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，可能还需要积极地从大气和海洋中去除二氧化碳（CO2）。越来越多的人一致认为，至少社会对二氧化碳去除（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能够证明它已经“起作用”，也就是说，它已经导致从大气中吸收额外的二氧化碳，而不是由于大气中二氧化碳浓度上升而自然发生的二氧化碳，这里称为“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，本项目将确定可能部署CDR的海洋区域的自然背景碳吸收、其可变性以及已知的确定性程度。将确定为提高对背景海洋碳汇的了解和自信地测量来自CDR的额外信号所需的额外采样要求。这项工作将支持未来观测系统的发展，并最终支持基于观测的基准的未来发展，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。该项目由国家海洋和大气管理局通过国家海洋学伙伴计划共同支持。该项目的目标是：1)在区域尺度上量化海气CO2通量变率和综合背景海洋碳汇的不确定性；2)为减少这些不确定性而确定额外数据收集的要求。在全球范围内成功的前期工作之后，这些目标将通过开发和应用一个“测试平台”来实现。该试验台将是一个高分辨率（1/10°）海洋模型，将对美国西海岸和东海岸、夏威夷和白令海等地区现有地表二氧化碳分压观测的时空格局进行采样。机器学习重建将基于这些样本进行重建，以重建全场，时变的pCO2。测试平台的独特优势是，重建的保真度可以通过与原始全模型场的比较来评估。这种方法可以评估稀疏数据和最先进的机器学习技术如何很好地结合起来限制海洋表面碳通量。在第二阶段，观测系统模拟实验（OSSEs）将建立最优的观测设计，进一步减少重建的不确定性。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。

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.