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.

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.

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.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) 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) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. 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 year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. 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. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (data-products).There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 spreadsheet. The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because adjustments were applied only for global fluxes. What is in the files? (1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, southfgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitudesfco2: Surface ocean fCO2, dimensions: time, latitude, longitudearea: Area per pixel, dimensions: latitude, longitudearea_reg: Total surface ocean area covered by native grid, for global, north, tropics, south (2) The files for the GOBMs contain the following fields, for simulation A (‘contemporary simulation’, including effects of rising CO2, climate change and variability) and simulation B (‘control simulation’, constant CO2, no climate change and variability). Temporal resolution: monthly fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitudesfco2: Surface ocean fCO2, dimensions: time, latitude, longitudearea: Area per pixel, dimensions: latitude, longitude(3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.Fair data use statement:The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.Citation: Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2022 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).Acknowledgement: Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output.”Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudget.org/

The Paris Agreement aims to constrain global warming to ‘well below 2 °C’ and to ‘pursue efforts’ to limit it to 1.5 °C above pre-industrial levels. We quantify global and regional risk-related metrics associated with these levels of warming that capture climate change–related changes in exposure to water scarcity and heat stress, vector-borne disease, coastal and fluvial flooding and projected impacts on agriculture and the economy, allowing for uncertainties in regional climate projection. Risk-related metrics associated with 2 °C warming, depending on sector, are reduced by 10–44% globally if warming is further reduced to 1.5 °C. Comparing with a baseline in which warming of 3.66 °C occurs by 2100, constraining warming to 1.5 °C reduces these risk indicators globally by 32–85%, and constraining warming to 2 °C reduces them by 26–74%. In percentage terms, avoided risk is highest for fluvial flooding, drought, and heat stress, but in absolute terms risk reduction is greatest for drought. Although water stress decreases in some regions, it is often accompanied by additional exposure to flooding. The magnitude of the percentage of damage avoided is similar to that calculated for avoided global economic risk associated with these same climate change scenarios. We also identify West Africa, India and North America as hotspots of climate change risk in the future.

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.

Introduction To determine the association between mean glycated haemoglobin (HbA1c) or glycaemic variability and the development of diabetic retinopathy (DR) in people with diabetes. Methods An observational cohort study with people registered with a DR eye screening service between October 2012 and October 2017. Those who had no DR at the start of the study were followed for a maximum of 5 years. HbA1c measures were used to calculate HbA1c variability and mean HbA1c to assess any relationship between these and the risk of developing new onset DR. Results A total of 2511 individuals were followed up for up to 5 years. Of these, 542 (21.6%) developed DR. After adjustment, HbA1c variability was not significantly associated with the development of DR (p = 0.3435). However, the mean HbA1c was (p < 0.0001). Those with type 1 diabetes had an odds of 1.63 (95% CI 1.11–2.40) of a retinopathy diagnosis compared to those with type 2 diabetes. Conclusions We have shown that mean HbA1c is associated with an increased risk of developing diabetic retinopathy. However, after adjustment for sex, age, diabetes type and the mean, the HbA1c variability no longer remained significant. Our data suggest that optimizing long-term glycaemic control remains paramount.

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.

In this article, we analyze the impacts of climate change on Antarctic marine ecosystems. Observations demonstrate large-scale changes in the physical variables and circulation of the Southern Ocean driven by warming, stratospheric ozone depletion, and a positive Southern Annular Mode. Alterations in the physical environment are driving change through all levels of Antarctic marine food webs, which differ regionally. The distributions of key species, such as Antarctic krill, are also changing. Differential responses among predators reflect differences in species ecology. The impacts of climate change on Antarctic biodiversity will likely vary for different communities and depend on species range. Coastal communities and those of sub-Antarctic islands, especially range-restricted endemic communities, will likely suffer the greatest negative consequences of climate change. Simultaneously, ecosystem services in the Southern Ocean will likely increase. Such decoupling of ecosystem services and endemic species will require consideration in the management of human activities such as fishing in Antarctic marine ecosystems.

There is increasing recognition of the importance of gelatinous zooplankton (GZ) within the ocean. However, observations of GZ and understanding of their ecosystem role, lags behind other zooplankton. Increasing pressures on the ocean, including climate change and overfishing, will likely impact GZ. This thesis aims to identify the role of GZ in the marine ecosystem and carbon cycle using observations and a model. This is achieved by (1) an analysis of GZ abundance and biomass from a global database, (2) the addition of GZ as a Plankton Functional Type in the global biogeochemical model PlankTOM11, (3) an analysis of the effect of GZ on carbon export in PlankTOM11 and (4) a case study on the effects of overfishing and climate change on GZ in PlankTOM11. Model developments made use of available vital rates and biomass data. Parameterisation of mortality was the largest source of uncertainty for GZ; therefore, mortality was tuned based on the resulting biomass generated by PlankTOM11. GZ had the largest influence on macrozooplankton biomass and influenced the whole plankton ecosystem through trophic cascades. PlankTOM11 showed trophic level as the most important characteristic of GZ for increasing export. There is evidence that GZ mortality plays an important role in export, but this is not replicated in PlankTOM11, likely due to particulate organic carbon (OC) representation as smaller and with slower sinking speeds than GZ carcases. Further partitioning of OC should improve the representation of GZ mortality and its influence on export. The case study found overfishing reduced GZ biomass, in opposition to other studies. The lack of fish predation on GZ may be a key factor. Climate and overfishing acted synergistically on the ecosystem. GZ play a key role in marine ecosystems by influencing plankton community structures through trophic cascades, thus influencing carbon export.

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 and 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 (2008–2017), EFF was 9.4±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.7±0.02 GtC yr−1, SOCEAN 2.4±0.5 GtC yr−1, and SLAND 3.2±0.8 GtC yr−1, with a budget imbalance BIM of 0.5 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in EFF was about 1.6 % and emissions increased to 9.9±0.5 GtC yr−1. Also for 2017, ELUC was 1.4±0.7 GtC yr−1, GATM was 4.6±0.2 GtC yr−1, SOCEAN was 2.5±0.5 GtC yr−1, and SLAND was 3.8±0.8 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.7 % (range of 1.8 % to 3.7 %) based on national emission projections for China, the US, 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. The analysis presented here shows that the mean and trend in the five components of the global carbon budget are consistently estimated over the period of 1959–2017, 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 show (1) no consensus in the mean and trend in land-use change emissions, (2) a persistent low agreement among 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, originating 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 the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018, 2016, 2015a, b, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018.