Mapping Dissolved Oxygen using Observations and Machine Learning

使用观察和机器学习绘制溶解氧图

• 批准号: 2123546
• 负责人: Takamitsu Ito
• 金额: $ 34.79万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123546&HistoricalAwards=false
• 关键词: Mapping; Dissolved; Oxygen; using; Observations

Oxygen is produced by algae in the sunlit surface waters and is released into the atmosphere. This process contributes to about the half of atmospheric oxygen. However, there is a growing consensus in the scientific community that the global ocean oxygen inventory has declined in recent decades. Ocean heat uptake causes the reduction of solubility, and changes in circulation and biogeochemical processes associated with the ocean warming can further change ocean oxygen content. The reduction of dissolved oxygen can have far-reaching impacts on the marine habitats. Recent estimates of the global oxygen decline are in the range of 0.5-3.3% over the period of 1970- 2010. Distribution of the historical O2 measurements is irregular and sparse, causing significant uncertainty in these estimates. The objective of this project is to determine changes in the dissolved oxygen content of the oceans based on observational data and machine learning techniques. The overarching hypothesis of this project is that there are significant, regional relationships between O2 and other observed quantities. Dissolved oxygen is ultimately controlled by the combination of ocean circulation, air-sea gas transfer and biological processes. These processes can be linked with other observed quantities such as temperature (T) and salinity (S), but such relationships can be complex and non-linear. Therefore, it is difficult to determine a universal relationship that governs the distribution of O2 based on the first principle. However, machine learning algorithms can extract empirical relationships between O2 and other variables from existing observations, allowing us to estimate O2 where direct observation is not available. The work will also support one graduate and one undergraduate student research and outreach activities at local events.In this project, machine learning will be used to fill data gaps in the historical O2 dataset and to generate an improved, gridded estimates of O2 from 1960 to present. This approach takes advantage of the large amount of accumulated in-situ observations over multiple decades including not only O2 itself but also other related variables such as T and S. The proposed work revolves around three hypotheses. First, the current estimates of global O2 trend and variability are strongly influenced by relatively data-rich regions such as North Atlantic and North Pacific. Machine-learning based O2 dataset with an improved gap-fill approaches is hypothesized to better represent relatively data-poor regions such as tropics and southern hemisphere oceans. Secondly, the current estimates indicate that less than half of O2 decline is explained by the solubility effect. The global O2-heat relationship measures the reinforcing effects of ocean ventilation and biogeochemistry. Machine learning can estimate empirical relationships between O2, T and other physical variables, which can be manipulated to perform sensitivity experiments. The empirical model of O2 can constrain the regional and global O2-heat relationship. Thirdly, it is hypothesized that observed O2 decline in the tropical thermocline are driven by the combination of natural climate variability and long-term trends. In the proposed work, sensitivity experiments are performed with the empirical model of O2 to evaluate the influences of long-term trends and decadal-scale changes associated with the modes of natural climate variability.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.

氧气由藻类在日光地面水中产生，并释放到大气中。这个过程有助于大约一半的大气氧。但是，科学界越来越共识，即近几十年来，全球海洋氧气库存下降。海洋热吸收会导致溶解度的降低，并且与海洋变暖相关的循环和生物地球化学过程的变化可以进一步改变海洋氧含量。溶解氧的减少可能对海洋栖息地产生深远的影响。在1970年至2010年期间，全球氧气下降的最新估计值在0.5-3.3％的范围内。历史O2测量的分布是不规则且稀疏的，在这些估计中导致了明显的不确定性。 该项目的目的是根据观察数据和机器学习技术来确定海洋溶解的氧气含量的变化。 该项目的总体假设是O2与其他观察到的数量之间存在显着的区域关系。溶解的氧气最终由海洋循环，空气气体转移和生物过程的组合控制。这些过程可以与其他观察到的数量（例如温度（T）和盐度（S）相关，但是这种关系可能是复杂且非线性的。因此，很难根据第一个原则确定控制O2分布的普遍关系。但是，机器学习算法可以从现有观察结果中提取O2与其他变量之间的经验关系，从而使我们能够估算没有直接观察的O2。这项工作还将支持当地活动的一名研究生和一项学生的研究和外展活动。在该项目中，机器学习将用于填补历史O2数据集中的数据空白，并从1960年至展示。这种方法利用了数十年来的大量积累的原位观察结果，不仅包括O2本身，还包括其他相关变量，例如T和S。拟议的工作围绕三个假设旋转。首先，当前对全球O2趋势和可变性的估计受到北大西洋和北太平洋等相对富含数据的地区的强烈影响。假设基于机器学习的O2数据集具有改进的缝隙填充方法，以更好地代表相对数据贫乏的区域，例如热带地区和南半球海洋。其次，当前的估计表明，溶解度效应少于O2下降的一半。全球O2加热关系衡量了海洋通风和生物地球化学的增强作用。机器学习可以估计O2，T和其他物理变量之间的经验关系，这些变量可以操纵以执行灵敏度实验。 O2的经验模型可以限制区域和全球O2加热关系。第三，假设观察到热带热跃层的O2下降是由自然气候变化和长期趋势的组合驱动的。在拟议的工作中，使用O2的经验模型进行敏感性实验，以评估与自然气候变异性模式相关的长期趋势和际阶级变化的影响。该奖项反映了NSF的法定任务，并被视为值得的值得通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。