OPP-PRF: The predictive capacity of ocean heat gain and autumn freeze up dates on seasonal sea ice extent from three reanalyses and the CESM2 Large Ensemble

OPP-PRF：根据三次再分析和 CESM2 大型集合对季节性海冰范围的海洋增热和秋季结冰日期的预测能力

• 批准号: 2219266
• 负责人: Meghan Helmberger
• 金额: $ 28万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219266&HistoricalAwards=false
• 关键词: OPP; PRF; predictive; capacity; ocean

This project focuses on improving sea ice extent predictability in the Arctic. Using freely available data from a number of simulations, this research will use ocean heat patterns to assess how the timing of autumn sea ice freeze up relates to seasonal predictability. The researcher expects this project to contribute significantly to the understanding of environmental influences on sea ice change across the Arctic Ocean, by applying statistics to evaluate which environmental variables are the most significant for sea ice loss. Over the last 40 years, sea ice has declined in all months, leaving thinner and smaller sea ice floes, and such changes are expected to continue due to climate warming. These changes produce not only climate impacts, but also economic impacts, which is why accurately forecasting sea ice extent on seasonal scales is important. As the Arctic loses sea ice cover, it becomes more accessible to shipping and resource extraction but also less reliable for Indigenous communities and threatened species that depend on the ice. Sea ice can grow to be multiple meters thick causing regions of the Arctic to be impassible, unless the entity has access to an icebreaker ship. Better knowing the factors contributing to sea ice change will improve models and inform the public so they can plan more efficiently. This project aims to engage minority groups and encourage the development of greater scientific understanding of the Arctic climate for people at many educational levels. This will be done by creating a short course for high school and middle school students called a Data Puzzle (https://datapuzzles.org/) and employing an undergraduate intern. While basic physical processes impacting sea ice loss in the Arctic are well recognized, little quantitative information exists on the magnitudes, variability, and trends in seasonal Arctic Ocean heat uptake and release. This project will make novel use of ocean heat gain data from three retrospective models in conjunction with output from the newly available Community Earth System Model version 2 Large Ensemble (CESM2-LE). Using sea ice concentration (SIC) data from the combined Passive Microwave sea ice record, the total days with open water at each grid cell will be calculated to assess how open water days and the date of autumn freeze up have varied and changed. Lastly, a seasonal autoregressive integrated moving average model (SARIMA) will be used to predict seasonal SIC at lead times of one, two and three months. Total ocean heat gain, average cloud cover, total column water, and number of open water days will be used as predictors in the multivariate model. The researcher will use statistical models to estimate the influence of various Arctic climate characteristics on sea ice extent and future seasonal predictions. This project will significantly contribute to the understanding of the Arctic climate and aid in model improvement.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.

该项目的重点是提高北极海冰范围的可预测性。这项研究将使用来自多个模拟的免费数据，利用海洋热量模式来评估秋季海冰冻结的时间与季节可预测性的关系。研究人员预计该项目将通过应用统计数据来评估哪些环境变量对海冰损失最重要，从而为了解环境对北冰洋海冰变化的影响做出重大贡献。在过去的 40 年里，海冰在所有月份都在减少，留下了更薄更小的海冰浮冰，而且由于气候变暖，这种变化预计将持续下去。这些变化不仅会产生气候影响，还会产生经济影响，这就是为什么在季节性尺度上准确预测海冰范围非常重要。随着北极失去海冰覆盖，航运和资源开采变得更加容易，但对于依赖冰的土著社区和受威胁物种来说，可靠性也随之降低。海冰可能会增长到数米厚，导致北极地区无法通行，除非该实体能够使用破冰船。更好地了解导致海冰变化的因素将改进模型并告知公众，以便他们能够更有效地进行规划。该项目旨在吸引少数群体的参与，并鼓励不同教育水平的人们对北极气候有更深入的科学了解。这将通过为高中生和中学生创建一个名为“数据拼图”(https://datapuzzles.org/) 的短期课程并雇用一名本科生实习生来完成。虽然影响北极海冰消失的基本物理过程已得到广泛认可，但关于北冰洋季节性热量吸收和释放的幅度、变化和趋势的定量信息却很少。该项目将新颖地利用来自三个回顾性模型的海洋热量增益数据以及新推出的社区地球系统模型版本 2 大型集合体 (CESM2-LE) 的输出。使用来自组合被动微波海冰记录的海冰浓度（SIC）数据，将计算每个网格单元处有开放水域的总天数，以评估开放水域天数和秋季结冰日期的变化和变化。最后，季节性自回归综合移动平均模型 (SARIMA) 将用于预测提前期为 1、2 和 3 个月的季节性 SIC。总海洋热量增益、平均云量、总柱水和开放水域天数将用作多变量模型中的预测因子。研究人员将使用统计模型来估计各种北极气候特征对海冰范围和未来季节预测的影响。该项目将极大地有助于了解北极气候并帮助模型改进。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。