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?

北极海冰的萎缩和变薄，利用北极丰富自然资源的愿望日益增加，以及北极航线潜在的竞争力，都为整个北冰洋的海上通道提供了更多的机会。随着商业航运兴趣的增长，以及当地社区对海冰安全旅行的关注，越来越需要可靠的海冰预报。然而，海冰预报科学落后于天气预报几十年。为此，成立了海冰预测网络（SIPN），将海冰预报员、数据生产者和利益相关者聚集在一起，推进海冰预测。SIPN在激励海冰预测界方面非常成功，但它仍然主要集中在泛北极范围，而不是针对特定地区和/或社区进行量身定制的预测。作为UoM地球观测科学中心（ceo）的新加拿大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计划集中在一个单一的首要研究问题上：我们如何在3到6个月的提前时间内预测加拿大北极地区的海冰状况？要回答这个问题，我们需要研究如何改进对海冰状况的探测，并利用各种观测和建模平台评估可预测性的来源。具体而言，总司将重点发展对哈德逊湾和巴芬湾海冰变化控制机制的更好理解，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。结果将通过公共信息网络进行传播，该网站目前每年的浏览量超过24 000次。一个关键的组成部分将是评估当地社区的需求，并调整我们对有用的海冰状态变量的预测（即海冰厚度，冰退缩和前进的时间）。以下相互关联的目标将有助于实现我的总体目标：海冰检测-我们如何通过一个完整的年度周期来提高对哈德逊和巴芬湾海冰地球物理状态的检测？2.海冰过程——影响这些区域内海冰变化的可预测性来源是什么？3.遥感-遥感和基于地面的测量如何在一系列空间和时间尺度上改进(1)和(2)的确定？4.气候模式-现有的耦合海洋-海洋-冰-大气模式在小空间尺度上预测(1)和(2)的效果如何？5.改进预报-我们如何整合观测时间序列数据和数值模式来改进季节性冰预报？6.对社区的好处-我们如何减少在海冰上安全旅行的风险和脆弱性？