Recent satellite-derived sea ice volume flux through the Fram Strait: 2011–2015

The Fram Strait(FS) is the primary region of sea ice export from the Arctic Ocean and thus plays an important role in regulating the amount of sea ice and fresh water entering the North Atlantic seas. A 5 a(2011–2015) sea ice thickness record retrieved from Cryo Sat-2 observations is used to derive a sea ice volume flux via the FS. Over this period, a mean winter accumulative volume flux(WAVF) based on sea ice drift data derived from passivemicrowave measurements, which are provided by the National Snow and Ice Data Center(NSIDC) and the Institut Francais de Recherche pour d'Exploitation de la Mer(IFREMER), amounts to 1 029 km~3(NSIDC) and1 463 km~3(IFREMER), respectively. For this period, a mean monthly volume flux(area flux) difference between the estimates derived from the NSIDC and IFREMER drift data is –62 km~3 per month(–18×10~6 km~2 per month).Analysis reveals that this negative bias is mainly attributable to faster IFREMER drift speeds in comparison with slower NSIDC drift data. NSIDC-based sea ice volume flux estimates are compared with the results from the University of Bremen(UB), and the two products agree relatively well with a mean monthly bias of(5.7±45.9) km~3 per month for the period from January 2011 to August 2013. IFREMER-based volume flux is also in good agreement with previous results of the 1990 s. Compared with P1(1990/1991–1993/1994) and P2(2003/2004–2007/2008), the WAVF estimates indicate a decline of more than 600 km~3 in P3(2011/2012–2014/2015). Over the three periods, the variability and the decline in the sea ice volume flux are mainly attributable to sea ice motion changes, and second to sea ice thickness changes, and the least to sea ice concentration variations.     

基于遥感数据的近40 a弗雷姆海峡海冰输出研究

北极海冰作为一个巨大的淡水资源库, 每年向全球输送大量淡水资源, 从北极输出的海冰在向南输送的过程中融化, 对海洋水循环与水环境产生影响, 进而影响全球气候变化, 弗雷姆海峡作为北极海冰输出的主要通道, 对其研究显得尤为重要。为了解弗雷姆海峡海冰长期输出量, 利用美国冰雪数据中心（NSIDC）发布的海冰密集度、海冰厚度与海冰漂移速度数据, 计算得到 1979 年至 2019 年弗雷姆海峡海冰输出面积通量与 2010 至 2019 年弗雷姆海峡海冰输出体积通量, 并在此基础上分析弗雷姆海峡近 40 a 海冰输出量的变化状况以及弗雷姆海峡海冰输出的年际变化、季节变化, 并分析了影响弗雷姆海峡海冰输出量的可能原因。结果表明: 近 40 a 弗雷姆海峡年均海冰输出面积通量为 7.83×10⁵ km²,近 10 a 弗雷姆海峡海冰年均输出体积通量为 1.34×10⁶ km³, 从长期来看, 弗雷姆海峡海冰输出面积通量呈略微增加趋势, 弗雷姆海峡海冰输出体积通量在 2010—20...     

基于卫星观测的2003-2013年北极海冰体积变化估算

北极海冰正处于快速减退时期,北极海冰体积变化是全球气候变化的重要指示因子。本文利用两种卫星高度计数据（ICESat和CryoSat-2）反演得到的海冰厚度数据,结合星载辐射计提取的海冰密集度数据以及海冰年龄数据,估算了近期的北极海冰体积以及一年冰和多年冰体积变化。CryoSat-2观测时段（2011-2013年）与ICESat观测时段（2003-2008年）相比,北极海冰体积在秋季（10-11月）和冬季（2-3月）分别减少了1 426 km3和412 km3。其中,秋季和冬季的一年冰的体积增加了702 km3和2 975 km3。相反,多年冰分别减少了2 108 km3和3 206 km3。多年冰的大量流失是造成北极海冰净储量下降的主要原因。     

北极海冰输出研究综述

北极海冰对全球气候变化起重要的指示作用。除了海水冻结和融化过程以外,通过弗拉姆海峡(Fram Strait)的海冰输出也是影响北极海冰质量变化的重要动力机制。观测数据中的多源卫星遥感数据(尤其是辐射计观测数据)在获取大尺度连续观测方面具有独特的优势,在研究北极海冰输出面积通量变化方面有着广泛应用。本文总结了北极弗拉姆海峡、其他通道(S-FJL、FJL-SZ、加拿大群岛、Nares海峡通道)海冰输出面积或体积通量,着重介绍了弗拉姆海峡不同年龄海冰输出情况,并总结和分析了影响北极海冰输运的大尺度大气活动模态。最后,本文阐明北极海冰输出方面现有研究的不足之处以及未来的突破方向。     

1979-2012年北极海冰运动学特征初步分析

利用美国冰雪数据中心(NSIDC)发布的海冰速度和范围数据,本文分析了1979—2012年间北极海冰的运动学特征,以及北极海冰运动与分布范围演变之间的关系。结合欧洲中期天气预报中心(ECMWF)发布的2007和2012年高分辨率的气压场、风场数据,探讨了北极风场和气压场与海冰运动、辐散辐合和海冰面积的关系。结果表明,在1979-2012年间北极海冰平均运动速度呈显著增强的趋势,冬季海冰平均运动速度增加趋势明显强于夏季;北极、波弗特-楚科奇海域和弗拉姆海峡的冬、夏季海冰平均运动速度的增加率分别为2.1%/a和1.7%/a、2.0%/a和1.6%/a以及4.9%/a和2.2%/a。1979-2012年北极海冰平均运动速度和范围的相关性为-0.77,二者存在显著的负相关关系。北极冬季和夏季风场的长期变化趋势与海冰平均运动速度的变化趋势一致,冬季和夏季的相关系数分别为0.50和0.48。风场和气压场对海冰的运动、辐散及重新分布发挥着重要作用。2007年夏季,第234~273天波弗特海域一直被高压系统控制,波弗特涡旋加强,使得波弗特海域海冰聚集在北极中央区;顺时针的风场促使海冰向格陵兰岛和加拿大北极群岛以北聚合。2012年,白令海峡和楚科奇海域处于低压和高压系统的交界处,盛行偏北风,海冰从北极东部往西部输运,加拿大海盆的多年海冰因离岸运动而辐散,向楚科奇海域的海冰输运增加,受太平洋入流暖水影响,移入此区域的海冰加速融化,从而加剧海冰的减少。     

基于CryoSat-2卫星测高数据的北极海冰体积估算方法

近30年来,北极海冰正发生着剧烈的变化。海冰体积是量化海冰变化的重要指标之一。本文以2015年CryoSat-2卫星测高数据和OSI SAF海冰类型产品为基础。提取了浮冰出水高度、积雪深度、海冰密集度、海冰类型等属性信息,通过数据内插、投影变换、栅格转换、空间重采样等工作将海冰属性信息统一为25 km×25 km分辨率的栅格数据集。根据流体静力学平衡原理,逐个估算栅格像元对应的海冰厚度值,将其与对应的海冰面积相乘,估算了北极海冰密集度大于75%海域的海冰体积,并分析了海冰厚度和体积的月变化和季节变化特征。用NASA IceBridge海冰厚度产品对反演的海冰厚度进行验证。结果表明二者相关系数为0.72,有较高的一致性。北极海冰平均厚度春季最大,夏季最小,分别约为2.99 m和1.77 m,最厚的海冰集中在格陵兰沿岸北部和埃尔斯米尔半岛以北海域。多年冰平均厚度大于一年冰。冬季海冰体积最大,约为23.30×103 km3,经过夏季的融化,减少了近70%。一年冰体积季节波动较大,而多年冰体积相对稳定,季节变化不明显。     

Arctic summer sea ice phenology including ponding from 1982 to 2017

Information on the Arctic sea ice climate indicators is crucial to business strategic planning and climate monitoring. Data on the evolvement of the Arctic sea ice and decadal trends of phenology factors during melt season are necessary for climate prediction under global warming. Previous studies on Arctic sea ice phenology did not involve melt ponds that dramatically lower the ice surface albedo and tremendously affect the process of sea ice surface melt. Temporal means and trends of the Arctic sea ice phenology from 1982 to 2017 were examined based on satellite-derived sea ice concentration and albedo measurements. Moreover, the timing of ice ponding and two periods corresponding to it were newly proposed as key stages in the melt season. Therefore, four timings, i.e., date of snow and ice surface melt onset (MO), date of pond onset (PO), date of sea ice opening (DOO), and date of sea ice retreat (DOR); and three durations, i.e., melt pond formation period (MPFP, i.e., MO–PO), melt pond extension period (MPEP, i.e., PO–DOR), and seasonal loss of ice period (SLIP, i.e., DOO–DOR), were used. PO ranged from late April in the peripheral seas to late June in the central Arctic Ocean in Bootstrap results, whereas the pan-Arctic was observed nearly 4 days later in NASA Team results. Significant negative trends were presented in the MPEP in the Hudson Bay, the Baffin Bay, the Greenland Sea, the Kara and Barents seas in both results, indicating that the Arctic sea ice undergoes a quick transition from ice to open water, thereby extending the melt season year to year. The high correlation coefficient between MO and PO, MPFP illustrated that MO predominates the process of pond formation.     

Characteristics of sea ice kinematics from the marginal ice zone to the packed ice zone observed by buoys deployed during the 9th Chinese Arctic Expedition

Sea ice growth and consolidation play a significant role in heat and momentum exchange between the atmosphere and the ocean. However, few in situ observations of sea ice kinematics have been reported owing to difficulties of deployment of buoys in the marginal ice zone (MIZ). To investigate the characteristics of sea ice kinematics from MIZ to packed ice zone (PIZ), eight drifting buoys designed by Taiyuan University of Technology were deployed in the open water at the ice edge of the Canadian Basin. Sea ice near the buoy constantly increased as the buoy drifted, and the kinematics of the buoy changed as the buoy was frozen into the ice. This process can be determined using sea ice concentration, sea skin temperature, and drift speed of buoy together. Sea ice concentration data showed that buoys entered the PIZ in mid-October as the ice grew and consolidated around the buoys, with high amplitude, high frequency buoy motions almost ceasing. Our results confirmed that good correlation coefficient in monthly scale between buoy drift and the wind only happened in the ice zone. The correlation coefficient between buoys and wind was below 0.3 while the buoys were in open water. As buoys entered the ice zone, the buoy speed was normally distributed at wind speeds above 6 m/s. The buoy drifted mainly to the right of the wind within 45° at wind speeds above 8 m/s. During further consolidation of the ice in MIZ, the direct forcing on the ice through winds will be lessened. The correlation coefficient value increased to 0.9 in November, and gradually decreased to 0.7 in April.     

桑沟湾海冰厚度遥感反演研究

为解决现场调查数据覆盖不足的问题,利用卫星遥感数据(Landsat TM和ETM+)对桑沟湾海域的海冰厚度进行了反演。与Zubov模型计算结果相比,本反演结果与之接近(相关系数0.89)。由遥感影像提取结果看出,桑沟湾海冰厚度随时间和空间变化明显。在轻冰年份,桑沟湾基本无冰。在偏重冰年和重冰年份,桑沟湾出现大量浮冰,并且海冰在水动力和风应力的作用下,呈现由近岸到离岸冰厚不断减小的趋势。重冰年份桑沟湾南侧由于受潮汐和风力推动作用下发生挤压变形,近岸出现平均冰厚较大的海冰(20 cm),桑沟湾中部也出现平均厚度约5~10 cm的海冰。     

Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model (FIO-ESM) climate forecast system, satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) are assimilated into this system, using the method of localized error subspace transform ensemble Kalman ?lter (LESTKF). Five-year (2014–2018) Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation. Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent. All the biases of ice concentration, ice cover, ice volume, and ice thickness can be reduced dramatically through ice concentration and thickness assimilation. The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system. The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation. Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-to-seasonal (S2S) Prediction Project, FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast. Since sea ice thickness in the PIOMAS is updated in time, it is a good choice for data assimilation to improve sea ice prediction skills in the near-real-time Arctic sea ice seasonal prediction.