2018 Vol. 40, No. 11
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2018, 40(11): 1-13.
doi: 10.3969/j.issn.0253-4193.2018.11.001
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The changes of the Arctic sea ice affect the global material balance, energy exchange and climate change. Here we analyzed the seasonal and interannual variation of sea ice area, thickness and volume in the Arctic during 2010-2017 based on CryoSat-2 measurements and OSI SAF sea ice concentration and sea ice type products. We discussed the effects of sea surface air temperature anomalies and summer wind field anomalies on Arctic sea ice combined with the NCEP/NCAR reanalysis data. The results show that the increase of sea ice area has large fluctuations during freezing period, while the increase of thickness shows declined tendency. The decrease of sea ice thickness fluctuates during melting period, while the decrease of sea ice area accelerates year by year after 2013. The change trend of sea ice volume is more similar to that of sea ice area, and the reduced rate of melting season is larger than the increased rate of freezing season. The atmospheric temperature anomaly of the Arctic sea surface during the melting season is positively correlated with the amount of sea ice melting. The summer wind field affects the convergence and divergence of sea ice and plays an important role in the transport of sea ice in the Fram Strait. It promotes the transport of warm surface water in the Arctic Ocean to the deep ocean.
The changes of the Arctic sea ice affect the global material balance, energy exchange and climate change. Here we analyzed the seasonal and interannual variation of sea ice area, thickness and volume in the Arctic during 2010-2017 based on CryoSat-2 measurements and OSI SAF sea ice concentration and sea ice type products. We discussed the effects of sea surface air temperature anomalies and summer wind field anomalies on Arctic sea ice combined with the NCEP/NCAR reanalysis data. The results show that the increase of sea ice area has large fluctuations during freezing period, while the increase of thickness shows declined tendency. The decrease of sea ice thickness fluctuates during melting period, while the decrease of sea ice area accelerates year by year after 2013. The change trend of sea ice volume is more similar to that of sea ice area, and the reduced rate of melting season is larger than the increased rate of freezing season. The atmospheric temperature anomaly of the Arctic sea surface during the melting season is positively correlated with the amount of sea ice melting. The summer wind field affects the convergence and divergence of sea ice and plays an important role in the transport of sea ice in the Fram Strait. It promotes the transport of warm surface water in the Arctic Ocean to the deep ocean.
2018, 40(11): 14-22.
doi: 10.3969/j.issn.0253-4193.2018.11.002
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Arctic sea ice is undergoing a rapid decline, and the Arctic sea ice volume is an important indicator of global climate change. Sea ice thickness data from two kinds of satellite altimetry data (ICESat and CryoSat-2), together with sea ice concentration data and sea ice age data derived from space borne radiometers, the total and the first-year/multi-year Arctic sea ice volume from 2003 to 2013 were estimated. Comparison between the ICESat period from 2003 to 2008 and the CryoSat-2 period from 2011 to 2013, the Arctic sea ice volume had been decreased by 1 426 km3 and 412 km3 in autumn (October-November) and winter (February-March), respectively. These changes are mainly caused by significant reduction of the multi-year sea ice volume in these ten years, the ice volume in autumn and winter had been decreased by 2 108 km3 and 3 206 km3, respectively. Therefore, the dramatic depletion of multi-year sea ice is the main factor of causing Arctic sea ice volume loss.
Arctic sea ice is undergoing a rapid decline, and the Arctic sea ice volume is an important indicator of global climate change. Sea ice thickness data from two kinds of satellite altimetry data (ICESat and CryoSat-2), together with sea ice concentration data and sea ice age data derived from space borne radiometers, the total and the first-year/multi-year Arctic sea ice volume from 2003 to 2013 were estimated. Comparison between the ICESat period from 2003 to 2008 and the CryoSat-2 period from 2011 to 2013, the Arctic sea ice volume had been decreased by 1 426 km3 and 412 km3 in autumn (October-November) and winter (February-March), respectively. These changes are mainly caused by significant reduction of the multi-year sea ice volume in these ten years, the ice volume in autumn and winter had been decreased by 2 108 km3 and 3 206 km3, respectively. Therefore, the dramatic depletion of multi-year sea ice is the main factor of causing Arctic sea ice volume loss.
2018, 40(11): 23-32.
doi: 10.3969/j.issn.0253-4193.2018.11.003
Abstract:
Thermal conductivity of snow (ks) is an important physical parameter for sea ice thermodynamics, which controls the conductive heat flux through the ice. The winter temperature profiles from ice mass balance buoys (IMB) on Arctic perennial sea ice can clearly distinguish the snow-ice interface. Considering the temporal variation of the temperature near the snow-ice interface, a new method for determining the ks was proposed by exploiting the continuity of the heat flux at the snow-ice interface. Influenced by different circumstance, the ks on different IMB ranged from 0.23 W/(m·K) to 0.41 W/(m·K), with a mean value of (0.32±0.08) W/(m·K). Maximum conductive heat flux through perennial sea ice occurred from November to March, about 14 W/m2 to 16 W/m2. In freezing season, the contribution of the specific heat flux from ice cooling in the upward heat lose through ice to atmosphere decreased gradually, from 100% in September to 35% in December, and maintained around 10% from January to March. In summer time, as the heating from ice surface to bottom, temperature of the sea ice upper layer was higher than the lower layer, and the conductive heat transferred downward. Until the solar radiation disappeared in September, air temperature decreased, the conductive heat transferred upward again. As the ice bottom conductive heat flux revealed, a portion of heat transferred from ice to ice-ocean interface in summer time. The low thermal conductivity of snow made it an effective insulator thereby impacting the growth and decay of the underlying sea ice, as well as reducing the transfer of heat between the ocean and atmosphere. The snow covered sea ice upper layer conductive heat flux still showed good relationship with air temperature. For every 1℃ decrease in air temperature, the conductive heat flux increased 0.59 W/m2.
Thermal conductivity of snow (ks) is an important physical parameter for sea ice thermodynamics, which controls the conductive heat flux through the ice. The winter temperature profiles from ice mass balance buoys (IMB) on Arctic perennial sea ice can clearly distinguish the snow-ice interface. Considering the temporal variation of the temperature near the snow-ice interface, a new method for determining the ks was proposed by exploiting the continuity of the heat flux at the snow-ice interface. Influenced by different circumstance, the ks on different IMB ranged from 0.23 W/(m·K) to 0.41 W/(m·K), with a mean value of (0.32±0.08) W/(m·K). Maximum conductive heat flux through perennial sea ice occurred from November to March, about 14 W/m2 to 16 W/m2. In freezing season, the contribution of the specific heat flux from ice cooling in the upward heat lose through ice to atmosphere decreased gradually, from 100% in September to 35% in December, and maintained around 10% from January to March. In summer time, as the heating from ice surface to bottom, temperature of the sea ice upper layer was higher than the lower layer, and the conductive heat transferred downward. Until the solar radiation disappeared in September, air temperature decreased, the conductive heat transferred upward again. As the ice bottom conductive heat flux revealed, a portion of heat transferred from ice to ice-ocean interface in summer time. The low thermal conductivity of snow made it an effective insulator thereby impacting the growth and decay of the underlying sea ice, as well as reducing the transfer of heat between the ocean and atmosphere. The snow covered sea ice upper layer conductive heat flux still showed good relationship with air temperature. For every 1℃ decrease in air temperature, the conductive heat flux increased 0.59 W/m2.
2018, 40(11): 33-45.
doi: 10.3969/j.issn.0253-4193.2018.11.004
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In recent years, there emerges a phenomenon that Central Arctic should experience anomalous low sea ice concentration. To analyze explicitly the causes, the Low Concentration in Central Arctic (LCCA) index is defined by using the ERA-Interim reanalysis data. Within the period of June to September from 2009 to 2016, there were 6 cases recognized as the peaks of LCCA index. The results show that the leading factor of low sea ice concentration is not the local thermal condition. Dynamically, the drifting pattern of sea ice and the location of region with low sea ice concentration response consistently to the atmospheric circulation. Particularly, cyclones used to be found north of 70°N before the 6 peaks of LCCA index occurred. These cyclones moved towards north with hot air from lower latitudes causing divergence and rapid melting of sea ice. In 3 cases of 6, cyclones were accompanied with Dipole Anomaly (DA) pattern. LCCA index correlates positively with northward heat advection across the circle of 84°N as well as the divergence of Central Arctic sea ice. Before the LCCA peak days, the northward heat advection has greater effects on sea ice than the dynamic divergence.
In recent years, there emerges a phenomenon that Central Arctic should experience anomalous low sea ice concentration. To analyze explicitly the causes, the Low Concentration in Central Arctic (LCCA) index is defined by using the ERA-Interim reanalysis data. Within the period of June to September from 2009 to 2016, there were 6 cases recognized as the peaks of LCCA index. The results show that the leading factor of low sea ice concentration is not the local thermal condition. Dynamically, the drifting pattern of sea ice and the location of region with low sea ice concentration response consistently to the atmospheric circulation. Particularly, cyclones used to be found north of 70°N before the 6 peaks of LCCA index occurred. These cyclones moved towards north with hot air from lower latitudes causing divergence and rapid melting of sea ice. In 3 cases of 6, cyclones were accompanied with Dipole Anomaly (DA) pattern. LCCA index correlates positively with northward heat advection across the circle of 84°N as well as the divergence of Central Arctic sea ice. Before the LCCA peak days, the northward heat advection has greater effects on sea ice than the dynamic divergence.
2018, 40(11): 46-53.
doi: 10.3969/j.issn.0253-4193.2018.11.005
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In this study, we evaluated the 24-120 h Arctic sea ice concentration forecasts provided by National Marine Environmental Forecasting Center during the 7th Chinese National Arctic Research Expedition (CHINARE 2016). The Arctic sea ice forecast system was based on the MIT general circulation model (MITgcm) ice-ocean coupled model with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data Nudged. We compared the numerical forecast products with the satellite data, reanalysis data and ship-based in situ sea ice concentration observations during CHINARE 2016. It was shown that the Arctic sea ice concentration forecasts were smaller than the satellite data. The mean biases between 24 h, 72 h, 120 h forecasts and satellite data were -2.7%, -3.1% and -3.2%. The numerical sea ice concentration forecasts were better than the climatological means and the inertial forecasts. But the forecast skill was required to improve when the Arctic sea ice had surged rapid melting or freezing. Moreover, the forecast biases were larger compared with ship in situ observations in the marginal ice zone. The mean biases between 24 h, 72 h, 120 h forecasts and ship in situ data were 8.8%, 12.0% and 14.5%.
In this study, we evaluated the 24-120 h Arctic sea ice concentration forecasts provided by National Marine Environmental Forecasting Center during the 7th Chinese National Arctic Research Expedition (CHINARE 2016). The Arctic sea ice forecast system was based on the MIT general circulation model (MITgcm) ice-ocean coupled model with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data Nudged. We compared the numerical forecast products with the satellite data, reanalysis data and ship-based in situ sea ice concentration observations during CHINARE 2016. It was shown that the Arctic sea ice concentration forecasts were smaller than the satellite data. The mean biases between 24 h, 72 h, 120 h forecasts and satellite data were -2.7%, -3.1% and -3.2%. The numerical sea ice concentration forecasts were better than the climatological means and the inertial forecasts. But the forecast skill was required to improve when the Arctic sea ice had surged rapid melting or freezing. Moreover, the forecast biases were larger compared with ship in situ observations in the marginal ice zone. The mean biases between 24 h, 72 h, 120 h forecasts and ship in situ data were 8.8%, 12.0% and 14.5%.
2018, 40(11): 54-63.
doi: 10.3969/j.issn.0253-4193.2018.11.006
Abstract:
In summer 2017, for the first time, the Chinese R/V Xuelong successfully passed through the Central Arctic Passage (CAP) during the Chinese National Arctic Research Expedition (CHINARE 2017), the ship-based sea ice observations were carried out during this cruise. The results showed that the CAP was mainly occupied by thick first-year ice, the average sea ice concentration (SIC) and thickness along the CAP were 0.64 and 1.5 m, respectively; the ice floes in the central Arctic Ocean are significantly larger than the sea ice edge area. The 5 commonly used passive microwave satellite retrieved SIC datasets with a spatial resolution higher than 10 km were inter-compared and assessed using the ship-based SIC. The point to point comparison showed the AMSR2 SIC datasets (Bootstrap algorithm) released by University of Bremen had the largest bias and rms (root mean square) values with 0.19 and 0.28, while the AMSR2 SIC datasets (OSHD and TUD algorithm, respectively) released by Ocean and Sea Ice Satellite Application Facility (OSI SAF) were with the smallest bias of -0.02 and 0.01, and the rms values were both 0.20. The daily mean comparison showed that the AMSR2 SIC dataset (Bootstrap algorithm) released by University of Bremen and the AMSR2/OSI SAF (TUD) dataset had the largest (0.15 and 0.20) and smallest (0.0 and 0.11) mean bias and rms values, respectively.
In summer 2017, for the first time, the Chinese R/V Xuelong successfully passed through the Central Arctic Passage (CAP) during the Chinese National Arctic Research Expedition (CHINARE 2017), the ship-based sea ice observations were carried out during this cruise. The results showed that the CAP was mainly occupied by thick first-year ice, the average sea ice concentration (SIC) and thickness along the CAP were 0.64 and 1.5 m, respectively; the ice floes in the central Arctic Ocean are significantly larger than the sea ice edge area. The 5 commonly used passive microwave satellite retrieved SIC datasets with a spatial resolution higher than 10 km were inter-compared and assessed using the ship-based SIC. The point to point comparison showed the AMSR2 SIC datasets (Bootstrap algorithm) released by University of Bremen had the largest bias and rms (root mean square) values with 0.19 and 0.28, while the AMSR2 SIC datasets (OSHD and TUD algorithm, respectively) released by Ocean and Sea Ice Satellite Application Facility (OSI SAF) were with the smallest bias of -0.02 and 0.01, and the rms values were both 0.20. The daily mean comparison showed that the AMSR2 SIC dataset (Bootstrap algorithm) released by University of Bremen and the AMSR2/OSI SAF (TUD) dataset had the largest (0.15 and 0.20) and smallest (0.0 and 0.11) mean bias and rms values, respectively.
2018, 40(11): 64-72.
doi: 10.3969/j.issn.0253-4193.2018.11.007
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An arctic sea ice concentration dataset was operationally produced in National Satellite Meteorological Center (NSMC) using FY-3 data and Nasa Team2 algorithm. Currently, there are several long time series sea ice concentration data sets are operational produced in different organizations. Several typical operational data sets were compared with the FY-3 sea ice concentration data set include:(1) 1978-present global SIC data set using SSM/I, SSMIS data and Nasa Team (NT) algorithm from National Snow and Ice Data Center (NSIDC); (2) 1978-present global SIC data set using SSM/I, SSMIS data and Boot Strap (BS) algorithm from NSIDC; (3) 2004-present northern hemisphere sea ice coverage data (IMS) set using multi-remote sensing data, ship observation and model result from National Oceanic and Atmospheric Administration. Analysis results show that there are deviation between different data set in north polar area. Based on the higher resolution data set (IMS), we compared the other 3 kinds of data sets, the total biases are more than 1 million km2. For NT2 data set, there is a significant overestimate. After the bias correction, the overestimate of NT2 was improved. The further bias analysis found that most of the overestimated area of NT, BS, NT2 compared with IMS was located in land/sea border area. For both overestimated area and underestimated, summer error is larger than winter error. For the total bias, NT2 have the lowest value while BS have the highest value separately. Results show that the FY-3 Arctic sea ice concentration dataset have a similar accuracy with other operational sea ice concentration datasets.
An arctic sea ice concentration dataset was operationally produced in National Satellite Meteorological Center (NSMC) using FY-3 data and Nasa Team2 algorithm. Currently, there are several long time series sea ice concentration data sets are operational produced in different organizations. Several typical operational data sets were compared with the FY-3 sea ice concentration data set include:(1) 1978-present global SIC data set using SSM/I, SSMIS data and Nasa Team (NT) algorithm from National Snow and Ice Data Center (NSIDC); (2) 1978-present global SIC data set using SSM/I, SSMIS data and Boot Strap (BS) algorithm from NSIDC; (3) 2004-present northern hemisphere sea ice coverage data (IMS) set using multi-remote sensing data, ship observation and model result from National Oceanic and Atmospheric Administration. Analysis results show that there are deviation between different data set in north polar area. Based on the higher resolution data set (IMS), we compared the other 3 kinds of data sets, the total biases are more than 1 million km2. For NT2 data set, there is a significant overestimate. After the bias correction, the overestimate of NT2 was improved. The further bias analysis found that most of the overestimated area of NT, BS, NT2 compared with IMS was located in land/sea border area. For both overestimated area and underestimated, summer error is larger than winter error. For the total bias, NT2 have the lowest value while BS have the highest value separately. Results show that the FY-3 Arctic sea ice concentration dataset have a similar accuracy with other operational sea ice concentration datasets.
2018, 40(11): 73-84.
doi: 10.3969/j.issn.0253-4193.2018.11.008
Abstract:
The Gravity Recovery and Climate Experiment (GRACE) mission launched in 2002 provided a new opportunity to estimate the global mass variations with high temporal-spatial resolution. We use the GRACE RL05 data from January 2003 to December 2014 to estimate the Greenland ice mass variations. We applied 500 km Gaussian smoothing, a decorrelation filtering and a forward modelling to reduce the land-ocean leakage effects to obtain the time series of Greenland's ice sheet mass, analyzes the long-term trend of Greenland ice sheet, and compared with the ICESat results. Results show that the mass change rate of the Greenland ice sheet from January 2003 to December 2014 is (-260±43) Gt/a, equivalent to (0.72±0.12) mm/a of the global sea level change, which occupied the 25.8% contribution to sea level rise. And the Greenland ice sheet melting has a very strong regional differences, mainly concentrated in the edge of the area, while for the central region, the central inland ice sheet has a tendency to increase. And we further compared with the ICESat result to verify our results. The result of the ICESat show hat the mass change rate of the Greenland ice sheet is from (-174±43) Gt/a to (-184.8±28.2) Gt/a, and the result of GRACE is about (-209.4±26.3) Gt/a, and have a good consistency, and the characteristics of regional distribution have also a good agreement.
The Gravity Recovery and Climate Experiment (GRACE) mission launched in 2002 provided a new opportunity to estimate the global mass variations with high temporal-spatial resolution. We use the GRACE RL05 data from January 2003 to December 2014 to estimate the Greenland ice mass variations. We applied 500 km Gaussian smoothing, a decorrelation filtering and a forward modelling to reduce the land-ocean leakage effects to obtain the time series of Greenland's ice sheet mass, analyzes the long-term trend of Greenland ice sheet, and compared with the ICESat results. Results show that the mass change rate of the Greenland ice sheet from January 2003 to December 2014 is (-260±43) Gt/a, equivalent to (0.72±0.12) mm/a of the global sea level change, which occupied the 25.8% contribution to sea level rise. And the Greenland ice sheet melting has a very strong regional differences, mainly concentrated in the edge of the area, while for the central region, the central inland ice sheet has a tendency to increase. And we further compared with the ICESat result to verify our results. The result of the ICESat show hat the mass change rate of the Greenland ice sheet is from (-174±43) Gt/a to (-184.8±28.2) Gt/a, and the result of GRACE is about (-209.4±26.3) Gt/a, and have a good consistency, and the characteristics of regional distribution have also a good agreement.
Land water and glaciers contributions to global sea level change from satellite gravity measurements
2018, 40(11): 85-95.
doi: 10.3969/j.issn.0253-4193.2018.11.009
Abstract:
The Gravity Recovery and Climate Experiment (GRACE) satellite mission launched in 2002 provided an opportunity to estimate the global land and ocean water mass variations with high temporal-spatial resolution. In this paper, we use the GRACE RL05 data from January 2003 to December 2014 to estimate the ocean mass variations. We applied 500 km Gaussian smoothing, a decorrelation filtering and a forward modelling to reduce the land-ocean leakage effects. Land water and glaciers contributions to global sea level change are investigated. Results show that the long-term trend of the mass-induced sea level variations is (2.09±0.54) mm/a, which has a good agreement with the steric sea level change of (2.07±0.62) mm/a from the satellite altimetry and Argo data. The contribution of land water to sea level change is (0.15±0.25) mm/a. The glacier melting contribution to sea level rise is (0.72±0.12) mm/a in Greenland, (0.59±0.10) mm/a in Antarctica, and (0.63±0.09) mm/a for the mountain glaciers (including Alaska, Iceland, Canadian Arctic, High Mountain Asia and Patagonia). Furthermore, the impact of the GRACE gravity field coefficients from different GRACE analysis centers (CSR, JPL and GFZ), first-order coefficient and the second-order coefficient to sea level change are discussed. The impact of first-order coefficient to the mass-induced sea level variations is (0.10±0.08) mm/a, and the second-order coefficient to the mass-induced sea level variations is (0.16±0.04) mm/a. The results from CSR are consistent with GFZ results, while the JPL's results are slightly smaller.
The Gravity Recovery and Climate Experiment (GRACE) satellite mission launched in 2002 provided an opportunity to estimate the global land and ocean water mass variations with high temporal-spatial resolution. In this paper, we use the GRACE RL05 data from January 2003 to December 2014 to estimate the ocean mass variations. We applied 500 km Gaussian smoothing, a decorrelation filtering and a forward modelling to reduce the land-ocean leakage effects. Land water and glaciers contributions to global sea level change are investigated. Results show that the long-term trend of the mass-induced sea level variations is (2.09±0.54) mm/a, which has a good agreement with the steric sea level change of (2.07±0.62) mm/a from the satellite altimetry and Argo data. The contribution of land water to sea level change is (0.15±0.25) mm/a. The glacier melting contribution to sea level rise is (0.72±0.12) mm/a in Greenland, (0.59±0.10) mm/a in Antarctica, and (0.63±0.09) mm/a for the mountain glaciers (including Alaska, Iceland, Canadian Arctic, High Mountain Asia and Patagonia). Furthermore, the impact of the GRACE gravity field coefficients from different GRACE analysis centers (CSR, JPL and GFZ), first-order coefficient and the second-order coefficient to sea level change are discussed. The impact of first-order coefficient to the mass-induced sea level variations is (0.10±0.08) mm/a, and the second-order coefficient to the mass-induced sea level variations is (0.16±0.04) mm/a. The results from CSR are consistent with GFZ results, while the JPL's results are slightly smaller.
2018, 40(11): 96-104.
doi: 10.3969/j.issn.0253-4193.2018.11.010
Abstract:
Based on the data of the ERA-Interim mean sea level pressure field in the European Center for Medium-Range Weather Forecasts, this paper uses the cyclone automatic identification and tracking algorithm to establish the cyclone data from July to October in the east and west of the Northwest Passage of the Arctic from 1979 to 2015. The data include cyclone basic latitude and longitude position information and the cyclone center minimum pressure value. Based on this set of data, this paper analyzes the climatological characteristics, spatial density distribution, cyclone intensity characteristics, cyclone deepening and the activity of explosive cyclones over the Northwest Passage from July to October. The number of cyclones at the east and west region of the Northwest Passage is significantly different. The number of cyclones at the east is more than twice that of the west, and the trend of the number of cyclones at both regions is inconsistent. The trend of the number of cyclones at the west region shows an insignificant decrease but an insignificant increase in east region. The overall cyclone intensity of the northwest channel was weak. The cyclone with the lowest atmospheric pressure reaching 980 hPa accounted for only 5% of the total number of cyclones. The lowest center of cyclone pressure is concentrated between 990-1 000 hPa. Since 1979, the overall trend of cyclone intensity in the eastern region has increased, and the cyclone in the western region also significantly increased before 2002, but the overall cyclone intensity in the western region turned weak after 2002. Cyclone life history concentrated in less than 7 days, the eastern cyclone within a day significantly more than the number of cyclones. The western region of the density distribution of cyclones is mainly distributed in the north of Beaufort Sea north of 74°N. The eastern region is mainly distributed in the northeast of Baffin Bay and the southeast of Baffin Island. In recent years, there has been a slight shift in the eastward and westward shifts of the main density distribution area of the cyclone. The growth of explosive cyclones in the Northwest Passage is concentrated near 70°N coasts, especially in northern Canada and near the west coast of Greenland. The North Atlantic Oscillation Index is significantly positively correlated with the number of cyclones in the eastern region.
Based on the data of the ERA-Interim mean sea level pressure field in the European Center for Medium-Range Weather Forecasts, this paper uses the cyclone automatic identification and tracking algorithm to establish the cyclone data from July to October in the east and west of the Northwest Passage of the Arctic from 1979 to 2015. The data include cyclone basic latitude and longitude position information and the cyclone center minimum pressure value. Based on this set of data, this paper analyzes the climatological characteristics, spatial density distribution, cyclone intensity characteristics, cyclone deepening and the activity of explosive cyclones over the Northwest Passage from July to October. The number of cyclones at the east and west region of the Northwest Passage is significantly different. The number of cyclones at the east is more than twice that of the west, and the trend of the number of cyclones at both regions is inconsistent. The trend of the number of cyclones at the west region shows an insignificant decrease but an insignificant increase in east region. The overall cyclone intensity of the northwest channel was weak. The cyclone with the lowest atmospheric pressure reaching 980 hPa accounted for only 5% of the total number of cyclones. The lowest center of cyclone pressure is concentrated between 990-1 000 hPa. Since 1979, the overall trend of cyclone intensity in the eastern region has increased, and the cyclone in the western region also significantly increased before 2002, but the overall cyclone intensity in the western region turned weak after 2002. Cyclone life history concentrated in less than 7 days, the eastern cyclone within a day significantly more than the number of cyclones. The western region of the density distribution of cyclones is mainly distributed in the north of Beaufort Sea north of 74°N. The eastern region is mainly distributed in the northeast of Baffin Bay and the southeast of Baffin Island. In recent years, there has been a slight shift in the eastward and westward shifts of the main density distribution area of the cyclone. The growth of explosive cyclones in the Northwest Passage is concentrated near 70°N coasts, especially in northern Canada and near the west coast of Greenland. The North Atlantic Oscillation Index is significantly positively correlated with the number of cyclones in the eastern region.
2018, 40(11): 105-115.
doi: 10.3969/j.issn.0253-4193.2018.11.011
Abstract:
Satellite remote sensing is an important method to study the distribution and variation of sea surface winds and ocean waves in the Arctic seas. Based on remote sensing data of the orbiting multi-source satellites, the observation capability of the sea surface wind and the ocean wave in the Arctic Ocean is analyzed from three aspects:spatial coverage of remote sensing observations, time coverage and remote sensing data merging. It is concluded as follows. The ASCAT and HY-2A scatterometers can be used for sea surface wind remote sensing observation in the Arctic seas and the multi-satellite joint observation can obtain the sea surface wind remote sensing data with the spatial and temporal resolution of better than 12 hours and 0.1° in the Arctic Ocean. Based on the HY-2A, CryoSat-2, SARAL and Sentinel-3 altimeters, the remote sensing observations of the Arctic seas waves can be realized. The multi-satellite joint observations can obtain the ocean wave remote sensing data of the spatial and temporal resolution of 1 day and 0.25° in the Arctic seas. Based on sea surface wind and ocean wave fusion data in 2016, it is concluded that sea surface wind and ocean wave in the Arctic seas are high from January to March and then decrease to the minimum in July, then gradually increase. The results show that sea surface wind and ocean wave in the Arctic seas can be monitored by multi-source scatterometers and altimeters with high spatial and temporal resolution.
Satellite remote sensing is an important method to study the distribution and variation of sea surface winds and ocean waves in the Arctic seas. Based on remote sensing data of the orbiting multi-source satellites, the observation capability of the sea surface wind and the ocean wave in the Arctic Ocean is analyzed from three aspects:spatial coverage of remote sensing observations, time coverage and remote sensing data merging. It is concluded as follows. The ASCAT and HY-2A scatterometers can be used for sea surface wind remote sensing observation in the Arctic seas and the multi-satellite joint observation can obtain the sea surface wind remote sensing data with the spatial and temporal resolution of better than 12 hours and 0.1° in the Arctic Ocean. Based on the HY-2A, CryoSat-2, SARAL and Sentinel-3 altimeters, the remote sensing observations of the Arctic seas waves can be realized. The multi-satellite joint observations can obtain the ocean wave remote sensing data of the spatial and temporal resolution of 1 day and 0.25° in the Arctic seas. Based on sea surface wind and ocean wave fusion data in 2016, it is concluded that sea surface wind and ocean wave in the Arctic seas are high from January to March and then decrease to the minimum in July, then gradually increase. The results show that sea surface wind and ocean wave in the Arctic seas can be monitored by multi-source scatterometers and altimeters with high spatial and temporal resolution.
2018, 40(11): 116-127.
doi: 10.3969/j.issn.0253-4193.2018.11.012
Abstract:
In this paper, SST data of AMSR2, GMI, WindSat and HY-2A RM in 2016 are used to analyze the space-time coverage and the accuracy of remote sensing SST in the Arctic. The results show that, the space borne microwave radiometer SST retrievals coverage rate and effective coverage days in winter are lower than that in summer, and the effective SST coverage rate of GMI is lower, and AMSR2 is higher. When AMSR2, GMI, WindSat and HY-2A RM space borne microwave radiometer SST data are combined used, the SST coverage rate can be between 12%-15% in February, and the number of effective observation days is better than 26 days. The SST coverage rate is higher than 26% in the whole August, and the number of effective observation days is better than 29 days. The error of the space borne microwave radiometer SST data in the Arctic is larger than that of the global average. The accuracy of AMSR2 data is the best one, the accuracy of WindSat data is close to that of AMSR2. The RMSE of GMI SST is about 2 times larger than AMSR2, and the accuracy of HY-2A RM data is lower than that of any other space borne microwave radiometer.
In this paper, SST data of AMSR2, GMI, WindSat and HY-2A RM in 2016 are used to analyze the space-time coverage and the accuracy of remote sensing SST in the Arctic. The results show that, the space borne microwave radiometer SST retrievals coverage rate and effective coverage days in winter are lower than that in summer, and the effective SST coverage rate of GMI is lower, and AMSR2 is higher. When AMSR2, GMI, WindSat and HY-2A RM space borne microwave radiometer SST data are combined used, the SST coverage rate can be between 12%-15% in February, and the number of effective observation days is better than 26 days. The SST coverage rate is higher than 26% in the whole August, and the number of effective observation days is better than 29 days. The error of the space borne microwave radiometer SST data in the Arctic is larger than that of the global average. The accuracy of AMSR2 data is the best one, the accuracy of WindSat data is close to that of AMSR2. The RMSE of GMI SST is about 2 times larger than AMSR2, and the accuracy of HY-2A RM data is lower than that of any other space borne microwave radiometer.
2018, 40(11): 128-140.
doi: 10.3969/j.issn.0253-4193.2018.11.013
Abstract:
The detection limit of satellite chlorophyll algorithm at large solar zenith angles (SZA) in polar regions was assessed using the vector radiative transfer model of PCOART-SA which has accounted for the spherical-shell atmosphere. It was found that the geometric parameters between sun and sensor, particularly the solar zenith angle, have significant influence in the detection limit of satellite chlorophyll algorithm. The minimum chlorophyll concentration detected by satellite is about 0.136 μg/L at large SZA of 80°, while the minimum value is 0.012 8 μg/L at 30°. Because of the high absorption resulted by chlorophyll, the satellite detection at large SZA is difficult, and thus requires high radiometric sensitivity sensor, more accurate calibration and atmospheric correction.
The detection limit of satellite chlorophyll algorithm at large solar zenith angles (SZA) in polar regions was assessed using the vector radiative transfer model of PCOART-SA which has accounted for the spherical-shell atmosphere. It was found that the geometric parameters between sun and sensor, particularly the solar zenith angle, have significant influence in the detection limit of satellite chlorophyll algorithm. The minimum chlorophyll concentration detected by satellite is about 0.136 μg/L at large SZA of 80°, while the minimum value is 0.012 8 μg/L at 30°. Because of the high absorption resulted by chlorophyll, the satellite detection at large SZA is difficult, and thus requires high radiometric sensitivity sensor, more accurate calibration and atmospheric correction.
2018, 40(11): 141-148.
doi: 10.3969/j.issn.0253-4193.2018.11.014
Abstract:
In this paper, an iceberg detection method based on image blocks is proposed using iterative CFAR algorithm. Considering the large computational burden and low computational efficiency of sliding windows, the SAR image is blocked first to detect iceberg shown as bright objects in blocks. The Gauss model is used to characterize the statistical distribution of backscatter coefficient. The iceberg detection threshold can be simply expressed as a linear combination of mean and variance (μ+nσ), compared with which the iceberg pixels are detected. Considering the impact of large size icebergs in the same scene, the identified iceberg pixels as seeds are grown to detect large size icebergs. In order to reduce the error of Gauss model to characterize the block statistical distribution and improve the accuracy of iceberg detection, iterative processing is done for a single block. The method is validated by two RADARSAT-2 images acquired on November 22 and 29, 2013 in polar sea area. The results show that these icebergs with large number, size change and embedded into, which is common on the poles, can be effectively detected by the method in this paper, the accuracy rate is more than 85%, and it has high operation efficiency.
In this paper, an iceberg detection method based on image blocks is proposed using iterative CFAR algorithm. Considering the large computational burden and low computational efficiency of sliding windows, the SAR image is blocked first to detect iceberg shown as bright objects in blocks. The Gauss model is used to characterize the statistical distribution of backscatter coefficient. The iceberg detection threshold can be simply expressed as a linear combination of mean and variance (μ+nσ), compared with which the iceberg pixels are detected. Considering the impact of large size icebergs in the same scene, the identified iceberg pixels as seeds are grown to detect large size icebergs. In order to reduce the error of Gauss model to characterize the block statistical distribution and improve the accuracy of iceberg detection, iterative processing is done for a single block. The method is validated by two RADARSAT-2 images acquired on November 22 and 29, 2013 in polar sea area. The results show that these icebergs with large number, size change and embedded into, which is common on the poles, can be effectively detected by the method in this paper, the accuracy rate is more than 85%, and it has high operation efficiency.
2018, 40(11): 149-156.
doi: 10.3969/j.issn.0253-4193.2018.11.015
Abstract:
The classification of sea ice is one of the most important applications in the field of remote sensing monitoring, and its accuracy is of great significance in assessing the ice conditions, ensuring the safety of navigation and opening up the Arctic channel. In order to solve the sea ice classification problems, this paper proposed an improved SAR sea ice classification method, which used Sentinel-1 data and texture feature analysis. In this method, the gray level co-occurrence matrix (GLCM) was used to extract the eigenvalue, and the suitable of texture features for sea ice classification was obtained, then we used support vector machine to carried out sea ice classification. The experimental results showed that the proposed method can recognize three types of ice, which are first year ice, multiyear ice and open water. Compared with the traditional methods of Neural Net and Maximum Likelihood, it is feasible to use SVM classification method and texture feature to monitor sea ice type. It also showed that multi-feature is helpful to improve the classification accuracy of SAR image, which verifies the effectiveness of this method and provides a new idea for sea ice classification.
The classification of sea ice is one of the most important applications in the field of remote sensing monitoring, and its accuracy is of great significance in assessing the ice conditions, ensuring the safety of navigation and opening up the Arctic channel. In order to solve the sea ice classification problems, this paper proposed an improved SAR sea ice classification method, which used Sentinel-1 data and texture feature analysis. In this method, the gray level co-occurrence matrix (GLCM) was used to extract the eigenvalue, and the suitable of texture features for sea ice classification was obtained, then we used support vector machine to carried out sea ice classification. The experimental results showed that the proposed method can recognize three types of ice, which are first year ice, multiyear ice and open water. Compared with the traditional methods of Neural Net and Maximum Likelihood, it is feasible to use SVM classification method and texture feature to monitor sea ice type. It also showed that multi-feature is helpful to improve the classification accuracy of SAR image, which verifies the effectiveness of this method and provides a new idea for sea ice classification.
2018, 40(11): 157-165.
doi: 10.3969/j.issn.0253-4193.2018.11.016
Abstract:
The Polar Prediction Project (PPP, 2013-2022) and Year of Polar Prediction (YOPP, mid-2017 to mid-2019) were initiated by the World Meteorological Organization (WMO), to effectively tackle the challenges of global climate change and polar warming. Eight key research goals, e.g., user applications and societal benefit, verification, observations, modelling, data assimilation, ensemble forecasting, predictability and forecast error diagnosis, global linkages, as well as the selected activities were discussed. Suggestions related to the in-situ observation, numerical prediction and information service, are also given to the Chinese polar research community to well take this opportunity of the PPP/YOPP implementation.
The Polar Prediction Project (PPP, 2013-2022) and Year of Polar Prediction (YOPP, mid-2017 to mid-2019) were initiated by the World Meteorological Organization (WMO), to effectively tackle the challenges of global climate change and polar warming. Eight key research goals, e.g., user applications and societal benefit, verification, observations, modelling, data assimilation, ensemble forecasting, predictability and forecast error diagnosis, global linkages, as well as the selected activities were discussed. Suggestions related to the in-situ observation, numerical prediction and information service, are also given to the Chinese polar research community to well take this opportunity of the PPP/YOPP implementation.
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