AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-4025-2015Uncertainties of satellite-derived surface skin temperatures in the
polar oceans: MODIS, AIRS/AMSU, and AIRS onlyKangH.-J.YooJ.-M.yjm@ewha.ac.krJeongM.-J.WonY.-I.Department of Atmospheric Science and Engineering, Ewha Womans
University, Seoul 120-750, Republic of KoreaDepartment of Science Education, Ewha Womans University, Seoul
120-750, Republic of KoreaDepartment of Atmospheric and Environmental Sciences, Gangneung-Wonju
National University, Gangneung 210-702, Republic of KoreaWyle ST&E, NASA/GSFC, Maryland, USAJ.-M. Yoo (yjm@ewha.ac.kr)2October20158104025404111February20154May201517July20159September2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/8/4025/2015/amt-8-4025-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/4025/2015/amt-8-4025-2015.pdf
Uncertainties in the satellite-derived surface skin temperature (SST) data
in the polar oceans during two periods (16–24 April and 15–23 September)
2003–2014 were investigated and the three data sets were intercompared as
follows: MODerate Resolution Imaging Spectroradiometer Ice Surface
Temperature (MODIS IST), the SST of the Atmospheric Infrared
Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS
only algorithm was developed in preparation for the degradation of the
AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice
boundary and colder down to -2.04 K in the polar sea ice regions of both the
Arctic and Antarctic than that of the AIRS/AMSU. This difference in the
results could have been caused by the surface classification method. The
spatial correlation coefficient of the AIRS only to the AIRS/AMSU
(0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU
(0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU
had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere
high latitudes and a bias of -0.109 K with a RMSE of 0.852 K over the
Southern Hemisphere high latitudes. There was a systematic disagreement
between the AIRS retrievals at the boundary of the sea ice, because the AIRS
only algorithm utilized a less accurate GCM forecast over the
seasonally varying frozen oceans than the microwave data. The three data sets
(MODIS, AIRS/AMSU and AIRS only) showed significant warming rates
(2.3 ± 1.7 ∼ 2.8 ± 1.9 K decade-1) in the northern
high regions (70–80∘ N) as expected from the ice-albedo feedback. The
systematic temperature disagreement associated with surface type
classification had an impact on the resulting temperature trends.
Introduction
The satellite observations of the polar oceans have been more challenging
than those of non-frozen ocean and land, because it is more difficult to
identify clouds over the various surfaces (Tobin et al., 2006). The surface
skin temperature (SST) is one of the most important climate variables that
is related to the surface energy balance and the thermal state of the
atmosphere (Jin et al., 1997). Compared to ground-based observations,
satellite-observed SST data play a crucial role in climate study and model
development by providing uniform resolution data encompassing the entire globe. The
retrievals of AIRS data over the last decade have a significant contribution
to various climate studies and model evaluations (Aumann et al., 2003; Tian
et al., 2013; Yoo et al., 2013). AIRS retrievals have produced atmospheric
temperature, moisture, and ozone profiles on a global scale by the AIRS
method itself or together with other instruments (Liu at al., 2008). A lot
of comparisons of the AIRS/AMSU data against data from numerical forecast
model analysis fields, radiosondes, lidar, and retrievals from high-altitude
aircraft have been used to assess the accuracy of the retrievals (Tobin et
al., 2006; Susskind et al., 2014). The AIRS retrieval algorithm has been
developed and validated gradually with clear sky and clear/cloudy conditions
over a non-frozen ocean and then the non-polar land and polar cases (Tobin
et al., 2006).
The AIRS/AMSU has an advantage of measuring the radiation penetrating
through clouds and polar darkness, and has high spectral resolution and
coarse spatial resolution (Dong et al., 2006). However, the AIRS only
algorithm using only AIRS observations has been developed due to the
degradation of the AMSU-A. Microwave and multispectral radiometers were used
for global mapping of the sea ice extent and dynamics, while the visible,
near-infrared, and infrared sensors could obtain details on the ice
concentration, snow/ice albedo, thickness, and IST during clear-sky
conditions (Hall et al., 2004; Scott et al., 2014). The MODIS on Earth
Observing System (EOS) Aqua, used as infrared measurements, was influenced
by water and cloud contamination, but had a higher spatial resolution (Dong
et al., 2006). In order to remove the cloud effects in the MODIS IST
algorithm, MODIS cloud mask products were used (Hall et al., 2004).
Since AIRS and MODIS were co-located on Aqua, they have often been used to
make a synergistic algorithm and they have been compared to each other
frequently (Molnar and Susskind, 2005; Li et al., 2012; Liu et al., 2014).
Li et al. (2012) compared the atmospheric instability from at a single AIRS
field of view (FOV; ∼ 13.5 km at nadir) sounding with that of
AIRS/AMSU sounding (∼ 45 km), utilizing the MODIS cloud
information. Molnar and Susskind (2005) validated the accuracy of the
AIRS/AMSU cloud products using MODIS cloud analyses, which have a higher
spatial resolution than that of AIRS. Knuteson et al. (2006) compared the
MODIS Collection 4 (C4) with the AIRS Version 3 (V3) on the land surface
temperature (LST) for the eastern half of the US, showing that the monthly
differences were approximately 3 K. Lee et al. (2013) investigated the
characteristics of the differences between the MODIS land surface skin
temperature/sea surface temperature and the AIRS/AMSU surface skin
temperature across the globe, and found that the MODIS C5 product was
systematically lower by 1.7 K than the AIRS/AMSU V5 product over land in the
50∘ N–50∘ S regions, but it was higher by 0.5 K than the AIRS/AMSU product
over ocean. Particularly in the sea ice regions, the MODIS annual averages
were larger than the AIRS/AMSU values, due to the differential errors in
ice/snow emissivity between the retrieval methods (or channels) for the two
data products. The differences between the MODIS and AIRS methods were
reduced when the MODIS IST and AIRS/AMSU surface skin temperatures were
compared for 9 days. The possible reasons for this include the local time of
satellite observation difference between them due to the different swath
width in the high latitude regions, and the emissivity difference between
microwave and infrared channels, but more comparison studies are necessary
for a longer period to pin down the reasons of such skin temperature
discrepancies between MODIS and AIRS/AMSU.
The primary purpose of this study was to investigate a relative degree of
agreement (or disagreement) among different SST data sets using the MODIS IST
C5, the SST of the AIRS/AMSU, and AIRS only V6. The second purpose of this
paper was to analyze the temperature trend differences affected by the
temperature differences among different data products. The data sets used in
this study were described in Sect. 2. In Sect. 3, we compared the MODIS
and AIRS only data with the AIRS/AMSU values. We also analyzed the
temperature trends from the three satellite-based data sets in Sect. 4, and
in the conclusion we summarized our study.
Data and methods
The Aqua satellite carrying the AIRS, AMSU and MODIS instruments was
launched on 4 May 2002 with the Earth's Radiant Energy System (CERES),
Humidity Sounder for Brazil (HSB) and the Advanced Microwave Scanning
Radiometer-EOS (AMSR-E). It has far exceeded its designed life span of 6
years and has a chance of operating into the 2020's (http://aqua.nasa.gov/).
The Aqua satellite orbits the earth every 98.8 min with an equatorial
crossing time going north (ascending) at 1:30 p.m. local time (daytime) and
going south (descending) at 1:30 a.m. (nighttime) in a sun-synchronous, near
polar orbit with an inclination of 98.2∘ and an operational
altitude of 705 km (Tian et al., 2013).
As shown in Table 1, we used the data sets of MODIS IST (e.g., Hall and Riggs, 2015) and SSTs of AIRS/AMSU and AIRS only over the Northern Hemisphere
during 16–24 April and over the Southern Hemisphere on 15–23 September from
2003 to 2014 in order to avoid the polar night when the visible channels of
the MODIS did not operate (Hall et al., 2004). The sea surface temperature
observed from infrared channels of satellites indicates the values at the
skin of sea water, in contrast with the sea surface temperature measured
from buoys, of which values represent the temperature of bulk water near the
sea surfaces. The infrared sea SST was measured at depths of approximately
10 µm within the oceanic skin layer (∼ 500 µm) at the water side of the air–sea interface where the
conductive and diffusive heat transfer processes dominated (Emery et al.,
2001; Donlon et al., 2002; Liou, 2002).
The information on the satellite-observed surface skin temperature
(Tskin) Level 3 (L3) data used in this study. Three data sets of
Tskin were compared over the Northern Hemisphere during 16–24
April 2003–2014, and over the Southern Hemisphere during 15–23 September
2003–2014. The abbreviations used in this table are as follows: temp
(temperature), IST (ice surface skin temperature), OBS (observation), and
SFC (surface).
Satellite-observed data setVersionTempAreaSpatialNumberSatellite sensorAbbreviationReference(Collection)typeresolutionof OBSMODIS ISTMYD29E1D/5SkinPolar ocean4km × 4km1 day-1Aqua MODISTskin (MODIS)Hall et al. (2004)AIRS/AMSU SFC skin tempAIRX3STD/6SkinGlobe1∘× 1∘2 day-1Aqua AIRS/AMSU-ATskin (AA_V6)Susskind et al. (2014)AIRS only SFC skin tempAIRS3STD/6SkinGlobe1∘× 1∘2 day-1Aqua AIRSTskin (AO_V6)Susskind et al. (2014)
As an imaging spectroradiometer, the MODIS with 36 bands has retrieved
various physical parameters such as aerosol optical thickness, land and
water surface temperature, leaf area index, and snow cover, etc. (Barnes et
al., 1998; Hall and Riggs, 2007). MODIS produced the “sea ice by
reflectance” and “IST” in order to identify sea ice (Riggs et al., 1999).
The “sea ice by reflectance” was determined by the normalized difference
snow index (NDSI), and the reflectance of Band 1 (0.645 µm) and
Band 2 (0.858 µm). The NDSI was calculated using Band 4 (0.555 µm) and Band 7 (2.130 µm). IST is used as another
method for identifying sea ice. The IST derived from the “split-window
method” in Eq. (1), where bands 31 and 32 are centered at approximately 11 and 12 µm, respectively. The method was applied
in order to identify the ice when the IST was less than 271.5 K. The cutoff
temperature between water and ice (271.5 K) may vary depending on the region
and season. The IST is calculated as follows:
IST=a+bT11+c(T11-T12)+d[(T11-T12)(sec(θ)-1)],
where T11 is the brightness temperature (K) in 11 µm,
T12 is the brightness temperature (K) in 12 µm, and θ is the scan angle from nadir. The difference between the T11 and the
skin temperature from the LOWTRAN can be less than 3 K for a skin
temperature between 230 and 260 K (Key et al., 1997). Since the value of
T11 itself was a good estimate, coefficients a–d were defined for the
following temperature ranges: T11 < 240 K, 240 K ≤T11≤ 260 K, and 260 K < T11 (Riggs et al., 2006).
The IST algorithm was only applied to the polar ocean pixels that were
determined to be clear by the MODIS cloud mask using visible reflectance
(Hall et al., 2004). The surface in the IST algorithm was assumed to be snow
(Key et al., 1997). MODIS ISTs were provided as daily polar fields with a 4 km × 4 km resolution.
The AIRS spectrometer is a high spectral resolution spectrometer with
2378 channels in the thermal infrared spectrum and 4 bands in the visible
spectrum (Won, 2008). The AIRS and AMSU were coupled in order to play a role
as an advanced sounding system under clear and cloudy conditions (Aumann et
al., 2003). The AIRS/AMSU algorithm is independent of the GCM, except for
the use of GCM surface pressure to determine the bottom boundary conditions
(Molnar and Susskind, 2005). V6 is the most current retrieval algorithm
since the launch of AIRS instrument, and detailed descriptions are given in
Olsen (2013b). The primary products from AIRS suite include the atmospheric
temperature-humidity profiles, ozone profiles, sea/land surface skin
temperature (SST), and cloud related parameters such as the outgoing
longwave radiation (OLR) (Susskind et al., 2011). In the AIRS/AMSU
algorithm, the surface classification was conducted using the brightness
temperature difference in 23 GHz (AMSU ch1) and 50 GHz (AMSU ch3). The
difference (brightness temperature at 23 GHz minus brightness temperature at
50 GHz) had a negative value on the sea ice and a positive value on the
water (Grody et al., 1999; Hewison and English, 1999). Also, the brightness
temperature difference between 23 GHz (AMSU ch1) and 31 GHz (AMSU ch2) could
distinguish the age of the sea ice (Kongoli et al., 2008). The accuracy of
AIRS/AMSU SST can be affected by surface misclassification, which is caused
by the surface emissivity changes, the pixel mixed with the various surface
types, and the ice pixel pooled with water.
After the surface type classification from the AMSU retrieval, the initial
state for atmospheric and surface parameters, cloud parameters and OLR was
generated using the Neural Network methodology (Susskind et al., 2011,
2014). The methodology was used to approximate some functions between the
input and output vectors by training (Gardner and Dorling, 1998). Next, the
initial clear column radiances were generated, which were based on the
initial state and the observed infrared radiances. The surface and
atmospheric variables, including the surface skin temperature, were
retrieved by updating the cloud-cleared infrared radiance, iteratively. The
cloud properties and outgoing longwave radiation were then retrieved,
followed by the error estimates and quality control. In the AIRS only V6,
shortwave window region 3.76–4.0 µm was used in order to derive
the surface skin temperature and surface spectral emissivity (ε).
AMSU channels 4–5 had not been available since 2007 and 2010, respectively,
due to radiometric noise. In preparation for the degradation of the other
AMSU channels, the AIRS only algorithm was developed excluding the AMSU
observations. The algorithm was similar to that of AIRS/AMSU, but it did not
use the AMSU-A observations in any step of the physical retrieval process
and the quality control methodology. The AIRS only algorithm has utilized
the forecast surface temperature from the NOAA Global Forecast System (GFS)
in order to determine whether the oceanic surface is highly likely to be
liquid or frozen, instead of AMSU observations (Olsen, 2013a; Susskind et
al., 2014).
AIRS/AMSU L2 product (AIRX2RET) is based on 3 × 3 AIRS FOVs
coexisting within a single AMSU footprint, and the horizontal resolution of
AIRS and AMSU is approximately 13.5 and 45 km, respectively (Aumann et
al., 2003; Zheng et al., 2015). The L2 data set of AIRS only is provided on 3 × 3 AIRS FOVs (∼ 45 km) like that of AIRS/AMSU. The
two L3 products of the AIRS/AMSU and AIRS only data sets used in this study have also
been analyzed under the same spatial resolution of a 1∘× 1∘ (∼ 100 km × 100 km) grid (Table 1).
We calculated the climatology and anomaly values from the yearly 9-day mean
temperatures in a 1∘× 1∘ grid for the 12-year period
of Aqua satellite observations in order to estimate the temperature anomaly
trends. The trends of MODIS IST were derived only when the number of
yearly data was at least 10 out of 12 entire years at each grid point. The
trends of the AIRS/AMSU and AIRS only were derived only when the number of
yearly data sets was 12, covering the entire years of the analysis at each
grid. The bootstrap method (Wilks, 1995) was used to calculate at a 95 %
confidence interval. In the method, 10 000 linear temperature trends were
generated by random sampling, allowing repetition of 10 000 yearly anomaly
temperature data sets. Then, we estimated the 95 % confidence interval of
10 000 temperature trends.
Comparison of the satellite-derived surface skin temperatures:
IST vs. SST
Figure 1a shows the spatial coverage and the averaged value of the MODIS IST
over the Southern Hemisphere during 15–23 September 2003–2014. In order
to solve the spatial resolution mismatches, the original resolution of the
MODIS data with a 4 km × 4 km grid (Fig. 1a) was re-gridded to a
1∘× 1∘ grid in the case of MODIS data present over 50 % (Fig. 1b). A grid spacing of 1∘ corresponds to approximately
111 km on the equator, and it becomes reduced poleward. In the zonal averaged SST analysis, this 50 % criterion was used. During the same
period, the spatial distributions of the climatological Tskin (AA_V6) and Tskin (AO_V6) were also
shown in Fig. 1c–d, respectively. As expected, the MODIS and AIRS showed the
spatial distribution of the climatological SST, warmer at the lower
latitudes than the higher latitudes. The SST distributions over the Northern
Hemisphere during 16–24 April 2003–2014 have been shown in Kang and Yoo (2015).
Figure 2a displays the number of years when both Tskin (MODIS) and
Tskin (AA_V6) are available at each grid point over
the Southern Hemisphere. The number near 60∘ S was smaller than that of the
other regions because the MODIS IST algorithm only produced its data in the
cloud-free pixels. Similar distributions by the clouds were shown in Fig. 2b
and 2d for the same reason. The reduced number of observations near 60∘ S had a spatial distribution similar to that of the frontal cloud bands that were
likely associated with the mid-/high-latitude depressions encircling the
Antarctica (e.g., Jakob, 1999; Comiso and Stock, 2001; Lachlan-Cope, 2010;
Boucher et al., 2013). Figure 2c shows the number of years when both
Tskin (AO_V6) and Tskin (AA_V6)
were available at each grid. Most of the grids had both Tskin (AO_V6) and Tskin (AA_V6) for a
period of more than 10 years.
(a) 12-year composite skin temperatures (K) of the MODIS IST over
the Southern Hemisphere during 15–23 September 2003–2014. The original
MODIS data (MYD29E1D) have a 4 km × 4 km spatial resolution. Their
spatial resolution has been reconstructed to 1∘× 1∘ in (b) in order to compare this data with the AIRS/AMSU
data. (c)–(d) is the surface skin temperatures of the AIRS/AMSU and AIRS
only over the Southern Hemisphere ocean during 15–23 September 2003–2014,
respectively.
The number of co-located observations of (a)Tskin (MODIS)
and Tskin (AA_V6), (b)Tskin (MODIS) and
Tskin (AO_V6), and (c)Tskin (AO_V6) and Tskin (AA_V6) over the Southern Hemisphere
during 15–23 September 2003–2014. (d) Same as in (c) except for three
different data sets (Tskin (MODIS), Tskin (AA_V6), and Tskin (AO_V6)).
The distributions of (a)Tskin (MODIS) minus Tskin (AA_V6),
(c)Tskin (MODIS) minus Tskin (AO_V6), and (e)Tskin (AO_V6) minus
Tskin (AA_V6) over the Northern Hemisphere during
16–24 April 2003–2014. The scatter plots of (b)Tskin (MODIS)
vs. Tskin (AA_V6), (d)Tskin (MODIS) vs.
Tskin (AO_V6), and (f)Tskin (AO_V6) vs. Tskin (AA_V6).
Figure 3a presents the spatial distribution of the temporal difference in a
1∘× 1∘ grid between the climatological Tskin (MODIS) and Tskin (AA_V6) during 16–24
April 2003–2014 over the Northern Hemisphere. In general, Tskin (MODIS) at
60–70∘ N was higher than the Tskin (AA_V6). Tskin (MODIS) was about 3 K higher than the Tskin (AA_V6)
on the Hudson Bay and near Greenland, whereas it was about -2 K lower near
the center of the Arctic Ocean. The relationship between the climatological
Tskin (MODIS) and Tskin (AA_V6) was presented
in the scatter diagrams (Fig. 3b). The scatter plot revealed a temperature
interval which deviated from the simple linear line. The discontinuous shape
appeared at the freezing point (∼ 273 K) and the turning point
(∼ 260 K) in terms of Tskin (MODIS), changing the
coefficient of the MODIS IST algorithm. In the interval, Tskin (MODIS) was systematically higher than the Tskin (AA_V6) in the 260–273 K range of Tskin (MODIS). The slope in the
range was 0.85, lower than the slope for the whole regression line (0.97).
There was a better agreement in the 240–260 K range, where the difference
between the T11 and the SST in the LOWTRAN was less than 3 K (Key et
al., 1997). The better agreement in the range greater than 280 K was also
shown.
Figure 3c was the same as Fig. 3a except for Tskin (MODIS) vs.
Tskin (AO_V6). The differences between the two data sets
were very similar to those in Fig. 3a. However, Tskin (MODIS) was
more than 4 K higher than Tskin (AO_V6) in some
regions near the Greenland and the Barents Sea. The slope (0.93) in the 260–273 K range of Tskin (MODIS) also indicated a deviation from the
total slope (0.98) in the scatter plot (Fig. 3d), similar to that in Fig. 3b. Figure 3e showed the difference between Tskin (AO_V6) to Tskin (AA_V6). Overall, the agreement was much
better than the previous two cases (Fig. 3a and c), except for in the
Greenland Sea, the Barents Sea, and the Okhotsk Sea. Both Tskin (AO_V6) and Tskin (AA_V6) agreed with
each other (r=0.999) well except for near the freezing point.
Same as in Fig. 3 except for the data taken during 15–23 September
2003–2014, over the Southern Hemisphere.
Figure 4 showed discrepancies among the three types of SST data sets over the
Southern Hemisphere during 15–23 September 2003–2014. It has been noted
that there was a latitudinal band encircling Antarctica at 60–70∘ S, where
Tskin (MODIS) was higher than both the Tskin (AA_V6) and Tskin (AO_V6) (Fig. 4a
and c). The circular region corresponded to the sea ice/water boundary which
was expected to move seasonally. This implies a systematic difference
between Tskin (MODIS) and Tskin (AA_V6) in the
sea ice classification. The corresponding scatter plots also revealed a
discontinuous (i.e., not linear) shape in the 260–273 K range of
Tskin (MODIS) (Fig. 4b and d). The slopes in that range were 0.84–0.94,
which were smaller than the slope (0.98) in the whole range. In addition,
Tskin (MODIS) showed lower temperature values than Tskin (AA_V6) and Tskin (AO_V6) near the
Antarctic peninsula, in the region from Weddell Sea to Ross Sea (Fig. 4a and c).
The comparison between two types of AIRS data sets also showed the circular
pattern around Antarctica where Tskin (AO_V6) was
lower by 1.5–5.6 K than Tskin (AA_V6) (Fig. 4e). The
discrepancy near the sea ice/water boundary was clear, possibly due to the
difference in the sea ice detection method between the two data sets. The
uncertainty of the SST at the sea ice boundary was distinguished from the
other regions. Both Tskin (AO_V6) and Tskin (AA_V6) were in good agreement, other than the sea
ice/water boundary regions. The scatter pattern of the Tskin (AA_V6) vs. that of the Tskin (AO_V6) showed that the two data sets generally agreed with each other, but the
disagreement near the freezing point again occurred indicating a cold bias
of AIRS only with respect to AIRS/AMSU (Fig. 4f).
Annual-average spatial distributions of the Tskin (MODIS)
minus Tskin (AA_V6) over the Southern Hemisphere
during 15–23 September.
Statistical comparisons of the climatological 9-day composite data
during 2003–2014 over both hemispheres; Tskin (MODIS) vs. Tskin (AA_V6), Tskin (MODIS) vs. Tskin
(AO_V6), and Tskin (AO_V6) vs. Tskin
(AA_V6). The values in this table were calculated based on
the 12-year composite mean values. The values in parentheses indicate the
12-year mean values and their standard deviations during 2003–2014. Bias:
Tskin (MODIS) minus Tskin (AA_V6), Tskin (MODIS) minus Tskin (AO_V6), and Tskin (AO_V6) minus Tskin (AA_V6), r:
correlation coefficient, RMSE: root mean square error.
RegionTskin (MODIS) vs. Tskin (AA_V6) Tskin (MODIS) vs. Tskin (AO_V6) Tskin (AO_V6) vs. Tskin (AA_V6) Bias (K)rRMSE (K)Bias (K)rRMSE (K)Bias (K)rRMSE (K)35–90∘ N-0.1690.9941.491-0.2890.9931.5630.1330.9990.574(-0.161±0.231)(0.990±0.002)(1.909±0.156)(-0.324±0.308)(0.990±0.003)(1.963±0.260)(0.137±0.130)(0.997±0.001)(1.018±0.131)40–90∘ S0.0260.9891.4800.2030.9851.756-0.1410.9980.750(-0.010±0.218)(0.982±0.003)(2.082±0.144)(0.035±0.282)(0.980±0.003)(2.184±0.119)(-0.139±0.079)(0.994±0.001)(1.272±0.092)60–90∘ N0.2230.9861.5010.5970.9861.5910.1680.9980.590(0.194±0.357)(0.973±0.009)(1.986±0.227)(-0.013±0.475)(0.972±0.011)(2.033±0.370)(0.170±0.214)(0.992±0.003)(1.027±0.216)60–90∘ S0.1980.9681.8470.3060.9592.173-0.1090.9920.852(0.368±0.537)(0.906±0.023)(2.871±0.276)(0.295±0.620)(0.898±0.021)(2.987±0.271)(-0.108±0.142)(0.976±0.005)(1.498±0.112)
Same as Fig. 5 except for Tskin (AO_V6) minus
Tskin (AA_V6).
Figure 5 showed the annual-average spatial distributions for Tskin (MODIS) minus Tskin (AA_V6) in the Southern Hemisphere from 15–23 September 2003–2014. Although the 9-day composite
values were used in each year, Tskin (MODIS) data did not exist in
some areas. It was because the MODIS IST algorithm was valid only for
cloud-free pixels. The systematic positive values at the boundary of the sea
ice consistently occurred, while the negative ones occurred on some areas of
the sea ice near Antarctica every year.
Figure 6 presented the interannual variation of the spatial distribution of
Tskin (AO_V6) minus Tskin (AA_V6) for the study period. As already seen in Fig. 4e, the values of
Tskin (AO_V6) compared to Tskin (AA_V6) show systematic negative values encircling
Antarctica during the period. In addition, there were positive values over
the sea-ice prevailing areas inside the circle, with the location varying
from year to year, which must be related to the difference in the surface
type characterization.
Table 2 showed the statistics of bias, spatial correlation coefficient (r),
and root mean square error (RMSE) obtained from the 12-year climatologies of
2003–2014 in order to analyze the systematic error among the three types of
satellite-observed temperatures quantitatively. This analysis for each
hemispheric vernal period has been performed over the two regions (35–90,
60–90∘ N) of the Northern Hemisphere during 16–24 April, and over the regions
(40–90, 60–90∘ S) of the Southern Hemisphere during 15–23 September. The
spatial correlation coefficient between the two satellite data sets was
computed in this study as follows. (i) The climatological 9-day composite
data of SSTs during 2003–2014 were computed in a 1∘× 1∘ grid of the two data sets, respectively. (ii) We computed the
spatial correlation coefficient between the two data sets, using their
climatological values in a 1∘× 1∘ grid within
a given latitude band. The values in parentheses indicated the average
obtained from the statistics for each year and their corresponding standard
deviations. Based on the climatology values, the SST of the AIRS retrievals
were comparable with respect to the Tskin (MODIS) (r= 0.959–0.994).
Tskin (MODIS) tended to systematically exceed the AIRS retrievals
over the polar oceans (bias = 0.198–0.597 K). Hall et al. (2004) reported
the accuracy of Tskin (MODIS) with the bias values of 1.2–1.3 K near
the South Pole and the Arctic Ocean. The RMSE of 1.847 K for Tskin (MODIS) vs. the Tskin (AA_V6) over 60–90∘ S in our study
was slightly higher than that in the study of Hall et al. (2004).
From the intercomparison of the three data sets, the bias (-0.109–0.597) and
RMSE (0.590–2.173) over the high latitude belt (60–90∘ N and ∘ S) tended to be
larger, and the correlation coefficients (r= 0.959–0.986) was smaller than
those over 35–90∘ N and 40–90∘ S among the three comparisons (Table 2). This
result indicated that there was more disagreement over the high latitudes
than over other regions. The spatial correlation coefficient (0.992–0.999)
between Tskin (AO_V6) and Tskin (AA_V6) was greater than those (0.968–0.994) between
Tskin (MODIS) and Tskin (AA_V6). In the high
latitudes Tskin (AO_V6) with respect to Tskin (AA_V6) had a positive bias of 0.168 K with a RMSE of 0.590 K in the Northern Hemisphere, but a bias of -0.109 K with a RMSE of 0.852 K
in the Southern Hemisphere. The high correlations (r= 0.998–0.999)
between the AIRS/AMSU and AIRS only (i.e., AIRS retrievals) over the 35–90∘ N
and 40–90∘ S areas showed that the AIRS only can be a good alternative for
the AIRS/AMSU, except for at the region of the sea ice boundary (r= 0.992
over the 60–90∘ S). The disagreement between Tskin (AA_V6) and Tskin (AO_V6) at the region where the sea ice
and water mixed appeared, because the AIRS only used less accurate GCM
forecast data for surface classification over the potentially frozen oceans.
Figure 7 presents the zonal mean temperature difference among the three
satellite-observed data sets in a 1∘× 1∘ grid
over the Northern Hemisphere during 16–24 April 2003–2014 and over the
Southern Hemisphere during 15–23 September 2003–2014. The red, blue and
green lines represent the zonally averaged annual values of Tskin (MODIS) minus Tskin (AA_V6), Tskin (MODIS)
minus Tskin (AO_V6), and Tskin (AO_V6) minus Tskin (AA_V6),
respectively. The climatological annual values have been calculated from the
interannually varying yearly data, shown in Fig. 8. The black dashed line,
the difference between the original MODIS IST data (4 km × 4 km) and converted
Tskin (MODIS) (1∘× 1∘) indicated the
possible error from the conversion of spatial resolution. The differences by
the conversion over both hemispheres were within 0.3 and 0.5 K,
respectively. The original Tskin (MODIS), converted Tskin (MODIS), Tskin (AA_V6), and Tskin (AO_V6) were chosen under the same condition in space and
time, and each grid (1∘× 1∘) of a degree
latitudinal band.
Zonal averaged values of Tskin (MODIS) minus Tskin (AA_V6) (red solid line), Tskin (MODIS) minus
Tskin (AO_V6) (blue solid line), and Tskin (AO_V6) minus Tskin (AA_V6) (green
solid line). The difference in spatial grid averages of the MODIS data
between 4 km × 4 km and 1∘× 1∘ is shown by the black
dashed line. The difference values are calculated at one degree interval
along each latitudinal belt. The climatological data periods are 16–24 April
2003–2014 over the Northern Hemisphere, and 15–23 September
2003–2014 over the Southern Hemisphere.
Zonal averaged values of (a)Tskin (MODIS) minus Tskin (AA_V6), (b)Tskin (MODIS) minus Tskin (AO_V6), (c)Tskin (AO_V6) minus
Tskin (AA_V6) over the Northern Hemisphere from 16 to 24 April
2003–2014, and over the Southern Hemisphere from 15 to 23 September 2003–2014. The values in each year represent the corresponding color
lines. The thick black line indicates the mean difference values.
It is hard to see in Fig. 3a the systematic difference due to the sea ice
detection over the Northern Hemisphere because of the continental
distribution. However, Fig. 7 clearly showed that the difference among the
Tskin (MODIS), Tskin (AA_V6), and Tskin (AO_V6) existed over the Northern Hemisphere.
Tskin (MODIS) was warmer than Tskin (AA_V6) in 56–81∘ N and 54–69∘ S, while cooler than Tskin (AA_V6) in the
other latitudinal zone. It has been noted that the peak of the difference
between Tskin (MODIS) and two AIRS data sets in the Northern Hemisphere high-latitude region took place in a broader region than in the
Southern Hemisphere. Tskin (MODIS) was up to 1.65 K higher than the
AIRS data sets at the boundaries of the sea ice/water, whereas it was lower
by up to -2.04 K over the sea ice region. The MODIS IST algorithm was the
optimized on the snow/ice surface type, and thus the underestimation of
Tskin (MODIS) in the 35–54∘ N and 40–55∘ S may not be unexpected. In
general, the overestimation of Tskin (MODIS) to the AIRS retrievals
occurred at the sea ice boundary and the underestimation occurred in the sea
ice region that can be covered with snow/ice.
The grey solid lines in Fig. A1a–b mean the 5 % significance level of the
differences between Tskin (MODIS) and Tskin (AA_V6), and between Tskin (AO_V6) and Tskin
(AA_V6) over a possibly frozen region (poleward from 50∘ N and
50∘ S, respectively). Based on the t test (von Storch and Zwiers, 1999) at
significance level of p < 0.05, the temperature disagreement between
Tskin (MODIS) and Tskin (AA_V6) (red solid line) is
significant in 50–55, 58–70, 89–90∘ N, 50–53 and 57–62∘ S (Fig. A1a).
Considering the uncertainty of MODIS due to the conversion of spatial
resolution (black dashed line), the temperature disagreement in 57–62∘ S can
become insignificant. However, the discrepancy in 58–70∘ N is significant
even if the uncertainty of MODIS is considered. The difference between
Tskin (AO_V6) and Tskin (AA_V6) in
53–60∘ S is significant (Fig. A1b).
The color-coded lines in Fig. 8 interannually represent the differences in
temperature among the three data sets for individual years. The thick black
lines indicated the yearly difference averages. There was a significant
degree of interannual variation in the difference between Tskin (MODIS) and the two AIRS data sets (Fig. 8a–b). The variation was larger in
2009, 2010 and 2011 over the regions northward of 60∘ N and southward of 55∘ S
where sea ice existed. Figure 8b shows a value of Tskin (MODIS) minus
Tskin (AO_V6) that was similar to that in Fig. 8a.
Tskin (MODIS) was lower than Tskin (AO_V6) at
the ice surface, but higher than Tskin (AO_V6) at the
boundary of the sea ice. Figure 8c showed the interannual variation of
Tskin (AO_V6) minus Tskin (AA_V6). The interannual variation of the difference between the AIRS retrievals
was much larger in the high latitude than in the mid-latitudes. The maximum
difference of 1.56 K between the AIRS retrievals was found at 87–88∘ N in
2011.
The rate of the surface skin temperature change (K decade-1) of the
MODIS, AIRS/AMSU, and AIRS only in each 10∘ latitudinal belt over
the Northern Hemisphere (NH) during 16–24 April and over the Southern Hemisphere (SH) during 15–23 September 2003–2014, using their colocated
data in a 1∘× 1∘ grid. The ±values
define the 95 % confidence intervals for the trends. The symbol “∗” means
the significant value at a 95 % confidence interval. Note that the rates
are subject to large uncertainty due to the short periods of the
satellite-based temperature records.
There could be several reasons for the observed differences between
Tskin (MODIS) and Tskin (AA_V6). The main one
can be attributed to the difference in the channel used for the retrievals
of the skin temperature. The AIRS/AMSU V6 only utilized shortwave window
channels for the surface skin temperature, while the MODIS IST algorithm
used the longwave window regions. The shortwave window could be mixed with
the solar radiation during the daytime, but it was suitable for temperature
sounding (Chahine, 1974, 1977; Susskind et al., 2014). The advantage of the
longwave window was that its range corresponded to the peak of the infrared
radiation emitted from the earth (Prakash, 2000). On the other hand, the
longwave window radiation could be affected more by clouds. In order to
avoid cloud contamination, the MODIS IST algorithm analyzed the pixel when
the MODIS cloud mask was reported as clear sky (Hall et al., 2004). The
MODIS cloud mask using visible reflectance had a high accuracy during the
daytime, but a lower accuracy during the nighttime due to low illumination.
As another reason for the temperature difference, Lee et al. (2013)
suggested that there were substantial differences in observation time
between MODIS and AIRS in the high latitude regions, since the different
scan angles of the two instruments resulted in different footprints, which
could lead to the observed difference in temperature. However, we suggested
that the surface type classification method could be the primary reason for
the temperature difference between the MODIS-based and AIRS-based data sets.
AIRS/AMSU SST was retrieved after the surface type was classified. On the
other hand, the MODIS IST was calculated without the surface type
classification step. Then, the MODIS algorithm categorized pixels being ice
if IST was less than the cutoff temperature. MODIS IST was calculated on the
snow, sea ice, and ocean, assuming the surface was snow-covered (sea ice).
The IST was utilized as a criterion for identifying the ice/water which
might cause significant disagreement between the Tskin (MODIS) and
Tskin (AA_V6) in the range of 260–273 K.
Comparison of the surface skin temperature trends: IST vs. SST
In order to further investigate the effects of the difference among the
satellite-observed temperatures from different measurement techniques or
algorithms on the temperature anomaly trend, we calculated the trend in some
latitude belts, using the three satellite-observed temperature data sets at
each grid during 16–24 April 2003–2014 (in the Northern Hemisphere) and
15–23 September 2003–2014 in the Southern Hemisphere. During this period,
an unusually extensive surface melting event was observed in 2012 (Nghiem et
al., 2012; Hall et al., 2013; Comiso and Hall, 2014).
Table 3 shows the temperature anomaly trend with a 95 % confidence level
on the 10∘ latitude belt. We arranged the data of MODIS IST,
AIRS/AMSU, and AIRS only under the same condition in space and time. The
significant warming trend in 70–80∘ N was estimated in the following order:
AIRS/AMSU (2.83 K decade-1) > AIRS only (2.71 K decade-1) > Tskin (MODIS) (2.30 K decade-1). The
warming (0.10 to 0.38 K decade-1) at 40–50∘ N and 50–60∘ S, and the cooling (-0.08 to -1.94 K decade-1)
at 80–90, 60–70, 50–60∘ N, 60–70 and 70–80∘ S of the three data sets
occurred, but the trends were not significant. Comiso and Hall (2014)
reported the SST trend using the Goddard Institute for Space Studies (GISS)
data set as 0.60 K decade-1 and the trend using the Advanced Very High
Resolution Radiometer (AVHRR) data set as 0.69 K decade-1 in the Arctic
(> 64∘ N) during 1981–2012. Our result in 70–80∘ N, compared with
the above studies, seems to indicate an acceleration in the Arctic warming.
The warming trend in the northern hemispheric high latitudes had been known
to be caused in part by the well-known positive feedback among snow/ice,
surface albedo and temperature (Curry et al., 1995; Comiso and Hall, 2014).
Tskin (MODIS) had a greater cooling tendency compared to Tskin (AA_V6) in the higher latitude regions (70–90∘ N
and 60–80∘ S) (Table 3). The trend difference between the two temperatures was -0.69 K decade-1
at 70–80∘ S. The trend difference of the Tskin (AA_V6) and Tskin (AO_V6) (i.e., AIRS
only minus AIRS/AMSU) was the largest (-0.26 K decade-1) at 60–70∘ N. The
cooling trend (-0.90 K decade-1) of the Tskin (AO_V6)
was greater than that (-0.65 K decade-1) of Tskin (AA_V6) at the latitude band.
Figure 9a–b showed the SST anomaly trends from the Tskin (MODIS) in a
1∘× 1∘ grid over the Northern Hemisphere during 16–24 April
2003–2014 and over the Southern Hemisphere during 15–23 September
2003–2014. The Tskin (MODIS) trend was calculated on the grid,
which had available data that existed for over 10 years. Figure 9c–d and e–f showed the trend data for Tskin (AA_V6) and
Tskin (AO_V6), respectively, which all had 12-year
data, individually. The trend distributions in all three of the data sets
were similar over the Northern Hemisphere. Warming trend in the Beaufort
Sea, East Siberian Sea and Kara Sea was detected, while cooling was observed
in the Hudson Bay and near Greenland. The significant warming trend appeared
at 70–80∘ N as shown in Table 3, and the trend based on the spatial
distribution varied depending on the regions (Fig. 9a, c and e). According
to Comiso and Hall (2014), a strong warming trend (> 1.5 K decade-1) existed near the Kara Sea and Baffin Bay among the entire Arctic,
consistent with the noticeable trend revealed near the Kara Sea in our
study. Over the Southern Hemisphere, there were not enough data to derive a
trend for Tskin (MODIS) mostly due to clouds. The trend analysis over
the sea ice regions from Tskin (AA_V6) and Tskin (AO_V6) showed a strong cooling trend, especially near the
Antarctic peninsula between the Weddell and Ross Seas (Fig. 9d and f). The
cooling trend was generally dominant over the Southern Hemisphere. Marshall
et al. (2014) suggested that based on the model experiments, the cooling
trend around Antarctica as opposed to the warming trend around the Arctic
Ocean was the result of the offset between the greenhouse gas and ozone hole
responses, emphasizing the larger cooling effects associated with the
Antarctic ozone hole.
Satellite-derived 9-day anomaly trends (K yr-1) in a grid box
of 1∘× 1∘ over the Northern Hemisphere during
16–24 April 2003–2014, for the (a)Tskin (MODIS), (c)Tskin (AA_V6), and (e)Tskin (AO_V6), and
over the Southern Hemisphere during 16–24 September 2003–2014, for the
(b)Tskin (MODIS), (d)Tskin (AA_V6), and
(f)Tskin (AO_V6).
(a) 12-year mean of Tskin (MODIS) minus Tskin (AA_V6) (K) over the Northern Hemisphere during 16–24 April
2003–2014, and (b) difference in the thermal trend (K decade-1) between
Tskin (MODIS) and Tskin (AA_V6). (c)–(d)
are the same as (a)–(b) except for over the Southern Hemisphere during
16–24 September 2003–2014, respectively. (a) is the same as Fig. 3a
in Kang and Yoo (2015).
The 12-year mean of the Tskin (MODIS) minus Tskin (AA_V6) (Fig. 10a and c) and of the trend difference
between Tskin (MODIS) and Tskin (AA_V6) (Fig. 10b and d) were compared in order to reveal the relationship between the
temperature difference and the corresponding trend difference over the
Northern Hemisphere during 16–24 April 2003–2014 and over the Southern Hemisphere during 15–23 September 2003–2014. Tskin (MODIS) was
higher than Tskin (AA_V6) over the bays of Hudson and
Baffin, and Bering Sea (Fig. 10a). The warming trend of the Tskin (MODIS) was also greater than that of the Tskin (AA_V6) over the Hudson Bay and near the Kara Sea (Fig. 10b). The data for the
trend difference in the Southern Hemisphere was not sufficient due to the
missing data of Tskin (MODIS) in the cloudy condition (Fig. 10d).
Figure 11 showed over both hemispheres the 12-year mean of the Tskin (AO_V6) minus Tskin (AA_V6) (Fig. 11a
and c) and the corresponding trend difference of the Tskin (AO_V6) and Tskin (AA_V6) (Fig. 11b
and d). The relationship of the temperature difference and trend difference
over the Southern Hemisphere in Fig. 10 was hard to analyze due to the
absence of a Tskin (MODIS) trend (Fig. 10c–d). However, Fig. 11c–d
clearly showed that the temperature difference had a significant impact on
the trend difference over the Southern Hemisphere. The trend of the
Tskin (AA_V6) and Tskin (AO_V6)
agreed well except for at the region of the sea ice boundary, implying that
the algorithm identifying the sea ice affected the SST trend.
Uncertainties among satellite observations (Tskin (MODIS), Tskin
(AA_V6), and Tskin (AO_V6)) in the sea
ice region of the Northern Hemisphere are generally similar to those of the
Southern Hemisphere in terms of zonal averages. However, the systematic
difference between the observations can be more clearly seen in the latter
region than in the former region due to more oceanic regions in the Southern Hemisphere (Figs. 10–11, and see also Fig. 7).
Same as Fig. 10 except for Tskin (AO_V6)
minus Tskin (AA_V6). (a) is the same as Fig. 3c
in Kang and Yoo (2015).
Table 4 quantitatively showed how the temperature differences among the
three types of SST affected each trend difference over the hemispheric
regions poleward either from 50∘ N (shown in the left side of the table)
during 16–24 April 2003–2014 or from 50∘ S during 15–23
September 2003–2014. In the upper portion, the average of the temperature difference
and the trend difference in the grid corresponding to the temperature
difference condition was used, whereas the average values on the grids that
had the same signs for the temperature difference and the trend difference
were used in the lower portion. Only the cases where grid number was greater
than 100 were considered. The warmer temperature led to relatively warming
trend, the cooler temperature led to relatively cooling trend. When the
Tskin (MODIS) was greater than Tskin (AA_V6) in
the regions poleward from 50∘ S, the trend difference was in the reduced
cooling trend (i.e., warmer direction) as -0.96, -0.66, and -0.21 K decade-1
with the conditions of Tskin (MODIS) minus Tskin (AA_V6) rising as more than 1, 1.5, and 2 K,
respectively. The uncertainty of the satellite-derived temperatures had a
substantial effect on the uncertainty of the temperature trends. The data
set has been reduced in the lower section of Table 4. The sample size can
affect the estimated impact of ΔT on ΔTrend, but it looks
like that the impact on the trends in the lower section is almost consistent
with that in the upper section despite the reduced sample sizes.
Uncertainties of the satellite-derived surface skin temperature
rate (or trend; ΔTrend) due to the temperature difference (ΔTskin) for the cases of Tskin (MODIS) minus Tskin (AA_V6) and Tskin (AO_V6) minus
Tskin (AA_V6) in the upper portion of the table. Also,
the values of uncertainties provided in the lower portion of the table
indicate the cases of ±ΔTrend with respect to ±ΔTskin (double signs in the same order). The uncertainties are not
shown when the number of the grid (1∘× 1∘)
points (i.e., No. of grids in the table) is less than 100.
ΔTskin (K)Poleward from 50∘ N Poleward from 50∘ S Tskin (MODIS) vs. Tskin (AA_V6) Tskin (AO_V6) vs. Tskin (AA_V6) Tskin (MODIS) vs. Tskin (AA_V6) Tskin (AO_V6) vs. Tskin (AA_V6) No. ofΔTskinΔTrendNo. ofΔTskinΔTrendNo. ofΔTskinΔTrendNo. ofΔTskinΔTrendgrids(Kdecade-1)grids(Kdecade-1)grids(Kdecade-1)grids(Kdecade-1)≥1.021552.01-0.1095––4252.01-0.963781.370.03≥1.515062.340.0419––2532.25-0.661041.800.24≥2.09402.710.195––1342.69-0.2122––≤-1.01839-1.59-0.45236-2.25-0.34224-1.71-0.43877-2.19-0.37≤-1.5921-1.94-0.45162-2.72-0.47115-2.18-0.16654-2.52-0.55≤-2.0367-2.27-0.60109-3.20-0.7855––472-2.82-0.69≥1.09122.151.2140––1392.091.361791.401.01≥1.57072.411.228––94––51––≥2.04992.701.221––64––15––≤-1.01309-1.59-0.90126-2.51-2.02122-1.69-2.42500-2.26-1.96≤-1.5643-1.96-0.9289––61––387-2.55-2.06≤-2.0272-2.28-1.0269––27––293-2.81-2.06Conclusions
The satellite-derived L3 products of MODIS IST and two SSTs from AIRS/AMSU
and AIRS only were investigated with a comparative analysis during the
vernal periods of 2003–2014: 16–24 April over the Northern Hemisphere and
15–23 September over the Southern Hemisphere. The original MODIS IST data
were regridded onto a 1∘× 1∘ grid box for
comparison with the AIRS retrievals. The difference between the original
MODIS IST and the converted one was within 0.5 K in a latitudinal belt.
The differences among the three types of satellite derived SST data were
most prominent over the sea ice regions. Tskin (MODIS) and Tskin (AA_V6) were comparable (r= 0.97–0.99), but there
was systematic disagreement occurred in the Tskin (MODIS) range of
260–273 K. The southern hemispheric high latitude (60–90∘ S) was the
primary contributor to the disagreement between them. In comparison with the
Tskin (AA_V6) in a latitudinal belt, the Tskin (MODIS) was higher by up to 1.65 K than Tskin (AA_V6) on the boundary of the sea ice/water, whereas it was lower by up to
-2.04 K in the sea ice region.
The spatial correlation coefficients (0.992–0.999) of the Tskin (AO_V6) and Tskin (AA_V6) over both
hemispheres were greater than those (0.968–0.994) between Tskin (MODIS) and Tskin (AA_V6). The Tskin (AO_V6) compared to the Tskin (AA_V6)
had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere
high latitudes and a bias of -0.109 K with a RMSE of 0.852 K over the
southern hemispheric high latitudes. There was a systematic disagreement
between the Tskin (AA_V6) and Tskin (AO_V6) at the sea ice boundary. It is likely due to the
fact that the AIRS only algorithm utilized a less accurate GCM forecast than
the microwave data over the seasonally varying frozen oceans.
The temperature differences among the three types of data sets showed a high
degree of interannual variations over the latitudinal belts where sea ice
existed. The significant warming rates (2.3 ± 1.7 ∼ 2.8 ± 1.9 K decade-1) were revealed by all three data sets in the northern
hemispheric high-latitude regions (70–80∘ N) could be interpreted as the
ice-albedo feedback. The discrepancies between the trends of the Tskin (AA_V6) and Tskin (AO_V6) occurred at
the sea ice boundary. When the Tskin (AA_V6) trends
were compared to those of the Tskin (MODIS) or Tskin (AO_V6) in a 1∘× 1∘ grid, the
warmer temperature difference tended to lead to a relative warming trend,
whereas the cooler temperature difference tended to lead to a relative
cooling trend.
The systematic disagreement between the Tskin (MODIS) and Tskin (AA_V6) could be caused by (1) the channels used for the
surface skin temperature, (2) the cloud contamination, (3) the difference in
local time of observation between the MODIS and AIRS, and (4) the surface
type classification method. Whereas the AIRS/AMSU V6 used only the shortwave
window channels for the surface skin temperature, MODIS IST used the
longwave window regions. The MODIS IST product utilized the MODIS cloud mask
with visible reflectance, which had lower accuracy during the night (Hall et
al., 2004). Lee et al. (2013) reported that the local times of observation
between the MODIS and AIRS were almost the same from 60∘ N–60∘ S, but they
were quite different in the high latitude regions. It is likely that the
main cause to the observed SST differences near the sea ice boundary was in
the way the surface type was classified. The AIRS/AMSU algorithm conjugated
the emissivity difference in the low and high frequency microwave bands (23
and 50 GHz) in order to identify sea ice. However, MODIS IST was calculated
without the surface type classification.
The AIRS/AMSU L2 data offer the surface type (coastline, land, ocean, two
types of sea ice, two types of snow, and glacier/snow), and the AIRS/AMSU L3
data provide the number of these various surface types in a grid. The AIRS
only L2 also offer the surface type (coastline, land, ocean, two types of
sea ice, and snow), and its L3 data provide the number of these various
surface types in a grid. Under the condition without ground truth, the
direct validation has a limit because the surface classifications of
AIRS/AMSU and AIRS only have some difference. Although the AIRS only has
utilized the forecast surface temperature from the GFS, there is a good
agreement in SST between AIRS/AMSU and AIRS only in most regions. However,
the disagreement between them over the land regions of the Sahara desert,
parts of Spain and in the US with snow cover at night has been reported
(Dang et al., 2012).
We have investigated the effect in the difference of spatial resolution
between L2 and L3 products, utilizing the L2 products (Tskin (MODIS),
Tskin (AA_V6), and Tskin (AO_V6))
for a year of 2003. Overall the uncertainties among the three L2 data sets
are similar to those of the L3, but the magnitude of Tskin (MODIS)
minus Tskin (AA_V6) for L2 data sets is somewhat
different from that for the L3 data sets (not shown). Based on the L3
products in this study, it seems to be sufficient to allow us to show the
systematic characteristics of the uncertainties. Although the detailed
analysis of L2 is beyond the scope of this study, further studies are
warranted.
The SST in the polar region is a useful parameter being used to derive the
climate change signal, although it has been challenging to measure an
accurate SST. Based on our results from detailed comparative investigation,
we cautiously suggested that the observed difference and uncertainty among
the satellite-derived SSTs were likely caused by the different sea ice
detecting methods used in each algorithm. This study suggested that the ice
forecast derived from other microwave satellite data could improve the AIRS
only product from the better accuracy of surface classification. In
addition, the methods also affected the temperature trend. In this study, we
aimed to help in understanding characteristics of the infrared and microwave
measurements for the surface skin temperature, and the method for
identifying sea ice. We believe the results of this study can be useful for
the interpretation and the modeling of the climate change associated with
the temperature trends.
The difference values (a) between Tskin (MODIS) and Tskin
(AA_V6), and (b)Tskin (AA_V6) and
Tskin (AO_V6) over a possibly frozen region; shown in
Fig. 7. The 5 % significance level is presented as grey solid lines, and
the shaded areas are statistically significant at the 0.05 level.
Acknowledgements
This study was supported by the National Research Foundation of Korea (NRF)
grant funded by the Korean Government (MSIP) (No. 2009-0083527) and the
Korean Ministry of Environment as the Eco-technopia 21 project (No. 2012000160003). We thank Goddard Earth Sciences Data Information and Services
Center for AIRS/AMSU data, and NASA National Snow and Ice Data Center for
MODIS IST data. We also thank Bob Iacovazzi Jr., J. M. Blaisdell, and
C.-Y. Liu for their constructive comments.
Edited by: B. Kahn
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