Role of the Overturning Circulation in Carbon Accumulation (ROCCA)
Role of the Overturning Circulation in Carbon Accumulation (ROCCA)
批准号:
NE/Y005252/1
负责人:
Richard Williams
金额:
$1.27万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
Human activities have caused atmospheric CO2 levels to increase dramatically, but their growth has been slowed by the oceans absorbing approximately one quarter of this anthropogenic carbon (Canth). Globally, the North Atlantic Ocean stores the highest quantities of Canth, due to local CO2 uptake from the atmosphere, and large-scale ocean currents, particularly the Atlantic Meridional Overturning Circulation (AMOC) delivering waters high in Canth to northern locations where they cool, get denser and sink to great depths away from contact with the atmosphere.Models project that the size of this carbon sink will reduce in the coming decades despite continued atmospheric CO2 increases, as surface warming increases stratification, decreases CO2 solubility, and AMOC weakening slows the transport of dense waters to depth. However there is substantial model spread regarding flux peak, and decline timing. The same models show a large range in ocean carbon transports, often related to AMOC representation. The balance between air- sea fluxes and ocean transports to North Atlantic Canth accumulation is thus not well constrained both now and into the future, and subject to large uncertainties.Previous observational studies have attempted to quantify the contributions of these processes to Canth accumulation in order to assist with model verification and validation. However, it is not currently possible to directly measure anthropogenic air-sea CO2 fluxes - they are chemically identical to those with 'normal', non-human-derived CO2. And while they can be calculated indirectly from trans-ocean basin decadal repeat cruises, this approach is subject to large uncertainties. It is thus impossible to constrain why fluxes (or carbon transports) vary on shorter timescales, or how they interact with the AMOC. For this we require frequent estimates of ocean transports combined with frequent estimates of how quickly carbon concentrations are increasing in the ocean.This project will look to do precisely that. Firstly, we will generate new high-resolution estimates of Canth transports across the subtropical and subpolar boundaries of the North Atlantic, relying on the outputs from the RAPID (10day) and OSNAP (monthly) mooring arrays. At RAPID, we will extend to 2024 the 2004-2013 time-series we published in 2021 and that identified a stable, northward Canth transport that was highly variable over all time scales (weekly, monthly, seasonally, annually, interannually), and highly correlated to the AMOC.We will collect new sub-seasonal water samples in Florida Straits, at the western boundary. The waters that flow through the Straits represent the vast majority of the upper, northward-flowing part of the overturning circulation but we don't currently account for any variability in water mass characteristics (chemical or otherwise) in the transport calculation there, so are not fully characterising the AMOC:carbon coupling.We'll generate a novel Canth transports time-series for 2014-2022 at the OSNAP, identifying how it co-varies with AMOC, and RAPID carbon transports. We'll track the changing interior (anthropogenic) carbon signal using novel, publicly-available datasets based on ship and autonomous platform data. Combined, we'll form a North Atlantic budget with transports at the southern and northern boundaries, and evolving concentrations in the interior. The residual will represent Canth entering (or leaving) through the surface - the air-sea flux.The contributions of air-sea fluxes and ocean circulation to regional carbon accumulation will be determined, better understanding how, with AMOC, they work together to store carbon. The calculation scheme, its components and transport/air-sea flux/AMOC relationships will be tested in earth system models, before observations are compared to simulation outputs. Our findings will help improve the accuracy of climate models, which is crucial for predicting the effects of climate change.
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