Journal of Oceanography, Vol. 53, pp. 161 to 172. 1997
Characteristics of the Satellite-Derived Sea Surface
Temperature in the Oceans around Japan
YOSHIMI KAWAI and HIROSHI K AWAMURA
Center for Atmospheric and Oceanic Studies, Faculty of Science, Tohoku University,
Aoba-ku, Sendai 980-77, Japan
(Received 8 February 1996; in revised form 30 August 1996; accepted 2 September 1996)
We compare the sea surface temperatures (SSTs) derived from NOAA/AVHRR data
through the multi-channel sea surface temperature (MCSST) algorithm with the in situ
SSTs reported from ships and buoys during November 1988–May 1991 in the oceans
around Japan. The weekly averages of both the SSTs are computed in 1°-grids. We find
from this comparison that the satellite-derived SSTs are lower than the in situ SSTs by
more than 0.5°C on the average in the Yellow Sea, the north of the Japan Sea and around
the Kuril Islands. On the other hand, the satellite SSTs are higher than the in situ ones by
more than 0.5°C in the regions of the Tsushima and Tsugaru warm currents. Furthermore,
we find that the most dominant variation of the differences is an annual cycle in the regions
north of 40° latitudinal line. In these regions, the satellite-derived SSTs are higher (lower)
than the in situ SSTs by more than 1.0°C in summer (winter) on the average. Because there
is not such a large amount of water vapor or volcanic aerosols that cause a large error
through the MCSST algorithm over the regions, the systematic biases may be related to
vertical temperature structures in the ocean surface layer, formed by the strong monsoon.
In order to obtain more accurate SSTs from satellite observations in these regions, the
characteristics of the vertical temperature structures in the ocean surface layer are
needed to be investigated in detail.
than 0.7°C.
For example, Strong and McClain (1984) compared the
SSTs derived from AVHRR data through the multi-channel
sea surface temperature (MCSST) methods (we refer to the
SSTs as “MCSSTs”) with the in situ SSTs observed with
drifting buoys. They showed that the bias (average of
MCSST-minus-in situ SST differences) was –0.01°C and
the RMS error between them was 0.54°C during November
1981–August 1982 over the Atlantic, Pacific and Indian
Oceans. McClain et al. (1985) also showed that the global
bias and RMS error of the match-up data in 1985 which
consisted of the MCSSTs derived from NOAA-9/AVHRR
data and the SSTs observed with drifting buoys were near
–0.1°C and 0.5°C, respectively.
Barton (1995) discussed selected AVHRR SST algorithms to investigate recent developments in the SST derivation. He used three sets of the atmospheric conditions for
typical subtropical, midlatitude summer, and midlatitude
winter, and the corresponding infrared measurements by the
AVHRR. Although the algorithms have been developed for
different AVHRR sensors, different regions, and by theoretical and empirical techniques, there is very good agree-
1. Introduction
Sea surface facing the lower boundary of the atmosphere
influences atmospheric conditions and climate. For example,
the sea surface temperature (SST) variation in the tropical
Pacific Ocean is one of the indices of the El Niño and
southern oscillation phenomena (ENSO) which causes global
climate changes. Exchange of heat and water between the
atmosphere and the oceans is done through the sea surface.
SST is one of its important controllers and is also controlled
through the exchanging processes. Therefore, it is very
important to understand SST behavior in the global oceans.
In order to investigate climatic changes, a global SST
data set that is dense, accurate and well-ordered is necessary.
Satellite observations of the ocean are expected to satisfy
this requirement. The global SST observations using the
advanced very high resolution radiometer (AVHRR) sensors
on the TIROS-N/NOAA series satellites have been going on
for more than 17 years by the National Oceanic and Atmospheric Administration (NOAA). Many studies on SST
derivation from satellite observations have been done, and
it has been proved that satellite-derived SSTs agree well
with in situ SSTs with root mean square (RMS) errors of less
161
Copyright  The Oceanographic Society of Japan.
Keywords:
⋅ Sea surface
temperature,
⋅ satellite-derived
SST,
⋅ MCSST,
⋅ systematic bias,
⋅ seasonal cycle,
⋅ surface layer,
⋅ oceans around
Japan.
ment between the derived SST values for the three sets of
data, indicating a robustness of the basic differential absorption algorithm.
On the other hand, it has been reported that there are
systematic biases between satellite-derived and in situ SSTs
over particular areas and conditions. Reynolds et al. (1989)
showed day-minus-night MCSST differences for February
1989. In the northwestern tropical Pacific Ocean, the
nighttime MCSSTs were higher than the daytime ones by
more than 0.5°C. This pattern had persisted since the new
NOAA-11 satellite became operational in November 1988.
The comparisons between daytime MCSSTs and driftingbuoy SSTs in this area from November 1988 showed that the
former SSTs were estimated lower than the latter ones.
Since it is assumed in the MCSST algorithm that the infrared
absorption due to water vapor is small, the results of the
comparison may suggest that the large errors are caused by
a large amount of water vapor in the tropical regions.
Bates and Diaz (1991) compared MCSSTs with the in
situ SSTs recorded in the comprehensive ocean-atmosphere
data set (COADS) for each ocean, and showed that the
MCSSTs were lower than the in situ SSTs by 0.19°C to
0.64°C, and the bias averaged over the whole oceans was
–0.29°C. It may be the reason why the MCSSTs are lower
than the COADS SSTs that ship intake temperatures have a
warm bias relative to high-quality (i.e., bucket measurement)
surface observations because the majority of the COADS
data are reported from ship engine room measurements of
the temperatures of water brought in to cool the engine. They
have indicated that sparsity of ship data, especially in the
southern hemisphere, or the difference physically caused by
the measurement of very thin skin temperature (measured
by satellite) and upper-ocean bulk temperature (by ship) can
be the reasons for the satellite-in situ temperature differences.
Sakaida and Kawamura (1992) have shown that when
the MCSSTs derived from NOAA-11/AVHRR data are
compared with the in situ SSTs observed with moored buoys
around Japan, the RMS error between them is about 0.6°C.
They have also pointed out that some MCSST values are
systematically higher than the corresponding buoy SSTs in
the Japan Sea in summer.
Hepplewhite (1989) compared the oceanic skin temperatures measured from a ship using an infrared radiometer
with the bulk temperature observed through a bucket sampling. As a result, it was found that the bulk-minus-skin
temperature differences range from –0.3°C to +1.2°C and
the mean difference was +0.3°C. Schluessel et al. (1990) also
showed that these differences range between –1.0°C and
+1.0°C with mean differences of 0.1°C to 0.2°C depending
on wind and surface heat flux conditions.
These studies imply that there might be a systematic
difference between satellite-derived and in situ SSTs associated with the bulk-skin temperature difference, which is
caused by regional specific conditions of the atmosphere
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Y. Kawai and H. Kawamura
and the ocean surface layer. It is also known that the SST
observation using the infrared bands from space has a bias
toward underestimating SST when there are volcanic aerosols in the atmosphere (e.g., Reynolds et al., 1989; Reynolds,
1993). In order to obtain accurate and dense SST data over
the global oceans, it is essential to investigate the causes of
these systematic differences, and to correct satellite-derived
SST if it has a systematic error.
However, there can be a large difference between
satellite-derived and in situ SSTs even though the atmospheric
correction of a SST deriving algorithm is appropriate because
satellite-derived SSTs and in situ SSTs are measured at
different depths.
In the sections that follow, we compare MCSSTs and in
situ SSTs around Japan, and examine differences between
them. We then discuss the causes of large-scale spatial
biases under no contamination by volcanic aerosols.
2. Data
2.1 The global MCSST data set
Since the infrared radiation emitted from the sea surface is attenuated by absorption gases in the atmosphere, it
is very difficult to measure SST accurately with only one
infrared channel from space. Anding and Kauth (1970)
proposed a method to estimate SST from space using the
differences of brightness temperatures measured with different bands of wavelength. The theoretical basis of a
multiple-window channel algorithm was developed in the
1970’s. The principle and history of improvement of the
MCSST algorithm are reviewed in McMillin and Crosby
(1984), McClain et al. (1985), and Barton (1995). In the
MCSST algorithms, the assumption used is that the amount
of absorption gases in the atmosphere is small, yielding a
linear relation between SST and satellite-measured brightness temperatures.
MCSSTs have been computed operationally from
AVHRR infrared data, by NOAA/NESDIS (National Environmental Satellite Data Information Service) since late
1981. The AVHRR on board the polar orbiting NOAA
satellite has two visible and three infrared channels. Their
band widths are: ch. 1, 0.58–0.68 µm; ch. 2, 0.725–1.10 µm;
ch. 3, 3.55–3.93 µm; ch. 4, 10.3–11.3 µm; ch. 5, 11.5–12.5
µm. The ch. 4 and ch. 5 are called split-window channels.
Since the AVHRR channels used to estimate SST and to
detect clouds in daytime are different from those in the
nighttime, the coefficients of the MCSST equations are
different between daytime and nighttime.
In the present study, we use the global MCSST data set,
produced from the NOAA/NESDIS MCSST retrievals, by
the University of Miami/Rosenstiel School of Marine and
Atmospheric Sciences (UM/RSMAS) and provided by the
Physical Oceanography Distributed Active Archive Center
(PO.DAAC) at the Jet Propulsion Laboratory (JPL) (Olson
Fig. 1. Number of the daytime NOAA/AVHRR observations available in a 1°-grid during November 1988–May 1991.
et al., 1988). In the global MCSST data set the cylindrical
equi-rectangular grid has dimensions of 2048 (longitude) by
1024 (latitude), and the spatial resolution is about 0.176° by
0.176°. The MCSSTs are binned at every week. We average
the MCSSTs in each 1°-grid in order to compare with in situ
data. The number of the daytime MCSST data in each 1°grid during the period November 1988–May 1991 is shown
in Fig. 1.
2.2 In situ SST data
The in situ SST data provided by the Japan Meteorological Agency (JMA) consist of ship, buoy and aircraft
observations. Most of them were reported from ships. A lot
of ship tracks are highly concentrated in the oceans around
Japan, so as the distribution of the SST data from voluntary
observing ships. In the present study, we compare the
MCSSTs with the in situ SSTs in the region extending from
20°N to 50°N and from 120°E to 160°E. Since satellitederived SST can be contaminated by volcanic aerosols after
the eruption of Mt. Pinatubo in the Philippines which had
occurred since June 1991 (Reynolds, 1993), we analyze the
data obtained during 10 November 1988 to 5 June 1991
before the eruption (134 weeks).
The quality of each SST is checked in the following
procedure before analyses: climatological SST in each 1°grid is calculated for every third part of a month from the 10day mean SST maps published by JMA from 1950 to 1992.
Then, the root mean square of the differences between the
climatological SSTs and the in situ SSTs is computed over
each 5°-grid. The in situ SST data whose residual from the
climatological SST exceeds three times of the root mean
square are eliminated.
After this procedure, we average the in situ SST data in
each 1°-grid for the weekly periods of the global MCSST
data set. The number of the in situ SST data in each 1°-grid
during the analyzed period is shown in Fig. 2(a). The
number of the data is large in the coastal regions around
Japan, in the Japan Sea, and along major ship routes, and
small in the Yellow Sea and the ocean southeast of Japan.
The larger amounts of data at the grids in the Japan Sea, the
East China Sea and the ocean south of Japan are collected by
the JMA moored buoys. The standard deviation (root mean
square of deviations from the weekly-averaged SSTs) of the
in situ SSTs in each 1°-grid is shown in Fig. 2(b). The
standard deviation is large in the middle of the Japan Sea and
the ocean east of Japan because of large spatial variability.
2.3 Procedure of analysis
We compute a mean difference and a scatter for each
1°-grid between the daytime MCSSTs and in situ SSTs. The
mean difference (bias) is defined as an average of the
MCSST-minus-in situ SST, and the scatter is a standard
deviation of the differences (note that the scatter is a bit
different from the RMS which is defined as an root mean
square of the differences).
We try to test the statistical significance of the SST
differences (biases). Individual variances of two kind of
SST data are needed to know whether or not the biases are
significant. However, we have no tool to know the variance
of the MCSSTs. Reynolds and Smith (1994) described the
new NOAA operational global SST analysis using optimum
interpolation in 1°-grids. They showed that the data/guess
error (standard deviation) ratios for ship and daytime satellite
data are 3.9 and 1.6 in global average, respectively. (The
guess error is common for both the data.) Therefore, we
assumed that the standard deviation of the daytime MCSSTs
Characteristics of MCSST in the Oceans around Japan
163
Fig. 2. (a) Number of the quality controlled in-situ SSTs available in a 1°-grid during November 1988–May 1991. (b) Standard deviation
of the in situ SSTs in a 1°-grid. The contour interval is 0.5°C with heavy contours at 1.0°C, 2.0°C. Regions with values equal to or
greater than 2.0°C and less than 2.5°C are shaded, and those with values equal to or greater than 2.5°C are striped.
is 1.6/3.9 times as large as that of the in situ SSTs. As a result
of the test, most of the mean differences were judged to be
significant because the numbers of both the data are large
(Figs. 1 and 2(a)) in spite of the large standard deviations of
the in situ SSTs (Fig. 2(b)). Furthermore, we examine the
significance of the SST differences with an another method
(described in Subsection 3.1).
In order to investigate the dominant spatial/temporal
variability of the differences, we perform the empirical
orthogonal function (EOF) analysis for the weekly differences. In order to increase reliability of the EOF analysis, the
following preparation processes are conducted: we select
the grids in which there are the SST difference data of more
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Y. Kawai and H. Kawamura
than 67 weeks (a half of the analyzed period), and make a
complete time series of the difference for each selected grid
filling in the weeks without data by linear interpolation.
These time series are low-pass filtered through the 7-week
running-mean to reduce variations with short periods. (The
smoothed data are used only for the EOF analysis. Neither
interpolation nor smoothing are done for the calculation of
biases and scatters.) Small scale spatial variations are
eliminated using the Gaussian filter whose e-folding and
cut-off scales in zonal and meridional directions are 1° and
3°, respectively. As a result, the temporal variations shorter
than about 3 months and the spatial ones less than about 500
km are eliminated.
3. Comparison between the MCSSTs and the in situ
SSTs
(a)
3.1 Analysis
The mean differences (biases) between the daytime
MCSSTs and the in situ SSTs during the whole analyzed
period are shown in Fig. 3. We calculated the 95%-confidence
intervals of the biases using the standard deviation of the
(b)
Fig. 3. Mean difference (bias) between the daytime MCSSTs and
the in situ SSTs (MCSSTs minus in situ SSTs) during November 1988–May 1991. The contour interval is 0.5°C with heavy
contours every 1.0°C starting with –2.0°C. Positive and statistically significant biases are shaded, and negative and statistically significant biases are striped. The asterisks show the
locations of the JMA buoy.
(c)
Fig. 4. Scatter of the differences between the daytime MCSSTs
and the in situ SSTs during November 1988–May 1991. The
contour interval is 0.5°C with heavy contours at 1.0°C, 2.0°C.
Values equal to or greater than 2.0°C and less than 2.5°C are
shaded, values equal to or greater than 2.5°C are striped.
Fig. 5. Spatial pattern of the EOFs of the differences between the
daytime MCSSTs and the in situ SSTs. The contour interval is
0.05 with heavy contours at –0.10, 0.0, +0.10. Regions with
values greater than +0.05 are heavy shaded, and those with
values less than –0.05 are striped. Grids not used for the EOF
analysis are light shaded. The asterisks show the locations of
the JMA buoy. (a) 1st mode, (b) 2nd mode, and (c) 3rd mode.
Characteristics of MCSST in the Oceans around Japan
165
Fig. 6. Principal component score of the EOFs during the analyzed period. (a) 1st mode, (b) 2nd mode, and (c) 3rd mode.
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differences (scatters) between both the weekly-averaged
SSTs, and the number of the weekly averaged data. If the
minimum (maximum) of the confidence interval of the bias
is positive (negative), the bias is judged to be significant.
The biases are smaller than –0.5°C and significant in the
Yellow Sea, the middle of the Japan Sea, and around the
Kuril Islands. On the other hand, the biases are larger than
+0.5°C and significant in the Tsushima and Tsugaru warm
currents regions. The biases are small over most of the
Pacific Ocean. The scatters during the analyzed period are
shown in Fig. 4. The scatters are larger than 2.0°C in the
Yellow Sea, the middle of the Japan Sea, and in the region
between 40°N and 45°N in the northwestern Pacific Ocean.
We perform the EOF analysis to look for the dominant
spatial/temporal variations of the differences between the
daytime MCSSTs and the in situ SSTs. The spatial patterns
of the 1st, 2nd and 3rd mode EOFs are shown in Fig. 5, and
the time series of the mode scores in Fig. 6. The contribution
rates of the 1st, 2nd and 3rd modes are 56.1%, 6.9% and
5.1%, respectively. The spatial pattern of the 1st mode EOFs
shows a significant variation in the Pacific Ocean south of
the Hokkaido Island and the Kuril Islands, the northern
Japan Sea, the Sea of Okhotsk (Fig. 5(a)). The regions with
the amplitudes of about 1–2°C (as obtained by combination
of Fig. 5(a) and Fig. 6(a)) in the 1st mode distribute north of
40°N around Japan. The largest amplitude appears in the Sea
of Okhotsk off the north coast of the Hokkaido Island and the
Kuril Islands.
A dominant annual cycle can be seen in the variation of
the 1st mode score (Fig. 6(a)). The 1st mode score becomes
Fig. 7. Seasonal variability of the biases. Otherwise same as Fig. 3. (a) Winter (Jan.–Mar.), (b) spring (Apr.–June), (c) summer (July–
Sep.), and (d) autumn (Oct.–Dec.).
Characteristics of MCSST in the Oceans around Japan
167
positive during July–September in 1989 and during June–
October in 1990. It is known that the year of 1990 was an
anomalously hot year in the studied period. The difference
of the peak score heights between the years of 1989 and 1990
might be due to the inter-annual variability of the SST or airtemperature field around Japan.
These facts mean that the most dominant spatial/temporal variation of the differences in the oceans around Japan
is the seasonal one in the regions north of 40°N. The 2nd and
3rd modes have less importance than the 1st mode since the
former contribution rates are about one tenth of the latter.
Therefore, we do not discuss the 2nd and 3rd modes so much
judging that their reliability is not high. However, it may be
pointed out that the 2nd mode shows an inter-annual variation
with an annual cycle (Fig. 6(b)), and the 3rd mode has a
spatial pattern similar to that of the biases shown in Fig. 3
(Fig. 5(c)).
In order to examine the 1st mode variability in detail,
we average the SST differences for each season. We define
winter as January–March, spring as April–June, summer as
July–August, and autumn as September–December. Spatial
variability of the seasonal biases is shown in Fig. 7. In
winter, the MCSSTs are lower than the in situ SSTs by more
than 1.0°C on the average in the region north of 40°N, and
in the Yellow Sea. Oppositely, the former SSTs are higher
than the latter ones by more than 1.0°C on the average in the
south of the Sea of Okhotsk, and the north of the Japan Sea
in summer. The biases in the regions of the Tsushima and
Tsugaru warm currents are positive through all seasons.
The seasonal spatial distributions of the scatters are
shown in Fig. 8. In winter and spring, the scatters are larger
than 2.0°C between 35°N and the Kuril Islands, and in the
Fig. 8. Seasonal variability of the scatters. Otherwise same as Fig. 4. (a) Winter, (b) spring, (c) summer, and (d) autumn.
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Y. Kawai and H. Kawamura
Yellow Sea. In summer, the regions with the scatters exceeding 2.0°C are located around the Kuril Islands, and the
scatters in the Yellow Sea are smaller than 2.0°C. The
larger-scatter regions shift to the ocean south of the Kuril
Islands in autumn, and appear in the Japan Sea and the East
China Sea again.
3.2 Discussion
It can be considered that the sources of the differences
between satellite-derived and in situ SSTs are (1) ship-borne
thermometer accuracy, (2) AVHRR radiometric calibration,
(3) atmospheric correction algorithm, (4) sub-pixel cloud
contamination, (5) mismatch of satellite and ship measurements due to the followings: vertical structure, horizontal
structure and temporal variability (Robinson and Ward,
1989).
Strong and McClain (1984) showed that the bias and
the root mean square difference are larger when compared
MCSSTs with ships-of-opportunity SSTs rather than with
drifting-buoy ones. The errors of voluntary-ship observations can not be negligible since the methods and the depths
of temperature measurements are not always consistent.
Though this can be the reason why the scatters in the present
study are larger over the entire studied region than those in
the previous researches using buoy SSTs, this can not
explain the seasonal variability of the large-scale spatial
biases and scatters.
In the MCSST algorithm it is assumed that the amount
of absorption gases in the atmosphere and the difference
between SST and mean atmospheric temperature are small.
Therefore, if either of them significantly exceed a mean
value, MCSSTs may have a large error against true SSTs.
However, the error of the SSTs derived through the splitwindow technique is smaller than 1°C even if the difference
between SST and mean atmospheric temperature is about
5°C (May and Holyer, 1993). The amount of water vapor in
the atmosphere over the analyzed region, which is in the
midlatitudes, is thought to be much smaller than that in the
Tropics. Therefore the contribution of the error caused
through the atmospheric correction to the large SST differences is small.
The regions where the scatters are more than 2.0°C
correspond to the polar front in the Japan Sea, the Kuroshio
front around 36°N, the Oyashio front around 41°N, and the
confluence zone with the fronts associated with the warm
eddies detached from the Kuroshio (Fig. 4). These fronts
have been well described and studied using AVHRR IR
images (e.g., Kawamura et al., 1986). Yoshida (1993) have
demonstrated that the SST front in the northwestern Pacific
Ocean moves from the region around 40°N to that around
the Kuril Islands during spring to summer (Fig. 9). In
summer both the scatters and the horizontal SST gradients
in the region around the Kuril Islands are largest over the
whole analyzed region (see Fig. 8(c)). It can be inferred from
the above comparison that the horizontal structure of SST
field affects the difference between MCSST and in situ SST.
The comparison is made for the 1°-grid- and weekly-averaged SSTs. Thus, the temporal and spatial variations of the
SST fronts within 1°-grids and a week may result in the
larger scatters in these regions through the temporal or
spatial difference of SST sampling between in situ and
satellite observations.
A correlation coefficient between the scatters and the
horizontal SST gradients computed from the daytime
MCSSTs for each season shows that both the scatters and the
SST gradients have a positive and statistically significant
correlation in every season (Table 1). Reynolds and Smith
(1994) showed that the guess errors in the global SST
analyses using optimum interpolation on 1°-grids reach
local maxima in the western boundary regions, especially in
the Kuroshio and Gulf Stream regions, where larger SST
variability is expected because of the warm eddies and the
frontal structures associated with the energetic currents.
Positive and negative heat fluxes through the water
surface produce vertical temperature gradients in the layer
below the surface. The process of the ocean surface layer has
been discussed for a long time in terms of the possible error
source in satellite SST measurements (e.g., Stewart, 1985).
A persistent one-way heat transfer through the ocean surface
may result in systematic biases between satellite-derived
and in situ SSTs. The coefficients of the MCSST equations
are determined through a linear regression method assuming that the relation between the skin and bulk temperatures
is random in time and in space. Therefore, if there is the
significant vertical temperature gradient which is steadily
formed in daytime or nighttime through a season in the
surface layer, the relation between the skin and bulk temperatures is not random anymore. Consequently, such a
vertical temperature structure may cause the seasonal variation of the MCSST-in situ SST difference.
The regions where the 1st mode EOFs are more than
+0.05 are north of the polar front in the Japan Sea and the
Oyashio front in the North Pacific (Fig. 5(a)). These regions
are strongly influenced by the monsoon winds because of
their locations. The cold dry monsoon from the Eurasian
Table 1. Correlation coefficients between the scatters and the
horizontal MCSST gradients in the 1°-grids for each season
and the whole analyzed period.
Correlation coefficients
Winter (Jan.–Mar.)
Spring (Apr.–June)
Summer (July–Sep.)
Autumn (Oct.–Dec.)
The whole analyzed period
0.58
0.57
0.68
0.54
0.64
Characteristics of MCSST in the Oceans around Japan
169
Fig. 9. Left: 30 years (1961–90) average monthly mean SST for February, May, August and November. Right: Southward gradient of
the 30 years average monthly mean SST for the months in the left panels (cited from Yoshida, 1993).
Continent blows over the regions from autumn to winter.
The amount of integrated water vapor in the continental air
over the Japan Sea or the Sea of Okhotsk is usually smaller
than 0.4 g/cm2 in winter. On the contrary, there is the moist
and warm monsoon blowing from the south-east oceans
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Y. Kawai and H. Kawamura
during summer, and anticyclones cover the regions to bring
sunny days. These characteristic patterns of the wind fields
may cause a persistent one-way heat transfer through the sea
surface. The quantitative investigation on the sea surface
heat flux and the wind fields are left for future studies.
A large negative bias appears in the Yellow Sea through
the studied period (Fig. 3) and has some seasonal features
(Fig. 7). The scatters are also large in the Yellow Sea (Figs.
4 and 8). The Yellow Sea is close to the Eurasian Continent
and included in the monsoon region. However, the variation
pattern of the biases is a bit different from that in the northern
Japan Sea and the Okhotsk Sea. One of the characteristics of
the Yellow Sea is that a large amount of fresh water is
pouring in from the two big rivers, the Yangtze River and the
Yellow River. Therefore density stratification is apt to be
made in the upper layer in the Yellow Sea. This stratification
structure due to the continuously supplied fresh water might
play a role in the appearance of the biases in the Yellow Sea.
Detailed examination of the fresh water effects is left for
future studies.
The locations of the JMA moored buoys are shown in
Figs. 3 and 5 with asterisks. All the JMA buoys are located
southerly out of the regions where the biases are large, and
both the biases and the first mode EOFs are nearly zero at
those stations. Sakaida and Kawamura (1992) reported that
the MCSSTs tuned against global drifting-buoy SSTs agree
well with the SSTs observed with the JMA moored buoys in
the oceans around Japan. It should be noted that, because of
the JMA buoy locations, the seasonal biases in the northern
oceans obtained in the present study could not be found in
their study.
4. Conclusion
We compared the MCSSTs derived from NOAA/
AVHRR data with the in situ SSTs which are reported from
ships and buoys in the oceans around Japan. Both the SSTs
obtained during November 1988–May 1991 were averaged
weekly in each 1°-grid. We found the existence of large
biases for the wide oceans averaged for the entire period
studied. The biases between the daytime MCSSTs and the in
situ SSTs are smaller than –0.5°C and significant in the
north of the Japan Sea, the Yellow Sea and around the Kuril
Islands. Those are larger than +0.5°C and significant in the
Tsushima and Tsugaru warm currents regions. The causes of
these biases are other than volcanic aerosols since we
selected the period from when the satellite data were not
contaminated by volcanic aerosols.
Furthermore, we found from the EOF analysis that the
most dominant spatial variation of the differences between
the daytime MCSSTs and the in situ SSTs is an annual cycle
in the regions north of 40°N. In these regions, the winter
biases are smaller than –1.0°C, and the summer ones are
larger than +1.0°C. The first mode pattern traces well the
seasonal distributions of the biases. The biases in the regions
of the Tsushima and Tsugaru warm currents are positive
through all seasons.
The regions where the scatters are larger than 2.0°C
correspond to the SST fronts. This fact means that the large
scatters in 1°-grids are mainly due to the horizontal SST
structure or the large SST temporal variability.
MCSSTs are tuned against the SSTs observed with the
buoys drifting around in the world oceans, and those agree
well with the moored-buoy SSTs in the oceans around Japan
(Sakaida and Kawamura, 1992). However, the seasonal
biases with the characteristic distribution patterns appear in
the oceans near the Eurasian continent and north of 40°N,
where there is no moored or drifting buoys.
In order to estimate SST more accurately from satellite
observations in the oceans around Japan, detailed characteristics of the vertically or horizontally complicated temperature structures in the ocean surface layer needs to be
investigated.
Acknowledgements
We would like to thank the Japan Meteorological
Agency for providing us with the in situ SST data set. We
would also like to acknowledge PO.DAAC (the Physical
Oceanography Distributed Active Archive Center) for
providing us with the global MCSST data set. Advice and
support from Dr. Futoki Sakaida of Kobe University of
Mercantile Marine are very useful and greatly appreciated.
We wish to thank all the members of the Physical Oceanography Group, Tohoku University for their valuable discussions. This study is supported by the international cooperative research program on GOOS (Global Ocean Observing System) sponsored by the Ministry of Education,
Science and Culture.
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