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，南极大部分地区的海冰覆盖面积都有所增加。在过去六年中的四年中，面积综合的海冰总面积增长到创纪录的最大值，但2015-16年夏季的冰盖却创纪录地低。虽然令人印象深刻，但很难评估这些最近记录在长期变化背景下的重要性，因为连续的卫星记录只能追溯到1978年。在确定观测到的海流变化是仅仅由于自然变率还是人为强迫响应时，海冰连续记录的有限长度是一个重要的混杂因素。因此，对南极海冰趋势的科学认识仍然不足，对未来南极海冰趋势的预测也缺乏信心。为应对这一挑战，该项目力求利用卫星观测的海冰、海平面气压、热带海面温度、厄尔尼诺/南方涛动指数、一些代用数据（冰芯等）、以及南大洋的温度数据。研究的目的是尽可能准确地收集和联合收割机这些辅助记录，同时保留与现有实地数据内在不确定性相关的可变性。 一系列统计方法用于使用灵活回归对卫星时代海冰数据之间的关系进行建模，将使用贝叶斯和多变量动态时空（MDST）方法。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.