Antarctic summer sea ice concentration and extent: comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea ice charts

Özsoy, Burcu; Xie, Hongjie; Ackley, S. F.; Ye, K.

doi:10.5194/tc-3-1-2009

Cited by 24 publications

(23 citation statements)

References 14 publications

(18 reference statements)

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“…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.…”

Section: Resultssupporting

confidence: 81%

“…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).…”

Section: Summary and Discussionmentioning

confidence: 99%

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

Hong

Shin

2010

Journal of Climate

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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.

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Section: Resultssupporting

confidence: 81%

Section: Summary and Discussionmentioning

confidence: 99%

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

Hong

Shin

2010

Journal of Climate

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“…During summer, a considerable fraction of the sea ice might be too wet to be recognized as ice by satellite microwave radiometry -causing an underestimation as well (see e.g. Worby and Comiso, 2004;Ozsoy-Cicek et al, 2009). In addition, low ice concentrations are often filtered by the weather filters applied (see Sect.…”

Section: Observed Differences To Ship-based Observationsmentioning

confidence: 99%

Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

et al. 2019

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Abstract. We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from −0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and −3.7 % (ASI), but similar for the Antarctic, −5.4 % and −5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends.

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“…Early satellite missions have also provided snapshot estimates of SIE since 1964 (Meier et al, 2013). However, the ability of these proxy and historical records to quantitatively estimate changes in SIE has been questioned Ackley et al (2003); Ozsoy-Cicek et al (2009). For example, Ozsoy-Cicek et al (2009) showed that the bias between the definition of the ice edge derived from ship logs compared to that derived from satellite data could be more than 10 6 km 2 during some seasons.…”

Section: Long-term Trends From Historical and Proxy Sourcesmentioning

confidence: 99%

“…However, the ability of these proxy and historical records to quantitatively estimate changes in SIE has been questioned Ackley et al (2003); Ozsoy-Cicek et al (2009). For example, Ozsoy-Cicek et al (2009) showed that the bias between the definition of the ice edge derived from ship logs compared to that derived from satellite data could be more than 10 6 km 2 during some seasons. Care must clearly be taken in comparing proxy and historical records with modern satellite observations, but as the only available information on longer time scales, these records comprise a valuable data set for putting recent changes in SIE into a longer-term context.…”

Section: Long-term Trends From Historical and Proxy Sourcesmentioning

confidence: 99%

Understanding the Seasonal Cycle of Antarctic Sea Ice Extent in the Context of Longer‐Term Variability

Eayrs

Holland

Francis

et al. 2019

Reviews of Geophysics

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Over the 40‐year satellite record, there has been a slight increasing trend in total annual mean Antarctic sea ice extent of approximately 1.5% per decade that is made up of the sum of significantly larger opposing regional trends. However, record increases in total Antarctic sea ice extent were observed during 2012–2014, followed by record lows (for the satellite era) through 2018. There is still no consensus on the main drivers of these trends, but it is generally believed that the atmosphere plays a significant role and that seasonal time scales and regional scale processes are important. Despite considerable yearly and regional variability, the mean seasonal cycle of growth and melt of Antarctic sea ice is strikingly consistent, with a slow growth but fast melt season. If we are to project trends in Antarctic sea ice and understand changes on longer time scales, we need to understand the mechanisms related to the seasonal cycle separately from those that drive variability. Twice‐yearly changes in the position and intensity of the zonal winds circling Antarctica are thought to drive the system by working with or against the evolving sea ice edge to slow the autumn advance and hasten the spring melt. Open water regions, created by divergence associated with the zonal winds, amplify the spring melt through increased warming of the upper ocean. Climate models fail to accurately reproduce mean Antarctic sea ice extent and overestimate its year‐to‐year variability, but they tend to capture the pattern and timing of the Antarctic seasonal cycle.

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Antarctic summer sea ice concentration and extent: comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea ice charts

Abstract: Abstract. Antarctic sea ice cover has shown a slight increase (

Cited by 24 publications

References 14 publications

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

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

Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

Understanding the Seasonal Cycle of Antarctic Sea Ice Extent in the Context of Longer‐Term Variability

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