ALBERT

Description: We estimate the global ocean N2O flux to the atmosphere and its confidence interval using a statistical method based on model perturbation simulations and their fit to a database of ΔpN2O (n=6136). We evaluate two submodels of N2O production. The first submodel splits N2O production into oxic and hypoxic pathways following previous publications. The second submodel explicitly represents the redox transformations of N that lead to N2O production (nitrification and hypoxic denitrification) and N2O consumption (suboxic denitrification), and is presented here for the first time. We perturb both submodels by modifying the key parameters of the N2O cycling pathways (nitrification rates, NH4+ uptake, N2O yields under oxic, hypoxic and suboxic conditions), and determine a set of optimal model parameters by minimisation of a cost function against 4 databases of N cycle observations derived from observed and model ΔpN2O concentrations. Our estimate of the global oceanic N2O flux resulting from this cost function minimisation is 2.4 ± 0.8 Tg N y−1, and is invariant to the choice of N2O submodel. These estimates suggest that the currently available observational data of surface ΔpN2O constrain the global N2O flux to a narrower range relative to the large range of results presented in the latest IPCC report.

Description: We estimate the global ocean N2O flux to the atmosphere and its confidence interval using a statistical method based on model perturbation simulations and their fit to a database of ΔpN2O (n =  6136). We evaluate two submodels of N2O production. The first submodel splits N2O production into oxic and hypoxic pathways following previous publications. The second submodel explicitly represents the redox transformations of N that lead to N2O production (nitrification and hypoxic denitrification) and N2O consumption (suboxic denitrification), and is presented here for the first time. We perturb both submodels by modifying the key parameters of the N2O cycling pathways (nitrification rates; NH4+ uptake; N2O yields under oxic, hypoxic and suboxic conditions) and determine a set of optimal model parameters by minimisation of a cost function against four databases of N cycle observations. Our estimate of the global oceanic N2O flux resulting from this cost function minimisation derived from observed and model ΔpN2O concentrations is 2.4 ± 0.8 and 2.5 ± 0.8 Tg N yr−1 for the two N2O submodels. These estimates suggest that the currently available observational data of surface ΔpN2O constrain the global N2O flux to a narrower range relative to the large range of results presented in the latest IPCC report.

Description: 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).

Description: Global ocean biogeochemistry models currently employed in climate change projections use highly simplified representations of pelagic food webs. These food webs do not necessarily include critical pathways by which ecosystems interact with ocean biogeochemistry and climate. Here we present a global biogeochemical model which incorporates ecosystem dynamics based on the representation of ten plankton functional types (PFTs): six types of phytoplankton, three types of zooplankton, and heterotrophic procaryotes. We improved the representation of zooplankton dynamics in our model through (a) the explicit inclusion of large, slow-growing macrozooplankton (e.g. krill), and (b) the introduction of trophic cascades among the three zooplankton types. We use the model to quantitatively assess the relative roles of iron vs. grazing in determining phytoplankton biomass in the Southern Ocean high-nutrient low-chlorophyll (HNLC) region during summer. When model simulations do not include macrozooplankton grazing explicitly, they systematically overestimate Southern Ocean chlorophyll biomass during the summer, even when there is no iron deposition from dust. When model simulations include a slow-growing macrozooplankton and trophic cascades among three zooplankton types, the high-chlorophyll summer bias in the Southern Ocean HNLC region largely disappears. Our model results suggest that the observed low phytoplankton biomass in the Southern Ocean during summer is primarily explained by the dynamics of the Southern Ocean zooplankton community, despite iron limitation of phytoplankton community growth rates. This result has implications for the representation of global biogeochemical cycles in models as zooplankton faecal pellets sink rapidly and partly control the carbon export to the intermediate and deep ocean.

Description: Regime shifts have been reported in many marine ecosystems, and are often expressed as an abrupt change occurring in multiple physical and biological components of the system. In the Gulf of Alaska, a regime shift in the late 1970s was observed, indicated by an abrupt increase in sea surface temperature and major shifts in the catch of many fish species. A thorough understanding of the extent and mechanisms leading to such regime shifts is challenged by data paucity in time and space. We investigate the ability of a suite of ocean biogeochemistry models of varying complexity to simulate regime shifts in the Gulf of Alaska by examining the presence of abrupt changes in time series of physical variables (sea surface temperature and mixed-layer depth), nutrients and biological variables (chlorophyll, primary productivity and plankton biomass) using change-point analysis. Our results show that some ocean biogeochemical models are capable of simulating the late 1970s shift, manifested as an abrupt increase in sea surface temperature followed by an abrupt decrease in nutrients and biological productivity. Models from low to intermediate complexity simulate an abrupt transition in the late 1970s (i.e. a significant shift from one year to the next) while the transition is smoother in higher complexity models. Our study demonstrates that ocean biogeochemical models can successfully simulate regime shifts in the Gulf of Alaska region. These models can therefore be considered useful tools to enhance our understanding of how changes in physical conditions are propagated from lower to upper trophic levels.

Description: 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.