Collaborative Research: Understanding Contemporary Variability in Antarctic Sea Ice: Ensemble Reconstruction of Sea Ice Extent and Concentration for the 20th Century

与北极形成对比的是，自1979年以来，大多数南极地区的海冰覆盖面积都有所增加。在过去六年中的四年中，综合面积的海冰总面积增长到了创纪录的最大值，但2015-16年夏季的冰盖达到了创纪录的低水平。虽然令人印象深刻，但很难在较长期变化的背景下评估这些最新记录的意义，因为连续的卫星记录只可以追溯到1978年。连续海冰记录的有限长度，是确定所观察到的海流变化是否仅由自然变化所致，还是代表强迫的人为反应的一个重大混杂因素。因此，对南极海冰趋势的科学理解仍然很差，对未来南极海冰趋势预测的信心也是如此。为了应对这一挑战，该项目试图利用卫星观测的海冰、海平面气压、热带海面温度、ENSO指数、一些替代数据(冰芯等)和现场南大洋温度数据之间的关系，重建海冰范围和海冰集中度。这项研究的目的是尽可能准确地收集和合并这些辅助记录，同时保留与现有实地数据的内在不确定性有关的可变性。将使用一系列统计方法来模拟卫星时代海冰数据之间的关系，使用灵活的回归、贝叶斯和多变量动态时空(MDST)方法。这项研究将包括加州大学洛杉矶分校和俄亥俄大学的本科生和研究生的跨学科培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In contrast to the Arctic, sea ice cover in most Antarctic regions has increased since 1979. The area-integrated total sea ice extent grew to record maximum values in four of the last six years, yet the 2015-16 summer was marked by record low ice cover. While impressive, it is difficult to assess the significance of these very recent records in the context of longer term variability, since the continuous satellite record only dates back to 1978. The limited length of the continuous sea ice record, is a significant confounding factor in ascertaining whether the observed current changes are due to natural variability alone, or represent a forced anthropogenic response. As a result, the scientific understanding of the Antarctic sea ice trends remains poor, as does confidence in projections of future Antarctic sea ice trends. To address this challenge, this project seeks to reconstruct sea ice extent and sea ice concentration, using the relationships between satellite-observed sea ice, sea level pressure, tropical sea surface temperature, ENSO indices, some proxy data (ice cores, etc.), and in situ Southern Ocean temperature data. The aim of the study is to collect and combine these ancillary records as accurately as possible while retaining the variability associated with the intrinsic uncertainty in the available field data. A range of statistical methods for modelling the relationship between satellite era sea-ice data using flexible regression, Bayesian and multivariate dynamic spatial temporal (MDST) methods will be used.The study will entrain cross-disciplinary training of undergraduate and a graduate student at UCLA and Ohio University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.