• Andersen, S., , R. Tonboe, , L. Kaleschke, , G. Heygster, , and L. T. Pedersen, 2007: Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice. J. Geophys. Res., 112 , C08004. doi:10.1029/2006JC003543.

    • Search Google Scholar
    • Export Citation
  • Armaly, B. F., , J. G. Ochoa, , and D. C. Look, 1972: Restrictions on the inversion of the Fresnel reflectance equations. Appl. Opt., 11 , 29072910.

    • Search Google Scholar
    • Export Citation
  • Bjørgo, E., , O. M. Johannessen, , and M. W. Miles, 1997: Analysis of merged SSMR-SSM/I time series of Arctic and Antarctic sea ice parameters 1978–1995. Geophys. Res. Lett., 24 , 413416.

    • Search Google Scholar
    • Export Citation
  • Cavalieri, D. J., 1994: A microwave technique for mapping thin sea ice. J. Geophys. Res., 99 , 1256112572.

  • Cavalieri, D. J., , P. Gloersen, , C. L. Parkinson, , J. C. Comiso, , and H. J. Zwally, 1997: Observed hemispheric asymmetry in global sea ice changes. Science, 272 , 11041106.

    • Search Google Scholar
    • Export Citation
  • Cavalieri, D. J., , M. Thorsten, , and J. C. Comiso, cited. 2009: AMSR-E/Aqua daily L3 25 km brightness temperature & sea ice concentration polar grids V002. [Available online at http://nsidc.org/data/docs/daac/ae_si25_25km_tb_and_sea_ice.gd.html].

    • Search Google Scholar
    • Export Citation
  • Cho, K., , A. Komaki, , and H. Shimoda, 2007: Comparison of passive microwave sea ice algorithms for detecting the trends of global warming. Proc. 28th Asian Conf. on Remote Sensing (ACRS 2007), Kuala Lampur, Malaysia, Asian Association on Remote Sensing, PL2. [Available online at http://www.a-a-r-s.org/acrs/proceeding/ACRS2007/Papers/PL2.pdf].

    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., 1986: Characteristics of Arctic winter sea ice from satellite multispectral microwave observations. J. Geophys. Res., 91 , 975994.

    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., , and K. Steffen, 2001: Studies of Antarctic sea ice concentrations from satellite observations and their applications. J. Geophys. Res., 106 , 3136131385.

    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., , D. J. Cavalieri, , and T. Markus, 2003: Sea ice concentration, ice temperature, and snow depth using AMSR-E data. IEEE Trans. Geosci. Remote Sens., 41 , 243252.

    • Search Google Scholar
    • Export Citation
  • Gloersen, P., , W. J. Campbell, , D. J. Cavalieri, , J. C. Comiso, , C. L. Parkinson, , and H. J. Zwally, 1992: Arctic and Antarctic sea ice, 1978–1987: Satellite passive microwave observations and analysis. NASA Spec. Publ., 511 , 290.

    • Search Google Scholar
    • Export Citation
  • Hollinger, J. P., 1991: DMSP special sensor microwave/imager calibration/validation. U.S. Naval Research Lab Final Rep. A626472, 314 pp.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2009a: Detection of Asian dust (Hwangsa) over the Yellow Sea by decomposition of unpolarized infrared reflectivity. Atmos. Environ., 43 , 58875893.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2009b: Retrieval of refractive index over specular surfaces for remote sensing applications. J. Appl. Remote Sens., 3 , 033560. doi:10.1117/1.3265997.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2010a: Decomposition of unpolarized emissivity. Int. J. Remote Sens., 31 , 21092114.

  • Hong, S., 2010b: Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing. Remote Sens. Environ., 114 , 11361140.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2010c: Global retrieval of small-scale roughness over land surfaces at microwave frequency. J. Hydrol., 389 , 121126. doi:10.1016/j.jhydrol.2010.05.036.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2010d: Surface roughness and polarization ratio in microwave remote sensing. Int. J. Remote Sens., 31 , 27092716.

  • Hong, S., , I. Shin, , and M-L. Ou, 2010: Comparison of the Infrared Surface Emissivity Model (ISEM) with a physical emissivity model. J. Atmos. Oceanic Technol., 27 , 345352.

    • Search Google Scholar
    • Export Citation
  • Hwang, B., , and D. G. Barber, 2008: On the impact of ice emissivity on sea ice temperature retrieval using passive microwave radiance data. IEEE Geosci. Remote Sens. Lett., 5 , 448452.

    • Search Google Scholar
    • Export Citation
  • Manninen, A. T., 1997: Surface roughness of Baltic Sea ice. J. Geophys. Res., 102 , 11191139.

  • Markus, T., , and D. J. Cavalieri, 2000: An enhanced NASA team sea ice algorithm. IEEE Trans. Geosci. Remote Sens., 38 , 13871398.

  • Meier, W. N., 2005: Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in arctic peripheral seas. IEEE Trans. Geosci. Remote Sens., 43 , 13241337.

    • Search Google Scholar
    • Export Citation
  • Nishio, F., , and J. C. Comiso, 2005: The polar sea ice cover from Aqua/AMSR-E. Proc. 25th Int. Geoscience and Remote Sensing Symp. (IGARSS), Seoul, South Korea, IEEE, 4933–4937.

    • Search Google Scholar
    • Export Citation
  • Ozsoy-Cicek, B., , H. Xie, , S. F. Ackley, , and K. Ye, 2009: Antarctic summer sea ice concentration and extent: Comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea ice charts. The Cryosphere Discuss., 3 , 19.

    • Search Google Scholar
    • Export Citation
  • Parkinson, C. L., , J. Comiso, , H. J. Zwally, , D. J. Cavalieri, , P. Gloersen, , and W. J. Campbell, 1987: Arctic sea ice, 1973–1976: Satellite passive microwave observations. NASA Spec. Publ., 489 , 296.

    • Search Google Scholar
    • Export Citation
  • Parkinson, C. L., , D. J. Cavalieri, , P. Gloersen, , H. J. Zwally, , and J. C. Comiso, 1999: Variability of the Arctic sea ice cover 1978–1996. J. Geophys. Res., 104 , 2083720856.

    • Search Google Scholar
    • Export Citation
  • Querry, M. R., 1969: Direct solution of the generalized Fresnel reflectance equations. J. Opt. Soc. Amer., 59 , 876877.

  • Sadiku, M. N. O., 1985: Refractive index of snow at microwave frequencies. Appl. Opt., 24 , 571575.

  • Shi, J., , K. Chen, , Q. Li, , T. Jackson, , P. O’Neill, , and L. Tsang, 2002: A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer. IEEE Trans. Geosci. Remote Sens., 40 , 26742686.

    • Search Google Scholar
    • Export Citation
  • Steffen, K., , and A. Schweiger, 1991: NASA team algorithm for sea ice concentration retrieval from Defense Meteorological Satellite Program Special Sensor Microwave Imager: Comparison with Landsat satellite imagery. J. Geophys. Res., 96 , 2197121987.

    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., and Coauthors, 2006: Impact on surface roughness on AMSR-E sea ice products. IEEE Trans. Geosci. Remote Sens., 44 , 31033117.

    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., , and W. L. Chapman, 2001: 20th-Century sea-ice variations from observational data. Ann. Glaciol., 33 , 444448.

  • Worby, A. P., , and J. C. Comiso, 2004: Studies of the Antarctic sea ice edge and sea ice extent from satellite and ship observations. Remote Sens. Environ., 92 , 98111.

    • Search Google Scholar
    • Export Citation
  • Zwally, H. J., , J. C. Comiso, , C. L. Parkinson, , W. J. Campbell, , F. D. Carsey, , and P. Gloersen, 1983: Antarctic sea ice 1973–1976 from satellite passive microwave observations. NASA Spec. Publ., 459 , 206.

    • Search Google Scholar
    • Export Citation
  • Zwally, H. J., , J. C. Comiso, , C. L. Parkinson, , D. J. Cavalieri, , and P. Gloersen, 2002: Variability of Antarctic sea ice 1979–1998. J. Geophys. Res., 107 , 3041.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Example of (a),(b) the CI, (c),(d) the σ, and (e),(f) the real part of n at the AMSR-E 18.7-GHz channel in the Arctic and Antarctic regions on 1 Jan 2007.

  • View in gallery

    Time series of CI. Interannual trends in the (a) Arctic and (b) Antarctic regions. Seasonal variation in the (c) Arctic and (d) Antarctic regions.

  • View in gallery

    As in Fig. 2, but for σ.

  • View in gallery

    As in Fig. 2, but for n.

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Global Trends of Sea Ice: Small-Scale Roughness and Refractive Index

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  • 1 National Meteorological Satellite Center, Korea Meteorological Administration, Jincheon-gun, Chungcheongbuk-do, South Korea
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Abstract

Sea ice is one of the most important parameters in the global climate system, specifically the exchange of energy and momentum between the ocean and the atmosphere. In previous studies, a steady decline in Arctic sea ice has been observed over recent decades. The aim of this study is to estimate and analyze the spatial and temporal characteristics of the averaged sea ice extent, surface roughness, and refractive index from March 2003 to September 2009. A unique inversion algorithm is used for deriving the surface roughness and refractive index from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) daily observations. Surface roughness significantly affects the microwave emission of the sea ice/snow surface. The sea ice, snow, and water show the dielectric contrast in the microwave frequencies. Consequently, the averaged roughness as well as the sea ice extent shows a downward trend, while the averaged refractive index shows the opposite signature. The increased trend of the refractive index in summer on both poles supports more sea ice melting in summer because of the increased temperature. This research can be applied to the climate change studies and supports the previous approaches based on the sea ice concentration in passive microwave remote sensing by the physical explanations.

Corresponding author address: Dr. Sungwook Hong, NMSC/KMA, Jincheon-gun, Chungcheongbuk-do 365-831, South Korea. Email: sesttiya@gmail.com

Abstract

Sea ice is one of the most important parameters in the global climate system, specifically the exchange of energy and momentum between the ocean and the atmosphere. In previous studies, a steady decline in Arctic sea ice has been observed over recent decades. The aim of this study is to estimate and analyze the spatial and temporal characteristics of the averaged sea ice extent, surface roughness, and refractive index from March 2003 to September 2009. A unique inversion algorithm is used for deriving the surface roughness and refractive index from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) daily observations. Surface roughness significantly affects the microwave emission of the sea ice/snow surface. The sea ice, snow, and water show the dielectric contrast in the microwave frequencies. Consequently, the averaged roughness as well as the sea ice extent shows a downward trend, while the averaged refractive index shows the opposite signature. The increased trend of the refractive index in summer on both poles supports more sea ice melting in summer because of the increased temperature. This research can be applied to the climate change studies and supports the previous approaches based on the sea ice concentration in passive microwave remote sensing by the physical explanations.

Corresponding author address: Dr. Sungwook Hong, NMSC/KMA, Jincheon-gun, Chungcheongbuk-do 365-831, South Korea. Email: sesttiya@gmail.com

1. Introduction

Sea ice is one of the most important parameters of the global climate system and covers a significant portion of the global oceans. Sea ice with its snow cover is an effective insulator that limits the exchange of energy and momentum between the ocean and the atmosphere (Comiso et al. 2003). Satellite-board passive microwave sensors (Zwally et al. 1983; Parkinson et al. 1987; Gloersen et al. 1992) have been observing global sea ice comprehensively and consistently. The Advanced Microwave Scanning Radiometer (AMSR) and AMSR for Earth Observing System (EOS; AMSR-E) sensors provide a significant improvement for monitoring the sea ice cover and the baseline for new polar climate datasets and the means to evaluate the quality and consistency of historical satellite data.

Sea ice is an inhomogeneous material consisting of ice, brine, air pockets, and other impurities, the relative percentages of which are different depending on the formation conditions and the history of the ice. Hemispherical differences in environmental conditions make the inhomogeneity of ice in the Arctic region generally different from that in the Antarctic region, leading to differences in the dielectric properties and the emissivity of sea ice in the two regions (Comiso et al. 2003). Sea ice concentration CI is used in characterizing the spatial sea ice cover and extent and in the long-term trend analyses and process studies (Bjørgo et al. 1997; Cavalieri et al. 1997; Parkinson et al. 1999; Zwally et al. 2002).

Four representative algorithms for sea ice studies are bootstrap (Comiso 1986), the calibration/validation (Hollinger 1991), the NASA team (Cavalieri et al. 1994), and the NASA team 2 (Markus and Cavalieri 2000). These algorithms depend on tie points, which are derived on the basis of the assumed pure surface types (e.g., 100% ice or 100% water); these tie points are then used as a baseline for determining the mixture of ice and water within each pixel. In general, the sea ice derived with a microwave imager underestimates the actual sea ice (Walsh and Chapman 2001). The comparison between the bootstrap and the NASA team algorithms showed small disagreements in the central Arctic region in winter but a large disagreement in the seasonal region and in summer, mainly caused by the temperature and emissivity effects (Cho et al. 2007).

Polarization and frequency are used as the sensitive indicators of sea ice concentration, since there exists a large contrast of microwave emissivity between sea ice and water. Other geophysical factors influencing the brightness temperatures of both the sea surface and the sea ice lead to errors of <10% in the estimates of CI and sea ice extent (Steffen and Schweiger 1991). In general, the representative algorithms for sea ice lead to significantly high errors during summer or in the regions of melt, thin ice, and near the ice edge (Meier 2005; Andersen et al. 2007).

Surface roughness measurements of sea ice are rarely published despite the significant effect of the surface roughness on the microwave emission of the sea ice/snow surface (Manninen 1997). Stroeve et al. (2006) investigated the effect of the surface roughness on AMSR-E sea ice products. They found that the sensitivity to small changes in the sensor incidence angle, such as that observed by the Scanning Multichannel Microwave Radiometer (SMMR; 50°), the Special Sensor Microwave Imager (SSM/I; 53°), and AMSR (55°), results in differences as large as 10% in the total ice fraction (between 2% and 5% for thick sea ice with a snow cover), since the horizontally polarized channels are more sensitive than the vertically polarized channels to snow and ice characteristics. Hong (2010b) developed a method to estimate the small-scale roughness σ and the refractive index n of sea ice surfaces using the AMSR-E data.

The objective of this study is to investigate the trends of the surface roughness and the refractive index of sea ice using the AMSR-E observation with as limited use of forward models as possible. In addition, this paper compares the results with the ice concentration and extent on the basis of the previous works.

2. Theoretical background and methods

a. Ice temperature, rough emissivity, and ice concentration

The AMSR-E sensor launched in 2002 measures vertically V and horizontally H polarized radiances at 6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz and basically has the capability of a combined SMMR and SSM/I system but with 2–3 times better spatial resolution. In this study, the AMSR-E daily level-3 25-km brightness temperature TB and ice concentration data product version 2 from March 2003 to September 2009 (Cavalieri et al. 2009) are used for investigating the trends of the ice concentration, small-scale surface roughness, and refractive index of surfaces.

The ice temperature is determined at AMSR-E 6.925 GHz (Comiso et al. 2003). The surface emissivities at other channels are then derived as follows:
i1520-0442-23-17-4669-e1
where CH is the AMSR-E channel and EV,Rough(6.9) is 0.98 for ice (see Markus and Cavalieri 2000) and 0.56 (Hwang and Barber 2008) for ocean.
In this study, the mixed emissivity between the ice and water due to the climate change is defined using the percentage of CI in AMSR-E level-3 data as follows:
i1520-0442-23-17-4669-e2
Among the most basic geophysical cryospheric parameters that are derived from passive microwave data is CI, which has been defined as the percentage fraction of sea ice within the field of view of the sensor. This percentage fraction is calculated using the mixing equation given by Nishio and Comiso (2005)
i1520-0442-23-17-4669-e3
where TI and TO are the brightness temperature of sea ice and open water, respectively, in the region of observation.

The sea ice algorithms are designed to obtain TI and TO as accurately as possible. The equation suggests that data from only one channel is required, but variations in emissivity and temperature make it necessary to use a combination of two or more channels for obtaining accurate retrievals (Gloersen et al. 1992; Comiso et al. 2003). However, different techniques can produce inconsistent results (Comiso and Steffen 2001).

Ice extent is often defined as the sum of the areas of the data elements (pixels) with at least 15% ice concentration, while the ice area is the sum of the products of the area of each pixel and the corresponding ice concentration (Cho et al. 2007). The total sea ice extent is computed by summing the number of pixels with at least 15% ice concentration multiplied by the area of one pixel. The AMSR-E daily level-3 25-km data are used for the calculations.

b. Inverse retrieval method for roughness and refractive index

Any changes in ice or snow properties affect TB (Stroeve et al. 2006). The roughness effect on V and H polarizations are different in both magnitude and direction, depending on the incidence angle, surface roughness, and dielectric properties (Shi et al. 2002). A unique algorithm to detect the surface roughness and the refractive index of sea ice is proposed (Hong 2010b). This algorithm is based on the characteristics of the polarization ratio and the Hong approximation (Hong 2009a,b, 2010a; Hong et al. 2010) as follows (Hong 2010b,d):
i1520-0442-23-17-4669-e4
In addition, the polarized reflectivities for rough and specular surfaces are described using the characteristics of the incidence angle of AMSR-E (Hong 2010b,c,d). Without a priori knowledge of the surface properties such as refractive index, roughness, and heterogeneity, the small-scale surface roughness is estimated as follows (Hong 2010b,c,d):
i1520-0442-23-17-4669-e5
where λ is the wavelength and θ is the AMSR-E incidence angle.

Accordingly, the sea ice roughness can be estimated for AMSR-E 18.7 GHz using Eq. (5). The refractive index is also estimated by using the direct solution of the generalized Fresnel equation (Querry 1969; Armaly et al. 1972; Hong 2010b).

3. Results

Figures 1a and 1b show the sea ice concentrations of the Arctic and Antarctic regions, respectively, on 1 January 2007. The edges of sea ice are melting. According to Stroeve et al. (2006), in a physical sense, heavily ridged ice typically contains areas of exposed ice blocks and areas of deep and layered snow within the ridges, which results in a reduction in the H-polarized emissivity. The scattering from layered snowpack acts to reduce the emissivity at H polarizations. Figures 1c and 1d show the example of the estimated small-scale surface roughness. The average of the surface roughness ranges roughly between 0.2 and 0.6 cm, as estimated by the measurements of the small-scale sea ice surface roughness in the Baltic Sea over a 3-yr period (Manninen 1997). Figures 1e and 1f show the retrieved result for the real part of the refractive index. Typically, the refractive index of ice at 0°C is 1.78 + 0.0024i. The real parts of the refractive indices of dry, moist, and wet snow are approximately 1.016, 1.120, and 1.584, respectively (Sadiku 1985). On the basis of the earlier-mentioned information on the refractive index of ice, snow, and water, the refractive index is retrieved within a reasonable range.

Figures 2a and 2b show the trend of the sea ice extent in the Arctic and the Antarctic regions, respectively. The lines indicate the trends of the reduction rates in the summer, winter, and year, respectively. The sea ice extent shrinks in both the Arctic and the Antarctic regions. The signatures of the sea ice reduction are opposite for the Arctic and the Antarctic regions. We analyze that the sea ice extent in the Arctic region is decreasing in all seasons. However, the sea ice extent in the Antarctic region is increasing overall, although it decreases during the summer. Figures 2c and 2d show the seasonal variations of the sea ice concentration in each year. These figures indicate that the amplitude of the seasonal variation is increasing. This result is in agreement with what the previous investigators have found. Although the extent of the sea ice cover appears to recover each year, it is shrinking.

Figures 3a and 3b show the time series of the averaged roughness using the same spatial and temporal AMSR-E data. The trend shows the reduction signature. The low roughness can be explained physically by the ice/snow melting. From Figs. 3c and 3d, we conclude that the averaged roughness decreases significantly from 0.43 to 0.33 cm during the summer in the Arctic region, while the seasonal variation of roughness ranges from 0.3 to 0.4 cm in the Antarctic region. The rough ice locations have ice concentrations close to 100% (Stroeve et al. 2006). The results show this type of roughness feature, especially during the melting seasons.

Figures 4a and 4b show the trend of the averaged refractive index. The annual trend shows the signature of the increase in the Arctic region, whereas the signature is opposite in the Antarctic region. Both regions show that the averaged refractive index is roughly 1.3 (moist snow) except for the summer season. During the summer, the refractive index increases to between 1.4 and 1.7, corresponding to wet snow or ice. This result shows good agreement with the fact that the surfaces in the Arctic and the Antarctic regions are generally covered by wet snow or slush during the summer (Ozsoy-Cicek et al. 2009). The seasonal variation increases in 2008. Figures 4c and 4d imply that the snow on the sea ice is dry in the Arctic region, whereas it is close to wet snow in the Antarctic region. The increasing trend of the averaged refractive index for both the Arctic and the Antarctic regions in summer is because of the relatively high melting snow and ice due to the increased temperature.

Table 1 summarizes the signatures of seasonal and annual mean trends in the sea ice extent, roughness, and refractive index. As a result, we can conclude that a decline in the sea ice extent accompanies a decline in the mean surface roughness and an increase in the mean refractive index for both the Arctic and the Antarctic surfaces, especially in the summer months.

4. Summary and discussion

Sea ice is one of the most important indicators of the global climate change. Passive satellite microwave remote sensing such as SSM/I and AMSR-E plays a key role in the estimation of the sea ice monitoring. Currently, sea ice concentration is used in characterizing the spatial sea ice cover and extent and in long-term trend analyses and process studies. This study used the roughness and the refractive index of sea ice surface to understand the physical changes of the sea ice cover and for the trend analyses. The AMSR-E daily level-3 25-km brightness temperature data from March 2003 to September 2009 were used.

After calculating the V- and H-polarized emissivity, the roughness and refractive index were estimated using the Hong approximation and the direct inversion of the Fresnel equations. The trends of the averaged values of the sea ice extent, roughness, and refractive index were investigated during the above-mentioned period in the Arctic and the Antarctic regions.

The seasonal and annual trends of sea ice extent agreed with those observed in previous studies. The seasonal and annual downward trends in the mean roughness are also shown, while the opposite trends appear in the mean refractive index. These results support the conclusion that the sea ice is melting.

However, there exists a problem—many values of ice concentration are missed when a 15% “cutoff” is applied (Ozsoy-Cicek et al. 2009). In addition, passive microwave satellite data for a sea ice edge show poor agreement during the melting (summer) season, although passive microwave satellite data for a sea ice edge agree well with the ship observations for the ice growth (winter) season (Worby and Comiso 2004). However, the trend of the averaged roughness and refractive index may not change because of the missing data because the missing data include the low ice concentrations. This means that the low ice concentration implies a low surface roughness and a high refractive index because the physical properties of the missing surfaces are closer to those of water rather than of snow or ice.

This study can support the trends of sea ice reduction due to climate change by physical explanations that are based on the surface roughness and the refractive index. Further, this study can present an alternative to approaching the remaining problem of monitoring and understanding the spatial and temporal variations of the Antarctic sea ice.

Acknowledgments

The authors are thankful for the financial support of the National Meteorological Satellite Center (Project 153-3100-3137-302-210-13) and National Institute of Meteorological Research (Project 153-3100-3136-303).

REFERENCES

  • Andersen, S., , R. Tonboe, , L. Kaleschke, , G. Heygster, , and L. T. Pedersen, 2007: Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice. J. Geophys. Res., 112 , C08004. doi:10.1029/2006JC003543.

    • Search Google Scholar
    • Export Citation
  • Armaly, B. F., , J. G. Ochoa, , and D. C. Look, 1972: Restrictions on the inversion of the Fresnel reflectance equations. Appl. Opt., 11 , 29072910.

    • Search Google Scholar
    • Export Citation
  • Bjørgo, E., , O. M. Johannessen, , and M. W. Miles, 1997: Analysis of merged SSMR-SSM/I time series of Arctic and Antarctic sea ice parameters 1978–1995. Geophys. Res. Lett., 24 , 413416.

    • Search Google Scholar
    • Export Citation
  • Cavalieri, D. J., 1994: A microwave technique for mapping thin sea ice. J. Geophys. Res., 99 , 1256112572.

  • Cavalieri, D. J., , P. Gloersen, , C. L. Parkinson, , J. C. Comiso, , and H. J. Zwally, 1997: Observed hemispheric asymmetry in global sea ice changes. Science, 272 , 11041106.

    • Search Google Scholar
    • Export Citation
  • Cavalieri, D. J., , M. Thorsten, , and J. C. Comiso, cited. 2009: AMSR-E/Aqua daily L3 25 km brightness temperature & sea ice concentration polar grids V002. [Available online at http://nsidc.org/data/docs/daac/ae_si25_25km_tb_and_sea_ice.gd.html].

    • Search Google Scholar
    • Export Citation
  • Cho, K., , A. Komaki, , and H. Shimoda, 2007: Comparison of passive microwave sea ice algorithms for detecting the trends of global warming. Proc. 28th Asian Conf. on Remote Sensing (ACRS 2007), Kuala Lampur, Malaysia, Asian Association on Remote Sensing, PL2. [Available online at http://www.a-a-r-s.org/acrs/proceeding/ACRS2007/Papers/PL2.pdf].

    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., 1986: Characteristics of Arctic winter sea ice from satellite multispectral microwave observations. J. Geophys. Res., 91 , 975994.

    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., , and K. Steffen, 2001: Studies of Antarctic sea ice concentrations from satellite observations and their applications. J. Geophys. Res., 106 , 3136131385.

    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., , D. J. Cavalieri, , and T. Markus, 2003: Sea ice concentration, ice temperature, and snow depth using AMSR-E data. IEEE Trans. Geosci. Remote Sens., 41 , 243252.

    • Search Google Scholar
    • Export Citation
  • Gloersen, P., , W. J. Campbell, , D. J. Cavalieri, , J. C. Comiso, , C. L. Parkinson, , and H. J. Zwally, 1992: Arctic and Antarctic sea ice, 1978–1987: Satellite passive microwave observations and analysis. NASA Spec. Publ., 511 , 290.

    • Search Google Scholar
    • Export Citation
  • Hollinger, J. P., 1991: DMSP special sensor microwave/imager calibration/validation. U.S. Naval Research Lab Final Rep. A626472, 314 pp.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2009a: Detection of Asian dust (Hwangsa) over the Yellow Sea by decomposition of unpolarized infrared reflectivity. Atmos. Environ., 43 , 58875893.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2009b: Retrieval of refractive index over specular surfaces for remote sensing applications. J. Appl. Remote Sens., 3 , 033560. doi:10.1117/1.3265997.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2010a: Decomposition of unpolarized emissivity. Int. J. Remote Sens., 31 , 21092114.

  • Hong, S., 2010b: Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing. Remote Sens. Environ., 114 , 11361140.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2010c: Global retrieval of small-scale roughness over land surfaces at microwave frequency. J. Hydrol., 389 , 121126. doi:10.1016/j.jhydrol.2010.05.036.

    • Search Google Scholar
    • Export Citation
  • Hong, S., 2010d: Surface roughness and polarization ratio in microwave remote sensing. Int. J. Remote Sens., 31 , 27092716.

  • Hong, S., , I. Shin, , and M-L. Ou, 2010: Comparison of the Infrared Surface Emissivity Model (ISEM) with a physical emissivity model. J. Atmos. Oceanic Technol., 27 , 345352.

    • Search Google Scholar
    • Export Citation
  • Hwang, B., , and D. G. Barber, 2008: On the impact of ice emissivity on sea ice temperature retrieval using passive microwave radiance data. IEEE Geosci. Remote Sens. Lett., 5 , 448452.

    • Search Google Scholar
    • Export Citation
  • Manninen, A. T., 1997: Surface roughness of Baltic Sea ice. J. Geophys. Res., 102 , 11191139.

  • Markus, T., , and D. J. Cavalieri, 2000: An enhanced NASA team sea ice algorithm. IEEE Trans. Geosci. Remote Sens., 38 , 13871398.

  • Meier, W. N., 2005: Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in arctic peripheral seas. IEEE Trans. Geosci. Remote Sens., 43 , 13241337.

    • Search Google Scholar
    • Export Citation
  • Nishio, F., , and J. C. Comiso, 2005: The polar sea ice cover from Aqua/AMSR-E. Proc. 25th Int. Geoscience and Remote Sensing Symp. (IGARSS), Seoul, South Korea, IEEE, 4933–4937.

    • Search Google Scholar
    • Export Citation
  • Ozsoy-Cicek, B., , H. Xie, , S. F. Ackley, , and K. Ye, 2009: Antarctic summer sea ice concentration and extent: Comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea ice charts. The Cryosphere Discuss., 3 , 19.

    • Search Google Scholar
    • Export Citation
  • Parkinson, C. L., , J. Comiso, , H. J. Zwally, , D. J. Cavalieri, , P. Gloersen, , and W. J. Campbell, 1987: Arctic sea ice, 1973–1976: Satellite passive microwave observations. NASA Spec. Publ., 489 , 296.

    • Search Google Scholar
    • Export Citation
  • Parkinson, C. L., , D. J. Cavalieri, , P. Gloersen, , H. J. Zwally, , and J. C. Comiso, 1999: Variability of the Arctic sea ice cover 1978–1996. J. Geophys. Res., 104 , 2083720856.

    • Search Google Scholar
    • Export Citation
  • Querry, M. R., 1969: Direct solution of the generalized Fresnel reflectance equations. J. Opt. Soc. Amer., 59 , 876877.

  • Sadiku, M. N. O., 1985: Refractive index of snow at microwave frequencies. Appl. Opt., 24 , 571575.

  • Shi, J., , K. Chen, , Q. Li, , T. Jackson, , P. O’Neill, , and L. Tsang, 2002: A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer. IEEE Trans. Geosci. Remote Sens., 40 , 26742686.

    • Search Google Scholar
    • Export Citation
  • Steffen, K., , and A. Schweiger, 1991: NASA team algorithm for sea ice concentration retrieval from Defense Meteorological Satellite Program Special Sensor Microwave Imager: Comparison with Landsat satellite imagery. J. Geophys. Res., 96 , 2197121987.

    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., and Coauthors, 2006: Impact on surface roughness on AMSR-E sea ice products. IEEE Trans. Geosci. Remote Sens., 44 , 31033117.

    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., , and W. L. Chapman, 2001: 20th-Century sea-ice variations from observational data. Ann. Glaciol., 33 , 444448.

  • Worby, A. P., , and J. C. Comiso, 2004: Studies of the Antarctic sea ice edge and sea ice extent from satellite and ship observations. Remote Sens. Environ., 92 , 98111.

    • Search Google Scholar
    • Export Citation
  • Zwally, H. J., , J. C. Comiso, , C. L. Parkinson, , W. J. Campbell, , F. D. Carsey, , and P. Gloersen, 1983: Antarctic sea ice 1973–1976 from satellite passive microwave observations. NASA Spec. Publ., 459 , 206.

    • Search Google Scholar
    • Export Citation
  • Zwally, H. J., , J. C. Comiso, , C. L. Parkinson, , D. J. Cavalieri, , and P. Gloersen, 2002: Variability of Antarctic sea ice 1979–1998. J. Geophys. Res., 107 , 3041.

    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

Example of (a),(b) the CI, (c),(d) the σ, and (e),(f) the real part of n at the AMSR-E 18.7-GHz channel in the Arctic and Antarctic regions on 1 Jan 2007.

Citation: Journal of Climate 23, 17; 10.1175/2010JCLI3697.1

Fig. 2.
Fig. 2.

Time series of CI. Interannual trends in the (a) Arctic and (b) Antarctic regions. Seasonal variation in the (c) Arctic and (d) Antarctic regions.

Citation: Journal of Climate 23, 17; 10.1175/2010JCLI3697.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for σ.

Citation: Journal of Climate 23, 17; 10.1175/2010JCLI3697.1

Fig. 4.
Fig. 4.

As in Fig. 2, but for n.

Citation: Journal of Climate 23, 17; 10.1175/2010JCLI3697.1

Table 1.

Trends of CI, σ, and n in the Arctic and Antarctic regions.

Table 1.
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