Collaborative Research: A global assessment of annual to decadal sea level predictability

合作研究：对每年至十年海平面可预测性的全球评估

• 批准号: 2148507
• 负责人: Christopher Little
• 金额: $ 40.92万
• 依托单位: Atmospheric and Environmental Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148507&HistoricalAwards=false
• 关键词: Collaborative; Research; global; assessment; annual

This study will determine the robustness of our current ability to predict sea-level variability at temporal scales from annual to decadal and at spatial scales that span the globe. The study will use a community model that considers processes in the ocean and in the atmosphere to simulate sea-level variability. The model results will be compared to data that are already available and determine the skill of the simulations and predictions, while elucidating the causes for model-data disparities. Ultimately, the study will propose whether sea-level variability can be described, and its predictability can be achieved, with oceanic and atmospheric processes. Appropriate representation of sea-level variability and predictability has fundamental implications for coastal communities worldwide. The principal investigator has ongoing efforts with government and private entities related to sea-level predictability.This project will quantify the ability of models to simulate and predict annual to decadal sea-level variability at a global scale. The skill of the Community Earth System Model (CESM) will be determined with horizontal resolutions of 1 and 0.1 degrees, while combining model results with tide gauge data, altimetry measurements and atmospheric pressure reanalysis. A mechanistic understanding of sea-level variability will be achieved by partitioning of remote and local forcing, as well as via the propagation of sea-level anomalies throughout the ocean. Sea-level anomalies will be deconstructed into various components: static vs dynamic, externally forced and unforced, manometric vs steric, local vs remote forcing. Intercomparisons of model simulations will identify the effects of model errors and random variability on the predictive skill throughout Earth. The mechanistic analyses will increase reliability of predictions; recommend observational and model experiments; and suggest limitations of ocean and climate variability representations. In terms of broader impacts, the project will have impacts on economic, cultural and ecological resources. The project will represent a collaboration with NCAR, facilitating the model (CESM) implementation. As predictions of sea-level variability are essential for coastal planning, this project will contribute to assess errors of CESM at scales of annual to decadal and relate them to heat, momentum and freshwater fluxes, advection and wave propagation. The PI has ongoing predictability efforts with local and state governments, as well as with the private sector.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.

这项研究将确定我们目前在从年度到十年的时间尺度和在全球范围的空间尺度上预测海平面变化的能力的稳健性。这项研究将使用一个考虑海洋和大气中的过程的社区模型来模拟海平面的变化。模型结果将与已有的数据进行比较，确定模拟和预测的技巧，同时阐明模型数据差异的原因。最终，这项研究将提出是否可以用海洋和大气过程来描述海平面变化，以及能否实现海平面变化的可预测性。适当反映海平面的可变性和可预测性对世界各地的沿海社区具有根本意义。首席调查员正在与政府和私人实体就海平面可预测性进行合作。该项目将量化模型在全球范围内模拟和预测年度至年代际海平面变化的能力。共同体地球系统模型(CESM)的技能将以1度和0.1度的水平分辨率确定，同时将模型结果与验潮仪数据、测高测量和大气压力再分析结合起来。对海平面变化的机械理解将通过划分远程和本地强迫以及通过海平面异常在整个海洋中的传播来实现。海平面异常将被分解成各种分量：静态与动态、外强迫与非强迫、压力式与静力式、局部强迫与远程强迫。模型模拟的相互比较将确定模型误差和随机变异性对整个地球的预测技能的影响。机械分析将提高预测的可靠性；推荐观测和模型实验；并提出海洋和气候变异性表示的局限性。就更广泛的影响而言，该项目将对经济、文化和生态资源产生影响。该项目将代表与NCAR的合作，促进模型(CESM)的实施。由于对海平面变化的预测对沿海规划至关重要，该项目将有助于评估CESM在年度至年代尺度上的误差，并将它们与热量、动量和淡水通量、平流和波浪传播联系起来。PI与地方和州政府以及私营部门进行了持续的可预测性工作。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Coastal Sea Level Observations Record the Twentieth-Century Enhancement of Decadal Climate Variability

沿海海平面观测记录了二十世纪十年间气候变化的增强

• DOI: 10.1175/jcli-d-22-0451.1
• 发表时间: 2023
• 期刊: Journal of Climate
• 影响因子: 4.9
• 作者: Little, Christopher M.