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?

北极海冰的缩小和变薄，加上人们越来越希望利用北极丰富的自然资源，以及北极航线的潜在竞争力，所有这些都增加了整个北冰洋的海上通道。随着人们对商业航运的兴趣日益增长，以及当地社区对海冰上安全旅行的关注，越来越需要可靠的海冰预报。尽管如此，海冰预报科学仍然落后于天气预报几十年。为此，成立了海冰预报网络，将海冰预报员、数据制作者和利益攸关方聚集在一起，推进海冰预报。SIPN在激励海冰预报界方面非常成功，但它仍然主要侧重于泛北极范围，而不是为特定区域和/或社区量身定制预报。作为UoM地球观测科学中心（CEOS）新的加拿大150研究主席，我将通过开发新的数据产品和预测模型继续我在海冰预测方面的工作，但特别关注与当地社区相关的加拿大地区。我的DG方案是围绕一个单一的首要研究问题：我们如何预测海冰条件在3至6个月的提前期在加拿大北极？要回答这个问题，需要研究如何改善海冰状况的检测，并使用各种观测和建模平台评估可预测性的来源。具体而言，总干事将侧重于提高对控制哈德逊湾和巴芬湾海冰变化的机制的认识，开发新的统计预报模型，并评估这些过程在气候模型中的表现。调查结果将通过SIPN传播，SIPN目前每年的页面访问量超过24 000页。一个关键组成部分将是评估当地社区的需求，并调整我们对海冰状态变量的预测，这些变量是有用的（即海冰厚度，冰退和冰进的时间）。以下相互关联的目标将有助于实现我的总体目标：1.海冰探测----我们如何在一个完整的年度周期内改进对哈德逊湾和巴芬湾海冰的地球物理状态的探测？2.海冰过程-影响这些地区海冰变化的可预测性来源是什么？ 3.遥感-遥感和地面测量如何能在一定的空间和时间尺度范围内改进对（1）和（2）的确定？4.气候模式-现有的海洋-海冰-大气耦合模式在小空间尺度上对（1）和（2）的预测效果如何？5.改进预报-我们如何将观测的时间序列数据和数值模型结合起来，以改进季节性冰情预报？6.对社区的好处-我们如何减少海冰上安全旅行的风险和脆弱性？