temperature trends in the skin/surface, mid-troposphere...
TRANSCRIPT
Asia-Pacific J. Atmos. Sci., 47(5), 439-455, 2011
DOI:10.1007/s13143-011-0029-4
Temperature Trends in the Skin/Surface, Mid-troposphere and Low Stratosphere
Near Korea from Satellite and Ground Measurements
Jung-Moon Yoo1, Young-In Won
2, Young-Jun Cho
3, Myeong-Jae Jeong
4, Dong-Bin Shin
3, Suk-Jo Lee
5, Yu-Ri Lee
1,
Soo-Min Oh1, and Soo-Jin Ban
5
1Department of Science Education, Ewha Womans University, Seoul, Korea2Wyle IS, NASA/GSFC, U.S.A.3Dept. of Atmospheric Sciences, Yonsei University, Seoul, Korea4Dept. of Atmospheric & Environmental Sciences, Gangneung-Wonju National University, Gangneung, Korea5National Institute of Environmental Research, Incheon, Korea
(Manuscript received 9 January 2011; revised 17 June 2011; accepted 14 July 2011)© The Korean Meteorological Society and Springer 2011
Abstract: Various types of satellite (AIRS/AMSU, MODIS) and
ground measurements are used to analyze temperature trends in the
four vertical layers (skin/surface, mid-troposphere, and low strato-
sphere) around the Korean Peninsula (123-132oE, 33-44
oN) during
the period from September 2002 to August 2010. The ground-based
observations include 72 Surface Meteorological Stations (SMSs), 6
radiosonde stations (RAOBs), 457 Automatic Weather Stations
(AWSs) over the land, and 5 buoy stations over the ocean. A strong
warming (0.052 K yr−1
) at the surface, and a weak warming (0.004~
0.010 K yr−1
) in the mid-troposphere and low stratosphere have been
found from satellite data, leading to an unstable atmospheric layer.
The AIRS/AMSU warming trend over the ocean surface around the
Korean Peninsula is about 2.5 times greater than that over the land
surface. The ground measurements from both SMS and AWS over
the land surface of South Korea also show a warming of 0.043~
0.082 K yr−1
, consistent with the satellite observations. The correlation
average (r = 0.80) between MODIS skin temperature and ground
measurement is significant. The correlations between AMSU and
RAOB are very high (0.91~0.95) in the anomaly time series,
calculated from the spatial averages of monthly mean temperature
values. However, the warming found in the AMSU data is stronger
than that from the RAOB at the surface. The opposite feature is
present above the mid-troposphere, indicating that there is a
systematic difference. Warming phenomena (0.012~0.078 K yr−1
) are
observed from all three data sets (SMS, AWS, MODIS), which have
been corroborated by the coincident measurements at five ground
stations. However, it should also be noted that the observed trends
are subject to large uncertainty as the corresponding 95% confidence
intervals tend to be larger than the observed signals due to large
thermal variability and the relatively short periods of the satellite-
based temperature records. The EOF analysis of monthly mean
temperature anomalies indicates that the tropospheric temperature
variability near Korea is primarily linked to the Arctic Oscillation
(AO), and secondarily to ENSO (El Niño and Southern Oscillation).
However, the low stratospheric temperature variability is mainly
associated with Southern Oscillation and then additionally with
Quasi-Biennial Oscillation (QBO). Uncertainties from the different
spatial resolutions between satellite data are discussed in the trends.
Key words: Temperature trend, AIRS/AMSU, MODIS, Korea,
AWS, RAOB, arctic oscillation
1. Introduction
Surface temperature is an important parameter for climate and
surface energy balance, while the skin temperature is one of the
key factors relating canopy and soil surfaces to biogeophysical
processes (Jin et al., 1997). Therefore, monitoring and under-
standing the trends of atmospheric and surface temperature are
crucial to study regional and global climate changes. In order
to better understand global temperature trends, we need to
have reliable and consistent observations in the various vertical
layers of the mid-troposphere and low stratosphere as well as
in the skin/surface. In this study, temperature trends in the four
layers near Korea (123-132oE, 33-44oN; Fig. 1) during the
recent eight year period have been investigated utilizing satellite
observations (e.g., Susskind et al., 2003; Mostovoy et al., 2005)
of the Atmospheric Infrared Sounder (AIRS)/the Advanced
Microwave Sounding Unit-A (AMSU-A; hereafter named
AMSU) and Moderate-Resolution Imaging Spectroradiometer
(MODIS) together with ground measurements.
The atmospheric soundings and error estimates from the
AIRS/AMSU Version 5.0 (V5) data have been performed in the
previous studies (Susskind et al., 2003; Pagano et al., 2006;
Susskind, 2007; Susskind and Blaisdell, 2008). The MODIS
V5 surface products have been separately developed over the
land and the ocean, respectively. The validations of the MODIS
Land Surface Temperature (LST) have been carried out in
many studies (Mostovoy et al., 2005; Wan, 2005; Coll et al.,
2009), while the comparison between the MODIS Sea Surface
Temperature (SST) and other SST observed product is available
in Yuan and Savtchenko (2003). The Level-3 products of AIRS/
AMSU and MODIS used in this study have been derived from
V5 retrieval algorithm (e.g., Olsen, 2007 for AIRS/AMSU;
Wan, 2009 for the MODIS LST). Susskind (2008) showed the
temperature anomaly trends in the layers of skin and 500 hPa
over the globe using the 5 year AIRS data to improve the data
Corresponding Author: Jung-Moon Yoo, Department of ScienceEducation, Ewha Womans University, Seoul 120-750, Korea.E-mail: [email protected]
440 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
from Version 4 to 5.
The satellite-observed trend estimates have been compared
with independent ground-based measurements from surface-
stations, buoys and radiosondes. The ground-based data have
been observed from 72 Surface Meteorological Stations (SMSs),
6 radiosonde stations (RAOBs), 457 Automatic Weather Stations
(AWSs) over land, and 5 buoy stations over ocean. Since the
accuracy of the two types of observations has not been properly
assessed in diverse environments, we need to intercompare them
in order to estimate their uncertainties. Hall et al. (2008) have
compared MODIS LSTs with AWS ones over the ice and snow
surface of Greenland to assess the satellite-derived surface
temperature. Because of the limitations of the ground measure-
ments, the intercomparison of temperatures between satellite-
based and ground-based data has been performed over the land
of South Korea, which is a part of the whole study area (123-
132oE, 33-44oN). The dense network for meteorological ground
observations (i.e., SMS and AWS) is available over the land.
In order to understand the relationship between thermal and
climate variability in the area, we investigate the correlation of
time series between monthly mean temperature anomalies and
the climate indices, in connection with Arctic Oscillation (AO;
Thompson and Wallace, 1998), Southern Oscillation (SO), and
Quasi-Biennial Oscillation (QBO). The El Niño and Southern
Oscillation (ENSO) and AO are known as the largest climate
perturbations on the Earth (Jerrejeva et al., 2003), and generally
have a great impact on the mid-latitude (including Korea)
climate. We have used the SO Index (SOI) as the atmospheric
component of ENSO for the correlation analysis between ther-
mal and climate variability. On the other hand, the extratropical
circulation and temperature in the stratosphere are known to be
influenced by the QBO (Labitzke and van Loon, 1988; Salby
et al., 1997). The three climate indices during the recent eight
years have been obtained from NOAA (2010a, 2010b, 2010c).
The purpose of this study is to investigate the thermal trends
and the possible relationship with the climate indices in the
skin/surface, mid-troposphere and low stratosphere near Korea
in the last eight years using the satellite and ground measure-
ments, and to examine uncertainties among them based on
their intercomparison.
2. Data and method
In this study, we have used three satellite-observed datasets and
four ground-based measurements taken during the recent eight-
year period (September 2002 to August 2010) that cover near
Korean Peninsula (123-132oE, 33-44oN) (Table 1 and Fig. 1).
Fig. 1. Location of a) meteorological stations of surface (+), buoy (△),and radiosonde (□), and b) the Automatic Weather Stations (AWSs)used to examine the thermal trend around the Korean Peninsula (123-132
oE, 33-44
oN) in this study.
Table 1. The data information during the period from Sep 2002 to Aug 2010 used in this study. Either ‘Near Korea’ or ‘Around the KoreanPeninsula’ means the area of (123-132o
E, 33-44oN) in the table. Here the abbreviations are as follows; OBS (OBServation), LST (Land Surface-
skin Temperature), MODIS (MODerate resolution Imaging Spectroradiometer), temp (temperature), AIRS (Atmospheric InfraRed Sounder),AMSU (Advanced Microwave Sounding Unit), RAOB (RAdiosonde OBservation), SST (Sea Surface-skin Temperature), SFC (surface), AWS(Automatic Weather Station), LECT (Local Equatorial Crossing Time), and SK (South Korea).
OBSTemperature
Temp type Area Spatial
resolution Number of
OBS Satellite sensor LECT Abbreviation References
MODIS LST Skin Near Korea 5 km × 5 km 2/day Aqua MODIS 01:30/13:30
Tskin (MODIS_LST)
Wan (2009)Coll et al. (2009)
MODIS SST Skin Near Korea 4 km × 4 km 2/day Aqua MODIS 01:30/13:30
Tskin (MODIS_SST)
AMSU tempprofile at 24 levels
Air Near Korea 1o×1o 2/day Aqua AMSU-A 01:30/13:30
T(AMSU)Olsen
(2007a)
AIRS/AMSU skin temp
Skin Near Korea 1o×1o 2/day Aqua AIRS/AMSU-A
01:30/13:30
Tskin (AIRS/AMSU)
Olsen (2007a)
AIRS/AMSU SFC air temp
SFC air Near Korea 1o×1
o 2/day
Aqua AIRS/AMSU-A
01:30/13:30
Tsfc (AIRS/AMSU)
Olsen (2007a)
RAOB temp Air SK 7 stations 2/day N/A N/A T(RAOB)
Station SFC air temp
SFC air SK 72 stations Every 1 hour N/A N/A TSTAT_sfc
AWS SFC air temp SFC air SK 457 stations Every 1 min N/A N/A TAWS_sfc
Buoy SST Skin SK 5 stations Every 1 hour N/A N/A TBUOY_SST
30 November 2011 Jung-Moon Yoo et al. 441
a. Satellite temperature data (MODIS, AIRS/AMSU)
The satellite Level 3 gridded data retrieved from the radio-
meter measurements of MODIS and AIRS/AMSU-A (hereafter
AMSU-A named AMSU) onboard the EOS Aqua satellites are
used in this study. AMSU has 12 channels within the 50-60
GHz portion of the oxygen band for temperature information
(Chahine et al., 2001). The AMSU retrieval for the atmospheric
temperature profile is performed before the cloud correction
because AMSU radiances are not influenced significantly by
non-precipitating clouds. The retrieval obtained from AMSU is
unbiased over coarse layers of the atmosphere, although local
errors still exist. According to the AIRS document (Harris,
2007), the AIRS/AMSU monthly Level 3 products can be used
for climate trend analysis over long timescales with the lowest
possible systematic errors. The AIRS/AMSU instrument has
been constructed to produce 1 km tropospheric layer mean
temperatures with an accuracy (or an RMS error) of 1 K (e.g.,
Susskind et al., 2006; Olsen et al., 2007a, 2007c). The portion
of the areas taken by land and ocean is almost equal in the
study region.
The MODIS data have spatial resolutions of 5 km × 5 km
over the land and of 4 km × 4 km over the ocean (e.g., Wan,
2005, 2009; Wang et al., 2008; Coll et al., 2009). The specific
data products used in our study are: level-3 version 5 monthly
Land Surface Temperature (LST) data as MYD11C3.5, and
Ocean level-3 version 4 Standard Mapped Image (SMI) data
for Sea Surface Temperature (SST) from MODIS. Validation
efforts have reported that the accuracy of MODIS land surface
skin temperature is better than 1K for most clear-sky cases in
the temperature range from −10oC to 50oC (Wan et al., 2004;
Wan and Li, 2008). For the statistical analysis, MODIS data
are re-binned on a common grid with a spatial resolution of
20 km × 20 km over both the land and ocean to avoid the
artificial land/ocean effect due to the resolution difference. In
addition, level 3 monthly standard retrieval data from AIRS/
AMSU (AIRX3STM) are used as well (e.g., Olsen, 2007a).
The AIRS/AMSU data have the resolution of 1o × 1o (~100
km × 100 km) over both the land and the ocean. The polar
orbiting Aqua satellite, providing observational data twice a
day, has a Local Equatorial Crossing Time (LECT) of morning
and afternoon at 01:30.
Surface temperature is usually the standard surface-air tem-
perature measured by a sheltered thermometer 1.5-3 m above a
grassy, well-ventilated surface while skin temperature, also
called radiometric temperature, is inferred using the thermal
emission from the earth’s surface. Skin temperature is often an
average temperature for a mixture of various canopy and soil
surfaces (Jin et al., 1997). It is more directly related to surface
boundaries than is the surface air temperature. As discussed in
Olsen et al. (2007a, 2007c), the temperature profile from the
AIRS products are level quantities; i.e., they are retrieved at the
corresponding pressure level not for a layer. AIRS/AMSU
surface air temperature is an extrapolated result of the atmo-
spheric temperature profile to the surface pressure, which is
still a quantity at a desired level.
The satellite data from AIRS/AMSU have been utilized to
examine both skin and surface temperatures. In addition, the
AMSU temperature data at the heights of 500 hPa and 50 hPa
have been used to analyze the thermal state of mid-troposphere
and low stratosphere, respectively (e.g., Olsen, 2007b). Also,
we analyzed skin temperatures from the MODIS observations,
which have higher spatial resolution than AIRS/AMSU. Monthly
mean temperature anomaly values in this study have been used
in the analyses of both thermal trend and Empirical Orthogonal
Functions (EOF; Kutzbach, 1967; Weare et al., 1976) to screen
out annual and seasonal cycles.
The 95% confidence intervals for the temperature trends are
calculated using a bootstrap method (Wilks, 1995). For each
temperature anomaly data set, 10000 new data sets were created
to produce 10000 linear trends through random sampling. The
random sampling was conducted by drawing data out of the
respective original record of temperature anomaly with repe-
tition allowed.
The uncertainty (i.e., error bar) of monthly temperature anom-
alies was calculated as follows: We first calculated anomalies
for each 1o × 1o bin, then averaged the anomalies over the whole
studied area for each month. In each month, the uncertainty of
the anomalies associated with sampling error is estimated by the
standard error (i.e., the population standard deviation divided by
the square root of the sample size; Bendat and Piersol, 2010).
Then the range of uncertainty is represented by the 95%
confidence interval (± 1.96 times the standard error (SE) of the
average; e.g., Luterbacher et al., 2004).
We calculated EOF, which excluded the seasonal cycle from
the two types of satellite data. Missing data were filled using an
interpolation scheme. Deviations from the long-term mean were
calculated. The long-term mean corresponding to the month in
question was used to remove the seasonal cycle. This procedure
requires to a covariance matrix. The matrices were diagonalized
and the resulting eigenvalues were re-arranged from largest to
smallest. The eigenvectors corresponding to the largest eigen-
values were calculated together with their time coefficients for
each month.
b. Ground measurements
In order to validate satellite observations, we have used
ground-based measurements as follows: 72 SMSs, 7 RAOBs,
457 AWSs over the land, and 5 buoy stations over the ocean
(Table 1 and Fig. 1). In more detail, the ground measurement
data from the meteorological and RAOB stations, and AWS are
available over the land, while data from only five buoys are
available over the ocean (Fig. 1). The surface meteorological
stations and buoy stations provide data in every hour, and the
RAOB performs twice a day (0000, 1200 UTC). The AWS
provides data every minute with the densest observational
network, but we used the hourly AWS data in this study (Fig.
1b). The ground-based measurements that we use in our analy-
sis are available only over South Korea in the study region.
442 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
c. Index of agreement between satellite and ground measure-
ments
In order to quantify the trend differences (i.e., uncertainties)
between various types of observations, we have introduced an
index of agreement (e.g., Willmott, 1982; Yoo and Carton,
1988; Brazel et al., 1993). The index of agreement is a measure
of relative error in predicted estimates to observed values, and a
dimensionless number ranging from 0.0 to 1.0 (Brazel et al.,
1993). Here 1.0 indicates that the estimates and observed
values are identical. The index has been utilized because the
correlation coefficient cannot explain differences in proportion-
ality (Willmott, 1982). In this study, the index shows how close
satellite-based monthly-mean temperatures or their anomalies
are to ground-based values in terms of rms error.
(1)
According to Willmott (1982), Oi and P
i are observed and
model-predicted variables, respectively, on the evaluation of
model performance. is the mean values of Oi. ,
, and total observations N = 96 (Note N = 56 in buoy
case). In this study, Oi can be the temperature at the surface
meteorological station (SMS), RAOB, and buoy, while Pi is
either the satellite-based temperature (AIRS/AMSU, MODIS)
or AWS.
3. Results
a. Satellite derived thermal trends of MODIS and AIRS/
AMSU in the skin/surface
Figure 2 and Table 2 show the anomaly time series of
monthly mean MODIS skin temperature [Tskin(MODIS)] and
AIRS/AMSU skin/surface temperatures [Tskin(AIRS/AMSU),
Tsfc (AIRS/AMSU)] over the land and ocean areas near Korea
during the recent eight year period. While Tskin(MODIS) tends
to show a weak cooling (−0.067 ± 0.702 K 8yr−1), Tsfc(AIRS/
AMSU) indicates a warming (0.230 ± 0.946 K 8yr−1) over the
land. The ground measurements of SMSs and AWSs show a
warming of 0.346~0.653 K 8yr−1, which is also twice as strong
d 1 Pi Oi–( )2
i 1=
N
∑ Pi′ Oi
′+( )2
i 1=
N
∑⁄ 0 d 1≤ ≤,–=
O Pi′ Pi O–=
Oi′ Oi O–=
Table 2. Satellite-derived temperature trends (K yr−1) at four different altitudes (skin/surface, the 500 and 50 hPa pressure levels) around the KoreanPeninsula (123-132oE, 33-44oN) during the period from Sep 2002 to Aug 2010. The temperature data at the skin and surface, and at the 500 and 50hPa pressure levels have been obtained from MODIS AIRS/AMSU, and AMSU, respectively. The values in parentheses indicate the temperaturetrends (K 8yr−1) for the whole eight year period. Also, the ‘SK’ in parentheses stands for the South Korea area, where is a part in this study area. The‘Stns’ means stations. The ± values define the 95% confidence intervals for the trends.
AreaMODIS TBuoy_SST
(5 Stns)
AIRS/AMSU TSTAT_sfc
(72 Stns)TAWS_sfc
(457 Stns)
AMSU
Tskin Tskin Tsfc T500hPa T50hPa
Land & Ocean
3.60 × 10−3
(0.029 ± 0.423)1.08 × 10−2
(0.086 ± 0.587)5.16 × 10−2
(0.413 ± 0.575)3.60 × 10−3
(0.029 ± 0.556)9.60 × 10−3
(0.077 ± 0.609)
Land−8.40 × 10−3
(−0.067 ± 0.702)3.60 × 10−3
(0.029 ± 0.770)2.88 × 10−2
(0.230 ± 0.713)1.80 × 10−2
(0.144 ± 0.568)−6.00 × 10−3
(−0.048 ± 0.597)
Land (SK)8.16 × 10−2
(0.653 ± 0.625)4.32 × 10−2
(0.346 ± 0.630)
Ocean1.44 × 10
−2
(0.115 ± 0.336)1.80 × 10
−2
(0.144 ± 0.491)7.32 × 10
−2
(0.586 ± 0.485)−1.08 × 10
−2
(−0.086 ± 0.562)2.40 × 10
−2
(0.192 ± 0.630)
Ocean (SK)−6.12 × 10−2
(−0.490 ± 0.800)
30-60N (L&O)
−6.94 × 10−3
(−0.056 ± 0.128)2.81 × 10−3
(0.022 ± 0.205)4.42 × 10−2
(0.354 ± 0.198)1.14 × 10−3
(0.009 ± 0.182)−6.74 × 10−3
(−0.054 ± 0.323)
Fig. 2. Times series of satellite-derived monthly temperature anom-alies (K) from Tskin(AIRS/AMSU), Tsfc (AIRS/AMSU), and Tskin
(MODIS) during the period from Sep 2002 to Aug 2010 over the a)land and b) ocean around the Korean Peninsula (123-132o
E, 33-44oN).
The AIRS/AMSU and MODIS data are derived in a grid box of 1o×1
o.
The thermal trends (K yr−1) of Tskin(AIRS/AMSU), Tsfc(AIRS/AMSU),and Tskin(MODIS) are shown in the red-dashed, blue-dotted and black-solid lines, respectively. The ± values define the 95% confidenceintervals for the trends, and the error bars denote the ± 1.96 SEs of thetemperature anomalies. Note for clarity that the Tskin(AIRS/AMSU),Tsfc (AIRS/AMSU), and Tskin(MODIS) time series are offset by 4.0 K.
30 November 2011 Jung-Moon Yoo et al. 443
as Tsfc(AIRS/AMSU) (0.230 ± 0.713 K 8yr−1) (Table 2). The
discrepancy between the satellite and ground measurements
may stem from errors in satellite based datasets and differences
in spatial and temporal representativeness between satellite and
ground observations. For instance, as mentioned in Table 1, the
observations of SMSs and AWSs have been carried out at the
72 and 457 sites, respectively. On the other hand, The MODIS
data are available in a grid of 5 km × 5 km over the land, which
covers much larger area than a single ground station.
The upper atmospheric temperature trends of T500(AMSU) at
500 hPa and T50(AMSU) at 50 hPa over the land show a weak
warming (0.144 ± 0.491 K 8yr−1) in mid-troposphere and a
weak cooling (−0.048 ± 0.597 K 8yr−1) in low stratosphere
(Table 2). Thus, the thermal trends of the surface, mid-tropo-
sphere and low stratosphere over the land respectively reveal a
strong warming, a weak warming and a weak cooling with
increasing altitude, which could lead to an unstable atmosphere.
However, the tendency over the ocean is not consistent with
that over land as there have been thermal trends of a strong
warming (0.586 ± 0.485 K 8yr−1) in the surface, a weak cooling
(−0.086 ± 0.562 K 8yr−1) in mid-troposphere, and a weak warm-
ing (0.192 ± 0.630 K 8yr−1) in low stratosphere over the ocean.
If averaged together, the overall trend of the land and ocean
would be a strong warming (0.413 ± 0.575 K 8yr−1) in the
surface, a weak warming (0.029~0.077 K 8yr−1) in the mid-
troposphere and low stratosphere, and the net result would help
to increase the instability of the atmosphere from the viewpoint
of radiative convective adjustment.
A similar analysis made for the whole mid-latitude region
(30~60oN) shows a strong warming (0.354 ± 0.198 K 8yr−1) in
the surface, a weak warming (0.009 ± 0.182 K 8yr−1) in mid-
troposphere, and a weak cooling (−0.054 ± 0.323 K 8yr−1) in
low stratosphere (Table 2). It is noted that the temperature
trends over the land near Korea are similar to those in the mid-
latitude. The surface warming trend (0.586 ± 0.485 K 8yr−1)
seen from the AIRS/AMSU data over the ocean surrounding the
Korean Peninsula is more than twice greater than that (0.230 ±
0.713 K 8yr−1) over the land near Korea. It is very likely that the
surface warming near Korea is affected more by a large scale
thermal variability rather than by a local urban effect. For the
skin layer result over the combined land and ocean near Korea,
the warming (0.086 ± 0.587 K 8yr−1) of Tskin(AIRS/AMSU) is
about three times as strong as the warming (0.029 ± 0.423 K
8yr−1) of Tskin(MODIS). This inconsistency between the two
different skin temperature datasets may originate from differ-
ences in algorithm specifics (i.e., channels) and also from
different spatial resolutions between two datasets.
Overall, the average trends of the skin/surface temperature
anomaly over the land and the ocean generally show a warming
(0.029~0.413 K 8yr−1) (Table 2). Compared to the trends of
both skin (−0.056~0.022 K 8yr−1) and surface (0.354 ± 0.198 K
8yr−1) temperatures over the mid-latitude (30~60N) belt, the
Tsfc(AIRS/AMSU) trend (0.413 ± 0.575 K 8yr−1) near Korea is
generally similar to the mid-latitude one, while the Tskin(MODIS)
and Tskin(AIRS/AMSU) trends (0.029~0.086 K 8yr−1) near Korea
are greater than the mid-latitude values.
Figure 2 shows the satellite-derived skin/surface temperature
anomaly values from MODIS and AIRS/AMSU near Korea
during the recent eight year period. A maximum is clearly seen
in February 2007 over the land (Fig. 2a). It is also noted that
there are few discrepancies observed in November 2003 and
November 2005, and there Tskin(AIRS/AMSU) anomaly is much
lower/higher than both Tskin(MODIS) and Tsfc(AIRS/AMSU).
Other than that, there is a general agreement in the anomaly
tendency among the three data sets. The temperature anomaly
variability over the ocean is not as strong as the case of the land,
and more discrepancies among datasets are visible (Fig. 2b).
The deep minima seen over the land case (November 2002,
February and December 2005, April 2010) are also present over
the ocean, but much less distinctly and mostly from Tsfc(AIRS/
AMSU). The 95% confidence range values (± 1.96 SE) for
monthly temperature anomalies in Fig. 2 are within ~0.7 K.
Figure 3a shows the correlations in monthly mean tempera-
ture anomalies between 72 SMSs and their corresponding
MODIS data in a 5 km × 5 km grid over the land of South
Korea during the recent eight year period. Similarly, the
correlations in the anomaly between 457 AWSs and their
corresponding MODIS data are shown in Fig. 3d. The average
correlation ( ), which is an arithmetic mean of correlations
between ground measurements and corresponding satellite data,
is shown to be significant; correlation r = 0.80 for Tskin(MODIS)
vs TSTAT_sfc and r = 0.77 for Tskin(MODIS) vs TAWS_sfc. It is seen
that the correlations at some stations near the western and
southern coastal area are much lower (r = 0.3~0.7) than the rest
of regions. However, this is not surprising, given that the
satellite-observed MODIS data represent the area-averaged
temperature values in a grid box of 5 km × 5 km, while the
ground surface observations are obtained from individual sites.
In addition, since the MODIS data are derived differently based
on the surface type, it is expected that the errors caused by the
differences in the algorithm could complicate and increase the
uncertainties over the coasts, which in turn make it difficult for
comparison. The correlation is found to be lowest (r = 0.55) in
Heuksando between TSTAT_sfc and Tskin(MODIS), and in Maldo
(r = 0.21) between TAWS_sfc and Tskin(MODIS).
The temperature trends at ground stations and the satellite
data corresponding to the ground locations are shown in Figs.
3b, 3c, 3e and 3f. The average of warming trends of TSTAT_sfc
and the corresponding Tskin(MODIS) are found to be 0.082 ±
0.078 K yr−1 and 0.016 ± 0.089 K yr−1, respectively (Figs. 3b
and 3c), with the ground-based results being steeper than the
values retrieved from the satellite. The same comparison in
trend between TAWS_sfc and Tskin(MODIS) also reveals a stronger
warming trend from ground-based data (Figs. 3e and 3f),
though the strength of trends is slightly reduced. As mentioned
in the earlier section, the surface temperature at the ground
stations of SMS and AWS is obtained from a given site, while
the satellite-observed Tskin(MODIS) is area-averaged over a
5 km × 5 km grid. Therefore, there may be a spatial sampling
difference in this type of comparison. It is also noted from
r
444 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
Figs. 3c and 3f that Tskin(MODIS) data reveal slight warmer
trends near the west coast of South Korea compared to the rest
of region, unlike the ground-based results (Figs. 3b and 3e)
where the trend values looks sporadic.
Fig. 3. a) Correlation in the monthly mean temperature anomaly during the period from Sep 2002 to Aug 2010 between surface temperature(TSTAT_sfc) at 72 SMSs and its corresponding satellite-derived MODIS skin temperature (Tskin(MODIS)) in a 5 km × 5 km grid, and the anomalytrends (K yr
−1) from the data of b) TSTAT_sfc and c) its corresponding Tskin(MODIS). d) The correlation between surface temperature (TAWS_sfc) at 457
Automatic Weather Stations (AWSs) and its corresponding Tskin(MODIS), and the anomaly trends from the data of e) TAWS_sfc and f) itscorresponding Tskin(MODIS). The ± values define the 95% confidence intervals for the trends.
30 November 2011 Jung-Moon Yoo et al. 445
b. AMSU temperature vs. RAOB at the heights of 1000, 500,
and 50 hPa
Figure 4 shows the time series of AMSU monthly tempera-
ture anomaly in the mid-troposphere (500 hPa; Fig. 4a) and
low stratosphere (50 hPa; Fig. 4b) near Korea in a similar
format as shown in Fig. 2. The results over the land and ocean
are represented by black solid and red dotted lines, respectively.
The linear regression analysis shows a warming trend of
0.144 ± 0.568 K 8yr−1 over the land, and a cooling (−0.086 ±
0.562 K 8yr−1) over the ocean at the 500 hPa level (Fig. 4a). As
indicated in Table 2, the trend for the land and ocean combined
case indicates a weak warming of 0.029 ± 0.556 K 8yr−1. Unlike
mid-troposphere, the low stratospheric temperature reveals a
weak cooling (−0.048 ± 0.597 K 8yr−1) over the land, and a
warming (0.192 ± 0.630 K 8yr−1) over the ocean (Fig. 4b). The
land and ocean combined result indicates a weak warming
trend of 0.077 ± 0.609 K 8yr−1 as shown in Table 2. In general,
the temperature anomalies vary in a similar way between land
and ocean, but the variability shows more extreme cases in low-
stratosphere where three sharp warm and cold peaks are
observed during three winter months, i.e., December 2003,
February 2005, and February 2009. The ± 1.96 SEs for the
anomalies in Fig. 4 are within ~0.4 K.
The time series of monthly mean temperature anomaly of
the RAOB (black-solid line) are analyzed at the three heights
(1000, 500, and 50 hPa) over the land of South Korea during
the recent eight-years (Fig. 5). Here the number of the RAOB
stations is 6 at 1000 hPa, and 7 at 500 and 50 hPa. The RAOB
at 1000 hPa was unavailable in the Baengnyeongdo station due
to its high elevation (Table 3). Also shown in Fig. 5 are the
results of AMSU data that are used to match with the locations
of the radiosonde stations. The time series of temperature
anomalies averaged from either five or six stations (and grids)
show a very similar pattern between the ground- and space-
based measurements. The correlation coefficient ranges bet-
ween 0.91~0.95 with its value larger in mid-troposphere and
low stratosphere than those in the surface. Considering the fact
that the radiosonde data have been obtained at individual spots
and the satellite data are from selected 1o × 1o grids (about
100 km × 100 km), the agreement between T(RAOB) and
T(AMSU) is exceptionally good and supports the reliability of
satellite data in the trend analysis. It is shown that both data sets
(from ground and space) reveal a warming trend over the last
eight years at all three levels. The warming rate fluctuates with
its value lower (0.058~0.173 K 8yr−1) in the mid-troposphere
compared to below (0.365~0.662 K 8yr−1 in surface) and above
(0.288~0.730 K 8yr−1 in low stratosphere). It is also shown that
the warming rate from T(AMSU) is lower than that from
T(RAOB) except the case in the surface. The ± 1.96 SEs for
the anomalies in Fig. 5 are within ~1.3 K.
Table 3 provides the detailed information (location, data
Fig. 5. Average time series of monthly anomalies (K) from satellite-derived AMSU air temperature (red-dashed line) and RAOB (blacksolid line) at a) 1000 hPa, b) 500 hPa, and c) 50 hPa pressure levelsduring the period from Sep 2002 to Aug 2010 at six radiosondestations in Korea. The average time series at 1000 hPa pressure levelhave been obtained from five radiosonde stations, excluding theBaengnyeongdo station due to the RAOB not available. The AMSUdata are derived in a grid box of 1o
× 1o. Here the data at Heuksando
have been excluded due to a shorter observational period. The ‘r’means correlation coefficient. The thermal trends of AMSU andRAOB are shown in the red-dotted and black-solid lines, respectively.The ± values define the 95% confidence intervals for the trends, andthe error bars denote the ± 1.96 SEs of the temperature anomalies.Note for clarity that the AMSU and RAOB time series are offset by4.0 K.
Fig. 4. Time series of the AMSU monthly temperature anomalies (K)during the period from Sep 2002 to Aug 2010 over the land (black-solid line) and the ocean (red-dashed line) around the Koreanpeninsula (123-132oE, 33-44oN) at the pressure altitudes of a) 500 hPaand b) 50 hPa. The thermal trends over the oceanic and land regionsare shown in the red-dashed and black-solid lines, respectively. The± values define the 95% confidence intervals for the trends, and theerror bars denote the ± 1.96 SEs of the temperature anomalies. Notefor clarity that the land and ocean time series are offset by 4.0 K.
446 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
period, altitude) at each station from which Fig. 5 is generated.
The correlation coefficients between T(RAOB) and T(AMSU)
at each station are found to be lower (r = 0.65~0.94) than those
in Fig. 5, where the correlations (0.91~0.95; Fig. 5) are calcu-
lated from the anomaly time series of monthly mean tempera-
ture values spatially averaged over 5~6 stations. In general, the
correlation between T(RAOB) and T(AMSU) increases with
height except over Osan and Gwangju where the correlation
coefficients are higher at mid-troposphere than at low strato-
sphere.
The correlation is the lowest in the surface regardless of
locations. The lowest correlation is found at the surface in
Heuksando (r = 0.65), where the surface level is relatively high
(79.4 m) compared to those in other stations. The RAOB at the
1000 hPa height in Baengnyeongdo is not available due to the
high elevation (144.4 m), thus excluded in our analysis. Both
RAOB and AMSU data are obtained twice per day (00, 12 UTC
for RAOB, ~01:50, 13:20 LST for AMSU), and thus the time of
measurements are not coincident with each other. It is highly
likely that the diurnal variation in temperature, which is larger
near the surface, results in the lower correlations at surface.
c. Effect of climate indices to satellite-derived thermal vari-
ability
The correlations in time series between the satellite-derived
temperature and climate indices have been examined in order
to analyze the cause for the recent eight-year temperature
variation at the four vertical layers (skin/surface, mid-tropo-
sphere, low stratosphere) near Korea (Table 4). Here the
symbols of ‘*’ and ‘**’ indicate the statistically significant
cases at a level of 0.05 and 0.01, respectively. The climate indices
are Arctic Oscillation (AO; NOAA, 2010a; Thompson and
Wallace, 1998), Southern Oscillation (SO; Horel and Wallace,
1981; NOAA, 2010b), and Quasi-Biennial Oscillation (QBO;
Salby et al., 1997; NOAA, 2010c). The AO is defined as the
surface pressure difference between the mid-latitudes of the
northern hemisphere and the Arctic, and ENSO (or the SO)
can influence extra-tropical regions through atmospheric oscil-
lations and teleconnection (WMO, 2010).
From the statistical analysis of satellite temperature variation
with climate indices, it is found that AO affects has the most
significant influence on the layers from skin/surface to mid-
troposphere, while SO is more effective in the low stratosphere
(Table 4). The correlations between the satellite-derived tem-
perature and AO are found positive (r = 0.34~0.42) in the layers
from surface to mid-troposphere, and negative (r = −0.29) in the
low stratosphere. The correlations near Korea are very similar
to those found in the northern hemispheric mid-latitude (30~
60oN). The satellite-derived time series of the low stratospheric
temperature show a statistically significant negative correlation
(r = −0.46) with SO, implying that the temperature variability
can be strongly tied to the ENSO events. Although the SO
effect on the skin/surface temperature is weak (r = 0.19~0.25)
Table 4. Correlation coefficients at four different heights between satellite-derived temperature and climate indices (AO, SO, QBO) near Koreaduring the period from Sep 2002 to Aug 2010. The coefficients have also been calculated over three separate areas of ‘Ocean & Land’, ‘Ocean’, and‘Land’, respectively. Here the abbreviations of AO, SO and QBO mean the climatic indices of ‘Arctic Oscillation’, ‘Southern Oscillation’, and‘Quasi-Biennial Oscillation’. Note: Level of significance (p): **p<0.01 (r = 0.238, df = 94), *p<0.05 (r = 0.170, df = 94).
ClimateIndex
MODIS AIRS/AMSU AMSU
Tskin Tskin Tsfc T500hPa T50hPa
Ocean & Land
Ocean LandOcean &
LandOcean Land
Ocean & Land
Ocean LandOcean &
LandOcean Land
Ocean & Land
Ocean Land
AO 0.418**
0.327**
0.408**
0.404**
0.341* 0.398
** 0.405
** 0.344
** 0.421
** 0.341
** 0.357
** 0.312
** −0.290
** −0.268
* −0.307
**
SO 0.190* 0.105 0.208
* 0.247
** 0.277
** 0.199
* 0.212
* 0.240
** 0.178
* 0.068 0.078 0.056 −0.458
** −0.452
** −0.457
**
QBO 0.115 0.205* 0.053 0.116 0.113 0.104 0.109 0.105 0.104 −0.082 −0.110 −0.051 0.304
** 0.298
** 0.304
**
AO(30-60N)
0.451** 0.236* 0.420** 0.371** 0.405** 0.338** 0.305** 0.173* 0.371** 0.394** 0.367** 0.306** −0.177*
−0.091 −0.245**
Table 3. Correlation coefficients in air temperature between AMSU and RAOB at three pressure levels (1000, 500, and 50 hPa) near Korea duringthe period from Sep 2002 to Aug 2010. The coefficient at 1000 hPa was not available in Baengnyeongdo because of its sea level height (144.4 m).
Station Lat (oN), Lon (oE)
Data period Height (m)Correlation between AMSU & RAOB
T1000hPa T500hPa T50hPa
Sokcho (38.25,128.56) Sep 2002~Aug 2010 17.8 0.825 0.884 0.934
Baengnyeongdo (37.97, 124.63) Sep 2002~Aug 2010 144.4 N/A 0.915 0.929
Osan (37.10, 127.03) Sep 2002~Aug 2010 52.0 0.817 0.941 0.838
Pohang (36.03, 129.38) Sep 2002~Aug 2010 1.9 0.782 0.887 0.941
Gwangju (35.12, 126.82) Sep 2002~Aug 2010 13.0 0.876 0.906 0.890
Gosan (33.29, 126.16) Sep 2002~Aug 2010 71.2 0.696 0.897 0.897
Heuksando (34.69, 125.45) May 2003~Aug 2010 79.4 0.651 0.893 0.937
30 November 2011 Jung-Moon Yoo et al. 447
compared to that of AO (r = 0.34~0.42), it is still significant at
a 0.05 significance level. Also, the QBO effect on the low
stratospheric temperature is found weak (r = 0.30) compared to
the SO (r = −0.46), but still significant at a 0.05 level. Based on
the statistical analysis using climate indices, it can be sum-
marized that the tropospheric temperature variation near Korea
primarily can be linked to AO, and secondarily to SO. On the
other hand, the lower stratospheric temperature variability is
mainly associated with SO, followed by QBO. These results
suggest that the climate indices near Korea should be utilized as
indicators for thermal variability in order to better understand
the long-term variability of temperature and climate change in
connection with global warming in the region.
Figures 6 and 7 show the EOF spatial pattern and corres-
ponding principle component time series for satellite-derived
monthly mean temperature anomaly in the three layers (skin/
surface, low stratosphere) near Korea during the recent eight
year period. As mentioned in Table 1, there is a difference in
spatial resolution between MODIS (~20 km × 20 km) and AIRS/
AMSU (~100 km × 100 km). As used in the previous analyses
of thermal trends, the anomaly data during the recent eight
year period have been also used for the EOF in order to remove
the annual cycle in monthly mean data.
The first mode of MODIS skin temperature [Tskin(MODIS)]
anomaly, which has 49.1% explained variance to the total
variance, reflects well the spatial and temporal variability of
AO (Figs. 6a and 7a). The correlation (r = 0.50) in time series
between Tskin(MODIS) and AO is very significant at a 0.01
Fig. 6. The EOFs of the covariance matrix of monthly mean temperature anomalies near Korea (123-132oE, 33-44oN) of the MODIS skintemperature a) mode 1, b) mode 2, c) the AIRS/AMSU surface temperature mode 1, and d) the AMSU temperature at 50 hPa mode 1. Forconvenience’ sake, the eigenvector values are multiplied by 1000.
448 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
level (Fig. 7a). According to this mode, the thermal variability
over the whole area near Korea has been substantially affected
by AO (Fig. 6a). The second mode of Tskin(MODIS), which has
15.2% explained variance to the total variance, can be asso-
ciated with the land/ocean contrast (Figs. 6b and 7b). Here it
should be noted that the temperatures of the southern area and
eastern coast of South Korea follow the pattern of the oceanic
temperatures in terms of temporal and spatial variability, rather
than that of the land (Figs. 6b and 7b). This feature also appears
in the EOF second mode (9.8%) for the AIRS/AMSU surface
temperature [Tsfc(AIRS/AMSU)] anomaly (not shown in this
study). The MODIS second mode is correlated marginally with
SO at a 0.05 level, and the ENSO events weakly affect the
thermal variability in this region (Fig. 7b).
The first mode of monthly mean Tsfc(AIRS/AMSU) anomaly,
which explains 71.7% variance to the total variance, indicates
well the AO characteristics near Korea during the recent eight-
year period (Figs. 6c and 7c). This pattern is similar to the
Tskin(MODIS) first mode, but the AO seems to pose more
impact on Tsfc(AIRS/AMSU) (71.7%) than on Tskin(MODIS)
(49.1%). In other words, the effect to surface temperature is
greater than to skin temperature. However, in terms of the
correlation with AO, Tsfc(AIRS/AMSU) and Tskin(MODIS) are
comparable to each other (r = 0.45~0.50). The AO effect to the
two types of temperatures is the largest in the Yellow Sea and
the metropolitan area near Seoul, and more conspicuous in
Tsfc(AIRS/AMSU) than in Tskin(MODIS). Meanwhile, the first
mode of monthly mean T50(AMSU) anomaly at 50 hPa (low
stratosphere) which explains 92.6% to the total variance repre-
sents well the SO spatial and temporal features (Figs. 6d and
7d). The negative correlation (r = −0.46) in time series between
T50(AMSU) and SO is very significant at a 0.01 level (Fig. 7d).
The correlation (r = 0.30) between T50(AMSU) and QBO is also
very significant at a 0.01 level, and the SO and QBO effects are
important in the T50(AMSU) first mode. In summary, in the
temperature variability near Korea during the recent eight years,
the AO phenomena have been associated with tropospheric
thermal state, while the ENSO events (or SO indices) have been
related to the low stratospheric temperature. Since the tropo-
sphere and low stratosphere interact with each other through
the radiative-convective adjustment, (e.g., Liu and Curry, 2004;
Bond and Harrison, 2006; Cagnazzo and Manzini, 2009; Cohen
et al., 2010; Scaife, 2010) the dynamical relationship between
AO and SO has to be understood in order to predict thermal
trends more accurately near Korea.
d. MODIS skin LST vs. ground surface measurements (SMS,
AWS)
Figure 8 presents the time series of monthly mean tem-
perature anomaly of five ground stations from three datasets as
Fig. 7. The principal component time series (black solid lines) ofmonthly mean temperature anomalies corresponding to the EOFs inFig. 6. A smoothing function of (0.25, 0.5, 0.25) is used on the timeseries. In order to examine the correlation in time series betweensatellite-derived temperatures and climate indices (red-dotted lines),the indices of AO (Arctic Oscillation) and SO (Southern Oscillation)have been applied to a) the MODIS skin temperature mode 1, b) mode2, c) the AIRS/AMSU surface temperature mode 1, and d) the AMSUtemperature at 50 hPa mode 1, respectively. e) The QBO (Quasi-Biennial Oscillation) index is applied to the AMSU at 50 hPa mode 1.The ‘r’ in the figure means correlation coefficient.
Fig. 8. Time series of monthly mean temperature anomalies (K) ofTSTAT_sfc (black-solid line), TAWS_sfc (red-dashed line), and Tskin(MODIS_LST) (blue-dotted line), spatially averaged over five common stations(or five common 5 km × 5 km grids), under the condition of spatialco-location within a 5 km × 5 km grid during the period from Sep2002 to Aug 2010. The ± values define the 95% confidence intervalsfor the trends, and the error bars denote the ± 1.96 SEs of thetemperature anomalies. Note for clarity that the TSTAT_sfc, TAWS_sfc, andTskin(MODIS_LST) time series are offset by 4.0 K.
30 November 2011 Jung-Moon Yoo et al. 449
follows; a) TSTAT_sfc from SMSs, b) TAWS_sfc from AWSs, and c)
Tskin(MODIS_LST) from satellite-derived MODIS skin tempera-
ture. We chose the five locations (see Fig. 9) where simul-
taneous measurements are available from three data sources
Fig. 9. Correlation in the monthly temperature anomaly a) between TSTAT_sfc, and TAWS_sfc b) between TAWS_sfc and satellite-derived Tskin
(MODIS_LST), and c) between TSTAT_sfc and Tskin (MODIS_LST) under the condition of spatial co-location within a 5 km × 5 km grid during theperiod from Sep 2002 to Aug 2010. The trend values (K yr−1
) of d) TSTAT_sfc, e) TAWS_sfc, and f) satellite-derived Tskin (MODIS_LST) during the sameperiod are given. The ± values define the 95% confidence intervals for the trends.
450 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
[TSTAT_sfc, TAWS_sfc, Tskin(MODIS)] that are co-located with each
other within a 5 km × 5 km grid. Then, anomaly time series of
spatially averaged temperatures over five stations are con-
structed and plotted for each data set as shown in Fig. 8. The
monthly mean values of Tskin(MODIS) used in Fig. 8 are
computed based on the daily averages of about two observations
a day (morning, afternoon 01:30). The monthly mean values of
TSTAT_sfc and TAWS_sfc are calculated from the daily averages of 24
hourly observations a day. Consequently, there are inherent but
not significant discrepancies in the geolocation and time of ob-
servation among the three data sets through the data preparation
process.
All three data sets agree to reveal a warming trend, even
though the rates of warming are slightly different. The warming
rates from two ground measurements (0.624 ± 0.676 K 8yr−1
for TSTAT_sfc and 0.518 ± 0.645 K 8yr−1
for TAWS_sfc, respectively)
are shown higher than the satellite-based result of 0.096 ±
0.740 K 8yr−1 from Tskin(MODIS_LST). These rates based on
five stations, especially the Tskin(MODIS_LST) are comparable
with values retrieved from the AIRS/AMSU skin/surface tem-
peratures over the land, which is 0.029~0.230 K 8yr−1 (from
Table 2). The ± 1.96 SEs for the anomalies in Fig. 8 are within
~2.1 K.
Table 5 enlists the correlation coefficients among three data
sets, where arithmetic averages of monthly data from five
locations are calculated and then the anomaly time series are
obtained. In spite of the geolocation difference in a 5km × 5km
grid between SMS and AWS at each location, the correlation
between the two ground data sets is found to be very high
(r = 0.97). The correlations of TSTAT_sfc and TAWS_sfc with respect to
Tskin(MODIS) are nearly the same values of 0.89 and 0.90,
respectively. It should be noted that the satellite-derived
Tskin(MODIS) represents the area average of a 5 km × 5 km grid
over land, while the ground measurements of TSTAT_sfc and TAWS_sfc
provide the site values near the stations. Considering the
difference in geolocation and time within ~30 minutes between
satellite and ground measurements in the morning and the
afternoon 01:30, the resultant high correlations are impressive.
Figures 9a-c and Table 6 show the correlations in monthly
mean temperature anomalies among the three datasets (i.e.,
TSTAT_sfc, TAWS_sfc, and Tskin(MODIS)) that are calculated in a
slightly different way. Instead of averaging data from five sta-
tions, anomalies are calculated using monthly data at individual
station (or at individual grid), then the correlation among data
sets are calculated from the anomaly time series. The mean
correlation coefficient ( ) is the arithmetic average of the values
from five stations. Likewise, the thermal trend is obtained at
individual location and shown in Figs. 9d-f (Table 6).
The correlations between TSTAT_sfc and TAWS_sfc are always
higher than their correlations with Tskin(MODIS) regardless of
locations. The thermal trends fluctuate depending on location,
but the ground measurements generally show stronger warming
rates than satellite-derived results, which was already discussed
in Fig. 8.
e. Intercomparison of SST; MODIS, AIRS/AMSU, and buoy
Figure 10 displays the temperature trends from the anomaly
time series in a similar format as Fig. 8, but over the ocean using
data from buoy stations and Tskin(MODIS_SST). The time series
of the monthly temperature anomaly reveals that the TBUOY_SST
is highly correlated (r = 0.82) with Tskin(MODIS_SST). The tem-
r
Table 5. The correlation coefficients among three different kinds ofmonthly temperature anomalies [TSTAT_sfc, TAWS_sfc, Tskin(MODIS_LST)],obtained from spatial averages over five common locations during theperiod from Sep 2002 to Aug 2010, respectively.
TSTAT_sfc TAWS_sfc Tskin(MODIS_LST)
TSTAT_sfc 1.000 0.981 0.912
TAWS_sfc 1.000 0.898
Tskin(MODIS_LST) 1.000
Table 6. The correlation coefficients between of three different kinds of observations [TSTAT_sfc, TAWS_sfc, Tskin(MODIS_LST)], and their anomalytrends (K yr−1) obtained from five common locations during the period from Sep 2002 to Aug 2010, respectively. The values in parentheses indicatethe temperature trends (K 8yr−1
) for the whole eight year period. The ± values define the 95% confidence intervals for the trends.
Correlation coefficient Trend
TSTAT_sfc & TAWS_sfc
TAWS_sfc & Tskin(MODIS)
TSTAT_sfc & Tskin(MODIS)
TSTAT_sfc TAWS_sfc Tskin(MODIS)
Seoul 0.970 0.842 0.8791.20 × 10
−2
(0.096 ± 0.780)9.84 × 10−2
(0.787 ± 0.757)0.000
(0.000 ± 0.815)
Ulleungdo 0.929 0.760 0.7981.07 × 10−1
(0.854 ± 0.718)6.00 × 10−2
(0.480 ± 0.706)5.88 × 10−2
(0.470 ± 0.778)
Jecheon 0.911 0.805 0.808−2.28 × 10−2
(−0.182 ± 0.706)4.32 × 10−2
(0.346 ± 0.709)−2.16 × 10−2
(−0.173 ± 0.958)
Daegu 0.971 0.826 0.8581.28 × 10
−1
(1.027 ± 0.749)8.40 × 10−2
(0.672 ± 0.700)2.64 × 10−2
(0.211 ± 0.800)
Gwangju 0.938 0.769 0.8131.66 × 10−1
(1.325 ± 0.635)3.72 × 10−2
(0.298 ± 0.643)−2.40 × 10−3
(−0.019 ± 0.710)
Average 0.944 0.800 0.8317.80 × 10−2
(0.624 ± 0.718)6.46 × 10−2
(0.516 ± 0.703)1.22 × 10−2
(0.098 ± 0.911)
30 November 2011 Jung-Moon Yoo et al. 451
perature trends from the averaged anomaly time series based on
the five buoy stations indicate a cooling rate of −0.490 ± 0.800
K 8yr−1, which is comparable to the corresponding satellite skin
temperatures (−0.374 ± 0.887 K 8yr−1) from Tskin(MODIS_SST).
The ± 1.96 SEs for the anomalies in Fig. 10 are within ~1.4 K.
Figure 11 shows detailed information on the individual buoy
locations as well as the correlation among data sets. It is shown
from the average of the five locations that TBUOY_SST is sig-
nificantly correlated (r = 0.59) with Tskin(MODIS_SST). In the
mean time, the cooling trends (−0.490 ± 0.800 K 8yr−1) seen at
the five locations are substantially different from the warming
rate (0.115~0.144 K 8yr−1) of the MODIS and AIRS/AMSU skin
temperatures (Tskin(MODIS_SST), Tskin(AIRS/AMSU)) over the
whole ocean surrounding the Korean Peninsula as shown in
Table 2. Thus, the temperature observations at the five buoy
spots may reflect the local trends possibly due to the sea
currents, and cannot represent the large-scale thermal phenom-
ena near Korea. Also, the correlations between the satellite and
selected ground buoy measurements are lower than those
between the satellite and SMS (or AWS) observations, implying
that either the satellite and ground measurements over ocean
may be less accurate than over land.
Fig. 10. Time series of monthly skin temperature anomalies (K) forfive buoy stations(TBUOY_SST) and Tskin(MODIS_SST) under the spatialcollocation during the period from Jan 2006 to Aug 2010. The MODISdata have been given in a grid box of about 4 km × 4 km. Here thebuoy data have been shown in black-solid line, while the satelliteMODIS data are shown in red-dashed line. The ± values define the95% confidence intervals for the trends, and the error bars denote the± 1.96 SEs of the temperature anomalies. Note for clarity that theTBUOY_SST and Tskin(MODIS_SST) time series are offset by 4.0 K.
Fig. 11. Correlation in the monthly skin temperature anomaly a) between TBUOY_SST and Tskin(MODIS_SST) under the condition of spatial co-location during the period from Jan 2006 to Aug 2010. The trend values (K yr−1
) of b) TBUOY_SST and c) Tskin(MODIS_SST) during the same periodare given. The ± values define the 95% confidence intervals for the trends.
452 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
f. Uncertainties among satellite-based and ground-based ob-
servations
We have calculated the uncertainties among satellite-based
and ground-based observations by intercomparing them in
terms of some statistics: bias, correlation coefficient (r), the root
mean square error (RMSE), and the index of agreement in (1).
As shown in Table 7, intercomparison among satellite-based
and ground-based measurements has been performed to quantify
the uncertainties among them in the relationships of Tskin-
(MODIS_LST) vs TSTAT_sfc, TAWS_sfc vs TSTAT_sfc, T1000hPa(AMSU)
vs T1000hPa(RAOB), T500hPa(AMSU) vs T500hPa(RAOB), T50hPa-
(AMSU) vs T50hPa(AMSU), and Tskin(MODIS_SST) vs TBUOY_SST.
The intercomparison has been done through monthly mean
temperature values and their anomalies, respectively, during
the period from September 2002 to August 2010. Here five co-
located locations are used over the land and ocean regions,
respectively. After calculating the statistics at each station, we
computed the arithmetic average for the five stations. As
mentioned earlier, the temperatures at the surface meteoro-
logical station (SMS), RAOB and buoy are observed variables,
while satellite-based temperature (AIRS/AMSU, MODIS) and
AWS are predicted estimates.
All correlations (i.e., r values) are significant at α = 0.01 in
Table 7. The index of agreement ranges from 0.752 to 0.970 in
the temperature anomaly time series. The comparison of TAWS_sfc
vs TSTAT_sfc, (i.e., AWS with respect to surface meteorological
station) over the land produced the smallest RMSE (0.418 K),
and the largest correlation (r = 0.944) and index of agreement
(d = 0.970) of all the comparisons. The comparison of Tskin-
(MODIS_SST) vs TBUOY_SST (i.e., MODIS with respect to buoy)
over the ocean showed the largest bias (0.012 K), the lowest
correlation (r = 0.592), and the lowest index of agreement
(d = 0.752) of all. The bias and RMSE in the time series of
monthly mean temperature anomalies is ≤ 0.012 K and ≤ 0.852
K, respectively. The RMSE (≤ 1.889 K) in the time series of
monthly mean temperature is about twice larger than that of the
anomaly time series. The correlation and index of agreement in
the monthly mean temperature time series are generally larger
due to strong seasonal cycle in troposphere than those in the
anomaly time series. However, this tendency is not clear in the
low stratosphere (50 hPa) because of weak seasonal variation.
4. Discussion and conclusions
Data from two satellites (AIRS/AMSU, MODIS) and ground
measurements (SMS, RAOB, AWS, buoy) have been used for
the analysis of temperature trends of four vertical layers (skin/
surface, mid-troposphere, and low stratosphere) near Korea
(123~132oE, 33~44oN) during the recent eight year period. It is
shown that the satellite-derived Tsfc(AIRS/AMSU) over the
land near Korea reveals a lower warming rate of 0.230 ±
0.713 K 8yr−1 than results derived from the surface observations
(SMSs and AWSs) which ranges between 0.346~0.653 K 8yr−1.
It is also shown that the surface warming trend (0.586 ±
0.485 K 8yr−1) from Tsfc(AIRS/AMSU) over the ocean near the
Korean Peninsula is about 2.5 times greater than that
(0.230 ± 0.713 K 8yr−1) over the land, which may be caused by
large scale thermal variability rather than by local urban effect.
The comparison of both space-based data show that Tskin(AIRS/
AMSU) over the whole area (i.e., land plus ocean) near Korea,
reveals stronger warming trend (0.086 ± 0.587 K 8yr−1) than
that (0.029 ± 0.423 K 8yr−1) from Tskin(MODIS). In general,
satellite-derived thermal trends show a stronger warming in the
Table 7. Intercomparison in monthly mean temperature values and their anomalies of Tskin(MODIS_LST) vs TSTAT_sfc, TAWS_sfc vs TSTAT_sfc, andT(AMSU) vs T(RAOB) at five collocated land locations, and Tskin(MODIS_SST) vs T BUOY_SST at five collocated sea locations in terms of somestatistics (Bias, r, RMSE, d) during the period from Sep 2002 to Aug 2010. Here the abbreviations of temperature variables are defined in Table 1.The temperatures of TSTAT_sfc, T(RAOB) and T BUOY_SST are used as observed variables (O
i) while Tskin(MODIS_LST), TAWS_sfc, T(AMSU) and
Tskin(MODIS_SST) are predicted variables (Pi). Bias: predicted value minus observed value (K), r: correlation coefficient, RMSE: root mean square
error in K, d: index of agreement.
Bias (K) r RMSE (K) d
TSTAT_sfc T(RAOB) TBUOY_SST TSTAT_sfc T(RAOB) TBUOY_SST TSTAT_sfc T(RAOB) TBUOY_SST TSTAT_sfc T(RAOB) TBUOY_SST
<Anomaly>
Tskin(MODIS_LST) 0.000 0.831 0.789 0.904
TAWS_sfc 0.001 0.944 0.418 0.970
T1000hPa(AMSU) 0.000 0.799 0.852 0.884
T500hPa(AMSU) 0.000 0.903 0.552 0.949
T50hPa(AMSU) 0.000 0.900 0.594 0.947
Tskin(MODIS_SST) 0.012 0.592 0.765 0.752
<Monthly mean>
Tskin(MODIS_LST) 0.229 0.993 1.250 0.994
TAWS_sfc −0.197 0.999 0.860 0.996
T1000hPa(AMSU) −1.176 0.991 1.889 0.985
T500hPa(AMSU) 0.810 0.997 1.137 0.995
T50hPa(AMSU) 0.897 0.907 1.084 0.871
Tskin(MODIS_SST) −0.316 0.983 1.147 0.986
30 November 2011 Jung-Moon Yoo et al. 453
surface than in the mid-troposphere and low stratosphere. It
should also be noted that the observed warming trends are
subject to large uncertainty since the 95% confidence intervals
for the temperature trends in this study are larger than the
signals due to large thermal variability and the relatively short-
term period of 96 months of the satellite-based temperature
records. However, we believe our analysis using monthly
averages with large uncertainties for the temperature trends
and/or anomalies can be still useful for climate studies, con-
sidering the lack of the long-term, homogeneous satellite data
with high quality and dense-network ground observations over
a large area. The cases similar to this study can be found in
some previous studies as follows; Angell (2003), Karl et al.
(2006), Randal and Wu (2006), Trenberth et al. (2007), and
IPCC (2007).
The satellite-derived skin/surface temperature trends over
the whole area are in good agreement with that of the mid-
latitude (30-60oN), and the temperature anomalies show more
variability over the land than over the ocean. The correlation
average (r = 0.77~0.80) between Tskin(MODIS) and ground meas-
urements (TSTAT_sfc or TAWS_sfc) are significant, even though the
correlations at stations near the western and southern coast are
very low (r = 0.3~0.7). It should be noted that the Tskin(MODIS)
is an area-averaged value over a 5 km × 5 km grid, while the
MODIS data over the land and ocean are derived from
different algorithms. Therefore, the latter could bring more
errors near the coast.
The comparison of T(RAOB) and T(AMSU) based on 5-6
stations shows very high correlations, despite the fact that the
satellite-derived AMSU temperature data are area-averaged
(1o × 1o; ~100 km × 100 km). The correlation coefficients tend
to increase with height. Both datasets reveal warming trends at
all three levels (1000, 500 and 50 hPa), with T(AMSU)
warming slightly higher than the T(RAOB) in the surface and
vice-versa above the mid-troposphere.
The statistical study with climate indices indicates that the
tropospheric temperature variability near Korea is primarily
related with AO, and secondarily with SO. On the other hand,
the low stratospheric temperature variability is shown to be
associated mainly with SO and then with QBO. It is also noted
that the temporal and spatial variability of temperature in the
southern land area and eastern coastal land of South Korea
follows the oceanic pattern, rather than the continental one.
Because of the importance as an indicator for thermal vari-
ability, the climate indices should be utilized in order to
understand the long-term predictability for temperature and its
impact on climate change together with model simulations
(e.g., Scaife et al., 2008).
The comparative study based on five stations all indicates
warming trends with slightly higher values, observed from
ground measurements. In spite of the geo-location difference
of few kilometers within the grid between SMS and AWS, the
correlation between the two ground surface datasets is shown
very high (r = 0.98) in the anomaly time series. The same
correlations of ground dataset with respect to Tskin(MODIS) are
also significant (0.91 and 0.90, respectively) even though
satellite data are averaged in a 5 km × 5 km grid and sampled
twice per day. Similar temperature trend analysis based on five
spots over ocean and the corresponding satellite skin tem-
peratures indicates cooling rates of −0.374 ~ −0.490 K 8yr−1.
The cooling trend is not consistent with the results based on the
whole ocean near Korea and may reflect the local thermal
effect due to the sea current. The correlations between the
satellite and buoy measurements are generally lower than
those between the satellite and SMS (or AWS) observations,
implying that the satellite and/or ground measurements over
the ocean may be less accurate than over the land.
Among limitations of the present study, the satellite-
overpass problem, not timed with respect to ground stations,
can be important for the temperature intercomparison on a
daily time scale. However, this problem is less important on a
monthly time scale, although the effect of the diurnal cycle
may still remain large over some regions (i.e., deserts, plateau,
sea ice). In addition, most satellite-based and ground-based
observations are not free from cloud contaminations. As a
result, the observed differences between satellite datasets, and
between satellite and ground observations may be due to
different samplings that are related to diurnal cycle and clouds.
Furthermore, we expect that the major differences may arise
from different channels (IR, microwave) that have been used,
and from the different spatial samplings between satellite-based
area-averaged and ground-based point measurement. This study
has emphasized intercomparison between various types of
temperature observations and the uncertainties associated with
temperature trends in terms of some statistics (i.e., index of
agreement). The aforementioned problems need more rigorous
investigation in future study.
Acknowledgements. This work was supported by the National
Research Foundation of Korea (NRF) grant funded by Korea
government (MEST) (No. 2010-0001905). We would like to
thank Goddard Earth Sciences Data Information and Services
Center (GES DISC) for providing AIRS/AMSU-A data. We
are also grateful to NASA Land Process Distributed Active
Archive Center (LP DAAC) for providing MODIS LST data.
We appreciate Korea Meteorological Administration (KMA)
meteorological resources division for the ground-based tem-
perature data.
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