Temperature Trends in the Skin/Surface, Mid-troposphere ...web.yonsei.ac.kr/arsl/Yoo_et_al_APJAS2011.pdf · temperature profile is performed before the cloud correction because AMSU

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.


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.


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


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.


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.


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


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.


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.


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.


[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)


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.


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


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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Documents

Temperature Trends in the Skin/Surface, Mid-troposphere ...web.yonsei.ac.kr/arsl/Yoo_et_al_APJAS2011.pdf · temperature profile is performed before the cloud correction because AMSU