. Changes in marine fog
In July, the occurrence of marine fog (cloud fraction
at the lowest model level) is reduced in the amip4K
simulation in the central North Pacific and the western
North Atlantic near Newfoundland (Figure 3(c)). The
maximum reduction reaches 6%. On the other hand, theoccurrence of marine fog is increased by up to 3% in
the eastern North Pacific (south of Alaska). In January,a dipole feature is found in the eastern North Pacific,
which corresponds to an area of frequent fog occurrence: fog decreases in the western part by up to 6%
and increases in the eastern part of this area by up to
3% (Figure 3(d)). Similar results are seen in the difference between amipFuture and amip (Figure 3(e,f)).
Thus, these changes are robust for a warmer climate
regardless of the pattern of SST increase.

© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.

Atmos. Sci. Let. 17: 548–555 (2016)

550

H. Kawai et al.

(a)

(b)

CGCM3 Simulation: July

60°N

60°N

30°N

30°N

EQ

EQ

30°S

30°S

60°S

60°S
0

60°E

120°E
5

2

180°
10

120°W

20

30

60°W

0

0

30°N

30°N

EQ

EQ

30°S

30°S

60°S

60°S
120°E

–100 2

5

180°
10

120°W
20

30

60°W

0

0

40

60°N

30°N

30°N

EQ

EQ

30°S

30°S

60°S

60°S
0

60°E

120°E
2

5

180°
10

20

120°W
30

60°E

60°W

0

0

40

10

120°E

120°W

20

30

60°W

0

60°W

0

60°W

0

40

5

180°
10

120°W
20

30

40

KU cloud mask: January

(f)

60°N

180°

Ship obs.: January–100 2

KU cloud mask: July

(e)

5

(d)
60°N

60°E

120°E
2

60°N

0

60°E

40

Ship obs.: July(c)

CGCM3 Simulation: January

60°E

120°E
2

5

180°
10

20

120°W
30

40

Figure 1. (a,b) Cloud fraction at the lowest model level in the MRI-CGCM3 simulation (1979–2009). (c,d) Shipboard fog
observational climatology (1954–2008) on a 5∘ × 5∘ grid, where grid points are shaded gray if the sample number is <100. (e,f)
Occurrence frequency of fog (2007–2009) derived using the KU cloud mask (0–240 m bin). Units are %; data are shown for July(left panels) and January (right panels).

4.2. Changes in meteorological fields
To understand changes in marine fog occurrence,changes in meteorological fields are examined.
Changes in SLP are shown in Figure 4(c) and (d).
In July, subtropical high-pressure systems occur in
the North Pacific and the North Atlantic (Figure 4(a)),
and western parts of these high-pressure systems correspond to areas of frequent fog occurrence, where
southerly winds are dominant. In a warmer climate, thehigh-pressure system in the North Pacific is weakened
between 180∘ and 150∘ W, which corresponds to weakened southerly winds in the central North Pacific andstrengthened southerlies in the eastern North Pacific.

Therefore, surface temperature advection, which is calculated from 10 m wind and SST (Klein et al., 1995), is
decreased in the central North Pacific and increased in
the eastern North Pacific (Figure 4(e)). This pattern is
consistent with the reduction in marine fog in the central North Pacific and the increase in the eastern North
Pacific. Note that locations with maximum changes
in fog occurrence correspond to the poleward side oflocations with large changes in surface temperatureadvection because climatological relative humidity
near surface is larger (closer to saturation) at higher
latitude (see Figure 2(c,d)) and fog occurrence is moresensitive to warm air advection there. The low-pressurearea over the North American continent is weakened© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.

Atmos. Sci. Let. 17: 548–555 (2016)

Changes in marine fog in a warmer climate

800

(a)

551

Cloud Frac.: Southerly
800

850

850

900

900

950

950

(b)

Cloud Frac.: Northerly

1000

1000
30°N

40°N

50°N

60°N

5

30°N

40°N

10

800

(c)

RH:

10

Southerly
800

850

850

900

900

950

950

1000

(d)

20

30

50°N
40

50

RH:

60°N

60

Northerly

1000
30°N

40°N

50°N

60°N

5

30°N

40°N
60

10

70

80

50°N
85

90

60°N

95

Figure 2. Vertical structure of clouds (a,b) and relative humidity (c,d) in MRI-CGCM3. Composites of daily data where the surface
wind is (a,c) southerly (V > 2 m s−1 ) and (b,d) northerly (V < −2 m s−1 ) are shown. The data are averaged between 170∘ E and170∘ W for July (1979–2009). Shading shows cloud fraction (%), contours show potential temperature (K), horizontal arrows show
meridional wind (m s−1 ), and vertical arrows show vertical wind (−hPa h−1 ). Vertical axis is pressure (hPa).

in a warmer climate, which corresponds to reductions
in southerly wind strength and temperature advection
near Newfoundland. This is also consistent with alocal reduction in marine fog. In January, the area of
frequent fog occurrence corresponds to the eastern partof the Aleutian low-pressure system (Figure 4(b)). In a
warmer climate, a negative SLP anomaly occurs off thewest coast of the United States, which corresponds toa decrease (increase) in southerly wind on the western(eastern) side of the anomaly. This pattern explains
the dipole pattern in marine fog changes in the eastern
North Pacific in January. In the Southern Ocean, marine
fog occurrence is reduced along 40∘ S and increased
along 60∘ S, and this can be attributed to decreases andincreases in temperature advection along 40∘ and 60∘ S,
respectively (Figure S1). In January, the changes inmarine fog occurrence over the Southern Ocean havea more zonally uniform structure than in July, and thisis consistent with more zonally uniform changes in the
SLP pattern during this month.
To confirm the robustness of the changes in SLP,
CMIP5 multi-model data are compared (Figure S2).
The analysis confirms all the characteristics of changes
in SLP mentioned above, including a weakened
high-pressure system in the central North Pacific and aweakened low-pressure area over the North American

continent in July, a negative anomaly off the west
coast of the United States in January, and more zonallyuniform changes in SLP over the Southern Ocean in
January than in July. In addition, these changes arequite similar in the amip4K and amipFuture simulations. Therefore, we can conclude that these changes
are robust and deserving of analysis and discussion.
The weakening of the high-pressure system in the central North Pacific and weakening of the low-pressurearea over the North American continent in July could
be explained by weakened monsoon circulations in a
warmer climate (e.g., Tanaka et al., 2005; Kitoh et al.,2013), which would weaken SLP contrasts between
land and ocean. The increase in SLP around 45∘ S canbe attributed to a poleward shift of the jet in a warmer
climate (e.g., Medeiros et al., 2015). The similarityof the patterns of SLP changes in the amip4K and
amipFuture simulations suggests that the main factor
for the SLP changes is stabilization of tropospherebecause of SST increase and consequent weakeningof major overturning circulations, including Hadley,
Walker and monsoon circulations.
Sugimoto et al. (2013) discussed trends in fog frequency in recent decades observed in coastal land areas
adjacent to the North Pacific, into which advection fog
frequently flows. They found that decreases in fog in© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.

Atmos. Sci. Let. 17: 548–555 (2016)

552

H. Kawai et al.

(a)

(b)

July

amip

60°N

60°N

50°N

50°N

40°N

40°N

30°N

30°N

20°N
120°E

150°E

180°

(c)

150°W

120°W

90°W

60°W

amip4K – amip

30°W

0

20°N
120°E

60°N

50°N

50°N

40°N

40°N

30°N

30°N

20°N
120°E

150°E

180°

(e)

150°W

120°W

90°W

60°W

amipFuture – amip

30°W

0

50°N

50°N

40°N

40°N

30°N

30°N
180°

150°W

120°W

90°W

60°W

30°W

150°W

120°W

2

5

10

150°E

180°

150°W

–4

–2

–1

(f)

July
60°N

150°E

180°

0

90°W

60°W

30

40

20

30°W

amip4K – amip

20°N
120°E

60°N

20°N
120°E

150°E

(d)

July

60°N

January

amip

120°W

–0.5

January

90°W

60°W

30°W

1

2

4

0.5

amipFuture – amip

20°N
120°E

150°E

180°

150°W

–4

–2

–1

120°W

–0.5

0

January

90°W

60°W

30°W

1

2

4

0.5

0

0

Figure 3. (a,b) Climatology of marine fog (cloud fraction at the lowest model level) in MRI-CGCM3, and the change from (c,d)
amip to amip4K, and (e,f) amip to amipFuture. Units are %; data are shown for July (left panels) and January (right panels).
(a) SLP, Wind(b) SLP, Wind

July

amip

60°N

60°N

50°N

50°N

40°N

40°N

30°N

30°N

20°N
120°E

150°E

180°

150°W

120°W

90°W

60°W

30°W

0

20°N
120°E

150°E

January

amip

180°

150°W

120°W

90°W

60°W

30°W

10

(c) SLP, Wind

amip4K – amip

10(d) SLP, Wind

July

60°N

60°N

50°N

50°N

40°N

40°N

30°N

30°N

20°N
120°E

150°E

180°

150°W

120°W

90°W

60°W

30°W

0

20°N
120°E

150°E

amip4K – amip

–6

60°N

50°N

50°N

40°N

40°N

30°N

30°N
150°E

180°

150°W

120°W

90°W

60°W

30°W

180°

150°W

0

–4

–2

(f) TempAdv

July

60°N

20°N
120°E

amip4K – amip

120°W

January

90°W

60°W

30°W

0
4

2

(e) TempAdv

0

20°N
120°E

–1

1

2

4

6

amip4K – amip

150°E

180°

150°W

–2

–1

–0.5

120°W
–0.2

0.2

January

90°W
0.5

60°W
1

30°W

0

2

Figure 4. (a,b) Climatology of sea level pressure (hPa) and 10 m wind (m s−1 ) in MRI-CGCM3, and (c,d) the change from amip to
amip4K. (e,f) Change in surface temperature advection (K day−1 ) from amip to amip4K. Data are shown for July (left panels) andJanuary (right panels).

recent decades correspond to reductions in warm air
advection. It is shown in this study that changes inmarine fog correspond well to changes in horizontal
temperature advection near the surface in a warmer
climate. This suggests that the changes in marine
fog can be well explained by large-scale circulation

changes. There are numerous studies related to Californian coastal fog in summer, and for instance, Johnstone and Dawson (2010) showed relationships among
the fog frequency, SLP, meridional wind, and SST, on
both interannual and interdecadal timescales. However,note that there is a difference in the mechanisms of fog© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.

Atmos. Sci. Let. 17: 548–555 (2016)

Changes in marine fog in a warmer climate(a) 45

220

30
25
20
15
10
5
0
–15 –10

200

(c) 9
amip
amip4K

Frequency (% / (1m s–1))

35

(b)
amip
amip4K

in-cloud LWC (mg kg–1)

Cld Frac (z=1) (%)

40

553

180
160
140
120
100

–5

0

5

10

15

–15 –10–5

V (m s–1)

0

5

10

15

V (m s–1)

amip
amip4K

8
7
6
5
4
3
2
1
0
–15 –10

–5

0

5

10

15

V (m s–1)

Figure 5. Relationships between meridional surface wind and (a) cloud fraction and (b) in-cloud liquid water content, at the lowest
model level. (c) Meridional surface wind frequency histogram. Daily data of every single grid is binned into meridional surface windbins of 1 m s−1 interval, and averaged over the North Pacific (160∘ E − 160∘ W, 40 − 50∘ N), for July (1979–2009). Blue and red
dots/bars show amip and amip4K data, respectively.

occurrence between Californian coastal fog, which issignificantly affected by the coastal topography including ocean upwelling and offshore air flow, and advection marine fog, which occurs over broad areas in
mid-latitude global oceans.

4.3. Relationship between surface wind and fog
Figure 5(a) shows the relationship between meridional surface wind and cloud fraction at the lowest
model level (i.e., fog frequency) over the North Pacific(160∘ E − 160∘ W, 40 − 50∘ N), using daily data for July
(1979–2009). It is clear that cloud fraction increases as
the southerly wind increases, and the slope of the relationship is 2.7%/(m s−1 ) for the wind speed 0–10 m s−1 .
This slope can quantitatively explain that the peak values of positive and negative changes in fog frequency
in the North Pacific are +3 and −6% (Figure 3(c)) andthe corresponding changes in meridional wind are +1
and −2 m s−1 , respectively (Figure 4(c)). Figure 5(b)shows a similar relationship between meridional wind
and in-cloud LWC, which is LWC in a grid divided
by the cloud fraction, at the lowest model level. It isalso clear that in-cloud LWC increases as the southerly
wind increases. In a warmer climate, in-cloud LWC
is increased for a given meridional wind. This meansthat the optical thickness of fog could increase for agiven meridional wind in a warmer climate, unless thecloud droplet number concentration decreases substantially. The increase in in-cloud LWC can be explainedby air masses that have larger specific humidity in a
warmer climate, and can therefore condense out morewater vapor for a given rate of temperature advection,
as approximately corresponds to a given meridional
wind. Similar characteristics are found over the Southern Ocean (0∘ − 360∘ E, 45∘ − 60∘ S), although the wind
is in the opposite direction (Figure S3).

4.4. Impact of changes in marine fog on cloud
feedback
The contribution of changes in marine fog to cloud
feedback in the model is now briefly examined. The
approximate cloud radiative effect (W m−2 ) of clouds

between the surface z0 and height z for upward shortwave radiation, CRE_SW↑(z − z0 ), is estimated as follows:()
CRE_SW↑ z–z0
≈ (SW↑(all sky, z) −SW↑(clear sky, z))
)
(
))
(
(
− SW↑ all sky, z0 −SW↑ clear sky, z0 ,where SW↑ denotes the upward shortwave radiative flux(the sign is positive for downward). The contributionof clouds between z0 and z to shortwave cloud feedback can be roughly estimated from the difference in
CRE_SW↑(z – z0 ) between the amip4K and amip simulations. Changes in CRE_SW↑(z − z0 ) due to clouds
between the surface and 960, 900 and 700 hPa are examined. For example, it is clear from Figure S4 that thegeographical pattern of change in CRE_SW↑(z – z0 )between the surface and 960 hPa essentially corresponds to the change in marine fog occurrence shown in
Figure 3 for July. Changes in CRE_SW↑(z – z0 ) are positive in areas with reduced fog, such as the central North
Pacific and the western North Atlantic, and changes arenegative in areas with increased fog, such as the eastern North Pacific. However, these geographical patterns
become less clear for changes in CRE_SW↑(z – z0 )between the surface and 900 hPa, and changes between
the surface and 700 hPa show quite different geographical patterns and much larger amplitudes compared withthe case between the surface and 960 hPa. This meansthat the impact of changes in marine fog on cloud feedback is not negligible, but is small, and that the contribution of low clouds, in an atmospheric layer higher
than fog, dominates for low-cloud feedback.

5. Summary
Changes in marine fog over the global mid-latitude
ocean in a warmer climate, which have received relatively little attention to date, were investigated in thisstudy, using AMIP and AMIP with increased SST
experiments. It was confirmed that simulated global© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.

Atmos. Sci. Let. 17: 548–555 (2016)

554

marine fog occurrence is represented relatively well in
MRI-CGCM3, by comparison with two independent
global observation datasets. The vertical structures ofmarine fog represented in the model were briefly discussed by showing composite cases corresponding tosoutherly and northerly winds.
Simulated global changes in marine fog occurrence
in different seasons were compared, and changes in
SLP were also examined. An important conclusion ofthis study is that the changes in marine fog correspondwell to changes in horizontal temperature advection
near the surface in a warmer climate in the simulations.
The results suggest that large-scale changes in marine
fog can be well explained by large-scale circulation
changes.
Regarding changes in characteristics of marine fog, it
was shown that in-cloud LWC of marine fog is consistently increased in a warmer climate for a given meridional wind; i.e., for a given horizontal surface temperature advection. In addition, it was confirmed that thecontribution of the changes in marine fog to cloud feedbacks is not negligible, but is small.
Robust, general characteristics of the changes inmarine fog are discussed in this study. However, it ispossible that the vertical resolution of climate models is
not high enough to capture more detailed characteristics
of changes in marine fog in a warmer climate (the lowest
model layer is about 85 m in MRI-CGCM3). Therefore,
simulations using models with higher vertical resolution would be desirable to reveal more detailed changes
in marine fog, in a future study. In addition, representations of fog distribution of CMIP5 multi-model data
will be examined and the changes in a warmer climate
will be studied in detail in future research.

Acknowledgements
The EECRA data were obtained from the Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S.
Department of Energy. The contact person for requesting the
KU cloud mask data used in this study is Hajime Okamoto(okamoto@riam.kyushu-u.ac.jp). This research was supported inpart by ‘the Program for Risk Information on Climate Change’and ‘the Social Implementation Program on Climate Change
Adaptation Technology’, both of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, and by
the Environment Research and Technology Development Fund
(2-1503) of the Ministry of the Environment. We acknowledge
the two anonymous reviewers for their constructive and insightful comments. We thank Shuhei Maeda for valuable discussions.
Figures were edited by Rikako Matsumoto.

Supporting information
The following supporting information is available:
Appendix S1. Abbreviations, experiment names, and terminologies.
Figure S1. Changes in marine fog occurrence and sea levelpressure in the southern hemisphere.

H. Kawai et al.

Figure S2. Changes in sea level pressure simulated by the
CMIP5 multi-model ensemble.
Figure S3. Relationships between meridional wind and cloud
fraction, and in-cloud liquid water content over the Southern
Ocean.
Figure S4. Changes in the approximate cloud radiative effect.
Table S1. List of CMIP5 models used for Figure S2.

References
Blossey PN, Bretherton CS, Zhang M, Cheng A, Endo S, Heus T, Liu
Y, Lock AP, De Roode SR, Xu KM. 2013. Marine low cloud sensitivity to an idealized climate change: the CGILS les intercomparison.
Journal of Advances in Modeling Earth Systems 5(2): 234–258, doi:
10.1002/jame.20025.
Bony S, Webb M, Bretherton CS, Klein SA, Siebesma P, Tselioudis
G, Zhang M. 2011. CFMIP: towards a better evaluation and understanding of clouds and cloud feedbacks in CMIP5 models. Clivar
Exchanges 56: 20–22.
Bretherton CS, Blossey PN. 2014. Low cloud reduction in agreenhouse-warmed climate: results from Lagrangian LES of a
subtropical marine cloudiness transition. Journal of Advances inModeling Earth Systems 6(1): 91–114, doi: 10.1002/2013MS000250.
Brient F, Bony S. 2013. Interpretation of the positive low-cloud feedback
predicted by a climate model under global warming. Climate Dynamics 40(9–10): 2415–2431, doi: 10.1007/s00382-011-1279-7.
Hagihara Y, Okamoto H, Yoshida R. 2010. Development of a combined CloudSat-CALIPSO cloud mask to show global cloud
distribution. Journal of Geophysical Research 115: D00H33, doi:
10.1029/2009JD012344.
Hahn CJ, Warren SG. 2009. Extended Edited Synoptic Cloud Reports
From Ships and Land Stations Over the Globe, 1952–1996 (2009
Update). NDP-026C. Carbon Dioxide Information Analysis Center,
Oak Ridge National Laboratory: Oak Ridge, TN.
Johnstone JA, Dawson TE. 2010. Climatic context and ecological implications of summer fog decline in the coast redwood region. Proceedings of the National Academy of Sciences of the United States of America 107(10): 4533–4538, doi: 10.1073/pnas.0915062107.
Kawai H. 2012. Examples of mechanisms for negative cloud feedbackof stratocumulus and stratus in cloud parameterizations. SOLA 8:
150–154, doi: 10.2151/sola.2012-037.
Kawai H, Yabu S, Hagihara Y, Koshiro T, Okamoto H. 2015. Characteristics of the cloud top heights of marine boundary layer clouds andthe frequency of marine fog over mid-latitudes. Journal of the Meteorological Society of Japan 93: 613–628, doi: 10.2151/jmsj.2015-045.
Kitoh A, Endo H, Krishna Kumar K, Cavalcanti IFA, Goswami P, Zhou
T. 2013. Monsoons in a changing world: a regional perspective ina global context. Journal of Geophysical Research [Atmospheres]
118(8): 3053–3065, doi: 10.1002/jgrd.50258.
Klein SA, Hartmann DL. 1993. The seasonal cycle of low stratiform
clouds. Journal of Climate 6: 1587–1606, doi: 10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2.
Klein SA, Hartmann DL, Norris JR. 1995. On the relationships among
low-cloud structure, sea surface temperature, and atmospheric circulation in the summertime Northeast Pacific. Journal of Climate 8:1140–1155.
Koračin D, Dorman CE, Lewis JM, Hudson JG, Wilcox EM, Torregrosa
A. 2014. Marine fog: a review. Atmospheric Research 143: 142–175,
doi: 10.1016/j.atmosres.2013.12.012.
Koshiro T, Shiotani M. 2014. Relationship between low stratiform cloudamount and estimated inversion strength in the lower troposphere overthe global ocean in terms of cloud types. Journal of the MeteorologicalSociety of Japan 92: 107–120, doi: 10.2151/jmsj.2014-107.
Medeiros B, Stevens B, Bony S. 2015. Using aquaplanets to understandthe robust responses of comprehensive climate models to forcing.
Climate Dynamics 44(7–8): 1957–1977, doi: 10.1007/s00382-0142138-0.
Norris JR. 1998a. Low cloud type over the ocean from surface
observations. Part I: relationship to surface meteorology and the© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.

Atmos. Sci. Let. 17: 548–555 (2016)

Changes in marine fog in a warmer climate

vertical distribution of temperature and moisture. Journal of Climate 11: 369–382, doi: 10.1175/1520-0442(1998)011<0369:
LCTOTO>2.0.CO;2.
Norris JR. 1998b. Low cloud type over the ocean from surface observations. Part II: geographical and seasonal variations. Journal of
Climate 11: 383–403, doi: 10.1175/1520-0442(1998)011<0383:
LCTOTO>2.0.CO;2.
Norris JR, Klein SA. 2000. Low cloud type over the ocean from surface
observations. Part III: relationship to vertical motion and the regionalsurface synoptic environment. Journal of Climate 13: 245–256, doi:
10.1175/1520-0442(2000)013<0245:LCTOTO>2.0.CO;2.
Sugimoto S, Sato T, Nakamura K. 2013. Effects of synoptic-scale control
on long-term declining trends of summer fog frequency over thepacific side of Hokkaido Island. Journal of Applied Meteorology andClimatology 52(10): 2226–2242, doi: 10.1175/JAMC-D-12-0192.1.
Tanaka HL, Ishizaki N, Nohara D. 2005. Intercomparison of the intensities and trends of Hadley, Walker and Monsoon Circulations in
the Global Warming Projections. SOLA 1: 77–80, doi: 10.2151/
sola.2005-021.
Tanimoto Y, Xie SP, Kai K, Okajima H, Tokinaga H, Murayama T,
Nonaka M, Nakamura H. 2009. Observations of marine atmosphericboundary layer transitions across the summer Kuroshio extension.
Journal of Climate 22: 1360–1374, doi: 10.1175/2008JCLI2420.1.
Taylor KE, Stouffer RJ, Meehl GA. 2012. An overview of CMIP5 and theexperiment design. Bulletin of the American Meteorological Society
93(4): 485–498, doi: 10.1175/BAMS-D-11-00094.1.
Teixeira J. 1999. Simulation of fog with the ECMWF prognostic cloud
scheme. Quarterly Journal of the Royal Meteorological Society 125:
529–552, doi: 10.1002/qj.49712555409.
Warren SG, Hahn CJ, London J, Chervin RM, Jenne RL. 1988. Global
Distribution of Total Cloud Cover and Cloud Type Amounts Over
the Ocean, NCAR/TN-317+STR. National Center for AtmosphericResearch: Boulder, CO.
Webb MJ, Lock AP. 2013. Coupling between subtropical cloud feedback
and the local hydrological cycle in a climate model. Climate Dynamics
41(7–8): 1923–1939, doi: 10.1007/s00382-012-1608-5.

555

Webb MJ, Lock AP, Bodas-Salcedo A, Bony S, Cole JNS, Koshiro
T, Kawai H, Lacagnina C, Selten FM, Roehrig R, Stevens B.
2014. The diurnal cycle of marine cloud feedback in climate models. Climate Dynamics 44: 1419–1436, doi: 10.1007/s00382-0142234-1.
Winker DM, Vaughan MA, Omar AH, Hu Y, Powell KA, Liu Z,
Hunt WH, Young SA. 2009. Overview of the CALIPSO mission
and CALIOP data processing algorithms. Journal of Atmosphericand Oceanic Technology 26: 2310–2323, doi: 10.1175/2009JTECHA
1281.1.
Yukimoto S, Yoshimura H, Hosaka M, Sakami T, Tsujino H, Hirabara M,
Tanaka TY, Deushi M, Obata A, Nakano H, Adachi Y, Shindo E, Yabu
S, Ose T, Kitoh A. 2011. Meteorological Research Institute Earth
System Model Version 1 (MRI-ESM1) – model description. Technical
Reports of the Meteorological Research Institute 64: 83. http://www.
mri-jma.go.jp/Publish/Technical/index_en.html (accessed: 12 August2016).
Yukimoto S, Adachi Y, Hosaka M, Sakami T, Yoshimura H, Hirabara M,
Tanaka TY, Shindo E, Tsujino H, Deushi M, Mizuta R, Yabu S, Obata
A, Nakano H, Koshiro T, Ose T, Kitoh A. 2012. A new global climate
model of Meteorological Research Institute: MRI-CGCM3 – model
description and basic performance. Journal of the MeteorologicalSociety of Japan 90A: 23–64.
Zhang M, Bretherton CS, Blossey PN, Austin PH, Bacmeister JT, Bony
S, Brient F, Cheedela SK, Cheng A, Del Genio AD, De Roode SR,
Endo S, Franklin CN, Golaz J-C, Hannay C, Heus T, Isotta FA,
Dufresne J-L, Kang I-S, Kawai H, Köehler M, Larson VE, Liu Y, Lock
Adrian P, Lohmann U, Khairoutdinov MF, Molod AM, Neggers RAJ,
Rasch P, Sandu I, Senkbeil R, Siebesma AP, Siegenthaler-Le DrianC, Stevens B, Suarez MJ, Xu K-M, von Salzen K, Webb MJ, Wolf A,
Zhao M. 2013. CGILS: results from the first phase of an international
project to understand the physical mechanisms of low cloud feedbacks
in single column models. Journal of Advances in Modeling Earth
Systems 5(4): 826–842, doi: 10.1002/2013MS000246.

© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.

Atmos. Sci. Let. 17: 548–555 (2016)