Biogeochemical ocean models are usually based on two size classes for particulate organic matter: small classes (1-100 𝜇m) and large classes (100-5000 𝜇m). Based on the measurements of particulate organic carbon (POC) concentration from UVP5 profiles and observations of environmental and ecosystem conditions we estimated an optimal number of size classes for POC that can be introduced in biogeochemical ocean models. We identified four size classes based on the correlation between POC concentration and environmental and ecosystem variables. It gives us information on the relationship between POC and surrounding temperature, chlorophyll-a concentration, nitrate, phosphate and oxygen levels as well as plankton functional types (PFTs). Further, we applied Machine Learning methods to reconstruct size classes of POC concentration and identify the most important drivers for each class. We showed that the concentration of POC smaller than 0.3 mm mostly depends on environmental characteristics while concentration of POC bigger than 0.3 mm strongly depends on PFTs.

Understanding how climate variability and climate change affects marine ecosystem dynamics and its cascading implications for the carbon cycle is a “known-unknown” that was highlighted in the past four Assessment reports of the IPCC. We present results from a novel set of global ocean biogeochemistry model branches which were designed to explore the role of marine ecosystem structure for carbon dynamics both globally and regionally, with a focus on the Southern Ocean.PlankTOM12 is a global ocean biogeochemistry model based on the representation of marine microorganisms grouped into twelve Plankton Functional Types (PFTs) as a function of their importance for the carbon cycle. PlankTOM12 uniquely represents explicitly heterotrophic bacteria/archaea, six types of phytoplankton , and five types of zooplankton . We build three distinct branches of PlankTOM12, with identical ecosystem framework and identical physical environment, but each branch with its own set of ecosystem parameters allowing different ecosystem formations. Branch 1 (called GCB) is the historical branch that underpinned much prior research on the carbon cycle using this model and contributed to the Global Carbon Budget 2023. Branch 2 (ECO) is optimised to reproduce the observed mean, seasonal cycle, and interhemispheric distribution of surface chlorophyll-a (Chla). Branch 3 (CO2) is optimised to reproduce the observed mean and seasonality of the partial pressure of surface ocean carbon dioxide (pCO2). Even though the parameterisations are optimised globally, many of the substantial differences between the three branches occur in the Southern Ocean. In particular, it was not possible to reproduce a good mean and seasonality for both Chla and pCO2 simultaneously in the Southern Ocean. Strikingly, each of the three PlankTOM12 model branches offers a different perspective on marine ecosystem dynamics. The branches differ most distinctly in the relative fraction of biomass that is distributed among PFTs: the GCB branch distributes most of its biomass in small phytoplankton PFTs and large zooplankton PFTs, the ECO branch distributes its biomass relatively evenly among PFTs, and the CO2 branch is intermediate with most biomass in the small phytoplankton PFTs and large zooplankton PFTs, but also substantial biomass in the medium-sized PFTs for both phyto- and zooplankton. We show how the differences in these ecosystem structures transfer through to differences in carbon dynamics, including primary and secondary production, sinking fluxes of organic carbon, calcium carbonate, and silica, and how they propagate to carbon export to the deep ocean and export efficiency. We present the response of the three branches to recent climate change and variability using hindcast simulations over 1948-2022, and discuss model evaluation based on available biogeochemistry and ecosystem observational data. Finally, we suggest future applications and questions which may be best addressed by each model branch.

Ocean deoxygenation due to anthropogenic warming represents a major threat to marine ecosystems and fisheries. Challenges remain in simulating the modern observed changes in the dissolved oxygen (O 2 ). Here, we present an analysis of upper ocean (0-700m) deoxygenation in recent decades from a suite of the Coupled Model Intercomparison Project phase 6 (CMIP6) ocean biogeochemical simulations. The physics and biogeochemical simulations include both ocean-only (the Ocean Model Intercomparison Project Phase 1 and 2, OMIP1 and OMIP2) and coupled Earth system (CMIP6 Historical) configurations. We examine simulated changes in the O 2 inventory and ocean heat content (OHC) over the past 5 decades across models. The models simulate spatially divergent evolution of O 2 trends over the past 5 decades. The trend (multi-model mean and spread) for upper ocean global O 2 inventory for each of the MIP simulations over the past 5 decades is 0.03 ± 0.39×1014 [mol/decade] for OMIP1, −0.37 ± 0.15×10 14 [mol/decade] for OMIP2, and −1.06 ± 0.68×10 14 [mol/decade] for CMIP6 Historical, respectively. The trend in the upper ocean global O 2 inventory for the latest observations based on the World Ocean Database 2018 is −0.98×10 14 [mol/decade], in line with the CMIP6 Historical multi-model mean, though this recent observations-based trend estimate is weaker than previously reported trends. A comparison across ocean-only simulations from OMIP1 and OMIP2 suggests that differences in atmospheric forcing such as surface wind explain the simulated divergence across configurations in O 2 inventory changes. Additionally, a comparison of coupled model simulations from the CMIP6 Historical configuration indicates that differences in background mean states due to differences in spin-up duration and equilibrium states result in substantial differences in the climate change response of O 2 . Finally, we discuss gaps and uncertainties in both ocean biogeochemical simulations and observations and explore possible future coordinated ocean biogeochemistry simulations to fill in gaps and unravel the mechanisms controlling the O 2 changes.

Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 ppb (parts per billion) 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. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N2O to the total enhanced effective radiative forcing of greenhouse gases was 6.4 % for 1750–2022. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N2O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N2O emissions. We use bottom-up (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) 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 have increased 40 % (or 1.9 Tg N yr−1) in the past 4 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 18.5 (lower–upper bounds: 10.6–27.0) Tg N yr−1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr−1. For the 2010–2019 period, the annual BU decadal-average emissions for both natural and anthropogenic sources were 18.2 (10.6–25.9) 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 by 31 % since the 1980s, 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 importance of reducing anthropogenic N2O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose the establishment of a global network for monitoring and modeling N2O from the surface through to the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al., 2023).

AbstractThe ocean will ultimately store most of the CO2 emitted to the atmosphere by human activities. Despite its importance, estimates of the 2000−2022 trend in the ocean CO2 sink differ by a factor of two between observation-based products and process-based models. Here we address this discrepancy using a hybrid approach that preserves the consistency of known processes but constrains the outcome using observations. We show that the hybrid approach reproduces the stagnation of the ocean CO2 sink in the 1990s and its reinvigoration in the 2000s suggested by observation-based products and matches their amplitude. It suggests that process-based models underestimate the amplitude of the decadal variability in the ocean CO2 sink, but that observation-based products on average overestimate the decadal trend in the 2010s. The hybrid approach constrains the 2000−2022 trend in the ocean CO2 sink to 0.42 ± 0.06 Pg C yr−1 decade−1, and by inference the total land CO2 sink to 0.28 ± 0.13 Pg C yr−1 decade−1.

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.

The ocean is an important sink for carbon, absorbing 27% of the anthropogenic CO2 emitted to the atmosphere from human activities. The variability of this oceanic CO2 sink has come under scrutiny recently because data-products that are based on surface ocean CO2 observations (fCO2) and various statistical methods estimate twice the variability produced by process-based ocean models. These data-based products (pCO2 products) also suggest a strong growth of the oceanic CO2 sink in the past decade not produced by ocean models. Here we develop a hybrid approach that makes use of both fCO2 observations and an ocean model through a novel two-step optimisation approach. First, the model parameters are optimised to best reproduce fCO2 observations over the 1970-2019 period. Second, for each year with sufficient observations, the CO2 flux that minimises the distance to fCO2 observations is used as best estimate. This approach preserves the coherence of the physical and biogeochemical processes as represented in ocean models, while calibrating the CO2 fluxes to observations as done in pCO2 products. This approach confirms the ocean model weaknesses for producing coherent values of pCO2 in the high-latitude regions. The interannual variability in ocean CO2 sink after the two-step optimisation remains broadly consistent with the variability produced by the process model after the first optimisation step, but the amplitude of the variability is larger and closer to the one suggested by pCO2 products. The two-step otpimisation also improves the model’s representation of some documented decadal trends in the oceanic CO2 sink: its stagnation in 1990s and its reinvigoration in the 2000s. However, the optimisation approach does not support a strong increase of the oceanic CO2 sink in the past decade as reported by some pCO2 products. The finding is confirmed with the separate optimisation by latitude bands, where even in the Northern band (> 30°N) where the density of observations is maximum, the two-step optimisation returns a modest trend in the CO2 sink. In this high-latitude region, the fCO2 observations also suggest a low increase of the oceanic CO2 sink in the past decade, while they supported the previous increasing trend in the 2000s. Therefore, although the two-optimisation approach presented here partly reconciles results from process-based ocean models and data-based pCO2 products, it also suggests that the current generation of pCO2 products could be sensitive to numerical artefacts that artificially enhance the recent trend.

A lot of effort has been put in the representation of surface ecosystem processes in global carbon cycle models, in particular through the grouping of organisms into Plankton Functional Types (PFTs) which have specific influences on the carbon cycle. In contrast, the transfer of ecosystem dynamics into carbon export to the deep ocean has received much less attention, so that changes in the representation of the PFTs do not necessarily translate into changes in sinking of particulate matter. Models constrain the air-sea CO2 flux by drawing down carbon into the ocean interior. This export flux is five times as large as the CO2 emitted to the atmosphere by human activities. When carbon is transported from the surface to intermediate and deep ocean, more CO2 can be absorbed at the surface. Therefore, even small variability in sinking organic carbon fluxes can have a large impact on air-sea CO2 fluxes, and on the amount of CO2 emissions that remain in the atmosphere. In this work we focus on the representation of organic matter sinking in global biogeochemical models, using the PlankTOM model in its latest version representing 12 PFTs. We develop and test a methodology that will enable the systematic use of new observations to constrain sinking processes in the model. The approach is based on a Neural Network (NN) and is applied to the PlankTOM model output to test its ability to reconstruction small and large particulate organic carbon with a limited number of observations. We test the information content of geographical variables (location, depth, time of year), physical conditions (temperature, mixing depth, nutrients), and ecosystem information (CHL a, PFTs). These predictors are used in the NN to test their influence on the model-generation of organic particles and the robustness of the results. We show preliminary results using the NN approach with real plankton and particle size distribution observations from the Underwater Vision Profiler (UVP) and plankton diversity data from Tara Oceans expeditions and discuss limitations.

Jellyfish are increasingly recognised as important components of the marine ecosystem, yet their specific role is poorly defined compared to that of other zooplankton groups. This paper presents the first global ocean biogeochemical model that includes an explicit representation of jellyfish and uses the model to gain insight into the influence of jellyfish on the plankton community. The Plankton Type Ocean Model (PlankTOM11) model groups organisms into plankton functional types (PFTs). The jellyfish PFT is parameterised here based on our synthesis of observations on jellyfish growth, grazing, respiration and mortality rates as functions of temperature and jellyfish biomass. The distribution of jellyfish is unique compared to that of other PFTs in the model. The jellyfish global biomass of 0.13 PgC is within the observational range and comparable to the biomass of other zooplankton and phytoplankton PFTs. The introduction of jellyfish in the model has a large direct influence on the crustacean macrozooplankton PFT and influences indirectly the rest of the plankton ecosystem through trophic cascades. The zooplankton community in PlankTOM11 is highly sensitive to the jellyfish mortality rate, with jellyfish increasingly dominating the zooplankton community as its mortality diminishes. Overall, the results suggest that jellyfish play an important role in regulating global marine plankton ecosystems across plankton community structure, spatio-temporal dynamics and biomass, which is a role that has been generally neglected so far.

The Southern Ocean plays an important role in the uptake, transport and storage of carbon by the global oceans. These properties are dominated by the response to the rise in anthropogenic CO2 in the atmosphere, but they are modulated by climate variability and climate change. Here we explore the effect of climate variability and climate change on ocean carbon uptake and storage in the Southern Ocean. We assess the extent to which climate change may be distinguishable from the anthropogenic CO2 signal and from the natural background variability. We use a combination of biogeochemical ocean modelling and observations from the GLODAPv2020 database to detect climate fingerprints in dissolved inorganic carbon (DIC). We conduct an ensemble of hindcast model simulations of the period 1920-2019, using a global ocean biogeochemical model which incorporates plankton ecosystem dynamics based on twelve plankton functional types. We use the model ensemble to isolate the changes in DIC due to rising anthropogenic CO2 alone and the changes due to climatic drivers (both climate variability and climate change), to determine their relative roles in the emerging total DIC trends and patterns. We analyse these DIC trends for a climate fingerprint over the past four decades, across spatial scales from the Southern Ocean, to basin level and down to regional ship transects. Highly sampled ship transects were extracted from GLODAPv2020 to obtain locations with the maximum spatiotemporal coverage, to reduce the inherent biases in patchy observational data. Model results were sampled to the ship transects to compare the climate fingerprints directly to the observational data. Model results show a substantial change in DIC over a 35-year period, with a range of more than +/- 30 µmol/L. In the surface ocean, both anthropogenic CO2 and climatic drivers act to increase DIC concentration, with the influence of anthropogenic CO2 dominating at lower latitudes and the influence of climatic drivers dominating at higher latitudes. In the deep ocean, the anthropogenic CO2 generally acts to increase DIC except in the subsurface waters at lower latitudes, while climatic drivers act to decrease DIC concentration. The combined fingerprint of anthropogenic CO2 and climatic drivers on DIC concentration is for an increasing trend at the surface and decreasing trends in low latitude subsurface waters. Preliminary comparison of the model fingerprints to observational ship transects will also be presented.

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.

Accurate quantification of the global budget of atmospheric carbonyl sulfide (COS) is needed given its role in atmospheric chemistry and the global carbon cycle. COS is the most abundant atmospheric sulfur gas. In the stratosphere, COS is photodissociated to provide a significant source of sulfate aerosol, a key agent of stratospheric ozone depletion. In the troposphere, measurements of the COS variation have the potential to provide constraints on the rates of CO2 assimilation by terrestrial plants and hence on primary productivity. Accurate knowledge of the global budget of COS and of its respective source and sink fluxes is therefore needed to understand its impact on ozone depletion and on the carbon cycle. Recent estimates of the global COS budget, however, reveal discrepancies between known sources and sinks. In particular the magnitude of the oceanic flux (the largest known source to the atmosphere) remains uncertain. The ocean provides a source of COS to the troposphere through direct emission, and potentially through emission of COS precursors such as carbon disulfide (CS2). Here we assess the role of the ocean in the global COS budget using a global atmospheric transport model (GEOS-Chem) in combination with recent estimates of COS source and sink fluxes, and with available oceanic and atmospheric measurements of COS. We compare different realizations of oceanic COS fluxes taken from ocean biogeochemistry models and from recent data syntheses, and assess their ability to reduce the uncertainty in the current global budget of COS.

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. Bottom-up and top-down approaches are used to quantify global nitrous oxide sources and sinks resulting from both natural and anthropogenic sources, revealing a 30% increase in global human-induced emissions between 1980 and 2016.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein et al., 2019).

Anthropogenically-derived nitrogen input to the northern Indian Ocean has increased significantly in recent decades, based on both observational and model-derived estimates. This external nutrient source is supplied by atmospheric deposition and riverine fluxes, and has the potential to affect the vulnerable biogeochemical systems of the Arabian Sea and Bay of Bengal, influencing productivity and oceanic production of the greenhouse-gas nitrous-oxide (N2O). We summarize current estimates of this external nitrogen source to the northern Indian Ocean from observations and models, highlight implications for regional marine N2O emissions using model-based analyses, and make recommendations for measurement and model needs to improve current estimates and future predictions of this impact. Current observationally-derived estimates of deposition and riverine nitrogen inputs are limited by sparse measurements and uncertainties on accurate characterization of nitrogen species composition. Ocean model assessments of the impact of external nitrogen sources on regional marine N2O production in the northern Indian Ocean estimate potentially significant changes but also have large associated uncertainties. We recommend an integrated program of basin-wide measurements combined with high-resolution modeling and more detailed characterization of nitrogen-cycle process to address these uncertainties and improve current estimates and predictions.

The literature on the relative contributions of pelagic calcifying taxa to the global ocean export of CaCO3 is divided. Studies based on deep sediment trap data tend to argue that either foraminifers or coccolithophores, both calcite producers, dominate export. However, the compilations of biomass observations for pteropods, coccolithophores, and foraminifers instead show that pteropods dominate the global ocean calcifier biomass and therefore likely also carbonate export. Here we present a new global ocean biogeochemical model that explicitly represents these three groups of pelagic calcifiers. We synthesize databases of the physiology of the three groups to parameterize the model and then tune the unconstrained parameters to reproduce the observations of calcifier biomass and CaCO3 export. The model can reproduce both these observational databases; however, substantial dissolution of aragonite above the aragonite saturation horizon is required to do so. We estimate a contribution of pteropods to shallow (100 m) export of CaCO3 of at least 33% and to pelagic calcification of up to 89%. The high production‐high dissolution configuration that shows closest agreement with all the observations has a CaCO3 production of 4.7 Pg C/year but CaCO3 export at 100 m of only 0.6 Pg C/year.

We provide new estimates of the air-sea flux of CCl4 using simulations from a global ocean biogeochemistry model (NEMO-PlankTOM) in combination with depth-resolved CCl4 observations from global oceanic databases. Estimates of global oceanic CCl4 uptake are derived from a range of model analyses, including prescribed parameterizations using reported values on hydrolysis and degradation, and analyses optimized using the global observational databases. We evaluate the sensitivity of our results to uncertainties in air-sea gas exchange parameterization, estimation period, and circulation processes. Our best constrained estimate of ocean CCl4 uptake for the period 1996-2000 is 20.1Gg/year (range 16.6-22.7), corresponding to estimates of the partial atmospheric lifetime with respect to ocean uptake of 124 (110-150) years. This new oceanic lifetime implies higher emissions of CCl4 than currently estimated and therefore a larger missing atmospheric source of CCl4. Plain Language Summary Carbon tetrachloride (CCl4) is a trace gas emitted to the atmosphere during the industrial manufacture of refrigerants and solvents. CCl4 is also an ozone-depleting agent affecting loss of the atmosphere's protective ozone layer, and CCl4 emissions are now controlled by the Montreal Protocol. However, recent analyses of observed atmospheric CCl4 concentration conclude that the current rate of atmospheric decline is slower than expected, and inconsistent with recent estimates of CCl4 emissions to and removal from the atmosphere. Estimated oceanic uptake of CCl4 has been noted as potentially the most uncertain of the atmospheric loss processes, and here we provide new quantification of this flux. Our methodology employs a novel approach and uses a global ocean biogeochemistry model which includes representation of known physical and biogeochemical processes influencing oceanic CCl4 uptake, together with a compiled global ocean database of CCl4 observations. The observational database is used to evaluate model simulations, and to derive optimized estimates of the oceanic uptake flux by minimizing the discrepancies between the model and observations. We also evaluate the sensitivity of our estimates of ocean CCl4 uptake to known uncertainties in the model representation of processes, including air-sea gas transfer and oceanic chemical and biogeochemical loss. Our best constrained estimate of ocean CCl4 uptake for the 1996-2000 period is 20.1Gg/year (range 16.6-22.7), which is greater than previous recent estimates, and implies a larger missing source to the atmosphere of CCl4.

The ocean is estimated to contribute up to similar to 20% of global fluxes of atmospheric nitrous oxide (N2O), an important greenhouse gas and ozone depletion agent. Marine oxygen minimum zones contribute disproportionately to this flux. To further understand the partition of nitrification and denitrification and their environmental controls on marine N2O fluxes, we report new relationships between oxygen concentration and rates of N2O production from nitrification and denitrification directly measured with N-15 tracers in the Eastern Tropical Pacific. Highest N2O production rates occurred near the oxic-anoxic interface, where there is strong potential for N2O efflux to the atmosphere. The dominant N2O source in oxygen minimum zones was nitrate reduction, the rates of which were 1 to 2 orders of magnitude higher than those of ammonium oxidation. The presence of oxygen significantly inhibited the production of N2O from both nitrification and denitrification. These experimental data provide new constraints to a multicomponent global ocean biogeochemical model, which yielded annual oceanic N2O efflux of 1.7-4.4Tg-N (median 2.8Tg-N, 1 Tg=10(12)g), with denitrification contributing 20% to the oceanic flux. Thus, denitrification should be viewed as a net N2O production pathway in the marine environment.