Using machine learning to quantify historical changes in ocean heat content

使用机器学习来量化海洋热含量的历史变化

• 批准号: 1948985
• 负责人: Timothy DeVries
• 金额: $ 36.41万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948985&HistoricalAwards=false
• 关键词: Using; machine; learning; quantify; historical

This proposal will estimate how much the global ocean has warmed over the past half century and look at the spatial and temporal patterns of changes in ocean heat content. The project will use a machine learning approach to combine historical data such that errors and biases are minimized. An exciting aspect of the project is that it will also estimate heat content for the deep, abyssal ocean (deeper than 2000m). Ocean heat content is an important indicator for how much excess heat the Earth system is accumulating and is thus important for improving understanding and prediction of climate change. The project will involve students, including providing internships for students from Historically Black Colleges and Universities.This project will use ensemble Artificial Neural Networks (EANN) to estimate the total ocean heat content over the past fifty years. The use of EANN machine learning methods will reduce systematic biases in the historical temperature data sets and yield an improved historical data set with error estimates. The project will then also look at spatial and temporal patterns of ocean warming. A novel aspect of the project is that it will include estimates of OHC for the abyssal ocean deeper than 2000m. The project has strong potential for broader impacts by providing a state-of-the-art estimate of ocean warming which could be used to constrain ocean climate models. The project also broadens the participation of underrepresented minority students through internships.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.

该提案将估计过去半个世纪全球海洋变暖的程度，并研究海洋热含量变化的空间和时间模式。该项目将使用机器学习方法来结合历史数据，从而最大限度地减少错误和偏差。该项目的一个令人兴奋的方面是，它还将估算深海（深度超过 2000 米）的热含量。海洋热含量是地球系统积累了多少多余热量的重要指标，因此对于提高对气候变化的理解和预测非常重要。该项目将让学生参与，包括为历史悠久的黑人学院和大学的学生提供实习机会。该项目将使用集合人工神经网络（EANN）来估计过去五十年的海洋总热量。 EANN 机器学习方法的使用将减少历史温度数据集中的系统偏差，并产生具有误差估计的改进历史数据集。该项目还将研究海洋变暖的空间和时间模式。该项目的一个新颖之处在于，它将包括对深度超过 2000 米的深海的 OHC 估计。该项目通过提供最先进的海洋变暖估计，可用于约束海洋气候模型，具有产生更广泛影响的强大潜力。该项目还通过实习扩大代表性不足的少数族裔学生的参与。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。

项目成果

Global Mean Sea Level Rise Inferred From Ocean Salinity and Temperature Changes

根据海洋盐度和温度变化推断的全球平均海平面上升

• DOI: 10.1029/2022gl101004
• 发表时间: 2023
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Bagnell, Aaron;DeVries, Tim
• 通讯作者: DeVries, Tim

Correcting Biases in Historical Bathythermograph Data Using Artificial Neural Networks

• DOI: 10.1175/jtech-d-19-0103.1
• 发表时间: 2020-10-01
• 期刊: JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
• 影响因子: 2.2
• 作者: Bagnell, Aaron;DeVries, Timothy
• 通讯作者: DeVries, Timothy