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  • 1
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: Ecosystem processes are important determinants of the biogeochemistry of the ocean, and they can be profoundly affected by changes in climate. Ocean models currently express ecosystem processes through empirically derived parameterizations that tightly link key geochemical tracers to ocean physics. The explicit inclusion of ecosystem processes in models will permit ecological changes to be taken into account, and will allow us to address several important questions, including the causes of observed glacial–interglacial changes in atmospheric trace gases and aerosols, and how the oceanic uptake of CO2 is likely to change in the future. There is an urgent need to assess our mechanistic understanding of the environmental factors that exert control over marine ecosystems, and to represent their natural complexity based on theoretical understanding. We present a prototype design for a Dynamic Green Ocean Model (DGOM) based on the identification of (a) key plankton functional types that need to be simulated explicitly to capture important biogeochemical processes in the ocean; (b) key processes controlling the growth and mortality of these functional types and hence their interactions; and (c) sources of information necessary to parameterize each of these processes within a modeling framework. We also develop a strategy for model evaluation, based on simulation of both past and present mean state and variability, and identify potential sources of validation data for each. Finally, we present a DGOM-based strategy for addressing key questions in ocean biogeochemistry. This paper thus presents ongoing work in ocean biogeochemical modeling, which, it is hoped will motivate international collaborations to improve our understanding of the role of the ocean in the climate system.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2023-10-27
    Description: Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance, which has been accumulating in the atmosphere since the pre-industrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 parts per billion (ppb) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr-1 in both 2020 and 2021. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), we present a global N2O budget that incorporates both natural and anthropogenic sources and sinks, and accounts for the interactions between nitrogen additions and the biochemical processes that control N2O emissions. We use Bottom-Up (BU: inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and Top-Down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions increased 40 % (or 1.9 Tg N yr-1) in the past four decades (1980–2020). Direct agricultural emissions in 2020, 3.9 Tg N yr−1 (best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources (including ‘Fossil fuel and industry’, ‘Waste and wastewater’, and ‘Biomass burning’ (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1). For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.3 (lower-upper bounds: 10.5–27.0) Tg N yr-1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr-1. For the period 2010–2019, the annual BU decadal-average emissions for natural plus anthropogenic sources were 18.1 (10.4–25.9) Tg N yr-1 and TD emissions were 17.4 (15.8–19.20 Tg N yr-1. The once top emitter Europe has reduced its emissions since the 1980s by 31 % while those of emerging economies have grown, making China the top emitter since the 2010s. The observed atmospheric N2O concentrations in recent years have exceeded projected levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6), underscoring the urgency to reduce anthropogenic N2O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose establishing a global network for monitoring and modeling N2O from the surface through the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al. 2023).
    Type: Article , PeerReviewed
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  • 3
    Publication Date: 2023-02-08
    Description: Nitrous oxide (N2O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N2O concentrations have contributed to stratospheric ozone depletion1 and climate change2, with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N2O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N2O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N2O emissions. We use bottom-up (inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and top-down (atmospheric inversion) approaches to provide a comprehensive quantification of global N2O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N2O emissions were 17.0 (minimum–maximum estimates: 12.2–23.5) teragrams of nitrogen per year (bottom-up) and 16.9 (15.9–17.7) teragrams of nitrogen per year (top-down) between 2007 and 2016. Global human-induced emissions, which are dominated by nitrogen additions to croplands, increased by 30% over the past four decades to 7.3 (4.2–11.4) teragrams of nitrogen per year. This increase was mainly responsible for the growth in the atmospheric burden. Our findings point to growing N2O emissions in emerging economies—particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N2O–climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N2O emissions exceeds some of the highest projected emission scenarios3,4, underscoring the urgency to mitigate N2O emissions.
    Type: Article , PeerReviewed
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  • 4
    Publication Date: 2024-02-07
    Description: Understanding the relationship between surface marine ecosystems and the export of carbon to depth by sinking organic particles is key to representing the effect of ecosystem dynamics and diversity, and their evolution under multiple stressors, on the carbon cycle and climate in models. Recent observational technologies have greatly increased the amount of data available, both for the abundance of diverse plankton groups and for the concentration and properties of particulate organic carbon in the ocean interior. Here we use synthetic model data to test the potential of using machine learning (ML) to reproduce concentrations of particulate organic carbon within the ocean interior based on surface ecosystem and environmental data. We test two machine learning methods that differ in their approaches to data-fitting, the random forest and XGBoost methods. The synthetic data are sampled from the PlankTOM12 global biogeochemical model using the time and coordinates of existing observations. We test 27 different combinations of possible drivers to reconstruct small (POCS) and large (POCL) particulate organic carbon concentrations. We show that ML can successfully be used to reproduce modelled particulate organic carbon over most of the ocean based on ecosystem and modelled environmental drivers. XGBoost showed better results compared to random forest thanks to its gradient boosting trees' architecture. The inclusion of plankton functional types (PFTs) in driver sets improved the accuracy of the model reconstruction by 58 % on average for POCS and by 22 % for POCL. Results were less robust over the equatorial Pacific and some parts of the high latitudes. For POCS reconstruction, the most important drivers were the depth level, temperature, microzooplankton and PO4, while for POCL it was the depth level, temperature, mixed-layer depth, microzooplankton, phaeocystis, PO4 and chlorophyll a averaged over the mixed-layer depth. These results suggest that it will be possible to identify linkages between surface environmental and ecosystem structure and particulate organic carbon distribution within the ocean interior using real observations and to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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  • 5
    Publication Date: 2015-11-06
    Description: We use a suite of eight ocean biogeochemical/ecological general circulation models from the Marine Ecosystem Model Intercomparison Project and Coupled Model Intercomparison Project Phase 5 archives to explore the relative roles of changes in winds (positive trend of Southern Annular Mode, SAM) and in warming- and freshening-driven trends of upper ocean stratification in altering export production and CO2 uptake in the Southern Ocean at the end of the 21st century. The investigated models simulate a broad range of responses to climate change, with no agreement on a dominance of either the SAM or the warming signal south of 44°S. In the southernmost zone, i.e., south of 58°S, they concur on an increase of biological export production, while between 44 and 58°S the models lack consensus on the sign of change in export. Yet in both regions, the models show an enhanced CO2 uptake during spring and summer. This is due to a larger CO2(aq) drawdown by the same amount of summer export production at a higher Revelle factor at the end of the 21st century. This strongly increases the importance of the biological carbon pump in the entire Southern Ocean. In the temperate zone, between 30 and 44°S, all models show a predominance of the warming signal and a nutrient-driven reduction of export production. As a consequence, the share of the regions south of 44°S to the total uptake of the Southern Ocean south of 30°S is projected to increase at the end of the 21st century from 47 to 66% with a commensurable decrease to the north. Despite this major reorganization of the meridional distribution of the major regions of uptake, the total uptake increases largely in line with the rising atmospheric CO2. Simulations with the MITgcm-REcoM2 model show that this is mostly driven by the strong increase of atmospheric CO2, with the climate-driven changes of natural CO2 exchange offsetting that trend only to a limited degree (∼10%) and with negligible impact of climate effects on anthropogenic CO2 uptake when integrated over a full annual cycle south of 30°S.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
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  • 6
    Publication Date: 2015-09-25
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev , info:eu-repo/semantics/conferenceObject
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  • 7
    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
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  • 8
    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 Ecological Modelling 261-262 (2013): 43–57, doi:10.1016/j.ecolmodel.2013.04.006.
    Description: Dynamic Green Ocean Models (DGOMs) include different sets of Plankton Functional Types (PFTs) and equations, thus different interactions and food webs. Using four DGOMs (CCSM-BEC, PISCES, NEMURO and PlankTOM5) we explore how predator–prey interactions influence food web dynamics. Using each model's equations and biomass output, interaction strengths (direct and specific) were calculated and the role of zooplankton in modeled food webs examined. In CCSM-BEC the single size-class adaptive zooplankton preys on different phytoplankton groups according to prey availability and food preferences, resulting in a strong top-down control. In PISCES the micro- and meso-zooplankton groups compete for food resources, grazing phytoplankton depending on their availability in a mixture of bottom-up and top-down control. In NEMURO macrozooplankton controls the biomass of other zooplankton PFTs and defines the structure of the food web with a strong top-down control within the zooplankton. In PlankTOM5, competition and predation between micro- and meso-zooplankton along with strong preferences for nanophytoplankton and diatoms, respectively, leads to their mutual exclusion with a mixture of bottom-up and top-down control of the plankton community composition. In each model, the grazing pressure of the zooplankton PFTs and the way it is exerted on their preys may result in the food web dynamics and structure of the model to diverge from the one that was intended when designing the model. Our approach shows that the food web dynamics, in particular the strength of the predator–prey interactions, are driven by the choice of parameters and more specifically the food preferences. Consequently, our findings stress the importance of equation and parameter choice as they define interactions between PFTs and overall food web dynamics (competition, bottom-up or top-down effects). Also, the differences in the simulated food-webs between different models highlight the gap of knowledge for zooplankton rates and predator–prey interactions. In particular, concerted effort is needed to identify the key growth and loss parameters and interactions and quantify them with targeted laboratory experiments in order to bring our understanding of zooplankton at a similar level to phytoplankton.
    Description: This work was supported with funding from Palmer LTER (NSF OPP-0823101) and C-MORE (NSF EF-0424599).
    Keywords: Dynamic Green Ocean Model ; Plankton Functional Types ; Zooplankton ; Food web dynamic ; Predator–prey interactions
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 9
    Publication Date: 2022-05-25
    Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biogeosciences 12 (2015): 6955-6984, doi:10.5194/bg-12-6955-2015.
    Description: Past model studies have projected a global decrease in marine net primary production (NPP) over the 21st century, but these studies focused on the multi-model mean rather than on the large inter-model differences. Here, we analyze model-simulated changes in NPP for the 21st century under IPCC's high-emission scenario RCP8.5. We use a suite of nine coupled carbon–climate Earth system models with embedded marine ecosystem models and focus on the spread between the different models and the underlying reasons. Globally, NPP decreases in five out of the nine models over the course of the 21st century, while three show no significant trend and one even simulates an increase. The largest model spread occurs in the low latitudes (between 30° S and 30° N), with individual models simulating relative changes between −25 and +40 %. Of the seven models diagnosing a net decrease in NPP in the low latitudes, only three simulate this to be a consequence of the classical interpretation, i.e., a stronger nutrient limitation due to increased stratification leading to reduced phytoplankton growth. In the other four, warming-induced increases in phytoplankton growth outbalance the stronger nutrient limitation. However, temperature-driven increases in grazing and other loss processes cause a net decrease in phytoplankton biomass and reduce NPP despite higher growth rates. One model projects a strong increase in NPP in the low latitudes, caused by an intensification of the microbial loop, while NPP in the remaining model changes by less than 0.5 %. While models consistently project increases NPP in the Southern Ocean, the regional inter-model range is also very substantial. In most models, this increase in NPP is driven by temperature, but it is also modulated by changes in light, macronutrients and iron as well as grazing. Overall, current projections of future changes in global marine NPP are subject to large uncertainties and necessitate a dedicated and sustained effort to improve the models and the concepts and data that guide their development.
    Description: C. Laufkötter and the research leading to these results have received funding from the European Community’s Seventh Framework Programme (FP7 2007–2013) under grant agreements no. 238366 (Greencycles II) and 264879 (CarboChange). M. Vogt and N. Gruber acknowledge funding by ETH Zürich. S. C. Doney and I. D. Lima acknowledge support from NSF (AGS-1048827).
    Repository Name: Woods Hole Open Access Server
    Type: Article
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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
    Type: Article
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