Using machine learning to quantify historical changes in ocean heat content

该提案将估算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时空模式。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。海洋热含量是衡量地球系统正在积累多少多余热量的重要指标，因此对提高对气候变化的认识和预测很重要。该项目将涉及学生，包括为来自传统黑人学院和大学的学生提供实习机会。该项目将使用集成人工神经网络（EANN）来估计过去50年的海洋总热含量。使用EANN机器学习方法将减少历史温度数据集的系统偏差，并产生带有误差估计的改进的历史数据集。该项目还将研究海洋变暖的时空模式。该项目的一个新颖之处在于，它将包括对深度超过2000米的深海的热含量的估计。该项目提供了最先进的海洋变暖估计，可用于限制海洋气候模型，因此具有广泛影响的强大潜力。该项目还通过实习扩大了代表性不足的少数族裔学生的参与。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.