mCDR 2023: Data requirements for quantifying natural variability and the background ocean carbon sink in marine carbon dioxide removal (mCDR) models

为了应对气候变化的预期影响，除了减排外，可能还需要积极消除大气和海洋中的二氧化碳(CO2)。越来越多的人达成共识，认为至少社会对二氧化碳清除(CDR)的部分需求将不得不来自海洋。任何海洋CDR战略的一个重要要求是有能力证明其“有效”，即它已导致大气中的额外二氧化碳被吸收，而不是由于大气中二氧化碳浓度上升而自然产生的二氧化碳，在这里被称为“背景”海洋碳汇。海洋模型预计将在这一努力中发挥关键作用。为了验证这些模型，该项目将确定可能进行CDR部署的海洋区域的自然背景碳吸收、其变异性以及已知的确定程度。将确定为加强对背景海洋碳汇的了解并自信地测量来自CDR的额外信号所需的额外采样的要求。这项工作将支持未来观测系统的发展，并最终支持以观测为基础的基准的未来发展，根据这些基准可以评估拟议的海洋CDR模型。该项目将为一名早期职业研究人员提供工资支持，使其成为海洋碳循环和机器学习方面的专家，这些技能对海洋科学和海洋CDR(MCDR)劳动力至关重要。该项目由美国国家海洋和大气管理局通过国家海洋伙伴计划共同支持。该项目的目标是：1)量化区域尺度上大气-海洋二氧化碳通量变率和综合背景海洋碳汇的不确定性；2)设定额外数据收集的要求，以减少这些不确定性。在全球范围内先前的工作取得成功之后，将通过开发和应用“试验床”来实现这些目标。这个试验床将是一个高分辨率(1/10°)海洋模型，将根据美国西海岸和东海岸、夏威夷和白令海地区现有地表二氧化碳观测的时空模式进行采样。将基于这些样本执行机器学习重建，以重建全场、时变的二氧化碳分压。试验台的独特优势是可以根据与原始全模型场的比较来评估重建的保真度。这种方法可以评估稀疏数据和最先进的机器学习技术结合在一起以限制表层海洋碳通量的程度。在第二阶段，观测系统模拟实验(OSSES)将建立可以进一步减少重建不确定性的最佳观测设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In order to combat the expected impacts of climate change, active removal of carbon dioxide (CO2) from the atmosphere and oceans will likely be needed in addition to emissions reductions. There is a growing consensus that at least some of society’s needs for carbon dioxide removal (CDR) will have to come from the ocean. An important requirement of any ocean CDR strategy is the ability to demonstrate that it has “worked,” i.e., that it has resulted in the uptake of additional CO2 from the atmosphere beyond what is already naturally occurring due to rising atmospheric CO2 concentrations, termed here the “background” ocean carbon sink. Ocean models are expected to play a key role in this effort. In the interest of validating these models, this project will determine the natural background carbon uptake, its variability, and the degree of certainty with which it is known, in areas of the ocean where CDR deployments are likely to take place. Requirements for additional sampling needed to improve understanding of the background ocean carbon sink and to confidently measure the additional signal from CDR will be determined. This work will support future observing system development, and ultimately the future development of observation-based benchmarks against which proposed marine CDR models can be evaluated. The project will provide salary support to an early career researcher to become an expert in ocean carbon cycling and machine learning, skills critical to ocean science and the marine CDR (mCDR) workforce. This project is being jointly supported by the National Oceanic and Atmospheric Administration, through the National Oceanographic Partnership Program.The objectives of this project are to 1) quantify uncertainties in air-sea CO2 flux variability and the integrated background ocean carbon sink on regional scales, and 2) set requirements for additional data collection that will reduce these uncertainties. Following successful prior work at the global scale, these objectives will be achieved by developing and applying a ‘testbed’. This testbed will be a high-resolution (1/10°) ocean model that will be sampled with the spatio-temporal pattern of existing surface pCO2 observations in regions on the West and East US Coast, Hawaii and the Bering Sea. Machine learning reconstructions will be performed based on these samples to reconstruct full field, time-varying pCO2. The unique advantage of a testbed is that the fidelity of the reconstructions can be evaluated based on comparison to the original full model fields. This approach allows for assessment of how well sparse data and state-of-the-art machine learning techniques can be combined to constrain surface ocean carbon fluxes. In a second phase, observing system simulation experiments (OSSEs) will establish optimal observing designs that can further reduce reconstruction uncertainties.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.