Composite analyses were performed in this study to reveal the difference in spring precipitation over southern China during multiyear La Ni?a events during 1901 to 2015. It was found that there is significantly below-...Composite analyses were performed in this study to reveal the difference in spring precipitation over southern China during multiyear La Ni?a events during 1901 to 2015. It was found that there is significantly below-normal precipitation during the first boreal spring, but above-normal precipitation during the second year. The difference in spring precipitation over southern China is correlative to the variation in western North Pacific anomalous cyclone(WNPC), which can in turn be attributed to the different sea surface temperature anomaly(SSTA) over the Tropical Pacific. The remote forcing of negative SSTA in the equatorial central and eastern Pacific and the local air-sea interaction in the western North Pacific are the usual causes of WNPC formation and maintenance.SSTA in the first spring is stronger than those in the second spring. As a result, the intensity of WNPC in the first year is stronger, which is more likely to reduce the moisture in southern China by changing the moisture transport, leading to prolonged precipitation deficits over southern China. However, the tropical SSTA signals in the second year are too weak to induce the formation and maintenance of WNPC and the below-normal precipitation over southern China. Thus, the variation in tropical SSTA signals between two consecutive springs during multiyear La Ni?a events leads to obvious differences in the spatial pattern of precipitation anomaly in southern China by causing the different WNPC response.展开更多
In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice ...In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.展开更多
In comparison with seasonal sea ice(first-year ice,FY ice),multiyear(MY)sea ice is thicker and has more opportunity to survive through the summer melting seasons.Therefore,the variability of wintertime MY ice plays a ...In comparison with seasonal sea ice(first-year ice,FY ice),multiyear(MY)sea ice is thicker and has more opportunity to survive through the summer melting seasons.Therefore,the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer.As a response,the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover.Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice.During the recent decades,many active and passive sensors onboard a variety of satellites(QuikSCAT,ASCAT,SSMIS,ICESat,CryoSat-2,etc.)have been used to monitor the dramatic loss of Arctic MY ice.The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations.We summarize the primary satellite data sources that are used to identify MY ice.The methodology to classify MY ice and derive MY ice concentration is reviewed.The interannual variability and trends in the MY ice time series in terms of coverage,thickness,volume,and age composition are evaluated.The potential causes associated with the observed Arctic MY ice loss are outlined,which are primarily related to the export and melting mechanisms.In addition,the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion,as well as the roles of synoptic weather,are analyzed.The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed.Moreover,some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed.展开更多
Loss of multiyear ice(MYI)is of great importance for Arctic climate and marine systems and can be monitored using active and passive microwave satellite data.In this paper,we describe an upgraded classification algori...Loss of multiyear ice(MYI)is of great importance for Arctic climate and marine systems and can be monitored using active and passive microwave satellite data.In this paper,we describe an upgraded classification algorithm using the data from the scatterometer and radiometer sensors onboard the Chinese Haiyang-2B(HY-2B)satellite to identify MYI and first-year ice(FYI).The proposed method was established based on K-means and fuzzy clustering(K-means+FC)and was used to focus on the transition zone where the ice condition is complex due to the highly commixing of MYI and FYI,leading to the high challenge for accurate classification of sea ice.The K-means algorithm was applied to preliminarily classify MYI using the combination of scatterometer and radiometer data,followed by applying fuzzy clustering to reclassify MYI in the transition zone.The HY-2B K-means+FC results were compared with the ice type products[including the Ocean and Sea Ice Satellite Application Facility(OSI SAF)sea ice type product and the Equal-Area Scalable Earth-Grid sea ice age dataset],and showed agreement in the time series of MYI extent.Intercomparisons in the transition zone indicated that the HY-2B K-means+FC results can identify more old ice than the OSI SAF product,but with an underestimation in identifying second-year ice.Comparisons between K-means and Kmeans+FC results were performed using regional ice charts and Sentinel-1 synthetic aperture radar(SAR)data.By adding fuzzy clustering,the MYI is more consistent with the ice charts,with the overall accuracy(OA)increasing by 0.9%–6.5%.Comparing against SAR images,it is suggested that more scattered MYI floes can be identified by fuzzy clustering,and the OA is increased by about 3%in middle freezing season and 7%–20%in early and late freezing season.展开更多
2022年汛期,国家气候中心准确预测了“全国气候年景总体偏差,区域性、阶段性旱涝灾害明显,降水空间差异显著,主要多雨区在我国北方”的总趋势,较好、较早把握了汛期主雨带位置和全国旱涝分布。对东亚夏季风和雨季季节进程“南海夏季风5...2022年汛期,国家气候中心准确预测了“全国气候年景总体偏差,区域性、阶段性旱涝灾害明显,降水空间差异显著,主要多雨区在我国北方”的总趋势,较好、较早把握了汛期主雨带位置和全国旱涝分布。对东亚夏季风和雨季季节进程“南海夏季风5月第3候爆发,长江中下游入梅偏早,梅雨量偏少,以及华北雨季开始偏早,雨量偏多”的预测与实况一致。对夏季台风生成个数较常年偏少,盛夏出现北上台风可能性大的预测与实况基本吻合。准确预测了全国平均气温趋势和高温异常特征。对“夏季我国中东部大部气温偏高,华东、华中、新疆等地高温日数较常年同期偏多,可能出现阶段性高温热浪”的预测与实况一致。主要不足之处是对长江中下游和川渝地区高温干旱的范围和极端程度估计不足。2022年汛期预测重点考虑连续La Ni a事件和印度洋偶极子负位相对东亚夏季风环流的影响,夏季西太平洋副热带高压强度偏强,脊线位置偏北,东亚夏季风偏强,初夏东北冷涡活跃,导致汛期主雨带位于东北、华北和西北地区东部等地。展开更多
A modified algorithm taking into account the first year（FY） and multiyear（MY） ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat（2003–2008）. E...A modified algorithm taking into account the first year（FY） and multiyear（MY） ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat（2003–2008）. Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns（2005–2008） leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.展开更多
基金The National Natural Science Foundation of China under contract Nos 41576029, 41976221 and 42030410the National Key Research and Development Program of China under contract No. 2019YFA0606702the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology。
文摘Composite analyses were performed in this study to reveal the difference in spring precipitation over southern China during multiyear La Ni?a events during 1901 to 2015. It was found that there is significantly below-normal precipitation during the first boreal spring, but above-normal precipitation during the second year. The difference in spring precipitation over southern China is correlative to the variation in western North Pacific anomalous cyclone(WNPC), which can in turn be attributed to the different sea surface temperature anomaly(SSTA) over the Tropical Pacific. The remote forcing of negative SSTA in the equatorial central and eastern Pacific and the local air-sea interaction in the western North Pacific are the usual causes of WNPC formation and maintenance.SSTA in the first spring is stronger than those in the second spring. As a result, the intensity of WNPC in the first year is stronger, which is more likely to reduce the moisture in southern China by changing the moisture transport, leading to prolonged precipitation deficits over southern China. However, the tropical SSTA signals in the second year are too weak to induce the formation and maintenance of WNPC and the below-normal precipitation over southern China. Thus, the variation in tropical SSTA signals between two consecutive springs during multiyear La Ni?a events leads to obvious differences in the spatial pattern of precipitation anomaly in southern China by causing the different WNPC response.
基金The National Natural Science Foundation of China under contract Nos 41330960 and 41276193 and 41206184
文摘In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.
基金the National Key Research and Development Program of China(No.2017YFC1404000)the National Natural Science Foundation of China(No.41406215)+3 种基金the NSFC-Shandong Joint Fund for Marine Science Research Centers(No.U1606401)the Qingdao National Laboratory for Marine Science and Technologythe Postdoctoral Science Foundation of China(No.014M561971)the Open Funds for the Key Laboratory of Marine Geology and Environment,Institute of Oceanology,Chinese Academy of Sciences(No.MGE2020KG04)。
文摘In comparison with seasonal sea ice(first-year ice,FY ice),multiyear(MY)sea ice is thicker and has more opportunity to survive through the summer melting seasons.Therefore,the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer.As a response,the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover.Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice.During the recent decades,many active and passive sensors onboard a variety of satellites(QuikSCAT,ASCAT,SSMIS,ICESat,CryoSat-2,etc.)have been used to monitor the dramatic loss of Arctic MY ice.The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations.We summarize the primary satellite data sources that are used to identify MY ice.The methodology to classify MY ice and derive MY ice concentration is reviewed.The interannual variability and trends in the MY ice time series in terms of coverage,thickness,volume,and age composition are evaluated.The potential causes associated with the observed Arctic MY ice loss are outlined,which are primarily related to the export and melting mechanisms.In addition,the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion,as well as the roles of synoptic weather,are analyzed.The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed.Moreover,some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed.
基金the National Key Research and Development Program of China under contract No.2021YFC2803301the Fundamental Research Funds for the Central Universities,China under contract Nos 2042024kf0037 and 2042022dx0001the Natural Science Foundation of Wuhan under cocntract No.2024040701010030.
文摘Loss of multiyear ice(MYI)is of great importance for Arctic climate and marine systems and can be monitored using active and passive microwave satellite data.In this paper,we describe an upgraded classification algorithm using the data from the scatterometer and radiometer sensors onboard the Chinese Haiyang-2B(HY-2B)satellite to identify MYI and first-year ice(FYI).The proposed method was established based on K-means and fuzzy clustering(K-means+FC)and was used to focus on the transition zone where the ice condition is complex due to the highly commixing of MYI and FYI,leading to the high challenge for accurate classification of sea ice.The K-means algorithm was applied to preliminarily classify MYI using the combination of scatterometer and radiometer data,followed by applying fuzzy clustering to reclassify MYI in the transition zone.The HY-2B K-means+FC results were compared with the ice type products[including the Ocean and Sea Ice Satellite Application Facility(OSI SAF)sea ice type product and the Equal-Area Scalable Earth-Grid sea ice age dataset],and showed agreement in the time series of MYI extent.Intercomparisons in the transition zone indicated that the HY-2B K-means+FC results can identify more old ice than the OSI SAF product,but with an underestimation in identifying second-year ice.Comparisons between K-means and Kmeans+FC results were performed using regional ice charts and Sentinel-1 synthetic aperture radar(SAR)data.By adding fuzzy clustering,the MYI is more consistent with the ice charts,with the overall accuracy(OA)increasing by 0.9%–6.5%.Comparing against SAR images,it is suggested that more scattered MYI floes can be identified by fuzzy clustering,and the OA is increased by about 3%in middle freezing season and 7%–20%in early and late freezing season.
文摘2022年汛期,国家气候中心准确预测了“全国气候年景总体偏差,区域性、阶段性旱涝灾害明显,降水空间差异显著,主要多雨区在我国北方”的总趋势,较好、较早把握了汛期主雨带位置和全国旱涝分布。对东亚夏季风和雨季季节进程“南海夏季风5月第3候爆发,长江中下游入梅偏早,梅雨量偏少,以及华北雨季开始偏早,雨量偏多”的预测与实况一致。对夏季台风生成个数较常年偏少,盛夏出现北上台风可能性大的预测与实况基本吻合。准确预测了全国平均气温趋势和高温异常特征。对“夏季我国中东部大部气温偏高,华东、华中、新疆等地高温日数较常年同期偏多,可能出现阶段性高温热浪”的预测与实况一致。主要不足之处是对长江中下游和川渝地区高温干旱的范围和极端程度估计不足。2022年汛期预测重点考虑连续La Ni a事件和印度洋偶极子负位相对东亚夏季风环流的影响,夏季西太平洋副热带高压强度偏强,脊线位置偏北,东亚夏季风偏强,初夏东北冷涡活跃,导致汛期主雨带位于东北、华北和西北地区东部等地。
基金The National Natural Science Foundation of China under contract Nos 41276082 and 41076031the Nonprofit Research Project for the State Oceanic Administration of China under contract No.201005010-2
文摘A modified algorithm taking into account the first year（FY） and multiyear（MY） ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat（2003–2008）. Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns（2005–2008） leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.
