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.

该项目着重于改善北极的海冰范围可预测性。使用来自许多模拟的免费数据，这项研究将使用海热模式来评估秋季海冰的时机如何冻结与季节性可预测性有关。研究人员预计，该项目通过应用统计数据来评估哪些环境变量对海冰损失最为重要，从而有助于对环境对环境变化的影响产生重大贡献。在过去的40年中，海冰在所有月份都在下降，造成较薄和较小的海冰浮动，并且由于气候变暖，这种变化预计将继续。这些变化不仅会产生气候影响，而且会产生经济影响，这就是为什么准确预测海冰范围对季节性尺度的原因很重要的原因。随着北极损失海冰的覆盖率，它变得更容易被运输和资源提取，但对于依赖冰的土著社区和受威胁物种的可靠性也不太可靠。除非实体可以使用破冰船，否则海冰可以长成多米，导致北极地区无法实现。更好地了解导致海冰变化的因素将改善模型并告知公众，以便他们更有效地计划。该项目旨在吸引少数群体，并鼓励在许多教育水平上为人们提供对北极气候的进一步科学理解。这将通过为高中和中学生创建一个称为数据难题的简短课程（https://datapuzzles.org/）并雇用本科实习生。尽管众所周知，影响北极海冰损失的基本物理过程，但几乎没有关于季节性北极海洋热吸收和释放的幅度，可变性和趋势的数量信息。该项目将与新近可用的社区地球系统模型2大型合奏（CESM2-LE）的输出结合使用三个回顾模型的海洋热收益数据进行新颖的新颖使用。使用从被动微波海冰记录中的海冰浓度（SIC）数据，将计算每个网格电池处的开放水的总天数，以评估开放的水天和秋天冻结的日期的变化和变化。最后，将使用季节性自动回归的综合运动平均模型（Sarima）来预测一个，两个和三个月的交货时间。总海热增益，平均云覆盖物，总圆柱水以及开放水天数的数量将用作多元模型中的预测因子。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性预测的影响。该项目将极大地有助于理解北极气候和辅助模型改进。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛的影响审查标准的评估值得支持的。