2015 Vol. 37, No. 11
Display Method:
2015, 37(11): .
Abstract:
2015, 37(11): 1-10.
doi: 10.3969/j.issn.0253-4193.2015.11.001
Abstract:
Over the past ten years sea ice changes have been the main feature in the Arctic rapidly changing. However, the ocean plays a key role in the ice-sea-air change system. The Arctic Ocean not only affects the ice melting and freezing processes, but also is the main source of energy of the atmospheric change. In the context of rapid changes in Arctic sea ice, the Arctic Ocean feature has also undergone a series of changes. After the Fourth International Polar Year, Chinese has made a series of scientific research in the Arctic physical oceanography. In this paper Arctic water masses, fronts, currents, the structure of the upper ocean and other major hydrological phenomena were summed up during the 2011-2014.
Over the past ten years sea ice changes have been the main feature in the Arctic rapidly changing. However, the ocean plays a key role in the ice-sea-air change system. The Arctic Ocean not only affects the ice melting and freezing processes, but also is the main source of energy of the atmospheric change. In the context of rapid changes in Arctic sea ice, the Arctic Ocean feature has also undergone a series of changes. After the Fourth International Polar Year, Chinese has made a series of scientific research in the Arctic physical oceanography. In this paper Arctic water masses, fronts, currents, the structure of the upper ocean and other major hydrological phenomena were summed up during the 2011-2014.
2015, 37(11): 11-22.
doi: 10.3969/j.issn.0253-4193.2015.11.002
Abstract:
The change of Arctic sea ice change rapidly mainly occurred in the edge of central Arctic region in recent years. The edge area of the central Arctic is tending to be covered by seasonal ice which was dominated by multiyear ice in the past. The analysis in this paper shows that the first two EOF modes of seasonal sea ice anomaly mainly shows the characteristics of sea ice of year 2007 and 2005. The second mode mainly reflects an extreme change of Arctic sea ice in 2005, while the first mode not only reflects the change of Arctic sea ice in 2007, but also reflects a phase shift of seasonal sea ice in winter during 2002-2010. During the study period, the seasonal sea ice variability mainly occurs in the Pacific sector of the Arctic Ocean. The phase of seasonal sea ice anomaly is negative before 2007 and shifts to positive after 2007, and the positive phase continues to 2010. The maximum anomalies of surface temperature in Pacific sector occur in 2007. And the anomaly high air pressure in Beaufort Sea region helps to reduce the summer sea ice in the Pacific sector, and the weakened westerly jet is in favor of positive anomaly high air pressure in Beaufort Sea region in summer and autumn. Also the clockwise ice velocity distribution is in favor of ice leaving from the Pacific sector, which will lead to the positive seasonal ice anomalies in winter maintained from 2007 to 2010 in Pacific sector.
The change of Arctic sea ice change rapidly mainly occurred in the edge of central Arctic region in recent years. The edge area of the central Arctic is tending to be covered by seasonal ice which was dominated by multiyear ice in the past. The analysis in this paper shows that the first two EOF modes of seasonal sea ice anomaly mainly shows the characteristics of sea ice of year 2007 and 2005. The second mode mainly reflects an extreme change of Arctic sea ice in 2005, while the first mode not only reflects the change of Arctic sea ice in 2007, but also reflects a phase shift of seasonal sea ice in winter during 2002-2010. During the study period, the seasonal sea ice variability mainly occurs in the Pacific sector of the Arctic Ocean. The phase of seasonal sea ice anomaly is negative before 2007 and shifts to positive after 2007, and the positive phase continues to 2010. The maximum anomalies of surface temperature in Pacific sector occur in 2007. And the anomaly high air pressure in Beaufort Sea region helps to reduce the summer sea ice in the Pacific sector, and the weakened westerly jet is in favor of positive anomaly high air pressure in Beaufort Sea region in summer and autumn. Also the clockwise ice velocity distribution is in favor of ice leaving from the Pacific sector, which will lead to the positive seasonal ice anomalies in winter maintained from 2007 to 2010 in Pacific sector.
2015, 37(11): 23-32.
doi: 10.3969/j.issn.0253-4193.2015.11.003
Abstract:
Satellite data since 1979 show that the monthly mean Arctic sea-ice extent has downward trends, with the largest trend in September. The Arctic sea-ice minimum extent is observed in September 2012. While the minimum Arctic sea-ice area in the following September 2013 increases 60% compared with 2012. The sea-ice increase areas mainly locate in the Pacific section of Arctic Ocean: East Siberian Sea, Chukchi Sea, and Beaufort Sea. By using methods of climatological anomalies and Empirical Mode Decomposition, we investigated the Arctic sea-ice data from the National Snow and Ice Data Center, lower atmospheric circulation pattern and the upper-ocean from ERA-Interim data in summer to explain this sea-ice sharp increase phenomenon. Results show that the increasing of the Arctic sea ice in 2013 compared with 2012 is related to the following five conditions: the surface air temperature (SAT) decrease, sea level pressure (SLP) increase, the cyclonic anomaly of wind field, surface special humidity (SH) and surface sea temperature (SST) decrease. All these five favorable conditions for sea-ice increase are closely related with by the ice-SAT, ice-SST and ice-SH positive feedbacks in the Arctic Ocean.
Satellite data since 1979 show that the monthly mean Arctic sea-ice extent has downward trends, with the largest trend in September. The Arctic sea-ice minimum extent is observed in September 2012. While the minimum Arctic sea-ice area in the following September 2013 increases 60% compared with 2012. The sea-ice increase areas mainly locate in the Pacific section of Arctic Ocean: East Siberian Sea, Chukchi Sea, and Beaufort Sea. By using methods of climatological anomalies and Empirical Mode Decomposition, we investigated the Arctic sea-ice data from the National Snow and Ice Data Center, lower atmospheric circulation pattern and the upper-ocean from ERA-Interim data in summer to explain this sea-ice sharp increase phenomenon. Results show that the increasing of the Arctic sea ice in 2013 compared with 2012 is related to the following five conditions: the surface air temperature (SAT) decrease, sea level pressure (SLP) increase, the cyclonic anomaly of wind field, surface special humidity (SH) and surface sea temperature (SST) decrease. All these five favorable conditions for sea-ice increase are closely related with by the ice-SAT, ice-SST and ice-SH positive feedbacks in the Arctic Ocean.
2015, 37(11): 33-40.
doi: 10.3969/j.issn.0253-4193.2015.11.004
Abstract:
In this study, Arctic sea ice during 1992-2013 simulated by FIO-ESM (First Institute of Oceanography-Earth System Model) based on ensemble adjustment Kalman filter data assimilation experiment is assessed. Although only global sea surface temperature and global sea level anomaly are assimilated to FIO-ESM and there is no sea ice assimilation, our study shows that the climatology and long-term trend of Arctic sea ice can also be well reproduced with this kind of data assimilation. The linear trends of Arctic sea ice extent during 1992-2013 from satellite observations and FIO-ESM simulations are -7.06×105 and -6.44×105 km2/(10 a), respectively. The correlation coefficient between modeled and observed Arctic sea ice extent anomalies is 0.78. Compared with the results from FIO-ESM in CMIP5 (Coupled Model Intercomparison Project Phase 5) experiment, the long-term trends of Arctic sea ice extent and sea ice concentration from data assimilation experiment fit the observations much better, so these results from FIO-ESM data assimilation experiment can be used as initial condition for Arctic climate projection.
In this study, Arctic sea ice during 1992-2013 simulated by FIO-ESM (First Institute of Oceanography-Earth System Model) based on ensemble adjustment Kalman filter data assimilation experiment is assessed. Although only global sea surface temperature and global sea level anomaly are assimilated to FIO-ESM and there is no sea ice assimilation, our study shows that the climatology and long-term trend of Arctic sea ice can also be well reproduced with this kind of data assimilation. The linear trends of Arctic sea ice extent during 1992-2013 from satellite observations and FIO-ESM simulations are -7.06×105 and -6.44×105 km2/(10 a), respectively. The correlation coefficient between modeled and observed Arctic sea ice extent anomalies is 0.78. Compared with the results from FIO-ESM in CMIP5 (Coupled Model Intercomparison Project Phase 5) experiment, the long-term trends of Arctic sea ice extent and sea ice concentration from data assimilation experiment fit the observations much better, so these results from FIO-ESM data assimilation experiment can be used as initial condition for Arctic climate projection.
2015, 37(11): 41-56.
doi: 10.3969/j.issn.0253-4193.2015.11.005
Abstract:
The albedo of melt ponds is greater than open water but less than bare sea ice. It's important to obtain accurate melt pond fraction information for the study of heat budget in the atmosphere-ice-ocean system. In numerical model, melt pond fractions impact the calculation of sea ice surface albedo significantly. In this paper, comparison is carried out among the three melt pond parameterization schemes in CICE5.0. The results show that each scheme owns strengths and weaknesses. The freezing conditions of the cesm scheme are more reasonable. Comparatively, for the topo scheme, with freezing conditions changed, the amplitude of inter-annual variability of averaged pond fractions, the melt ponds coverage extent and the length of peak season agree with MODIS results best. In addition, by fixing bugs in CICE5.0, melt water permeating through sea ice is analyzed. This process could cause some side effect; for example, nearly no ponds exist on multi-year ice in the lvl scheme. This indicates that the evolution of sea ice permeability or other physical processes remains to be improved in CICE model. Lastly, we gave some discussions for the improvement mainly on the topo scheme.
The albedo of melt ponds is greater than open water but less than bare sea ice. It's important to obtain accurate melt pond fraction information for the study of heat budget in the atmosphere-ice-ocean system. In numerical model, melt pond fractions impact the calculation of sea ice surface albedo significantly. In this paper, comparison is carried out among the three melt pond parameterization schemes in CICE5.0. The results show that each scheme owns strengths and weaknesses. The freezing conditions of the cesm scheme are more reasonable. Comparatively, for the topo scheme, with freezing conditions changed, the amplitude of inter-annual variability of averaged pond fractions, the melt ponds coverage extent and the length of peak season agree with MODIS results best. In addition, by fixing bugs in CICE5.0, melt water permeating through sea ice is analyzed. This process could cause some side effect; for example, nearly no ponds exist on multi-year ice in the lvl scheme. This indicates that the evolution of sea ice permeability or other physical processes remains to be improved in CICE model. Lastly, we gave some discussions for the improvement mainly on the topo scheme.
2015, 37(11): 57-67.
doi: 10.3969/j.issn.0253-4193.2015.11.006
Abstract:
Based on the data of Arctic sea ice concentration from National Snow and Ice Data Center (NSIDC), the variability of Arctic sea ice extent (SIE) from 1979 to 2012 has been analyzed. The result shows that the changes of Arctic SIE had two decadal shift points in 1997 and 2007 respectively, and experienced three different periods: during 1979-1996, the SIE is downtrend and with strong interannual variability. Arctic Oscillation(AO) is in its strong phase and has low frequency oscillation; during 1997-2006, the interannual variability of SIE is weak but the linear downtrend is strongest. The strength of AO is weakened and AO has shorter period oscillation; during 2007-2012, the sea ice experienced a strongest interannual variability and slower downtrend than in 1997-2006. The interannual variability of AO is stronger that induces two negative anomaly centers move toward Greenland north Atlantic and stretch to Bering Strait, respectively. This pattern is conducive to transport the cold air to the North America and Europe. Experiments with ECHAM5 atmospheric circulation model proved that the strong interannual variability of sea ice is the key for the changes of AO mode for 2007-2012 period.
Based on the data of Arctic sea ice concentration from National Snow and Ice Data Center (NSIDC), the variability of Arctic sea ice extent (SIE) from 1979 to 2012 has been analyzed. The result shows that the changes of Arctic SIE had two decadal shift points in 1997 and 2007 respectively, and experienced three different periods: during 1979-1996, the SIE is downtrend and with strong interannual variability. Arctic Oscillation(AO) is in its strong phase and has low frequency oscillation; during 1997-2006, the interannual variability of SIE is weak but the linear downtrend is strongest. The strength of AO is weakened and AO has shorter period oscillation; during 2007-2012, the sea ice experienced a strongest interannual variability and slower downtrend than in 1997-2006. The interannual variability of AO is stronger that induces two negative anomaly centers move toward Greenland north Atlantic and stretch to Bering Strait, respectively. This pattern is conducive to transport the cold air to the North America and Europe. Experiments with ECHAM5 atmospheric circulation model proved that the strong interannual variability of sea ice is the key for the changes of AO mode for 2007-2012 period.
2015, 37(11): 68-78.
doi: 10.3969/j.issn.0253-4193.2015.11.007
Abstract:
Atmospheric vertical structure is a significant element to study planetary boundary layer and simulate atmospheric circulation. Therefore, based on the GPS radiosonde data obtained from the 6th Chinese Arctic expedition during summer of 2014, the vertical structure of the troposphere and the boundary layer characteristics over high latitude region of the northern hemisphere and the Arctic region were analyzed. The results show that: (1) LRT and CPT can both estimate the tropopause. The NCEP reanalysis data can represent the characteristics of tropopause over lower latitude area properly, however, it is not as well in the Arctic region where ice concentration is more than 90%. It is of importance to carry out GPS radiosonde. (2) These GPS soundings reveal a vertical domain of low temperature and high wind speed, which correspond to the tropopause. When it is sunny or cloudless, the tropopause tends to stable along the variation of the latitude. On the contrary, when it is cloudy or rainy, the tropopause tends to decrease at latitudes form 60°N to 82°N. (3) The height of the CPT and high wind speed both remarkably decrease in the region with latitude higher than 75°N. (4) All 6 regions have inversions in the boundary layer. The peaks of wind speed in the boundary layer result to weaken the reversion or make the reversion disappear.
Atmospheric vertical structure is a significant element to study planetary boundary layer and simulate atmospheric circulation. Therefore, based on the GPS radiosonde data obtained from the 6th Chinese Arctic expedition during summer of 2014, the vertical structure of the troposphere and the boundary layer characteristics over high latitude region of the northern hemisphere and the Arctic region were analyzed. The results show that: (1) LRT and CPT can both estimate the tropopause. The NCEP reanalysis data can represent the characteristics of tropopause over lower latitude area properly, however, it is not as well in the Arctic region where ice concentration is more than 90%. It is of importance to carry out GPS radiosonde. (2) These GPS soundings reveal a vertical domain of low temperature and high wind speed, which correspond to the tropopause. When it is sunny or cloudless, the tropopause tends to stable along the variation of the latitude. On the contrary, when it is cloudy or rainy, the tropopause tends to decrease at latitudes form 60°N to 82°N. (3) The height of the CPT and high wind speed both remarkably decrease in the region with latitude higher than 75°N. (4) All 6 regions have inversions in the boundary layer. The peaks of wind speed in the boundary layer result to weaken the reversion or make the reversion disappear.
2015, 37(11): 79-91.
doi: 10.3969/j.issn.0253-4193.2015.11.008
Abstract:
The downward radiative fluxes, wind speed, near surface temperature, precipitation, humidity got from Climate Forecast System Reanalysis (CFSR) and the Japanese 25-year Reanalysis Project (JRA25) are compared in this article. We find that most significant biases between the two datasets are precipitation, downward fluxes for both shortwave and longwave radiation. Driving by these two datasets, model results forced by CFSR shows big differences on sea ice, Atlantic water and thermohaline structure in Canada basin compared to in situ observations, with the simulated geostrophic current on isopycnal surface 20% higher than that forced by JRA25 and a larger volume fluxes than that derived from SODA data. Sensitivity experiment forced by downward radiative fluxes from CFSR, which have been evaluated to be close to observed values, demonstrates comparable results to observational results. The cloud data plays a key role in modeling sea ice while freshwater flux brought by precipitation can change the heat transport of Atlantic inflow prominently and carry a further effect on sea ice in the Arctic. The overestimated precipitation in CFSR is the major for large biases of volume flux through Fram Strait, geostropic current on isopycnal surface and thermohaline structure in central Arctic. Although reanalysis wind have different resolution for the two datasets, our results indicates that it carries an ignorable effect on modeling sea ice and thermohaline structure on basin scale.
The downward radiative fluxes, wind speed, near surface temperature, precipitation, humidity got from Climate Forecast System Reanalysis (CFSR) and the Japanese 25-year Reanalysis Project (JRA25) are compared in this article. We find that most significant biases between the two datasets are precipitation, downward fluxes for both shortwave and longwave radiation. Driving by these two datasets, model results forced by CFSR shows big differences on sea ice, Atlantic water and thermohaline structure in Canada basin compared to in situ observations, with the simulated geostrophic current on isopycnal surface 20% higher than that forced by JRA25 and a larger volume fluxes than that derived from SODA data. Sensitivity experiment forced by downward radiative fluxes from CFSR, which have been evaluated to be close to observed values, demonstrates comparable results to observational results. The cloud data plays a key role in modeling sea ice while freshwater flux brought by precipitation can change the heat transport of Atlantic inflow prominently and carry a further effect on sea ice in the Arctic. The overestimated precipitation in CFSR is the major for large biases of volume flux through Fram Strait, geostropic current on isopycnal surface and thermohaline structure in central Arctic. Although reanalysis wind have different resolution for the two datasets, our results indicates that it carries an ignorable effect on modeling sea ice and thermohaline structure on basin scale.
2015, 37(11): 92-104.
doi: 10.3969/j.issn.0253-4193.2015.11.009
Abstract:
Correlation between sea ice concentration and cloudiness in the central Arctic is studied. The running correlation coefficients of daily sea ice concentration averaged for whole central Arctic with the daily averaged low cloud, medium cloud and high cloud are calculated. During the melting period in spring (April and May) and the freezing period in autumn (October and November), sea ice concentration and low cloud present significant negative correlation. This could be explained that the low clouds were formed by strong evaporation from the open water in gaps of sea ice during these periods. In the same period, sea ice concentration is negatively correlated with medium cloud only during the freezing period in autumn, which suggests that in autumn the low cloud could upwell to form medium cloud, but in spring the upwell is much week because of its stable stratification. There is no a significant correlation between sea ice concentration and high cloud, which is speculated that sea ice can't influence the high cloud and the high cloud is not formed by local eveporation and the influence of high cloud on sea ice is not ice concentration, but is ice thickness. In the periods of significant correlation between ice concentration and low cloud, there still exist some occasional inconsistent betweeen them. The exchanges of sea ice or cloud between the central Arctic and adjacent regions is suggested to cause the inconsistent.
Correlation between sea ice concentration and cloudiness in the central Arctic is studied. The running correlation coefficients of daily sea ice concentration averaged for whole central Arctic with the daily averaged low cloud, medium cloud and high cloud are calculated. During the melting period in spring (April and May) and the freezing period in autumn (October and November), sea ice concentration and low cloud present significant negative correlation. This could be explained that the low clouds were formed by strong evaporation from the open water in gaps of sea ice during these periods. In the same period, sea ice concentration is negatively correlated with medium cloud only during the freezing period in autumn, which suggests that in autumn the low cloud could upwell to form medium cloud, but in spring the upwell is much week because of its stable stratification. There is no a significant correlation between sea ice concentration and high cloud, which is speculated that sea ice can't influence the high cloud and the high cloud is not formed by local eveporation and the influence of high cloud on sea ice is not ice concentration, but is ice thickness. In the periods of significant correlation between ice concentration and low cloud, there still exist some occasional inconsistent betweeen them. The exchanges of sea ice or cloud between the central Arctic and adjacent regions is suggested to cause the inconsistent.
2015, 37(11): 105-117.
doi: 10.3969/j.issn.0253-4193.2015.11.010
Abstract:
Based on the HadISST sea ice concentration (SIC) data from 1961-2013, we define the Arctic sea ice seasonal melting index and analyze the spatial and temporal characteristics of Arctic sea ice seasonal melting extent. It turns out that in recent decades there are two significant decadal regime shift, late 1970s and the middle of 1990s respectively. Before the late 1970s, the oscillation of Arctic sea ice extent was decrease under the background of global warming, the seasonal melting extent (SME) was small but increased significantly; during the late 1970s and the middle of 1990s, the oscillation of the SME was maintain and had no significant trend; while after the middle of 1990s, although there is a hiatus of the global warming, the melting of Arctic sea ice is accelerating, especially after 2007, during which Arctic sea ice SME is greatly increased. Besides that, as the time goes by, the significant SME area expanded anticlockwise from the East Siberia sea to Beaufort Sea-north Canadian Arctic Archipelago gradually, and expand to the central Arctic at the same time. Correspondingly, the total frequency of Chinese freezing rain (CFR) is decreasing during the past several decades and also has significant decadal regime shift. The inter-annual amplitude of the total frequency of CFR was large during 1962-1979, then turned small during 1980-1996, with close relation to SST instead of sea ice. After 1997 the total frequency of CFR is at a low stage, but is increasing, which may mostly be influenced by Arctic sea ice variability. The key area of SST or SIC that influence CFR are different during different decadal epochs, resulting in certain atmospheric background circulation anomalies. There is a consistency between the change of CFR and the SME, which means that the decadal regime shift of the Arctic sea ice may be the cause of the decadal regime shift of Chinese freezing rain.
Based on the HadISST sea ice concentration (SIC) data from 1961-2013, we define the Arctic sea ice seasonal melting index and analyze the spatial and temporal characteristics of Arctic sea ice seasonal melting extent. It turns out that in recent decades there are two significant decadal regime shift, late 1970s and the middle of 1990s respectively. Before the late 1970s, the oscillation of Arctic sea ice extent was decrease under the background of global warming, the seasonal melting extent (SME) was small but increased significantly; during the late 1970s and the middle of 1990s, the oscillation of the SME was maintain and had no significant trend; while after the middle of 1990s, although there is a hiatus of the global warming, the melting of Arctic sea ice is accelerating, especially after 2007, during which Arctic sea ice SME is greatly increased. Besides that, as the time goes by, the significant SME area expanded anticlockwise from the East Siberia sea to Beaufort Sea-north Canadian Arctic Archipelago gradually, and expand to the central Arctic at the same time. Correspondingly, the total frequency of Chinese freezing rain (CFR) is decreasing during the past several decades and also has significant decadal regime shift. The inter-annual amplitude of the total frequency of CFR was large during 1962-1979, then turned small during 1980-1996, with close relation to SST instead of sea ice. After 1997 the total frequency of CFR is at a low stage, but is increasing, which may mostly be influenced by Arctic sea ice variability. The key area of SST or SIC that influence CFR are different during different decadal epochs, resulting in certain atmospheric background circulation anomalies. There is a consistency between the change of CFR and the SME, which means that the decadal regime shift of the Arctic sea ice may be the cause of the decadal regime shift of Chinese freezing rain.
2015, 37(11): 118-126.
doi: 10.3969/j.issn.0253-4193.2015.11.011
Abstract:
Total sea ice deformation rates are produced based on RADARSAT Geophysical Processer System (RGPS) dataset (divergence, vorticity and shear) in this paper, as well as the probability distribution of samples whose value of total sea ice deformation rate are lager than 0.01/d in the Arctic Ocean. The results show that mean value of total deformation rates (TDR) of whole dataset (from November 1996 to April 2008) is 0.020 4/d. There are 45.89% samples whose value of TDR are lager than 0.01/d.TDR in coast area are larger than those near North Polar. There are statistically significant differences in the average TDR between summer and winter. Both average TDR and occurrence probabilities of samples whose value of TDR are lager than 0.01/d in summer are larger than those in winter. Where probability of occurrence in summer is 59% which has 18% more than that in winter. It may be lead by the amplify effect of sea ice melting-broken-easier melting-easier broken in summer, and than it makes the Arctic sea ice TDR larger than in winter.
Total sea ice deformation rates are produced based on RADARSAT Geophysical Processer System (RGPS) dataset (divergence, vorticity and shear) in this paper, as well as the probability distribution of samples whose value of total sea ice deformation rate are lager than 0.01/d in the Arctic Ocean. The results show that mean value of total deformation rates (TDR) of whole dataset (from November 1996 to April 2008) is 0.020 4/d. There are 45.89% samples whose value of TDR are lager than 0.01/d.TDR in coast area are larger than those near North Polar. There are statistically significant differences in the average TDR between summer and winter. Both average TDR and occurrence probabilities of samples whose value of TDR are lager than 0.01/d in summer are larger than those in winter. Where probability of occurrence in summer is 59% which has 18% more than that in winter. It may be lead by the amplify effect of sea ice melting-broken-easier melting-easier broken in summer, and than it makes the Arctic sea ice TDR larger than in winter.
2015, 37(11): 127-134.
doi: 10.3969/j.issn.0253-4193.2015.11.012
Abstract:
Volume backscatter strength (Sv) is a key parameter for acoustic transmission. Study on volume backscattering strength in marginal ice zone (MIZ) in the Arctic plays an important role in the knowledge of the Arctic acoustic environment. Based on the investigation during the Sixth Chinese National Arctic Research Expedition in summer 2014, the characteristics of backscatter strength in MIZ are analyzed. The results show that Sv under the open water (ice concentration less than 15%) is significantly higher than that in seawater under the packed ice (ice concentration more than 50%). Ice melt causes increasing of opaque creatures and suspended sediment, and leads to increase of Sv. According to the characteristics of low Sv under the packed ice, the proposal about the parameters of LADCP setting is given.
Volume backscatter strength (Sv) is a key parameter for acoustic transmission. Study on volume backscattering strength in marginal ice zone (MIZ) in the Arctic plays an important role in the knowledge of the Arctic acoustic environment. Based on the investigation during the Sixth Chinese National Arctic Research Expedition in summer 2014, the characteristics of backscatter strength in MIZ are analyzed. The results show that Sv under the open water (ice concentration less than 15%) is significantly higher than that in seawater under the packed ice (ice concentration more than 50%). Ice melt causes increasing of opaque creatures and suspended sediment, and leads to increase of Sv. According to the characteristics of low Sv under the packed ice, the proposal about the parameters of LADCP setting is given.
2015, 37(11): 135-146.
doi: 10.3969/j.issn.0253-4193.2015.11.013
Abstract:
Seawater δ18O at a transect along 64.3°N near Bering strait from 2003 to 2012 was determined, and the fractions of sea-ice melted water (SIM) and river water (RW) were calculated using mass balance of salinity and δ18O. The spatial distribution and interannual variability of freshwater components in the Bering Strait were discussed. Our results showed that the signals of depleted δ18O, low salinity, warmer temperatures and higher river runoff fractions were found in the region affected by the Alaska Coastal Water (ACW) on the eastern part of the section, while high δ18O, high salinity, and lower sea-ice melted water fractions were observed in the western part with the influence of Anadyr water. The moderate properties were revealed in the middle part of the section with the influence of the Bering Shelf Water. The fractions of river runoff in the region affected by the Alaska Coastal Water were approximately twice as much as in regions affected by the Bering Shelf Water and the Anadyr Water. The interannual variation of river runoff fractions in regions affected by the Alaska Coastal Water showed a characteristic of 2010 > 2012 > 2003 > 2008, which was regulated by the interannual variation of the Yukon River discharge. The fractions of sea-ice melted water were similar in the regions affected by the Bering Shelf Water and the Alaska Coastal Water, and higher (~45%) than those in the region affected by the Anadyr Water. The interannual variation of sea-ice melted water fractions showed a characteristic of 2003>2008≈2012 > 2010, controlled by the interannual variation of sea ice cover in the Bering Sea. The freshwater pass through the Bering Strait was constitute of 46% river water and 54% sea-ice melted water in average. The fraction ratios of river water to sea-ice melted water in the regions affected by the Alaskan Coastal Water, the Bering Shelf Water, and the Anadyr Water increased during 2003 to 2012, indicating that the freshwater components in the Pacific inflow also play a role in sea ice melting in the Arctic Ocean.
Seawater δ18O at a transect along 64.3°N near Bering strait from 2003 to 2012 was determined, and the fractions of sea-ice melted water (SIM) and river water (RW) were calculated using mass balance of salinity and δ18O. The spatial distribution and interannual variability of freshwater components in the Bering Strait were discussed. Our results showed that the signals of depleted δ18O, low salinity, warmer temperatures and higher river runoff fractions were found in the region affected by the Alaska Coastal Water (ACW) on the eastern part of the section, while high δ18O, high salinity, and lower sea-ice melted water fractions were observed in the western part with the influence of Anadyr water. The moderate properties were revealed in the middle part of the section with the influence of the Bering Shelf Water. The fractions of river runoff in the region affected by the Alaska Coastal Water were approximately twice as much as in regions affected by the Bering Shelf Water and the Anadyr Water. The interannual variation of river runoff fractions in regions affected by the Alaska Coastal Water showed a characteristic of 2010 > 2012 > 2003 > 2008, which was regulated by the interannual variation of the Yukon River discharge. The fractions of sea-ice melted water were similar in the regions affected by the Bering Shelf Water and the Alaska Coastal Water, and higher (~45%) than those in the region affected by the Anadyr Water. The interannual variation of sea-ice melted water fractions showed a characteristic of 2003>2008≈2012 > 2010, controlled by the interannual variation of sea ice cover in the Bering Sea. The freshwater pass through the Bering Strait was constitute of 46% river water and 54% sea-ice melted water in average. The fraction ratios of river water to sea-ice melted water in the regions affected by the Alaskan Coastal Water, the Bering Shelf Water, and the Anadyr Water increased during 2003 to 2012, indicating that the freshwater components in the Pacific inflow also play a role in sea ice melting in the Arctic Ocean.
2015, 37(11): 147-154.
doi: 10.3969/j.issn.0253-4193.2015.11.014
Abstract:
The concentrations of nitrate, phosphate, silicate, Chl a were analyzed and in situ nutrients enrichment experiment was conducted in the summer of 2008 to discuss the impact of macronutrient limitation on phytoplankton biomass and community structure in the western Arctic Canada Basin. The results showed that there was a strong stratification in the upper 20 m at B80 station. Lower concentrations of dissolved inorganic nitrogen (DIN) and silicate (0.31 and 0.94 μmol/L, respectively) and serious deviation from the Redfield ratio of the N/P, N/Si (0.42 and 0.32, respectively) indicated N and Si limited in the upper layer of the Canada Basin. According to the trend of Chl a, nutrients uptake and phytoplankton community structure during the experiment deduced that nitrogen was the primary limited nutrient, while silicate inhibited the growth of siliceous phytoplankton in the upper layer of the Canada Basin. Meanwhile, the smaller half saturation constant (Ks) of nitrate can also prove that the phytoplankton growth rates were at a low level even the absence of nutrient limitation in the Arctic Basin. The phytoplankton species were dominated by nano-or pico-phytoplankton rather than diatom, which would be responsible for the higher nutrient assimilation ratios of N/P compared to the Redfield ratio.
The concentrations of nitrate, phosphate, silicate, Chl a were analyzed and in situ nutrients enrichment experiment was conducted in the summer of 2008 to discuss the impact of macronutrient limitation on phytoplankton biomass and community structure in the western Arctic Canada Basin. The results showed that there was a strong stratification in the upper 20 m at B80 station. Lower concentrations of dissolved inorganic nitrogen (DIN) and silicate (0.31 and 0.94 μmol/L, respectively) and serious deviation from the Redfield ratio of the N/P, N/Si (0.42 and 0.32, respectively) indicated N and Si limited in the upper layer of the Canada Basin. According to the trend of Chl a, nutrients uptake and phytoplankton community structure during the experiment deduced that nitrogen was the primary limited nutrient, while silicate inhibited the growth of siliceous phytoplankton in the upper layer of the Canada Basin. Meanwhile, the smaller half saturation constant (Ks) of nitrate can also prove that the phytoplankton growth rates were at a low level even the absence of nutrient limitation in the Arctic Basin. The phytoplankton species were dominated by nano-or pico-phytoplankton rather than diatom, which would be responsible for the higher nutrient assimilation ratios of N/P compared to the Redfield ratio.
Estimation of nutrients flux of water-sediment interface in the Chukchi Sea,the western Arctic Ocean
2015, 37(11): 155-164.
doi: 10.3969/j.issn.0253-4193.2015.11.015
Abstract:
Nutrients regeneration in pore water is one of the important ways to supply nutrients of upper water column in the shelf. The pore water in sediment of the central Chukchi Sea continental shelf, showed a typical benthic distribution of nutrients at water-sediment interface, in where physical and bioturbation was weak. The nutrient samples in multi-tubular short column sediment and water column were obtained from the Forth Chinese National Arctic Research Expedition, to measure the nutrient concentrations of pore water, overlying water and water column. The results show that, the typical distribution can be separated into three layers. The first layer is the exponential increasing layer (I), in which the concentrations of nutrients increased rapidly with depth. Then was the steady layer (Ⅱ), the sediment demineralization was equal to the nutrient transference and nutrients' concentrations were substantially constant at this stage. The third layer was a slowly descending layer (Ⅲ), in which NO3- and PO43- were reduced by bacteria and lost oxygen ions due to organic materials degradation depleting oxygen. By a two-layer mode and the Fick's first law of diffusion, diffusive fluxes of silicate, phosphate and nitrate in R06 station of the Chukchi Sea shelf can be calculated, and the fluxes were 1.660 mmol/(m2·d), 0.008 mmol/(m2·d) and 0.117 mmol/(m2·d), respectively. The diffusive fluxes of silicate for CC1, R06, C07 and S23 stations were 3.101 mmol/(m2·d), 1.660 mmol/(m2·d), 1.307 mmol/(m2·d) and 0.243 mmol/(m2·d), respectively, which show obvious distribution characteristics with latitude. Distribution of N* in the pore water suggested that a strong denitrification process in sedimentary environment of the Chukchi Sea shelf, which is an important sink for nitrate.
Nutrients regeneration in pore water is one of the important ways to supply nutrients of upper water column in the shelf. The pore water in sediment of the central Chukchi Sea continental shelf, showed a typical benthic distribution of nutrients at water-sediment interface, in where physical and bioturbation was weak. The nutrient samples in multi-tubular short column sediment and water column were obtained from the Forth Chinese National Arctic Research Expedition, to measure the nutrient concentrations of pore water, overlying water and water column. The results show that, the typical distribution can be separated into three layers. The first layer is the exponential increasing layer (I), in which the concentrations of nutrients increased rapidly with depth. Then was the steady layer (Ⅱ), the sediment demineralization was equal to the nutrient transference and nutrients' concentrations were substantially constant at this stage. The third layer was a slowly descending layer (Ⅲ), in which NO3- and PO43- were reduced by bacteria and lost oxygen ions due to organic materials degradation depleting oxygen. By a two-layer mode and the Fick's first law of diffusion, diffusive fluxes of silicate, phosphate and nitrate in R06 station of the Chukchi Sea shelf can be calculated, and the fluxes were 1.660 mmol/(m2·d), 0.008 mmol/(m2·d) and 0.117 mmol/(m2·d), respectively. The diffusive fluxes of silicate for CC1, R06, C07 and S23 stations were 3.101 mmol/(m2·d), 1.660 mmol/(m2·d), 1.307 mmol/(m2·d) and 0.243 mmol/(m2·d), respectively, which show obvious distribution characteristics with latitude. Distribution of N* in the pore water suggested that a strong denitrification process in sedimentary environment of the Chukchi Sea shelf, which is an important sink for nitrate.
2015, 37(11): 165-177.
doi: 10.3969/j.issn.0253-4193.2015.11.016
Abstract:
A Marinomonas strain isolated from an ice core sample collected from Canada Basin, Arctic Ocean, displayed high β-galactosidase activity in the preliminary screening process. To get its overall understanding towards its enzymatic properties, the galt gene obtained by hiTAIL-PCR amplification was inserted into plasmid pET-28a(+), then transformed into E.coli BL21 (DE3). The recombinant enzyme was purified to electrophoretic homogeneity by one step Ni2+ affinity chromatography. The highest yield was achieved with 0.07mM IPTG when cultivated in 20℃ for 22 h. The molecular weight of the monomeric GALT was estimated as about 6.6×104 g/mol and the native GALT was confirmed as homologous trimer through native-PAGE. The optimum temperature of GALT was 35℃ and showed good thermal stability at the temperature of 60℃ below. The optimum pH of GALT was 9.0 and was stable between pH 6.0 and 11.0. GALT showed high tolerance to salinity and the optimum NaCl concentration was 0.5 mol/L. Mineral ions Fe2+ and Mn2+ were identified as enzyme activators, Mg2+, K+, DTT and EDTA showed no significant influence towards the enzyme activity, while Zn2+ and L-glutathione inhibited the activity strongly. GALT was able to hydrolyse Gal β1-4 GlcNAc but unable to hydrolyze Gal β1-3 GalNAc and Gal β1-3 GlcNAc. In this study, the β-galactosidase gene from Marinomonas sp. BSi20584 was successfully expressed in E.coli system, and a systematic understanding of the enzymatic properties of the recombinant GALT was acquired, which will provide the detailed enzymatic data for further studies on its metabolic adaptation and potential applications.
A Marinomonas strain isolated from an ice core sample collected from Canada Basin, Arctic Ocean, displayed high β-galactosidase activity in the preliminary screening process. To get its overall understanding towards its enzymatic properties, the galt gene obtained by hiTAIL-PCR amplification was inserted into plasmid pET-28a(+), then transformed into E.coli BL21 (DE3). The recombinant enzyme was purified to electrophoretic homogeneity by one step Ni2+ affinity chromatography. The highest yield was achieved with 0.07mM IPTG when cultivated in 20℃ for 22 h. The molecular weight of the monomeric GALT was estimated as about 6.6×104 g/mol and the native GALT was confirmed as homologous trimer through native-PAGE. The optimum temperature of GALT was 35℃ and showed good thermal stability at the temperature of 60℃ below. The optimum pH of GALT was 9.0 and was stable between pH 6.0 and 11.0. GALT showed high tolerance to salinity and the optimum NaCl concentration was 0.5 mol/L. Mineral ions Fe2+ and Mn2+ were identified as enzyme activators, Mg2+, K+, DTT and EDTA showed no significant influence towards the enzyme activity, while Zn2+ and L-glutathione inhibited the activity strongly. GALT was able to hydrolyse Gal β1-4 GlcNAc but unable to hydrolyze Gal β1-3 GalNAc and Gal β1-3 GlcNAc. In this study, the β-galactosidase gene from Marinomonas sp. BSi20584 was successfully expressed in E.coli system, and a systematic understanding of the enzymatic properties of the recombinant GALT was acquired, which will provide the detailed enzymatic data for further studies on its metabolic adaptation and potential applications.
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