Canada Sea Ice Outlook

加拿大海冰展望

• 批准号: RGPIN-2020-05689
• 负责人: Stroeve, Julienne
• 金额: $ 2.19万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740785
• 关键词: Canada; Sea; Ice; Outlook

The shrinking and thinning of Arctic sea ice, together with an increasing desire to utilize the Arctic's abundant natural resources, and the potential competitiveness of Arctic shipping routes, all provide for increased marine access throughout the Arctic Ocean. With interest in commercial shipping growing, as well as concerns for safe travel on the sea ice by local communities, there is a growing need for reliable sea ice forecasts. Nevertheless, the science of sea ice forecasting lags weather forecasting by several decades. In response, the Sea Ice Prediction Network (SIPN) was formed to bring together sea ice forecasters, data producers and stakeholders to advance sea ice forecasting. SIPN has been highly successful at galvanizing the sea ice forecasting community, yet it remains largely focused on the pan-Arctic scale, rather than tailoring forecasts for specific regions and/or communities. As a new Canada 150 Research Chair at the Centre for Earth Observation Science (CEOS) at UoM, I will continue my work on sea ice forecasting by developing new data products and forecasting models, but with a specific focus on Canadian regions of relevance to local communities. My DG programme is focused around a single overarching research question: How well can we forecast sea ice conditions at 3- to 6-month lead times in the Canadian Arctic? To answer this question requires research into ways we can improve detection of sea ice conditions, and assessing sources of predictability using a variety of observational and modeling platforms. Specifically, the DG will focus on developing improved understanding of mechanisms controlling sea ice variability in Hudson and Baffin Bays, developing novel statistical forecasting models and assessing how well these processes are represented in climate models. Results will be disseminated through SIPN, which currently receives over 24,000 page views each year. A key component will be to assess the needs of local communities and tailor our forecasts of sea ice state variables that are useful (i.e. sea ice thickness, timing of ice retreat and advance). The following interlinked objectives will help to achieve my overarching objective: 1.Sea ice detection - How can we improve detection of the geophysical state of sea ice in Hudson and Baffin Bays through a full annual cycle? 2.Sea ice processes - What are the sources of predictability that influence sea ice variability within these regions? 3.Remote sensing - How can remote sensing and surface-based measurements improve the determination of (1) and (2) over a range of spatial and temporal time-scales? 4.Climate models - How well do existing coupled ocean-sea-ice-atmosphere models predict (1) and (2) at small spatial scales? 5.Improving forecasts - How can we integrate observational time-series data and numerical models to improve seasonal ice forecasts? 6.Benefits to communities - How can we reduce risk and vulnerabilities for safe travel on the sea ice?

北极海冰的缩小和稀疏，以及越来越多利用北极自然资源的愿望以及北极运输路线的潜在竞争力，都可以增加整个北极海洋的海洋通道。由于对商业运输的兴趣以及当地社区对海冰安全旅行的担忧，因此对可靠的海冰预测的需求越来越大。然而，海冰科学预测滞后的天气预报数十年。作为响应，形成了海冰预测网络（SIPN），以将海冰预报员，数据生产者和利益相关者汇集在一起​​，以推进海冰预测。 SIPN在激发海冰预测社区方面取得了非常成功的态度，但它仍然主要集中在泛北极规模上，而不是针对特定地区和/或社区的预测量身定制。作为UOM地球观察科学中心（CEO）的新加拿大150位研究主席，我将通过开发新的数据产品和预测模型来继续在海冰预测上的工作，但特别关注与当地社区相关的加拿大地区。我的DG计划集中在一个总体研究问题上：我们可以如何在加拿大北极3到6个月的领先时间预测海冰状况如何？要回答这个问题，需要研究如何改善海冰条件的检测，并使用各种观察和建模平台评估可预测性的来源。具体而言，DG将着重于建立对控制Hudson和Baffin Bays海冰变异性机制的改进理解，开发新的统计预测模型，并评估这些过程在气候模型中的表现效果。结果将通过SIPN传播，SIPN目前每年获得24,000多个页面浏览量。一个关键组成部分是评估当地社区的需求，并根据有用的海冰状态变量进行定制（即海冰厚度，冰撤退的时机和前进）。以下相互联系的目标将有助于实现我的总体目标：1。冰冰检测 - 我们如何通过完整的年度周期来改善哈德逊海冰地区海冰的地球物理状态的检测？ 2. SEA ICE过程 - 影响这些地区海冰可变性的可预测性来源是什么？ 3.弹性传感 - 在空间和时间尺度的一系列范围内，遥感和基于表面的测量如何改善（1）和（2）的测定？ 4.气候模型 - 现有的耦合海 - 冰冰 - 大气模型在小空间尺度上预测（1）和（2）？ 5.提出预测 - 我们如何整合观察时间序列数据和数值模型以改善季节性冰预测？ 6.对社区的利益 - 我们如何减少在海冰上安全旅行的风险和脆弱性？