Surface Carbon EOV Report

Surface carbon syntheses and impact of AtlantOS observation

LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean

A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) the reconstruction of pCO2 climatology, and (2) the reconstruction of pCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step (2) augmented by their anomalies. During each step, the FFNN model reconstructs the nonlinear relationships between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1∘×1∘ grid for the period from 2001 to 2016. Global ocean pCO2 was reconstructed with satisfying accuracy compared with independent observational data from SOCAT. However, errors were larger in regions with poor data coverage (e.g., the Indian Ocean, the Southern Ocean and the subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO2 with reasonable skill over the equatorial Pacific associated with ENSO (the El Niño–Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variability between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern Hemisphere and, in particular, in the southern Pacific and the Indian Ocean, as these regions suffer from poor data coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea–air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends

Observation system simulation experiments in the Atlantic Ocean for enhanced surface ocean pCO2 reconstructions

To derive an optimal observation system for surface ocean pCO2 in the Atlantic Ocean and the Atlantic sector of the Southern Ocean, 11 observation system simulation experiments (OSSEs) were completed. Each OSSE is a feedforward neural network (FFNN) that is based on a different data distribution and provides ocean surface pCO2 for the period 2008-2010 with a 5g€¯d time interval. Based on the geographical and time positions from three observational platforms, volunteering observing ships, Argo floats and OceanSITES moorings, pseudo-observations were constructed using the outputs from an online-coupled physical-biogeochemical global ocean model with 0.25g nominal resolution. The aim of this work was to find an optimal spatial distribution of observations to supplement the widely used Surface Ocean CO2 Atlas (SOCAT) and to improve the accuracy of ocean surface pCO2 reconstructions. OSSEs showed that the additional data from mooring stations and an improved coverage of the Southern Hemisphere with biogeochemical ARGO floats corresponding to least 25g€¯% of the density of active floats (2008-2010) (OSSE 10) would significantly improve the pCO2 reconstruction and reduce the bias of derived estimates of sea-air CO2 fluxes by 74g€¯% compared to ocean model outputs

Editorial: Biogeochemistry and Genomics of Silicification and Silicifiers

International audienc

Toward an improved design of the in-situ observing system for ocean reanalysis, analysis and forecasting: design of experiments

This report presents the work plan within the task 1.3 - Observing System Design Studie

The global pandemic has shown we need an action plan for the ocean

The COVID-19 pandemic is the first serious test of how science can inform decision-making in the face of an immediate global threat, yielding important lessons on how science, society and policy interact. The global societal and economic impact of COVID-19 has shown that we need to assess, plan and prepare for potential future changes. These insights are particularly important for the ocean science community because of the global connectivity of the ocean and its crucial role in the Earth's climate system and in supporting all life on Earth. With climate change already impacting society and ecosystems, implementing mitigation measures to avoid and reduce emissions of greenhouses gases is an immediate priority (IPCC, 2021). Irreversible changes are already underway in the oceans and their impacts over the coming decades will continue to affect human communities, requiring societal responses and adaptation across multiple scales (IPCC, 2019, 2021)

Inconsistent strategies to spin up models in CMIP5: implications for ocean biogeochemical model performance assessment

International audienceDuring the fifth phase of the Coupled Model Inter-comparison Project (CMIP5) substantial efforts were made to systematically assess the skill of Earth system models. One goal was to check how realistically representative marine biogeochemical tracer distributions could be reproduced by models. In routine assessments model historical hind-casts were compared with available modern biogeochemi-cal observations. However, these assessments considered neither how close modeled biogeochemical reservoirs were to equilibrium nor the sensitivity of model performance to initial conditions or to the spin-up protocols. Here, we explore how the large diversity in spin-up protocols used for marine biogeochemistry in CMIP5 Earth system models (ESMs) contributes to model-to-model differences in the simulated fields. We take advantage of a 500-year spin-up simulation of IPSL-CM5A-LR to quantify the influence of the spin-up protocol on model ability to reproduce relevant data fields. Amplification of biases in selected biogeochemical fields (O2, NO3, Alk-DIC) is assessed as a function of spin-up duration. We demonstrate that a relationship between spin-up duration and assessment metrics emerges from our model results and holds when confronted with a larger ensemble of CMIP5 models. This shows that drift has implications for performance assessment in addition to possibly aliasing estimates of climate change impact. Our study suggests that differences in spin-up protocols could explain a substantial part of model disparities, constituting a source of model-to-model uncertainty

The impacts of ocean acidification on marine trace gases and the implications for atmospheric chemistry and climate

Surface ocean biogeochemistry and photochemistry regulate ocean–atmosphere fluxes of trace gases critical for Earth’s atmospheric chemistry and climate. The oceanic processes governing these fluxes are often sensitive to the changes in ocean pH (or pCO2) accompanying ocean acidification (OA), with potential for future climate feedbacks. Here, we review current understanding (from observational, experimental and model studies) on the impact of OA on marine sources of key climate-active trace gases, including dimethyl sulfide (DMS), nitrous oxide (N2O), ammonia and halocarbons. We focus on DMS, for which available information is considerably greater than for other trace gases. We highlight OA-sensitive regions such as polar oceans and upwelling systems, and discuss the combined effect of multiple climate stressors (ocean warming and deoxygenation) on trace gas fluxes. To unravel the biological mechanisms responsible for trace gas production, and to detect adaptation, we propose combining process rate measurements of trace gases with longer term experiments using both model organisms in the laboratory and natural planktonic communities in the field. Future ocean observations of trace gases should be routinely accompanied by measurements of two components of the carbonate system to improve our understanding of how in situ carbonate chemistry influences trace gas production. Together, this will lead to improvements in current process model capabilities and more reliable predictions of future global marine trace gas fluxes

An Assessment of CO2 Storage and Sea‐Air Fluxes for the Atlantic Ocean and Mediterranean Sea Between 1985 and 2018

31 pages, 8 figures, 1 table.-- This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial LicenseAs part of the second phase of the Regional Carbon Cycle Assessment and Processes project (RECCAP2), we present an assessment of the carbon cycle of the Atlantic Ocean, including the Mediterranean Sea, between 1985 and 2018 using global ocean biogeochemical models (GOBMs) and estimates based on surface ocean carbon dioxide (CO2) partial pressure (pCO2 products) and ocean interior dissolved inorganic carbon observations. Estimates of the basin-wide long-term mean net annual CO2 uptake based on GOBMs and pCO2 products are in reasonable agreement (−0.47 ± 0.15 PgC yr−1 and −0.36 ± 0.06 PgC yr−1, respectively), with the higher uptake in the GOBM-based estimates likely being a consequence of a deficit in the representation of natural outgassing of land derived carbon. In the GOBMs, the CO2 uptake increases with time at rates close to what one would expect from the atmospheric CO2 increase, but pCO2 products estimate a rate twice as fast. The largest disagreement in the CO2 flux between GOBMs and pCO2 products is found north of 50°N, coinciding with the largest disagreement in the seasonal cycle and interannual variability. The mean accumulation rate of anthropogenic CO2 (Cant) over 1994–2007 in the Atlantic Ocean is 0.52 ± 0.11 PgC yr−1 according to the GOBMs, 28% ± 20% lower than that derived from observations. Around 70% of this Cant is taken up from the atmosphere, while the remainder is imported from the Southern Ocean through lateral transportF. F. Pérez and A. Velo were supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033 and by European Union under grant agreement no. 101094690 (EuroGO-SHIP), and with E. Huertas contributed to WATER:iOS CSIC PTI. M. Becker acknowledges funding from the Research Council of Norway through N-ICOS-2 (Grant 296012), and Nansen Legacy, Grant 276730. N. Goris was supported by the strategic project DYNASOR (DYnamics of the North Atlantic Surface and Overturning ciRculation) of the Bjerknes Centre for Climate Research. M. López-Mozos was supported by the Grant PRE2020-093138 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future.” J. Tjiputra acknowledges funding from EU funded H2020 projects TRIATLAS (no. 817578) and OceanICU (no. 101083922). A. Olsen appreciates support from the Research Council of Norway through N-ICOS-2 (Grant 296012), and Horizon Europe through Grant 101083922 (OceanICU Improving Carbon Understanding). J.D. Müller and N. Gruber acknowledge support from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 821003 (project 4C) and no. 820989 (project COMFORT). M. Gehlen acknowledges support from the European Union’s Horizon 2020 research and innovation program under grant agreements no. 820989 (project COMFORT) and no. 862923 (project AtlantECO), as well as from Horizon Europe through Grant 101083922 (OceanICU). T. Chau and M. Gehlen appreciate funding through the European Copernicus Marine Environment Monitoring Service (CMEMS) Grant 83-CMEMSTAC-MOB. J. Hauck acknowledges funding from the Initiative and Networking Fund of the Helmholtz Association (Helmholtz Young Investigator Group Marine Carbon and Ecosystem feedback in the Earth System [MarESys], Grant VH-NG-1301) and from ERC-2022-STG OceanPeak, Grant agreement 101077209. R. Wanninkof acknowledges funding from the NOAA/OAR Global Ocean Monitoring and Observation Program (GOMO)Peer reviewe