Submarine Ice Thickness Data Analysis for Climate Change and Model Improvement

气候变化的海底冰厚数据分析和模型改进

• 批准号: 0453825
• 负责人: D. Andrew Rothrock
• 金额: $ 36.17万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0453825&HistoricalAwards=false
• 关键词: Submarine; Ice; Thickness; Data; Analysis

Funds are provided to solidify knowledge of arctic sea ice thickness through analyses of observations, which will, in particular, elucidate interannual variations, and through a critical assessment of discrepancies between observed and simulated ice thickness. By the spring of 2005, submarine ice draft data should be publicly available from some 35 cruises that took place during three decades - 1969 to 1999. Two tasks are proposed to analyze these data. First, the data will be fit to a statistical representation using multiple regression analysis. This is meant to isolate interannual variability from spatial and seasonal variability. Second, optimally interpolated fields of thickness quantities will be produced, primarily the mean, but also the mode, the probability density function of thickness, and the open water fraction. Thickness data will be separated into winter and summer groups and analyzed to produce optimal fields for the entire 31-year data set, for several years at a thickness maximum, and for several years at a thickness minimum. Both representations of the analyzed data- the multiple regressions and the optimally interpolated fields- will be placed in a national archive for community use by the end of the project. An in-house, state-of-the-art, coupled ice-ocean model of the Arctic Ocean, treating the combined momentum, heat, and mass balances of sea ice, including a detailed treatment of sea ice thickness with snow cover, has been shown to simulate successfully observed mean ice thickness averaged over the Arctic Ocean and the general decline in ice thickness from the late 1980s to the present. However, the model is unsatisfactory at reproducing the spatial field of thickness, i.e. the variations across the Arctic Ocean. Substantial improvement in the model should be possible through focused attention on the spatial structure of the discrepancies between model and observations. It is proposed to analyze how these discrepancies are structured in space, time, and with thickness itself and to investigate what aspects of the ice-ocean model and its forcing data are most likely causing these discrepancies. Forcing data, such as cloudiness, radiation, and snowfall, and model components, such as rafting and ridging, are candidates for detailed investigation.Improved sea ice models are necessary to understand the interactions of the Arctic with the global climate system. If the timing and location of abnormally thin sea ice were reliably predictable, it would have implications for topics as varied as the safety of indigenous hunters and the routing of shipping through the Arctic.

提供资金，通过分析观测资料，特别是阐明年际变化，并通过对观测和模拟冰厚之间差异的严格评估，巩固对北极海冰厚度的认识。 到2005年春天，从1969年到1999年的30年中进行的大约35次巡航中获得的海底冰吃水数据应该是公开的。 提出了两个任务来分析这些数据。 首先，使用多元回归分析将数据拟合到统计表示。 这意味着将年际变化与空间和季节变化隔离开来。 第二，将产生厚度量的最佳内插场，主要是平均值，但也有模式，厚度的概率密度函数，和开放水域分数。 厚度数据将被分成冬季和夏季组并进行分析，以产生整个31年数据集的最佳字段，其中厚度最大的几年，厚度最小的几年。 分析数据的两种表示形式--多重回归和最佳插值字段--将在项目结束时被放置在国家档案馆中供社区使用。一个内部的，最先进的，北冰洋的冰-海耦合模型，处理海冰的综合动量，热量和质量平衡，包括详细处理海冰厚度与积雪，已被证明成功地模拟观测到的平均冰厚度在北冰洋和冰厚度的普遍下降，从1980年代末到现在。 然而，该模型在再现厚度的空间场，即整个北冰洋的变化方面并不令人满意。 通过集中注意模型与观测之间差异的空间结构，应该可以对模型进行实质性改进。 建议分析这些差异是如何在空间，时间和厚度本身的结构，并调查哪些方面的冰-海洋模式及其强迫数据最有可能导致这些差异。 强迫数据，如云量，辐射和降雪，以及模式组件，如漂流和脊，是详细调查的候选人。改进海冰模式是必要的，以了解北极与全球气候系统的相互作用。 如果可以可靠地预测海冰异常薄的时间和位置，那么它将对土著猎人的安全和北极航运路线等各种问题产生影响。