On the basis of the arctic monthly mean sea ice extent data set during 1953-1984, the arctic region is divided into eight subregions,and the analyses of empirical orthogonal functions, power spectrum and maximum entro...On the basis of the arctic monthly mean sea ice extent data set during 1953-1984, the arctic region is divided into eight subregions,and the analyses of empirical orthogonal functions, power spectrum and maximum entropy spectrum are made to indentify the major spatial and temporal features of the sea ice fluctuations within 32-year period. And then, a brief appropriate physical explanation is tentatively suggested. The results show that both seasonal and non-seasonal variations of the sea ice extent are remarkable, and iis mean annual peripheral positions as well as their interannu-al shifting amplitudes are quite different among all subregions. These features are primarily affected by solar radiation, o-cean circulation, sea surface temperature and maritime-continental contrast, while the non-seasonal variations are most possibly affected by the cosmic-geophysical factors such as earth pole shife, earth rotation oscillation and solar activity.展开更多
In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)col...In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE).A total of 3667 observations were collected in the Arctic summers of 2010,2012,2014,2016,and 2018.PM SIC were derived from the NASA-Team(NT),Bootstrap(BT)and Climate Data Record(CDR)algorithms based on the SSMIS sensor,as well as the BT,enhanced NASA-Team(NT2)and ARTIST Sea Ice(ASI)algorithms based on AMSR-E/AMSR-2 sensors.The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons.The correlation coefficients(CC),biases and root mean square deviations(RMSD)between PM SIC and OBS SIC were compared in terms of the overall trend,and under mild/normal/severe ice conditions.Using the OBS data,the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness.Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2)to 0.95(SSMIS NT),biases range from−3.96%(SSMIS NT)to 12.05%(AMSR-E/AMSR-2 NT2),and RMSD values range from 10.81%(SSMIS NT)to 20.15%(AMSR-E/AMSR-2 NT2).Floe size has a significant influence on the SIC retrievals of the PM products,and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions.Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products.Overall,the best(worst)agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2)SIC in the Arctic summer.展开更多
The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distri...The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer(AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35 × 10~5 km^2/yr, twice as fast as the rate between 1979 and 2006, and slightly slower than from 2002 to 2011. Area with >90% sea-ice concentration decreased by 1.32 × 10~7 km^2/yr, while locations with >50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54 × 10~5 km^2 annually over the past 11 years.展开更多
Based on data observed from 1979 to 2017,the influence of Arctic sea ice in the previous spring on the first mode of interannual variation in summer drought in the middle and high latitudes of Asia(MHA)is analyzed in ...Based on data observed from 1979 to 2017,the influence of Arctic sea ice in the previous spring on the first mode of interannual variation in summer drought in the middle and high latitudes of Asia(MHA)is analyzed in this paper,and the possible associated physical mechanism is discussed.The results show that when there is more sea ice near the Svalbard Islands in spring while the sea ice in the Barents-Kara Sea decreases,the drought distribution in the MHA shows a north-south dipole pattern in late summer,and drought weakens in the northern MHA region and strengthens in the southern MHA region.By analyzing the main physical process affecting these changes,the change in sea ice in spring is found to lead to the Polar-Eurasian teleconnection pattern,resulting in more precipitation,thicker snow depths,higher temperatures,and higher soil moisture in the northern MHA region in spring and less precipitation,smaller snow depths,and lower soil moisture in the southern MHA region.Such soil conditions last until summer,affect summer precipitation and temperature conditions through soil moisture–atmosphere feedbacks,and ultimately modulate changes in summer drought in the MHA.展开更多
In our previous study, a statistical linkage between the spring Arctic sea ice concentration （SIC） and the succeeding Chinese summer rainfall during the period 1968-2005 was identified. This linkage is demonstrated ...In our previous study, a statistical linkage between the spring Arctic sea ice concentration （SIC） and the succeeding Chinese summer rainfall during the period 1968-2005 was identified. This linkage is demonstrated by the leading singular value decomposition （SVD） that accounts for 19% of the co-variance. Both spring SIC and Chinese summer rainfall exhibit a coherent interannual variability and two apparent interdecadal variations that occurred in the late 1970s and the early 1990s. The combined impacts of both spring Arctic SIC and Eurasian snow cover on the summer Eurasian wave train may explain their statistical linkage. In this study, we show that evolution of atmospheric circulation anomalies from spring to summer, to a great extent, may explain the spatial distribution of spring and summer Arctic SIC anomalies, and is dynamically consistent with Chinese summer rainfall anomalies in recent decades. The association between spring Arctic SIC and Chinese summer rainfall on interannual time scales is more important relative to interdecadal time scales. The summer Arctic dipole anomaly may serve as the bridge linking the spring Arctic SIC and Chinese summer rainfall, and their coherent interdecadal variations may reflect the feedback of spring SIC variability on the atmosphere. The summer Arctic dipole anomaly shows a closer relationship with the Chinese summer rainfall relative to the Arctic Oscillation.展开更多
In this work,we examined long-term wave distributions using a third-generation numerical wave model called WAVE-WATCH-III(WW3)(version 6.07).We also evaluated the influence of sea ice on wave simulation by using eight...In this work,we examined long-term wave distributions using a third-generation numerical wave model called WAVE-WATCH-III(WW3)(version 6.07).We also evaluated the influence of sea ice on wave simulation by using eight parametric switches.To select a suitable ice-wave parameterization,we validated the simulations from the WW3 model in March,May,September,and December 2017 against the measurements from the Jason-2 altimeter at latitudes of up to 60°N.Generally,all parameterizations ex-hibited slight differences,i.e.,about 0.6 m root mean square error(RMSE)of significant wave height(SWH)in May and September and about 0.9 m RMSE for the freezing months of March and December.The comparison of the results with the SWH from the European Centre for Medium-Range Weather Forecasts for December 2017 indicated that switch IC4_M1 performed most effec-tively(0.68 m RMSE)at high latitudes(60°-80°N).Given this finding,we analyzed the long-term wave distributions in 1999-2018 on the basis of switch IC4_M1.Although the seasonal variability of the simulated SWH was of two types,i.e.,‘U’and‘sin’modes,our results proved that fetch expansion prompted the wave growth.Moreover,the interannual variability of the specific regions in the‘U’mode was found to be correlated with the decade variability of wind in the Arctic Ocean.展开更多
A series of shipborne sea ice observations were performed during the Chinese National Arctic Research Expedition in the Pacific Arctic sector between 2 August 2014 and 1 September 2014.Undeformed sea ice thickness(SIT...A series of shipborne sea ice observations were performed during the Chinese National Arctic Research Expedition in the Pacific Arctic sector between 2 August 2014 and 1 September 2014.Undeformed sea ice thickness(SIT)as well as area fractions of open water,melt pond,and sea ice(Aw,Ap,and Ai)were monitored using downward-oriented and oblique-oriented cameras.The results show that SIT varied between 20 and 220 cm throughout the whole cruise,with the average and standard deviation equaling 104.9 and 29.1 cm,respectively.Mean Aw and Ai were 0.52 and 0.44 in the marginal ice zone,respectively,while mean Aw decreased to 0.23 and mean Ai increased to 0.73 in the pack ice zone.Limited variation between 0 and 0.32 in Ap was seen throughout the whole cruise.Shipborne sea ice concentration was then rectified and mapped across a large transect to validate estimates derived from the satellite sensors Special Sensor Microwave Imager/Sounder(SSMIS)(25 km)and AMSR2(25 km).Overestimations were 9.5%and 9.9%for SSMIS and AMSR2 compared with measurements,respectively.The mean areal broadband surface albedo based on shipborne survey increased from 0.07 to 0.66 along the transect between 72°N and 81°N.展开更多
文摘On the basis of the arctic monthly mean sea ice extent data set during 1953-1984, the arctic region is divided into eight subregions,and the analyses of empirical orthogonal functions, power spectrum and maximum entropy spectrum are made to indentify the major spatial and temporal features of the sea ice fluctuations within 32-year period. And then, a brief appropriate physical explanation is tentatively suggested. The results show that both seasonal and non-seasonal variations of the sea ice extent are remarkable, and iis mean annual peripheral positions as well as their interannu-al shifting amplitudes are quite different among all subregions. These features are primarily affected by solar radiation, o-cean circulation, sea surface temperature and maritime-continental contrast, while the non-seasonal variations are most possibly affected by the cosmic-geophysical factors such as earth pole shife, earth rotation oscillation and solar activity.
基金The National Major Research High Resolution Sea Ice Model Development Program of China under contract No.2018YFA0605903the National Natural Science Foundation of China under contract Nos 51639003,41876213 and 41906198+1 种基金the Hightech Ship Research Project of China under contract No.350631009the National Postdoctoral Program for Innovative Talent of China under contract No.BX20190051.
文摘In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE).A total of 3667 observations were collected in the Arctic summers of 2010,2012,2014,2016,and 2018.PM SIC were derived from the NASA-Team(NT),Bootstrap(BT)and Climate Data Record(CDR)algorithms based on the SSMIS sensor,as well as the BT,enhanced NASA-Team(NT2)and ARTIST Sea Ice(ASI)algorithms based on AMSR-E/AMSR-2 sensors.The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons.The correlation coefficients(CC),biases and root mean square deviations(RMSD)between PM SIC and OBS SIC were compared in terms of the overall trend,and under mild/normal/severe ice conditions.Using the OBS data,the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness.Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2)to 0.95(SSMIS NT),biases range from−3.96%(SSMIS NT)to 12.05%(AMSR-E/AMSR-2 NT2),and RMSD values range from 10.81%(SSMIS NT)to 20.15%(AMSR-E/AMSR-2 NT2).Floe size has a significant influence on the SIC retrievals of the PM products,and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions.Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products.Overall,the best(worst)agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2)SIC in the Arctic summer.
基金Under the auspices of National Natural Science Foundation of China(No.41676171)Qingdao National Laboratory for Marine Science and Technology of China(No.2016ASKJ02)+1 种基金Natural Science Foundation of Shandong(No.ZR2015DM015)Yantai Science&Technology Project(No.2013ZH094)
文摘The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer(AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35 × 10~5 km^2/yr, twice as fast as the rate between 1979 and 2006, and slightly slower than from 2002 to 2011. Area with >90% sea-ice concentration decreased by 1.32 × 10~7 km^2/yr, while locations with >50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54 × 10~5 km^2 annually over the past 11 years.
基金jointly supported by the National Key R&D Program of China[grant number 2017YFE0111800]the National Science Foundation of China[grant numbers 41991281 and 41875110].
文摘Based on data observed from 1979 to 2017,the influence of Arctic sea ice in the previous spring on the first mode of interannual variation in summer drought in the middle and high latitudes of Asia(MHA)is analyzed in this paper,and the possible associated physical mechanism is discussed.The results show that when there is more sea ice near the Svalbard Islands in spring while the sea ice in the Barents-Kara Sea decreases,the drought distribution in the MHA shows a north-south dipole pattern in late summer,and drought weakens in the northern MHA region and strengthens in the southern MHA region.By analyzing the main physical process affecting these changes,the change in sea ice in spring is found to lead to the Polar-Eurasian teleconnection pattern,resulting in more precipitation,thicker snow depths,higher temperatures,and higher soil moisture in the northern MHA region in spring and less precipitation,smaller snow depths,and lower soil moisture in the southern MHA region.Such soil conditions last until summer,affect summer precipitation and temperature conditions through soil moisture–atmosphere feedbacks,and ultimately modulate changes in summer drought in the MHA.
基金supported by the National Key Basic Research and Development Project of China(Grant Nos2004CB418300 and 2007CB411505)Chinese COPES project(GYHY200706005)the Na-tional Natural Science Foundation of China(Grant No40875052)
文摘In our previous study, a statistical linkage between the spring Arctic sea ice concentration （SIC） and the succeeding Chinese summer rainfall during the period 1968-2005 was identified. This linkage is demonstrated by the leading singular value decomposition （SVD） that accounts for 19% of the co-variance. Both spring SIC and Chinese summer rainfall exhibit a coherent interannual variability and two apparent interdecadal variations that occurred in the late 1970s and the early 1990s. The combined impacts of both spring Arctic SIC and Eurasian snow cover on the summer Eurasian wave train may explain their statistical linkage. In this study, we show that evolution of atmospheric circulation anomalies from spring to summer, to a great extent, may explain the spatial distribution of spring and summer Arctic SIC anomalies, and is dynamically consistent with Chinese summer rainfall anomalies in recent decades. The association between spring Arctic SIC and Chinese summer rainfall on interannual time scales is more important relative to interdecadal time scales. The summer Arctic dipole anomaly may serve as the bridge linking the spring Arctic SIC and Chinese summer rainfall, and their coherent interdecadal variations may reflect the feedback of spring SIC variability on the atmosphere. The summer Arctic dipole anomaly shows a closer relationship with the Chinese summer rainfall relative to the Arctic Oscillation.
基金support from the National Key Research and Development Program of China(No.2016 YFC1401605)the Key Special Project for Introduced Tal-ents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(No.GML2019ZD0302)the National Natural Science Foundation of China(Nos.41806005 and 42076238).
文摘In this work,we examined long-term wave distributions using a third-generation numerical wave model called WAVE-WATCH-III(WW3)(version 6.07).We also evaluated the influence of sea ice on wave simulation by using eight parametric switches.To select a suitable ice-wave parameterization,we validated the simulations from the WW3 model in March,May,September,and December 2017 against the measurements from the Jason-2 altimeter at latitudes of up to 60°N.Generally,all parameterizations ex-hibited slight differences,i.e.,about 0.6 m root mean square error(RMSE)of significant wave height(SWH)in May and September and about 0.9 m RMSE for the freezing months of March and December.The comparison of the results with the SWH from the European Centre for Medium-Range Weather Forecasts for December 2017 indicated that switch IC4_M1 performed most effec-tively(0.68 m RMSE)at high latitudes(60°-80°N).Given this finding,we analyzed the long-term wave distributions in 1999-2018 on the basis of switch IC4_M1.Although the seasonal variability of the simulated SWH was of two types,i.e.,‘U’and‘sin’modes,our results proved that fetch expansion prompted the wave growth.Moreover,the interannual variability of the specific regions in the‘U’mode was found to be correlated with the decade variability of wind in the Arctic Ocean.
基金the National Key Research and Development Program of China[grant number 2016YFC1402702],[grant number 2015CB953901]the National Natural Science Foundation of China[grant number 41676187],[grant number 41428603],[grant number 41376186],[grant number 41722605]+1 种基金the High Technology of Ship Research Project of the Ministry of Industry and Information Technology[grant number[2013]417],[grant number[2013]412]Academy of Finland[grant number 283101]。
文摘A series of shipborne sea ice observations were performed during the Chinese National Arctic Research Expedition in the Pacific Arctic sector between 2 August 2014 and 1 September 2014.Undeformed sea ice thickness(SIT)as well as area fractions of open water,melt pond,and sea ice(Aw,Ap,and Ai)were monitored using downward-oriented and oblique-oriented cameras.The results show that SIT varied between 20 and 220 cm throughout the whole cruise,with the average and standard deviation equaling 104.9 and 29.1 cm,respectively.Mean Aw and Ai were 0.52 and 0.44 in the marginal ice zone,respectively,while mean Aw decreased to 0.23 and mean Ai increased to 0.73 in the pack ice zone.Limited variation between 0 and 0.32 in Ap was seen throughout the whole cruise.Shipborne sea ice concentration was then rectified and mapped across a large transect to validate estimates derived from the satellite sensors Special Sensor Microwave Imager/Sounder(SSMIS)(25 km)and AMSR2(25 km).Overestimations were 9.5%and 9.9%for SSMIS and AMSR2 compared with measurements,respectively.The mean areal broadband surface albedo based on shipborne survey increased from 0.07 to 0.66 along the transect between 72°N and 81°N.
