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  • 1
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    Unknown
    PANGAEA
    In:  Supplement to: Pfeil, Benjamin; Olsen, Are; Bakker, Dorothee C E; Hankin, Steven; Koyuk, Heather; Kozyr, Alexander; Malczyk, Jeremy; Manke, Ansley; Metzl, Nicolas; Sabine, Christopher L; Akl, John; Alin, Simone R; Bellerby, Richard G J; Borges, Alberto Vieira; Boutin, Jacqueline; Brown, Peter J; Cai, Wei-Jun; Chavez, Francisco P; Chen, Arthur; Cosca, Catherine E; Fassbender, Andrea J; Feely, Richard A; González-Dávila, Melchor; Goyet, Catherine; Hardman-Mountford, Nicolas J; Heinze, Christoph; Hood, E Maria; Hoppema, Mario; Hunt, Christopher W; Hydes, David; Ishii, Masao; Johannessen, Truls; Jones, Steve D; Key, Robert M; Körtzinger, Arne; Landschützer, Peter; Lauvset, Siv K; Lefèvre, Nathalie; Lenton, Andrew; Lourantou, Anna; Merlivat, Liliane; Midorikawa, Takashi; Mintrop, Ludger J; Miyazaki, Chihiro; Murata, Akihiko; Nakadate, Akira; Nakano, Yoshiyuki; Nakaoka, Shin-Ichiro; Nojiri, Yukihiro; Omar, Abdirahman M; Padín, Xose Antonio; Park, Geun-Ha; Paterson, Kristina; Pérez, Fiz F; Pierrot, Denis; Poisson, Alain; Ríos, Aida F; Santana-Casiano, Juana Magdalena; Salisbury, Joe; Sarma, Vedula V S S; Schlitzer, Reiner; Schneider, Bernd; Schuster, Ute; Sieger, Rainer; Skjelvan, Ingunn; Steinhoff, Tobias; Suzuki, Toru; Takahashi, Taro; Tedesco, Kathy; Telszewski, Maciej; Thomas, Helmuth; Tilbrook, Bronte; Tjiputra, Jerry; Vandemark, Doug; Veness, Tony; Wanninkhof, Rik; Watson, Andrew J; Weiss, Ray F; Wong, Chi Shing; Yoshikawa-Inoue, Hisayuki (2013): A uniform, quality controlled Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data, 5(1), 125-143, https://doi.org/10.5194/essd-5-125-2013
    Publication Date: 2024-05-02
    Description: A well-documented, publicly available, global data set of surface ocean carbon dioxide (CO2) parameters has been called for by international groups for nearly two decades. The Surface Ocean CO2 Atlas (SOCAT) project was initiated by the international marine carbon science community in 2007 with the aim of providing a comprehensive, publicly available, regularly updated, global data set of marine surface CO2, which had been subject to quality control (QC). Many additional CO2 data, not yet made public via the Carbon Dioxide Information Analysis Center (CDIAC), were retrieved from data originators, public websites and other data centres. All data were put in a uniform format following a strict protocol. Quality control was carried out according to clearly defined criteria. Regional specialists performed the quality control, using state-of-the-art web-based tools, specially developed for accomplishing this global team effort. SOCAT version 1.5 was made public in September 2011 and holds 6.3 million quality controlled surface CO2 data points from the global oceans and coastal seas, spanning four decades (1968-2007). Three types of data products are available: individual cruise files, a merged complete data set and gridded products. With the rapid expansion of marine CO2 data collection and the importance of quantifying net global oceanic CO2 uptake and its changes, sustained data synthesis and data access are priorities.
    Keywords: 0306SFC_PRT; 061ASFC_PRT; 06AQ19860627-track; 06AQ19860928-track; 06AQ19911114-track; 06AQ19911210-track; 06AQ19921005-track; 06AQ19930128-track; 06AQ19930228-track; 06AQ19931019-track; 06AQ19940524-track; 06AQ19951206-track; 06AQ19960320-track; 06AQ19980411-track; 06AQ19990327-track; 06AQ20001004-track; 06AQ20001026-track; 06BE19961010-track; 06CK20060523-track; 06CK20060715-track; 06CK20060821-track; 06GA19960613-track; 06GA276_3; 06LB19831130-track; 06LB19840107-track; 06LB19840629-track; 06LB19850110-track; 06LB19850313-track; 06LB19850812-track; 06LB19860116-track; 06LB19860323-track; 06LB19860801-track; 06LB19861011-track; 06LB19861214-track; 06LB19870221-track; 06LB19870501-track; 06LB19870721-track; 06LB19870920-track; 06LB19871126-track; 06LB19871231-track; 06LB19880204-track; 06MT18_1; 06MT19910903-track; 06MT19920510-track; 06MT19921229-track; 06MT19941012-track; 06MT19941119-track; 06MT19950714-track; 06MT19960607-track; 06MT19960622-track; 06MT19970106-track; 06MT19970516-track; 06MT19970707-track; 06MT19970814-track; 06MT19981228-track; 06MT20021015-track; 06MT20060714; 06MT20060714-track; 06MT22_5; 06MT30_2; 06MT30_3; 06MT37_2; 06MT39_4; 06MT39_5; 06P119910616-track; 06P119950901-track; 06PO20050321; 06PO20050322-track; 07AL19951011-track; 07AL19960218-track; 07AL19970503-track; 07AL19990718-track; 07AL19991101-track; 07AL19991129-track; 07AL20000113-track; 07AL20000210-track; 07AL20000305-track; 07AL20010513-track; 07AL20010607-track; 07AL20010709-track; 07AL20010802-track; 09AR0103; 09AR19910926-track; 09AR19921019-track; 09AR19930105-track; 09AR19930311-track; 09AR19930807-track; 09AR19931119-track; 09AR19940101-track; 09AR19940831-track; 09AR19941213-track; 09AR19950717-track; 09AR19950916-track; 09AR19960119-track; 09AR19960822-track; 09AR19970910-track; 09AR19971114-track; 09AR19980228-track; 09AR19980404-track; 09AR19980715-track; 09AR19990716-track; 09AR20011031-track; 09AR9401; 09AR9404; 09AR9407; 09AR9501; 09AR9502; 09AR9601; 09AR9604; 09AR9701; 09AR9703; 09AR9707; 09AR9801; 09AR9806; 09AR9901; 09FA20000927-track; 09SS19951116-track; 09SS19990205-track; 11BE19940413-track; 11BE19950303-track; 11BE19950912-track; 11BE19970513-track; 11BE19970527-track; 11BE19970609-track; 11BE19970618-track; 11BE19970621; 11BE19970621-track; 11BE19970702-track; 11BE19980107-track; 11BE19980614-track; 11BE19980625-track; 11BE19980710-track; 11BE19990830-track; 11BE19990904-track; 11BE19990914-track; 11BE19990918-track; 11BE20010502-track; 11BE20010514-track; 11BE20010522-track; 11BE20020422-track; 11BE20020511-track; 11BE20020528-track; 11BE20021104-track; 11BE20030331-track; 11BE20030901; 11BE20030901-track; 11BE20031027; 11BE20031027-track; 11BE20031208; 11BE20031208-track; 11BE20040223; 11BE20040223-track; 11BE20040329; 11BE20040329-track; 11BE20040524; 11BE20040524-track; 11BE20040601-track; 11BE20041004; 11BE20041004-track; 11BE20060425; 11BE20060425-track; 11BE20060529-track; 11BE20070507-track; 18QA19730812-track; 18QA19731028-track; 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20070613_TC2; 20070620_TC2; 20070627_TC2; 20070703_TC2; 20070709_TC2; 20070716_TC2; 20070723_TC2; 20070730_TC2; 2007-07-BS; 20070806_TC2; 20070815_TC2; 20070820_TC2; 20070827_TC2; 2007-08-BS; 20070903_TC2; 20070910_TC2; 20070917_TC2; 20071001_TC2; 20071008_TC2; 20071010_TC2; 20071015_TC2; 20071023_TC2; 20071105_TC2; 20071115_TC2; 20071120_TC2; 20071128_TC2; 20071204_TC2; 20071211_TC2; 20071218_TC2; 20071225_TC2; 24N98L1; 24N98L2; 26GC20010421-track; 26GC20010831-track; 26NA20050107; 26NA20050107-track; 26NA20050115; 26NA20050115-track; 26NA20050130; 26NA20050130-track; 26NA20050207; 26NA20050207-track; 26NA20050317; 26NA20050317-track; 26NA20050321; 26NA20050321-track; 26NA20050402; 26NA20050402-track; 26NA20050420; 26NA20050420-track; 26NA20050502; 26NA20050502-track; 26NA20050511; 26NA20050511-track; 26NA20050523; 26NA20050523-track; 26NA20050531; 26NA20050531-track; 26NA20050614; 26NA20050614-track; 26NA20050624; 26NA20050624-track; 26NA20050714; 26NA20050714-track; 26NA20050720; 26NA20050720-track; 26NA20050730; 26NA20050730-track; 26NA20050805; 26NA20050805-track; 26NA20050815; 26NA20050815-track; 26NA20050824; 26NA20050824-track; 26NA20050914; 26NA20050914-track; 26NA20050927; 26NA20050927-track; 26NA20051005; 26NA20051005-track; 26NA20051018; 26NA20051018-track; 26NA20051026; 26NA20051026-track; 26NA20051110; 26NA20051110-track; 26NA20051117; 26NA20051117-track; 26NA20051130; 26NA20051130-track; 26NA20060518; 26NA20060518-track; 26NA20060527; 26NA20060527-track; 26NA20060607; 26NA20060607-track; 26NA20060617; 26NA20060617-track; 26NA20060628; 26NA20060628-track; 26NA20060708; 26NA20060708-track; 26NA20060719; 26NA20060719-track; 26NA20060728; 26NA20060728-track; 26NA20060809; 26NA20060809-track; 26NA20060818; 26NA20060818-track; 26NA20060830; 26NA20060830-track; 26NA20060908; 26NA20060908-track; 26NA20060920; 26NA20060920-track; 26NA20061011; 26NA20061011-track; 26NA20061021; 26NA20061021-track; 26NA20061128; 26NA20061128-track; 26NA20061202; 26NA20061202-track; 26NA20061214; 26NA20061214-track; 26NA20061225; 26NA20061225-track; 26NA20070103; 26NA20070103-track; 26NA20070112; 26NA20070112-track; 26NA20070125; 26NA20070125-track; 26NA20070205; 26NA20070205-track; 26NA20070216; 26NA20070216-track; 26NA20070323; 26NA20070323-track; 26NA20070329; 26NA20070329-track; 26NA20070410; 26NA20070410-track; 26NA20070418; 26NA20070418-track; 26NA20070427; 26NA20070427-track; 26NA20070509; 26NA20070509-track; 26NA20070518; 26NA20070518-track; 26NA20070530; 26NA20070530-track; 26NA20070610; 26NA20070610-track; 26NA20070622; 26NA20070622-track; 26NA20070701; 26NA20070701-track; 26NA20070712; 26NA20070712-track; 26NA20070721; 26NA20070721-track; 26NA20070802; 26NA20070802-track; 26NA20070811; 26NA20070811-track; 26NA20070901; 26NA20070901-track; 26NA20070912; 26NA20070912-track; 26NA20070923; 26NA20070923-track; 26NA20071003; 26NA20071003-track; 26NA20071014; 26NA20071014-track; 26NA20071024; 26NA20071024-track; 26NA20071103; 26NA20071103-track; 26NA20071114; 26NA20071114-track; 26NA20071124; 26NA20071124-track; 29HE050; 29HE19980729-track; 29HE20001028; 29HE20001028-track; 29HE20010306; 29HE20010306-track; 29HE20011027; 29HE20011027-track; 29HE20020305; 29HE20020305-track; 29HE20021028; 29HE20021028-track; 29HE20030409; 29HE20030409-track; 29HE20041021; 29HE20041021-track; 316N0154; 316N19810401-track; 316N19810416-track; 316N19810516-track; 316N19810619-track; 316N19810721-track; 316N19810821-track; 316N19810923-track; 316N19821202-track; 316N19821230-track; 316N19830130-track; 316N19831007-track; 316N19840111-track; 316N19871030-track; 316N19871123-track; 316N19871218-track; 316N19880128-track; 316N19940404-track; 316N19941201-track; 316N19950124-track; 316N19950310-track; 316N19950423-track; 316N19950611-track; 316N19950715-track; 316N19950829-track; 316N19951111-track; 316N19951205-track; 316N19961102-track; 316N19971005-track; 318M19780921-track; 318M19780928-track; 318M19790210-track; 318M19790308-track;
    Type: Dataset
    Format: application/zip, 1851 datasets
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  • 2
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    Unknown
    PANGAEA
    In:  Supplement to: Bakker, Dorothee C E; Pfeil, Benjamin; Smith, Karl; Hankin, Steven; Olsen, Are; Alin, Simone R; Cosca, Catherine E; Harasawa, Sumiko; Kozyr, Alexander; Nojiri, Yukihiro; O'Brien, Kevin M; Schuster, Ute; Telszewski, Maciej; Tilbrook, Bronte; Wada, Chisato; Akl, John; Barbero, Leticia; Bates, Nicolas R; Boutin, Jacqueline; Bozec, Yann; Cai, Wei-Jun; Castle, Robert D; Chavez, Francisco P; Chen, Lei; Chierici, Melissa; Currie, Kim I; de Baar, Hein J W; Evans, Wiley; Feely, Richard A; Fransson, Agneta; Gao, Zhongyong; Hales, Burke; Hardman-Mountford, Nicolas J; Hoppema, Mario; Huang, Wei-Jen; Hunt, Christopher W; Huss, Betty; Ichikawa, Tadafumi; Johannessen, Truls; Jones, Elizabeth M; Jones, Steve D; Jutterstrøm, Sara; Kitidis, Vassilis; Körtzinger, Arne; Landschützer, Peter; Lauvset, Siv K; Lefèvre, Nathalie; Manke, Ansley; Mathis, Jeremy T; Merlivat, Liliane; Metzl, Nicolas; Murata, Akihiko; Newberger, Timothy; Omar, Abdirahman M; Ono, Tsuneo; Park, Geun-Ha; Paterson, Kristina; Pierrot, Denis; Ríos, Aida F; Sabine, Christopher L; Saito, Shu; Salisbury, Joe; Sarma, Vedula V S S; Schlitzer, Reiner; Sieger, Rainer; Skjelvan, Ingunn; Steinhoff, Tobias; Sullivan, Kevin; Sun, Heng; Sutton, Adrienne; Suzuki, Toru; Sweeney, Colm; Takahashi, Taro; Tjiputra, Jerry; Tsurushima, Nobuo; van Heuven, Steven; Vandemark, Doug; Vlahos, Penny; Wallace, Douglas WR; Wanninkhof, Rik; Watson, Andrew J (2014): An update to the Surface Ocean CO2 Atlas (SOCAT version 2). Earth System Science Data, 6(1), 69-90, https://doi.org/10.5194/essd-6-69-2014
    Publication Date: 2024-06-12
    Description: The Surface Ocean CO2 Atlas (SOCAT), an activity of the international marine carbon research community, provides access to synthesis and gridded fCO2 (fugacity of carbon dioxide) products for the surface oceans. Version 2 of SOCAT is an update of the previous release (version 1) with more data (increased from 6.3 million to 10.1 million surface water fCO2 values) and extended data coverage (from 1968-2007 to 1968-2011). The quality control criteria, while identical in both versions, have been applied more strictly in version 2 than in version 1. The SOCAT website (http://www.socat.info/) has links to quality control comments, metadata, individual data set files, and synthesis and gridded data products. Interactive online tools allow visitors to explore the richness of the data. Applications of SOCAT include process studies, quantification of the ocean carbon sink and its spatial, seasonal, year-to-year and longerterm variation, as well as initialisation or validation of ocean carbon models and coupled climate-carbon models.
    Keywords: SOCAT; Surface Ocean CO2 Atlas Project
    Type: Dataset
    Format: application/zip, 2669 datasets
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  • 3
    Publication Date: 2019-02-01
    Description: Idealised and hindcast simulations performed with the stand-alone ocean carbon-cycle configuration of the Norwegian Earth System Model (NorESM-OC) are described and evaluated. We present simulation results of two different model versions at different grid resolutions and using two different atmospheric forcing data sets. Model version NorESM-OC1 corresponds to the version that is included in the fully coupled model NorESM-ME1, which participated in CMIP5. The main update between NorESM-OC1 and NorESM-OC1.2 is the addition of two new options for the treatment of sinking particles. We find that using a constant sinking speed, which has been the standard in NorESM's ocean carbon cycle module HAMOCC (HAMburg Ocean Carbon Cycle model) does not transport enough particulate organic carbon (POC) into the deep ocean below approximately 2000 m depth. The two newly implemented parameterisations, a particle aggregation scheme with prognostic sinking speed, and a simpler scheme prescribing a linear increase of sinking speed with depth, provide better agreement with observed POC fluxes. Additionally, reduced deep ocean biases of oxygen and remineralised phosphate indicate a better performance of the new parameterisations. For model version 1.2, a re-tuning of the ecosystem parameterisation has been performed, which (i) reduces previously too high primary production in high latitudes, (ii) consequently improves model results for surface nutrients, and (iii) reduces alkalinity and dissolved inorganic carbon biases at low latitudes. We use hindcast simulations with prescribed observed and constant (pre-industrial) atmospheric CO2 concentrations to derive the past and contemporary ocean carbon sink. For the period 1990–1999 we find an average ocean carbon uptake ranging from 2.01 to 2.58 Pg C yr-1 depending on model version, grid resolution and atmospheric forcing data set.
    Type: Article , PeerReviewed
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  • 4
    Publication Date: 2023-02-08
    Description: The Zero Emissions Commitment (ZEC) is the change in global mean temperature expected to occur following the cessation of net CO2 emissions and as such is a critical parameter for calculating the remaining carbon budget. The Zero Emissions Commitment Model Intercomparison Project (ZECMIP) was established to gain a better understanding of the potential magnitude and sign of ZEC, in addition to the processes that underlie this metric. A total of 18 Earth system models of both full and intermediate complexity participated in ZECMIP. All models conducted an experiment where atmospheric CO2 concentration increases exponentially until 1000 PgC has been emitted. Thereafter emissions are set to zero and models are configured to allow free evolution of atmospheric CO2 concentration. Many models conducted additional second-priority simulations with different cumulative emission totals and an alternative idealized emissions pathway with a gradual transition to zero emissions. The inter-model range of ZEC 50 years after emissions cease for the 1000 PgC experiment is −0.36 to 0.29 ∘C, with a model ensemble mean of −0.07 ∘C, median of −0.05 ∘C, and standard deviation of 0.19 ∘C. Models exhibit a wide variety of behaviours after emissions cease, with some models continuing to warm for decades to millennia and others cooling substantially. Analysis shows that both the carbon uptake by the ocean and the terrestrial biosphere are important for counteracting the warming effect from the reduction in ocean heat uptake in the decades after emissions cease. This warming effect is difficult to constrain due to high uncertainty in the efficacy of ocean heat uptake. Overall, the most likely value of ZEC on multi-decadal timescales is close to zero, consistent with previous model experiments and simple theory.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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  • 5
    Publication Date: 2024-02-07
    Description: Accurately predicting future ocean acidification (OA) conditions is crucial for advancing OA research at regional and global scales, and guiding society's mitigation and adaptation efforts. This study presents a new model-data fusion product covering 10 global surface OA indicators based on 14 Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with three recent observational ocean carbon data products. The indicators include fugacity of carbon dioxide, pH on total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. The evolution of these OA indicators is presented on a global surface ocean 1° × 1° grid as decadal averages every 10 years from preindustrial conditions (1750), through historical conditions (1850–2010), and to five future Shared Socioeconomic Pathways (2020–2100): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. These OA trajectories represent an improvement over previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs. The generated data product offers a state-of-the-art research and management tool for the 21st century under the combined stressors of global climate change and ocean acidification. The gridded data product is available in NetCDF at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html, and global maps of these indicators are available in jpeg at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/surface-oa-indicators.html. Key Points: - This study presents the evolution of 10 ocean acidification (OA) indicators in the global surface ocean from 1750 to 2100 - By leveraging 14 Earth System Models (ESMs) and the latest observational data, it represents a significant advancement in OA projections - This inter-model comparison effort showcases the overall agreements among different ESMs in projecting surface ocean carbon variables
    Type: Article , PeerReviewed
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  • 6
    Publication Date: 2024-02-07
    Description: Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020–2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate. Key Points: - Lockdown restrictions during COVID-19 have reduced emissions of aerosols and greenhouse gases - 12 CMIP6 Earth system models have performed coordinated experiments to assess the impact of this on climate - Aerosol amounts are reduced over southern and eastern Asia but there is no detectable change in annually averaged temperature or precipitation
    Type: Article , PeerReviewed
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  • 7
    Publication Date: 2024-04-29
    Type: Article , NonPeerReviewed
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  • 8
    Publication Date: 2022-05-25
    Description: Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 76 (2009): 113-133, doi:10.1016/j.jmarsys.2008.05.010.
    Description: Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983- 1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (〈 0.2 gC m-2d-2) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison six years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill.
    Description: This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G), as well as by numerous other grants to the various participating investigators
    Keywords: Primary production ; Modeling ; Remote sensing ; Satellite ocean color ; Statistical analysis ; Tropical Pacific Ocean (15°N to 15°S and 125°E to 95°W)
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
    Format: application/pdf
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  • 9
    Publication Date: 2022-05-25
    Description: © The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biogeosciences 10 (2013): 6225-6245, doi:10.5194/bg-10-6225-2013.
    Description: Ocean ecosystems are increasingly stressed by human-induced changes of their physical, chemical and biological environment. Among these changes, warming, acidification, deoxygenation and changes in primary productivity by marine phytoplankton can be considered as four of the major stressors of open ocean ecosystems. Due to rising atmospheric CO2 in the coming decades, these changes will be amplified. Here, we use the most recent simulations performed in the framework of the Coupled Model Intercomparison Project 5 to assess how these stressors may evolve over the course of the 21st century. The 10 Earth system models used here project similar trends in ocean warming, acidification, deoxygenation and reduced primary productivity for each of the IPCC's representative concentration pathways (RCPs) over the 21st century. For the "business-as-usual" scenario RCP8.5, the model-mean changes in the 2090s (compared to the 1990s) for sea surface temperature, sea surface pH, global O2 content and integrated primary productivity amount to +2.73 (±0.72) °C, −0.33 (±0.003) pH unit, −3.45 (±0.44)% and −8.6 (±7.9)%, respectively. For the high mitigation scenario RCP2.6, corresponding changes are +0.71 (±0.45) °C, −0.07 (±0.001) pH unit, −1.81 (±0.31)% and −2.0 (±4.1)%, respectively, illustrating the effectiveness of extreme mitigation strategies. Although these stressors operate globally, they display distinct regional patterns and thus do not change coincidentally. Large decreases in O2 and in pH are simulated in global ocean intermediate and mode waters, whereas large reductions in primary production are simulated in the tropics and in the North Atlantic. Although temperature and pH projections are robust across models, the same does not hold for projections of subsurface O2 concentrations in the tropics and global and regional changes in net primary productivity. These high uncertainties in projections of primary productivity and subsurface oxygen prompt us to continue inter-model comparisons to understand these model differences, while calling for caution when using the CMIP5 models to force regional impact models.
    Description: This work was supported by EU FP7 project CARBOCHANGE (under grant agreement No. 264879), EU FP7 project MEECE (under grant agreement No. 212085), EU FP7 project SOCCLI (under grant agreement No. 317699), and ANR project MACROES. S. C. Doney acknowledges the US National Science Foundation (AGS-1048827). This work has been supported by the Research Council of Norway through the EarthClim (207711/E10) and NOTUR/NorStore projects. M. Vichi acknowledges the support of the Italian Ministry of Education, University and Research and the Ministry for Environment, Land and Sea through the project GEMINA.
    Repository Name: Woods Hole Open Access Server
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  • 10
    Publication Date: 2022-05-25
    Description: Author Posting. © American Geophysical Union, 2010. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 24 (2010): GB3020, doi:10.1029/2009GB003655.
    Description: The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth-integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ 14C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll-a was derived from high-performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll-a time series were available.
    Description: This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G).
    Keywords: Marine primary productivity models ; BATS HOT trends ; Multidecadal climate forcing
    Repository Name: Woods Hole Open Access Server
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