Mapping Dissolved Oxygen using Observations and Machine Learning

氧气是由阳光照射的水面上的藻类产生的，并释放到大气中。这个过程贡献了大气中大约一半的氧气。然而，科学界越来越多的共识是，近几十年来，全球海洋氧气储量有所下降。海洋热吸收导致溶解度降低，与海洋变暖相关的环流和生物地球化学过程的变化可进一步改变海洋氧含量。溶解氧的减少会对海洋生境产生深远的影响。最近对1970年至2010年期间全球氧气减少量的估计在0.5-3.3%之间。历史上氧测量值的分布是不规则和稀疏的，这在这些估计中造成了很大的不确定性。该项目的目标是根据观测数据和机器学习技术确定海洋溶解氧含量的变化。这个项目的首要假设是O2和其他观测量之间存在显著的区域关系。溶解氧最终是由海洋环流、海气输送和生物过程共同控制的。这些过程可以与其他观测量联系起来，如温度(T)和盐度(S)，但这种关系可能是复杂和非线性的。因此，很难根据第一性原理确定支配氧分布的普遍关系。然而，机器学习算法可以从现有的观察中提取O2和其他变量之间的经验关系，使我们能够在无法直接观察的情况下估计O2。这项工作还将支持一名研究生和一名本科生在当地活动中的研究和推广活动。在这个项目中，机器学习将用于填补历史O2数据集中的数据空白，并生成1960年至今的改进的网格化O2估计。该方法利用了数十年来积累的大量原位观测数据，不仅包括O2本身，还包括其他相关变量，如T和s。首先，目前对全球氧趋势和变率的估计受到北大西洋和北太平洋等数据相对丰富的地区的强烈影响。假设基于机器学习的O2数据集具有改进的空白填充方法，可以更好地表示相对缺乏数据的地区，如热带和南半球海洋。其次，目前的估计表明，不到一半的O2下降是由溶解度效应解释的。全球o2 -热关系测量了海洋通风和生物地球化学的强化效应。机器学习可以估计O2， T和其他物理变量之间的经验关系，可以操纵这些变量进行灵敏度实验。O2的经验模型可以约束区域和全球的O2-热关系。第三，假设观测到的热带温跃层O2下降是由自然气候变率和长期趋势共同驱动的。本文采用O2经验模式进行敏感性试验，以评估与自然气候变率模态相关的长期趋势和年代际变化的影响。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。

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.