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We created, for the first time, a map of sea ice production in the Bering Sea, based on thin-ice thickness data from the Advanced Microwave Scanning Radiometers (AMSR-E and AMSR2) with a heat flux calculation. We used the AMSR-E thin-ice algorithm developed for the Arctic Ocean with some modification. We provided a 16-yr data set of ice production from the 2002/2003 to 2018/2019 seasons, excepting the 2011/2012 season. It is found that the Anadyr polynya has by far the highest sea ice production (average of 93 km(3)/yr) and accounts for more than 30% of all polynya ice production in the Bering Sea. The combined ice production in the Anadyr, Anadyr Strait, and St. Lawrence polynyas becomes the second-largest ice production during the AMSR-E period in the Northern Hemisphere. It is considered that the high ice production in the Anadyr polynya produces cold, saline, nutrient-rich water, so-called Anadyr Water, which would contribute to the formation of the cold halocline layer and high biological productivity. The ice production in the Anadyr polynya shows very large year-to-year variability. The record low ice extent year of the 2017/2018 season is also the lowest ice production year; the production is only one tenth of the highest value, observed during the 2015/2016 season. The high sensitivity of the wind direction and strength to the location of the Aleutian Low causes this large variability. We also built reconstruction schemes of ice production in the polynyas, using the offshore wind and air temperature, by multiple linear regression.

We created, for the first time, a map of sea ice production in the Bering Sea, based on thin-ice thickness data from the Advanced Microwave Scanning Radiometers (AMSR-E and AMSR2) with a heat flux calculation. We used the AMSR-E thin-ice algorithm developed for the Arctic Ocean with some modification. We provided a 16-yr data set of ice production from the 2002/2003 to 2018/2019 seasons, excepting the 2011/2012 season. It is found that the Anadyr polynya has by far the highest sea ice production (average of 93 km(3)/yr) and accounts for more than 30% of all polynya ice production in the Bering Sea. The combined ice production in the Anadyr, Anadyr Strait, and St. Lawrence polynyas becomes the second-largest ice production during the AMSR-E period in the Northern Hemisphere. It is considered that the high ice production in the Anadyr polynya produces cold, saline, nutrient-rich water, so-called Anadyr Water, which would contribute to the formation of the cold halocline layer and high biological productivity. The ice production in the Anadyr polynya shows very large year-to-year variability. The record low ice extent year of the 2017/2018 season is also the lowest ice production year; the production is only one tenth of the highest value, observed during the 2015/2016 season. The high sensitivity of the wind direction and strength to the location of the Aleutian Low causes this large variability. We also built reconstruction schemes of ice production in the polynyas, using the offshore wind and air temperature, by multiple linear regression.

We created, for the first time, a map of sea ice production in the Bering Sea, based on thin-ice thickness data from the Advanced Microwave Scanning Radiometers (AMSR-E and AMSR2) with a heat flux calculation. We used the AMSR-E thin-ice algorithm developed for the Arctic Ocean with some modification. We provided a 16-yr data set of ice production from the 2002/2003 to 2018/2019 seasons, excepting the 2011/2012 season. It is found that the Anadyr polynya has by far the highest sea ice production (average of 93 km(3)/yr) and accounts for more than 30% of all polynya ice production in the Bering Sea. The combined ice production in the Anadyr, Anadyr Strait, and St. Lawrence polynyas becomes the second-largest ice production during the AMSR-E period in the Northern Hemisphere. It is considered that the high ice production in the Anadyr polynya produces cold, saline, nutrient-rich water, so-called Anadyr Water, which would contribute to the formation of the cold halocline layer and high biological productivity. The ice production in the Anadyr polynya shows very large year-to-year variability. The record low ice extent year of the 2017/2018 season is also the lowest ice production year; the production is only one tenth of the highest value, observed during the 2015/2016 season. The high sensitivity of the wind direction and strength to the location of the Aleutian Low causes this large variability. We also built reconstruction schemes of ice production in the polynyas, using the offshore wind and air temperature, by multiple linear regression.

Sea-ice thickness in the Sea ofOkhotsk is estimated for 2004-2008 from ICESat derived free-board under the assumption of hydrostatic balance. Total ice thickness including snow depth (htot) averaged over 2004-2008 is 95 cm. The interannual variability of htot is large; from 77.5 cm (2008) to 110.4 cm (2005). The mode of htot varies from 50-60 cm (2007 and 2008) to 70-80 cm (2005). Ice thickness derived from ICESat data is validated from a comparison with that observed by Electromagnetic Induction Instrument (EM) aboard the icebreaker Soya near Hokkaido, Japan. Annual maps of htot reveal that the spatial distribution of h(tot) is similar every year. Ice volume of 6.3 x 10(11) m(3) is estimated from the ICESat derived h(tot) and AMSR-E derived ice concentration. A comparison with ice area demonstrates that the ice volume cannot always be represented by the area solely, despite the fact that the area has been used as a proxy of the volume in the Sea of Okhotsk. The ice volume roughly corresponds to that of annual ice production in the major coastal polynyas estimated based on heat budget calculations. This also supports the validity of the estimation of sea-ice thickness and volume using ICESat data.

Sea-ice thickness in the Sea ofOkhotsk is estimated for 2004-2008 from ICESat derived free-board under the assumption of hydrostatic balance. Total ice thickness including snow depth (htot) averaged over 2004-2008 is 95 cm. The interannual variability of htot is large; from 77.5 cm (2008) to 110.4 cm (2005). The mode of htot varies from 50-60 cm (2007 and 2008) to 70-80 cm (2005). Ice thickness derived from ICESat data is validated from a comparison with that observed by Electromagnetic Induction Instrument (EM) aboard the icebreaker Soya near Hokkaido, Japan. Annual maps of htot reveal that the spatial distribution of h(tot) is similar every year. Ice volume of 6.3 x 10(11) m(3) is estimated from the ICESat derived h(tot) and AMSR-E derived ice concentration. A comparison with ice area demonstrates that the ice volume cannot always be represented by the area solely, despite the fact that the area has been used as a proxy of the volume in the Sea of Okhotsk. The ice volume roughly corresponds to that of annual ice production in the major coastal polynyas estimated based on heat budget calculations. This also supports the validity of the estimation of sea-ice thickness and volume using ICESat data.

Sea-ice thickness in the Sea ofOkhotsk is estimated for 2004-2008 from ICESat derived free-board under the assumption of hydrostatic balance. Total ice thickness including snow depth (htot) averaged over 2004-2008 is 95 cm. The interannual variability of htot is large; from 77.5 cm (2008) to 110.4 cm (2005). The mode of htot varies from 50-60 cm (2007 and 2008) to 70-80 cm (2005). Ice thickness derived from ICESat data is validated from a comparison with that observed by Electromagnetic Induction Instrument (EM) aboard the icebreaker Soya near Hokkaido, Japan. Annual maps of htot reveal that the spatial distribution of h(tot) is similar every year. Ice volume of 6.3 x 10(11) m(3) is estimated from the ICESat derived h(tot) and AMSR-E derived ice concentration. A comparison with ice area demonstrates that the ice volume cannot always be represented by the area solely, despite the fact that the area has been used as a proxy of the volume in the Sea of Okhotsk. The ice volume roughly corresponds to that of annual ice production in the major coastal polynyas estimated based on heat budget calculations. This also supports the validity of the estimation of sea-ice thickness and volume using ICESat data.

Antarctic coastal polynyas are very high sea-ice production areas. The resultant large amount of brine rejection leads to the formation of dense water. The dense water forms Antarctic bottom water, which is the densest water in the global overturning circulation and a key player in climate change as a significant sink for heat and carbon dioxide. In this study, an algorithm was developed that uses Advanced Microwave Scanning Radiometer 2 (AMSR2) data (2012-present) to detect polynya area and estimates thin ice thickness by a method similar to that used to develop the algorithm for Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data. Landfast sea-ice areas were also detected using AMSR2 data. Ice production in the polynyas was estimated by a heat flux calculation using AMSR2 sea-ice data. In four major polynyas, AMSR2 ice production was compared with AMSR-E (2003-2011) ice production through comparison of them with Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) ice production. The comparison confirmed that the ice production from AMSR-E/2 data, which have higher spatial resolution than SSM/I-SSMIS data, can be used to analyze time series covering more than 10 years. For example, maps of annual ice production based on AMSR-E/2 data revealed detailed changes of the Mertz Polynya, where the ice production decreased significantly after the Mertz Glacier Tongue calving in 2010. Continuous monitoring of the coastal polynyas by the AMSR series sensors is essential for climate-change-related analyses in the Antarctic Ocean.

Antarctic coastal polynyas are very high sea-ice production areas. The resultant large amount of brine rejection leads to the formation of dense water. The dense water forms Antarctic bottom water, which is the densest water in the global overturning circulation and a key player in climate change as a significant sink for heat and carbon dioxide. In this study, an algorithm was developed that uses Advanced Microwave Scanning Radiometer 2 (AMSR2) data (2012-present) to detect polynya area and estimates thin ice thickness by a method similar to that used to develop the algorithm for Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data. Landfast sea-ice areas were also detected using AMSR2 data. Ice production in the polynyas was estimated by a heat flux calculation using AMSR2 sea-ice data. In four major polynyas, AMSR2 ice production was compared with AMSR-E (2003-2011) ice production through comparison of them with Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) ice production. The comparison confirmed that the ice production from AMSR-E/2 data, which have higher spatial resolution than SSM/I-SSMIS data, can be used to analyze time series covering more than 10 years. For example, maps of annual ice production based on AMSR-E/2 data revealed detailed changes of the Mertz Polynya, where the ice production decreased significantly after the Mertz Glacier Tongue calving in 2010. Continuous monitoring of the coastal polynyas by the AMSR series sensors is essential for climate-change-related analyses in the Antarctic Ocean.

Time series ice-draft data were obtained from moored ice-profiling sonar (IPS), in the coastal northeastern Chukchi Sea during 2009/10. Time series data show seasonal growth of sea-ice draft, occasionally interrupted by coastal polynya. The sea-ice draft distribution indicates a slightly lower abundance of thick, deformed ice compared with the eastern Beaufort Sea. In January, a rapid increase in the abundance of thick ice coincided with a period of minimal drift indicating compaction again the coast and dynamical thickening. The overall mean draft and corresponding derived thickness are 1.27 and 1.38 m, respectively. The evolution of modal ice thickness observed can be explained mostly by thermodynamic growth. The derived ice thicknesses are used to estimate heat losses based on ERA-interim data. Heat losses from the raw, 1 s IPS data are ∼50 and 100% greater than those calculated using IPS data averaged over spatial scales of ∼20 and 100 km, respectively. This finding demonstrates the importance of subgrid-scale ice-thickness distribution for heat-loss calculation. The heat-loss estimate based on thin ice data derived from AMSR-E data corresponds well with that from the 1 s observed ice-thickness data, validating heat-loss estimates from the AMSR-E thin ice-thickness algorithm.

Antarctic coastal polynyas are very high sea-ice production areas. The resultant large amount of brine rejection leads to the formation of dense water. The dense water forms Antarctic bottom water, which is the densest water in the global overturning circulation and a key player in climate change as a significant sink for heat and carbon dioxide. In this study, an algorithm was developed that uses Advanced Microwave Scanning Radiometer 2 (AMSR2) data (2012-present) to detect polynya area and estimates thin ice thickness by a method similar to that used to develop the algorithm for Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data. Landfast sea-ice areas were also detected using AMSR2 data. Ice production in the polynyas was estimated by a heat flux calculation using AMSR2 sea-ice data. In four major polynyas, AMSR2 ice production was compared with AMSR-E (2003-2011) ice production through comparison of them with Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) ice production. The comparison confirmed that the ice production from AMSR-E/2 data, which have higher spatial resolution than SSM/I-SSMIS data, can be used to analyze time series covering more than 10 years. For example, maps of annual ice production based on AMSR-E/2 data revealed detailed changes of the Mertz Polynya, where the ice production decreased significantly after the Mertz Glacier Tongue calving in 2010. Continuous monitoring of the coastal polynyas by the AMSR series sensors is essential for climate-change-related analyses in the Antarctic Ocean.

Sinking of dense water from Antarctic coastal polynyas produces Antarctic Bottom Water (AABW), which is the densest water in the global overturning circulation and is a key player in climate change as a significant sink for heat and carbon dioxide. Very recent studies have suggested that landfast sea ice (fast ice) plays an important role in the formation and variability of the polynyas and possibly AABW. However, they have been limited to regional and case investigations only. This study provides the first coincident circumpolar mapping of Antarctic coastal polynyas and fast ice. The map reveals that most of the polynyas are formed on the western side of fast ice, indicating an important role of fast ice in the polynya formation. Winds diverging from a boundary comprising both coastline and fast ice are the primary determinant of polynya formation. The blocking effect of fast ice on westward sea ice advection by the coastal current would be another key factor. These effects on the variability in sea ice production for 13 major polynyas are evaluated quantitatively. Furthermore, it is demonstrated that a drastic change in fast ice extent, which is particularly vulnerable to climate change, causes dramatic changes in the polynyas and possibly AABW formation that can potentially contribute to further climate change. These results suggest that fast ice and precise polynya processes should be addressed by next-generation models to produce more accurate climate projections. This study provides the boundary and validation data of fast ice and sea ice production for such models.

Sinking of dense water from Antarctic coastal polynyas produces Antarctic Bottom Water (AABW), which is the densest water in the global overturning circulation and is a key player in climate change as a significant sink for heat and carbon dioxide. Very recent studies have suggested that landfast sea ice (fast ice) plays an important role in the formation and variability of the polynyas and possibly AABW. However, they have been limited to regional and case investigations only. This study provides the first coincident circumpolar mapping of Antarctic coastal polynyas and fast ice. The map reveals that most of the polynyas are formed on the western side of fast ice, indicating an important role of fast ice in the polynya formation. Winds diverging from a boundary comprising both coastline and fast ice are the primary determinant of polynya formation. The blocking effect of fast ice on westward sea ice advection by the coastal current would be another key factor. These effects on the variability in sea ice production for 13 major polynyas are evaluated quantitatively. Furthermore, it is demonstrated that a drastic change in fast ice extent, which is particularly vulnerable to climate change, causes dramatic changes in the polynyas and possibly AABW formation that can potentially contribute to further climate change. These results suggest that fast ice and precise polynya processes should be addressed by next-generation models to produce more accurate climate projections. This study provides the boundary and validation data of fast ice and sea ice production for such models.

Sinking of dense water from Antarctic coastal polynyas produces Antarctic Bottom Water (AABW), which is the densest water in the global overturning circulation and is a key player in climate change as a significant sink for heat and carbon dioxide. Very recent studies have suggested that landfast sea ice (fast ice) plays an important role in the formation and variability of the polynyas and possibly AABW. However, they have been limited to regional and case investigations only. This study provides the first coincident circumpolar mapping of Antarctic coastal polynyas and fast ice. The map reveals that most of the polynyas are formed on the western side of fast ice, indicating an important role of fast ice in the polynya formation. Winds diverging from a boundary comprising both coastline and fast ice are the primary determinant of polynya formation. The blocking effect of fast ice on westward sea ice advection by the coastal current would be another key factor. These effects on the variability in sea ice production for 13 major polynyas are evaluated quantitatively. Furthermore, it is demonstrated that a drastic change in fast ice extent, which is particularly vulnerable to climate change, causes dramatic changes in the polynyas and possibly AABW formation that can potentially contribute to further climate change. These results suggest that fast ice and precise polynya processes should be addressed by next-generation models to produce more accurate climate projections. This study provides the boundary and validation data of fast ice and sea ice production for such models.

The formation of Antarctic Bottom Water-the cold, dense water that occupies the abyssal layer of the global ocean-is a key process in global ocean circulation. This water mass is formed as dense shelf water sinks to depth. Three regions around Antarctica where this process takes place have been previously documented. The presence of another source has been identified in hydrographic and tracer data, although the site of formation is not well constrained. Here we document the formation of dense shelf water in the Cape Darnley polynya (65 degrees -69 degrees E) and its subsequent transformation into bottom water using data from moorings and instrumented elephant seals (Mirounga leonina). Unlike the previously identified sources of Antarctic Bottom Water, which require the presence of an ice shelf or a large storage volume, bottom water production at the Cape Darnley polynya is driven primarily by the flux of salt released by sea-ice formation. We estimate that about 0.3-0.7 x 10(6) m(3) s(-1) of dense shelf water produced by the Cape Darnley polynya is transformed into Antarctic BottomWater. The transformation of this water mass, which we term Cape Darnley BottomWater, accounts for 6-13% of the circumpolar total., AuthorCount:13

From comparisons with thickness of sea ice from AVHRR and ice-profiling sonar data, we have developed an AMSR-E thin ice thickness algorithm for the Sea of Okhotsk. This algorithm can estimate ice thickness of ≤0.2 m without snow using the polarization ratio of AMSR-E brightness temperature at a 36.5 GHz channel from a linear relationship with AVHRR ice thickness. When a snow cover exists on the thin ice surface, as occurred a few times in each winter, it is shown that the algorithm cannot detect the thin ice. Sea-ice and dense shelf water (DSW) production in coastal polynya are estimated based on heat flux calculation with the daily AMSR-E ice thickness for 3 winters (December-March) of 2002/2003 - 2004/2005. The ice production is largest in the northwest shelf (NWS) polynya which accounts for ～45% of the sum of ice production in major coastal polynyas. The ice production in major coastal polynyas would cover the maximum ice area of the Okhotsk Sea if the average ice thickness is assumed to be 1 m. Variability of the ice production is mainly modulated by air temperature. In the NWS polynya, which is the main DSW production area, the annual DSW formation rate is estimated to be ～0.36 Sv.

From comparisons with thickness of sea ice from AVHRR and ice-profiling sonar data, we have developed an AMSR-E thin ice thickness algorithm for the Sea of Okhotsk. This algorithm can estimate ice thickness of ≤0.2 m without snow using the polarization ratio of AMSR-E brightness temperature at a 36.5 GHz channel from a linear relationship with AVHRR ice thickness. When a snow cover exists on the thin ice surface, as occurred a few times in each winter, it is shown that the algorithm cannot detect the thin ice. Sea-ice and dense shelf water (DSW) production in coastal polynya are estimated based on heat flux calculation with the daily AMSR-E ice thickness for 3 winters (December-March) of 2002/2003 - 2004/2005. The ice production is largest in the northwest shelf (NWS) polynya which accounts for ～45% of the sum of ice production in major coastal polynyas. The ice production in major coastal polynyas would cover the maximum ice area of the Okhotsk Sea if the average ice thickness is assumed to be 1 m. Variability of the ice production is mainly modulated by air temperature. In the NWS polynya, which is the main DSW production area, the annual DSW formation rate is estimated to be ～0.36 Sv.

From comparisons with thickness of sea ice from AVHRR and ice-profiling sonar data, we have developed an AMSR-E thin ice thickness algorithm for the Sea of Okhotsk. This algorithm can estimate ice thickness of ≤0.2 m without snow using the polarization ratio of AMSR-E brightness temperature at a 36.5 GHz channel from a linear relationship with AVHRR ice thickness. When a snow cover exists on the thin ice surface, as occurred a few times in each winter, it is shown that the algorithm cannot detect the thin ice. Sea-ice and dense shelf water (DSW) production in coastal polynya are estimated based on heat flux calculation with the daily AMSR-E ice thickness for 3 winters (December-March) of 2002/2003 - 2004/2005. The ice production is largest in the northwest shelf (NWS) polynya which accounts for ～45% of the sum of ice production in major coastal polynyas. The ice production in major coastal polynyas would cover the maximum ice area of the Okhotsk Sea if the average ice thickness is assumed to be 1 m. Variability of the ice production is mainly modulated by air temperature. In the NWS polynya, which is the main DSW production area, the annual DSW formation rate is estimated to be ～0.36 Sv.