A model of the Arctic Ocean carbon cycle

Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 116 (2011): C12020, doi:10.1029/2011JC006998.A three dimensional model of Arctic Ocean circulation and mixing, with a horizontal resolution of 18 km, is overlain by a biogeochemical model resolving the physical, chemical and biological transport and transformations of phosphorus, alkalinity, oxygen and carbon, including the air-sea exchange of dissolved gases and the riverine delivery of dissolved organic carbon. The model qualitatively captures the observed regional and seasonal trends in surface ocean PO4, dissolved inorganic carbon, total alkalinity, and pCO2. Integrated annually, over the basin, the model suggests a net annual uptake of 59 Tg C a−1, within the range of published estimates based on the extrapolation of local observations (20–199 Tg C a−1). This flux is attributable to the cooling (increasing solubility) of waters moving into the basin, mainly from the subpolar North Atlantic. The air-sea flux is regulated seasonally and regionally by sea-ice cover, which modulates both air-sea gas transfer and the photosynthetic production of organic matter, and by the delivery of riverine dissolved organic carbon (RDOC), which drive the regional contrasts in pCO2 between Eurasian and North American coastal waters. Integrated over the basin, the delivery and remineralization of RDOC reduces the net oceanic CO2 uptake by ~10%.This study has been carried out as part of ECCO2 and SASS (Synthesis of the Arctic System Science) projects funded by NASA and NSF, respectively. MM and MJF are grateful for support from the National Science Foundation (ARC-0531119 and ARC-0806229) for financial support. MM also acknowledges NASA for providing computer time, the use of the computing facilities at NAS center and also the Scripps post-doctoral program for further financial support that helped to complete the manuscript. RMK also acknowledges NOAA for support (NA08OAR4310820 and NA08OAR4320752).2012-06-1

Corrigendum to "Evaluating the ocean biogeochemical components of Earth system models using atmospheric potential oxygen and ocean color data" (Biogeosciences, 12, 193–208, 2015)

No abstract available

Evaluating CMIP5 ocean biogeochemistry and Southern Ocean carbon uptake using atmospheric potential oxygen: Present-day performance and future projection

Observed seasonal cycles in atmospheric potential oxygen (APO ~ O2 + 1.1 CO2) were used to evaluate eight ocean biogeochemistry models from the Coupled Model Intercomparison Project (CMIP5). Model APO seasonal cycles were computed from the CMIP5 air-sea O2 and CO2 fluxes and compared to observations at three Southern Hemisphere monitoring sites. Four of the models captured either the observed APO seasonal amplitude or phasing relatively well, while the other four did not. Many models had an unrealistic seasonal phasing or amplitude of the CO2 flux, which in turn influenced APO. By 2100 under RCP8.5, the models projected little change in the O2 component of APO but large changes in the seasonality of the CO2 component associated with ocean acidification. The models with poorer performance on present-day APO tended to project larger net carbon uptake in the Southern Ocean, both today and in 2100

Attribution of space-time variability in global-ocean dissolved inorganic Carbon

The inventory and variability of oceanic dissolved inorganic carbon (DIC) is driven by the interplay of physical, chemical, and biological processes. Quantifying the spatiotemporal variability of these drivers is crucial for a mechanistic understanding of the ocean carbon sink and its future trajectory. Here, we use the Estimating the Circulation and Climate of the Ocean-Darwin ocean biogeochemistry state estimate to generate a global-ocean, data-constrained DIC budget and investigate how spatial and seasonal-to-interannual variability in three-dimensional circulation, air-sea CO2 flux, and biological processes have modulated the ocean sink for 1995–2018. Our results demonstrate substantial compensation between budget terms, resulting in distinct upper-ocean carbon regimes. For example, boundary current regions have strong contributions from vertical diffusion while equatorial regions exhibit compensation between upwelling and biological processes. When integrated across the full ocean depth, the 24-year DIC mass increase of 64 Pg C (2.7 Pg C year−1) primarily tracks the anthropogenic CO2 growth rate, with biological processes providing a small contribution of 2 (1.4 Pg C). In the upper 100 m, which stores roughly 13 (8.1 Pg C) of the global increase, we find that circulation provides the largest DIC gain (6.3 Pg C year−1) and biological processes are the largest loss (8.6 Pg C year−1). Interannual variability is dominated by vertical advection in equatorial regions, with the 1997–1998 El Niño-Southern Oscillation causing the largest year-to-year change in upper-ocean DIC (2.1 Pg C). Our results provide a novel, data-constrained framework for an improved mechanistic understanding of natural and anthropogenic perturbations to the ocean sink. © 2022. The Authors

The ECCO-Darwin Data-Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean \u3cem\u3ep\u3c/em\u3eCO\u3csub\u3e2\u3c/sub\u3e and Air-Sea CO\u3csub\u3e2\u3c/sub\u3e Flux

Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO2. To address this challenge, we have updated and improved ECCO-Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint-based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data-constrained ECCO physics, a Green\u27s function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO-Darwin exhibits broad-scale consistency with observed surface ocean pCO2 and air-sea CO2 flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO2 uptake occur in subpolar seasonally stratified biomes, where ECCO-Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO-Darwin has a time-mean global ocean CO2 sink (2.47 ± 0.50 Pg C year−1) and interannual variability that are more consistent with interpolation-based products. Compared to interpolation-based methods, ECCO-Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO-Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate-related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property-conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies

The ECCO‐Darwin Data‐Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean pCO₂ and Air‐Sea CO₂ Flux

Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO₂. To address this challenge, we have updated and improved ECCO‐Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint‐based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data‐constrained ECCO physics, a Green's function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO‐Darwin exhibits broad‐scale consistency with observed surface ocean pCO₂ and air‐sea CO₂ flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO₂ uptake occur in subpolar seasonally stratified biomes, where ECCO‐Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO‐Darwin has a time‐mean global ocean CO₂ sink (2.47 ± 0.50 Pg C year⁻¹) and interannual variability that are more consistent with interpolation‐based products. Compared to interpolation‐based methods, ECCO‐Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO‐Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate‐related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property‐conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies

An analysis of the carbon balance of the Arctic Basin from 1997 to 2006

Author Posting. © The Authors, 2010. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Tellus B 62 (2010): 455-474, doi:10.1111/j.1600-0889.2010.00497.x.This study used several model-based tools to analyze the dynamics of the Arctic Basin between 1997 and 2006 as a linked system of land-ocean-atmosphere C exchange. The analysis estimates that terrestrial areas of the Arctic Basin lost 62.9 Tg C yr-1 and that the Arctic Ocean gained 94.1 Tg C yr-1. Arctic lands and oceans were a net CO2 sink of 108.9 Tg C yr-1, which is within the range of uncertainty in estimates from atmospheric inversions. Although both lands and oceans of the Arctic were estimated to be CO2 sinks, the land sink diminished in strength because of increased fire disturbance compared to previous decades, while the ocean sink increased in strength because of increased biological pump activity associated with reduced sea ice cover. Terrestrial areas of the Arctic were a net source of 41.5 Tg CH4 yr-1 that increased by 0.6 Tg CH4 yr-1 during the decade of analysis, a magnitude that is comparable with an atmospheric inversion of CH4. Because the radiative forcing of the estimated CH4 emissions is much greater than the CO2 sink, the analysis suggests that the Arctic Basin is a substantial net source of green house gas forcing to the climate system.This study was supported, in part, by the NSF Arctic System Science Program as part of the Arctic Carbon Cycle Synthesis Project (ARC-0531047, 0531082, 0531119, and 0554811)

Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion

Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0 and 30∘ N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the extra-tropical Northern Hemisphere that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through “expert judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.</p

Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion

Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0 and 30∘ N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the extra-tropical Northern Hemisphere that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through “expert judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.</p