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

mCDR 2023：海洋二氧化碳清除（mCDR）模型中量化自然变化和背景海洋碳汇的数据要求

• 批准号: 2333608
• 负责人: Galen McKinley
• 金额: $ 51.75万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333608&HistoricalAwards=false
• 关键词: mCDR; 2023; Data; requirements; quantifying

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.

为了应对气候变化的预期影响，除排放量减少外，还需要从大气和海洋中积极去除二氧化碳（CO2）。越来越多的共识是，至少某些社会对二氧化碳去除碳（CDR）的需求必须来自海洋。任何海洋CDR策略的重要要求是能够证明它具有“起作用”，即，它导致从大气中吸收了更多的二氧化碳，这是由于大气二氧化碳浓度上升而自然出现的，在这里称为“背景”海洋碳水槽。预计海洋模型将在这项工作中发挥关键作用。为了验证这些模型，该项目将在CDR部署可能发生的海洋地区确定自然背景碳吸收，其可变性以及已知的确定性程度。需要确定需要提高对背景海洋碳汇的理解并确定来自CDR的额外信号所需的其他采样的要求。这项工作将支持未来的观察系统开发，并最终对基于观察的基准的未来开发进行了对拟议的海洋CDR模型的评估。该项目将为早期职业研究员提供薪水支持，成为海洋碳循环和机器学习的专家，对海洋科学至关重要的技能和海洋CDR（MCDR）劳动力。该项目通过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）量化空气海洋二氧化碳变化的不确定性，以及在区域尺度上的综合背景碳碳汇，以及2）设定其他数据收集的需求，以减少这些不确定性。在全球范围成功的先前工作之后，这些目标将通过开发和应用“测试床”来实现。该测试床将是一种高分辨率（1/10°）海洋模型，将通过现有的Surface PCO2观测值的空间 - 周期性模式进行采样，该地区的西部和东美国海岸，夏威夷和综合海域。机器学习重建将根据这些样本进行重建，以重建全场PCO2。测试台的独特优势是，可以根据原始完整模型字段的比较来评估重建的忠诚度。这种方法可以评估稀疏数据和最先进的机器学习技术可以结合使用以限制表面海洋碳通量。在第二阶段，观察系统仿真实验（OSSES）将建立最佳的观察设计，以进一步减少重建不确定性。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得的支持。