NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP

Guo, Huan; John, Jasmin G.; Blanton, Chris; McHugh, Colleen; Nikonov, S. V.; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki; Balaji, .; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, T.; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul P. G.; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William J.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David; Ploshay, Jeffrey J.; Schwarzkopf, D.; Seman, Charles J.; Shao, Andrew; Silvers, Levi G.; Wyman, Bruce; Yan, Xiaolei; Zeng, Yujing; Adcroft, Alistair; Dunne, John; Held, Isaac M.; Krasting, John P.; Horowitz, Larry W.; Milly, C.; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong

doi:10.22033/esgf/cmip6.1414

Cited by 23 publications

(5 citation statements)

References 6 publications

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“…CanESM5-CanOE (Swart et al, 2019b(Swart et al, , 2019c agessc, epc100, o2, vo 3 GFDL-CM4 (Guo et al, 2018a(Guo et al, , 2018b agessc* (yr), o2 (gr), vo 1 GFDL-ESM4 (Krasting et al, 2018;John et al, 2018) agessc* (yr), epc100 (gr), o2 (gr), vo* 1 IPSL-CM6A-LR (Boucher et al, 2018(Boucher et al, , 2019 agessc, epc100, o2, vo 6 MIROC-ES2L (Hajima et al, 2019;Tachiiri et al, 2019) agessc, epc100, o2, vo 1 MPI-ESM1-2-HR (Jungclaus et al, 2019;Schupfner et al, 2019) epc100, o2, vo 2 MPI-ESM1-2-LR (Wieners et al, 2019a(Wieners et al, , 2019b agessc, epc100, o2, vo 10 MRI-ESM2-0 (Yukimoto et al, 2019a(Yukimoto et al, , 2019b epc100, o2, vo 1…”

Section: The Omz In Oxygen Coordinatesmentioning

confidence: 99%

Diverging Fates of the Pacific Ocean Oxygen Minimum Zone and Its Core in a Warming World

et al. 2022

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Section: The Omz In Oxygen Coordinatesmentioning

confidence: 99%

Diverging Fates of the Pacific Ocean Oxygen Minimum Zone and Its Core in a Warming World

et al. 2022

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“…Observed NH þ 4 concentrations can be obtained at the CLIVAR and Carbon Hydrographic Data Office (https://cchdo.ucsd.edu/, last retrieved on 10 June 2019) and SeaDataNet (https://www.seadatanet.org/, last retrieved on 28 August 2019). ESM4.1 model outputs are available on the ESGF portal (John et al, 2018;Krasting et al, 2018).…”

Section: Data Availability Statementmentioning

confidence: 99%

Ocean Ammonia Outgassing: Modulation by CO₂ and Anthropogenic Nitrogen Deposition

Paulot

Stock

John

et al. 2020

J Adv Model Earth Syst

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The imprint of anthropogenic activities on the marine nitrogen (N) cycle remains challenging to represent in global models, in part because of uncertainties regarding the source of marine N to the atmosphere. While N inputs of terrestrial origin present a truly external perturbation, a significant fraction of N deposition over the ocean arises from oceanic ammonia (NH 3) outgassing that is subsequently deposited in other ocean regions. Here, we describe advances in the Geophysical Fluid Dynamics Laboratory's (GFDL) Earth System Model 4 (ESM4.1) aimed at improving the representation of the exchange of N between atmosphere and ocean and its response to changes in ocean acidity and N deposition. We find that the simulated present-day NH 3 outgassing (3.1 TgN yr −1) is 7% lower than under preindustrial conditions, which reflects the compensating effects of increasing CO 2 (−16%) and N enrichment of ocean waters (+9%). This change is spatially heterogeneous, with decreases in the open ocean (−13%) and increases in coastal regions (+15%) dominated by coastal N enrichment. The ocean outgassing of ammonia is shown to increase the transport of N from N-rich to N-poor ocean regions, where carbon export at 100 m increases by 0.5%. The implications of the strong response of NH 3 ocean outgassing to CO 2 for the budget of NH 3 in the remote marine atmosphere and its imprint in ice cores are discussed.

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“…While BLINGv2 resolves fewer explicit ecosystem interactions than COBALTv2, both models perform well at representing the carbonate cycle and the transport of dissolved inorganic carbon and total alkalinity throughout the ocean 36 . Both models use the Model of the Ocean Carbonate System (Mocsy) routines for simulating components of the carbonate system 37 All simulations analyzed in this study [38][39][40][41][42] were part of NOAA-GFDL's contributions to the Sixth Coupled Model Intercomparison Project 43 (CMIP6) and are available through the Earth System Grid Federation (ESGF). The carbonate ion concentration in equilibrium with aragonite ("co3satarag") was not saved directly for CM4 and was calculated offline with the Mocsy routines using monthly mean fields of surface temperature, salinity, and dissolved inorganic carbon, total alkalinity, and phosphate.…”

Section: Methodsmentioning

confidence: 99%

Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning

Krasting

Palma

Sonnewald

et al. 2022

Commun Earth Environ

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Ocean acidification is a consequence of the absorption of anthropogenic carbon emissions and it profoundly impacts marine life. Arctic regions are particularly vulnerable to rapid pH changes due to low ocean buffering capacities and high stratification. Here, an unsupervised machine learning methodology is applied to simulations of surface Arctic acidification from two state-of-the-art coupled climate models. We identify four sub-regions whose boundaries are influenced by present-day and projected sea ice patterns. The regional boundaries are consistent between the models and across lower (SSP2-4.5) and higher (SSP5-8.5) carbon emissions scenarios. Stronger trends toward corrosive surface waters in the central Arctic Ocean are driven by early summer warming in regions of annual ice cover and late summer freshening in regions of perennial ice cover. Sea surface salinity and total alkalinity reductions dominate the Arctic pH changes, highlighting the importance of objective sub-regional identification and subsequent analysis of surface water mass properties.

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NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP

Cited by 23 publications

References 6 publications

Diverging Fates of the Pacific Ocean Oxygen Minimum Zone and Its Core in a Warming World

Diverging Fates of the Pacific Ocean Oxygen Minimum Zone and Its Core in a Warming World

Ocean Ammonia Outgassing: Modulation by CO₂ and Anthropogenic Nitrogen Deposition

Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning

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NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP

Cited by 23 publications

References 6 publications

Diverging Fates of the Pacific Ocean Oxygen Minimum Zone and Its Core in a Warming World

Diverging Fates of the Pacific Ocean Oxygen Minimum Zone and Its Core in a Warming World

Ocean Ammonia Outgassing: Modulation by CO2 and Anthropogenic Nitrogen Deposition

Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning

Contact Info

Product

Resources

About

Ocean Ammonia Outgassing: Modulation by CO₂ and Anthropogenic Nitrogen Deposition