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The carbon cycle component of the newly developed Earth system model of intermediate complexity CLIMBER-X is presented. The model represents the cycling of carbon through the atmosphere, vegetation, soils, seawater and marine sediments. Exchanges of carbon with geological reservoirs occur through sediment burial, rock weathering and volcanic degassing. The state-of-the-art HAMOCC6 model is employed to simulate ocean biogeochemistry and marine sediment processes. The land model PALADYN simulates the processes related to vegetation and soil carbon dynamics, including permafrost and peatlands. The dust cycle in the model allows for an interactive determination of the input of the micro-nutrient iron into the ocean. A rock weathering scheme is implemented in the model, with the weathering rate depending on lithology, runoff and soil temperature. CLIMBER-X includes a simple representation of the methane cycle, with explicitly modelled natural emissions from land and the assumption of a constant residence time of CH4 in the atmosphere. Carbon isotopes 13C and 14C are tracked through all model compartments and provide a useful diagnostic for model–data comparison. A comprehensive evaluation of the model performance for the present day and the historical period shows that CLIMBER-X is capable of realistically reproducing the historical evolution of atmospheric CO2 and CH4 but also the spatial distribution of carbon on land and the 3D structure of biogeochemical ocean tracers. The analysis of model performance is complemented by an assessment of carbon cycle feedbacks and model sensitivities compared to state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Enabling an interactive carbon cycle in CLIMBER-X results in a relatively minor slow-down of model computational performance by ∼ 20 % compared to a throughput of ∼ 10 000 simulation years per day on a single node with 16 CPUs on a high-performance computer in a climate-only model set-up. CLIMBER-X is therefore well suited to investigating the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to >100 000 years.

Observational reconstructions indicate a contemporary increase in coastal ocean CO2 uptake. However, the mechanisms and their relative importance in driving this globally intensifying absorption remain unclear. Here we integrate coastal carbon dynamics in a global model via regional grid refinement and enhanced process representation. We find that the increasing coastal ocean CO2 uptake is primarily driven by the upper ocean-atmosphere equilibration lag (41%), followed by climate-induced changes in the circulation (36%) and increasing riverine nutrient loads (23%). The comparatively weak riverine impact is mediated by enhanced advective export of organic carbon across the shelf break, thereby further adding to the carbon enrichment of the open ocean. The contribution of biological carbon fixation increases as the coastal ocean capacity to hold CO2 decreases under continuous climate change and ocean acidification. Our seamless coastal ocean integration advances carbon cycle model realism, which is relevant for addressing impacts of climate change mitigation efforts.

I will present the first global ocean-biogeochemistry model with a seamless integration of coastal carbon dynamics, ICON-Coast, and provide insights from recent simulations on the drivers of the increasing CO2 uptake efficiency of the coastal ocean. Based on the unstructured triangular grid of the model, we globally apply a mesh refinement in the coastal ocean to better resolve complex circulation features as well as ocean-shelf exchange. Moreover, we incorporate tidal currents including bottom drag effects, and extended the model's biogeochemistry component to account for key shelf-specific carbon transformation processes. In this way the model encompasses all coastal areas around the globe within a single, consistent ocean-biogeochemistry model, thus naturally accounting for two-way coupling of ocean-shelf feedback mechanisms at the global scale. First hindcast simulations over the 20th century indicate that the increasing CO2 uptake efficiency of the coastal ocean is mainly driven by the rising pCO2 in the atmosphere (40%), climate-induced changes in the circulation (40%) and increasing historical nutrient loads from rivers (20%). While river inputs caused a significant boost in organic carbon sequestration by enhanced biological productivity, this mainly induced a shift in the resource utilization, from dissolved inorganic carbon delivered by the open ocean towards absorbed CO2 from the atmosphere. Thus the comparatively weak riverine impact on the CO2 uptake at the sea surface is mediated by an enhanced advective export of organic carbon, this way further intensifying the carbonation of the open ocean.

The marine biological carbon pump substantially contributes to the glacial-interglacial CO2 change. Compared to the late Holocene, proxy data for the Last Glacial Maximum (LGM) generally agree on an increased export production, associated with an enhanced marine biological carbon pump, in the subantarctic region of the Southern Ocean (SO). By contrast, global export production during the LGM is poorly constrained due to the sparseness and uncertainty of proxy data. The efficiency of the biological pump is mainly controlled by phytoplankton growth, ocean circulation and the sinking and remineralisation of organic matter. Previous modelling studies primarily focused on the sensitivity regarding the former two factors. By far, few studies have discussed the impact of marine particle sinking on glacial ocean biogeochemistry.In this study, we examine the impact of two different sinking schemes for biogenic particles on the LGM ocean biogeochemistry in the Max Planck Institute Earth System Model (MPI-ESM). In the default sinking scheme, sinking velocities of particulate organic matter (POM), biogenic minerals (CaCO3 and opal) and dust are prescribed and kept the same between LGM and pre-industrial (PI) state. Such a scheme is also widely applied in other ocean biogeochemical models. In a new Microstructure, Multiscale, Mechanistic, Marine Aggregates in the Global Ocean (M4AGO) sinking scheme, the size, microstructure, heterogeneous composition, density and porosity of marine aggregates, consisting of POM, CaCO3, opal and dust, are explicitly represented, and the sinking speed is prognostically computed. We discuss the effect of the two particle sinking schemes under two LGM circulation states: “deep LGM AMOC” with a similar NADW/AABW boundary compared to PI, which is produced in many existing models, and “shallow LGM AMOC” with a shallower NADW/AABW boundary, which agrees better with proxy data. Furthermore, we conducted sensitivity studies regarding LGM dust deposition as the latter is subject to considerable uncertainties.We find that for the deep LGM AMOC, the difference between the impact of the two particle sinking schemes on the ocean biogeochemical tracers is small. On the contrary, for shallow LGM AMOC, the M4AGO scheme yields more remerineralised carbon in the deep ocean and, therefore, better agreement with δ13C data, suggesting the quantitative impact of particle sinking schemes strongly depends on the background LGM circulation state. For the default sinking scheme, increased glacial dust deposition increases iron fertilisation and thus leads to a rise in both primary production and export production. For the M4AGO scheme, however, the iron fertilisation effect is surpassed by the ballasting effect that reduces the surface nutrient concentration, and LGM primary production decreases with dust deposition. This preliminary result shows that the new marine aggregate sinking scheme adds further complexities to the marine biological carbon pump response to the climate states. Our further analysis will encompass the other nutrients and dissolved oxygen, as well as the comparison to corresponding proxy data.

The ocean plays an essential role in regulating Earth’s climate; it is also essential for regulating the Earth’s biogeochemical cycles of carbon, nitrogen, and oxygen. Long-term (at the end of this century) changes in ocean biogeochemical cycles will be determined by the pace of anthropogenic emissions and resulting climate change. For the ocean carbon cycle, Earth system models (ESMs) within the 6th Climate Model Intercomparison Project project that while the ocean carbon sink continues to grow with rising emissions, the fraction of emissions that is taken up declines as atmospheric CO2 rises, resulting in a positive carbon–climate feedback. By contrast, near-term (until 2040) changes will be masked by internal climate variability. ESMs in concert with observations are key to constrain the response of ocean biogeochemical cycles to ongoing climate change. Yet, predictive understanding of how ocean biogeochemical cycles would respond to rapid and strong changes in emissions is currently missing. Such knowledge is critical in support of monitoring and verification of political actions for strong and rapid decarbonization. I will talk about recent progress and challenges in our understanding of the long-term and near-term fate of the ocean biogeochemical cycles under changing climate.

The El Niño–Southern Oscillation (ENSO) widely modulates the global carbon cycle. More specifically, it alters the net uptake of carbon in the tropical ocean. Over the tropical Pacific, less carbon is released during El Niño, while the opposite is the case for La Niña. Here, the skill of Earth system models (ESMs) from the latest Coupled Model Intercomparison Project (CMIP6) to simulate the observed tropical Pacific CO2 flux variability in response to ENSO is assessed. The temporal amplitude and spatial extent of CO2 flux anomalies vary considerably among models, while the surface temperature signals of El Niño and La Niña phases are generally well represented. Under historical conditions followed by the high-warming Shared Socio-economic Pathway (SSP5-8.5) scenarios, about half the ESMs simulate a reversal in ENSO–CO2 flux relationship. This gradual shift, which occurs as early as the first half of the 21st century, is associated with a high CO2-induced increase in the Revelle factor that leads to stronger sensitivity of partial pressure of CO2 (pCO2) to changes in surface temperature between ENSO phases. At the same time, uptake of anthropogenic CO2 substantially increases upper-ocean dissolved inorganic carbon (DIC) concentrations (reducing its vertical gradient in the thermocline) and weakens the ENSO-modulated surface DIC variability. The response of the ENSO–CO2 flux relationship to future climate change is sensitive to the contemporary mean state of the carbonate ion concentration in the tropics. We present an emergent constraint between the simulated contemporary carbonate concentration with the projected cumulated CO2 fluxes. Models that simulate shifts in the ENSO–CO2 flux relationship simulate positive bias in surface carbonate concentrations.

The global carbon cycle is a complex system with many drivers, including slow ones such as the chemical weathering of rocks. At long enough timescales, changes in weathering rates influence CO2 consumption, but also the river loads of carbon, nutrients, and alkalinity. In particular, the global ocean inventory of alkalinity is a critical driver of carbon sequestration into the ocean. Thus, any transitory imbalance between the sources and sinks of alkalinity can lead to changes in ocean chemistry and impact atmospheric CO2 concentration. During the last deglaciation (ca. 19-11 ka BP), the Earth’s climate transitioned from cold and arid to comparatively warmer and wetter conditions. Simultaneously, large ice sheets melted and led to a significant rise of sea level (ca. +120 m), which reduced the size of the exposed continental shelves. Loess deposits were also gradually eroded. These changes logically influenced the chemical weathering of rocks because weathering rates depend on climate variables (runoff and temperature), land-sea distribution and lithology. Some modelling studies and proxy reconstructions suggest little net changes over this period. Yet, the deglacial changes of weathering rates remain poorly constrained.Most Earth System Models do not explicitly represent weathering and the consequent river fluxes. Moreover, the alkalinity inventory is often assumed constant in models, despite the fact that proxy data suggest an elevated total alkalinity at the Last Glacial Maximum (and the likely changes of its sources and sinks). These choices can potentially bias the model representation of the global carbon cycle, whose deglacial variations have been notoriously hard to simulate for decades. In this study, we calculate weathering fluxes of phosphorus and alkalinity (among others) using reconstructed lithological maps, and model results from transient runs of the last deglaciation and/or time-slice runs of the Last Glacial Maximum and pre-industrial period. To improve robustness, we compare the evolution and spatial distribution of weathering fluxes in different models. We demonstrate that while the increase of runoff during deglaciation enhances weathering, the rise of sea level and the erosion of loess deposits tend to have a counterbalancing effect on the river loads. Our model ensemble tends to show inconsistent deglacial changes of some river loads (e.g. for phosphorus), depending both on runoff biases and on the representation of land-sea distribution. Still, all models indicate a significant decrease of river alkalinity from the LGM to the pre-industrial. Using these findings, we discuss the implications of an explicit representation of weathering fluxes for the global carbon cycle in transient runs with Earth System Models.

El Niño-Southern Oscillation (ENSO) is not only a driver of global carbon cycle variability, but it also provides several mechanisms of predictability. Although most Earth system models (ESMs) can reproduce the relationship between ENSO and atmospheric CO2 concentrations, the question remains whether the ESMs agree on the origins of these ENSO-related GPP anomalies. We analyse the patterns of ENSO-induced GPP anomalies in 17 ESMs to determine from which regions these GPP anomalies come from, and whether the differences among the models are driven by climate forcing or biochemistry. While most of the GPP anomalies originate from Southeast Asia and northern South America, there are large deviations among the ESMs. The combined GPP anomaly of these two regions ranges between 26% and 75% of the global anomaly among the ESMs. To find out what causes the differences, we examined two major drivers of the GPP anomalies: the size of the ENSO-induced climate anomalies, and the sensitivity of GPP to climate. On the global average, ENSO-induced climate anomalies and GPP sensitivity have similar uncertainty among the ESMs, contributing equally to the variations in ENSO-induced GPP anomaly patterns. This analysis reveals model biases in teleconnection patterns and biochemistry. Addressing these biases is a tangible goal for model developers to decrease the uncertainty in the reproduction of the global carbon cycle, and to increase its predictability.

Nitrogen (N) plays a central role in marine biogeochemistry by regulating biological productivity, influencing the cycles of carbon, oxygen, and other nutrients, and controlling oceanic emissions of the potent greenhouse gas nitrous oxide (N2O). Although the marine N cycle consists of multiple chemical species, global biogeochemical models often employ simple parameterizations of N transformations and omit key tracers and processes. Here we present an extended numerical representation of the marine N cycle that includes explicit tracers for nitrate, dinitrogen, nitrous oxide, ammonium, and nitrite. The extended model simulates heterotrophic denitrification, DNRN, DNRA, anammox, and nitrification of ammonium and nitrite and thus allows for a detailed representation of a step-wise reduction of fixed nitrogen in hypoxic zones and oxidation of reduced N-species in oxic waters. The updated biogeochemical model is included in the new global ICOsahedral Non-hydrostatic Ocean model ICON-O developed at the Max Planck Institute for Meteorology. ICON-O features a flexible, triangular grid created by recursively subdividing the original 20 triangles of the icosahedron resulting, in our configuration, in an average resolution of 40 km and 235,403 triangles. We describe the tuning and spin-up of a pre-industrial control simulation and compare model results with global and local estimates to quantify the N cycle (e.g., rates of primary production, N2 fixation, nitrification, DNRN, DNRA, anammox). We further report results from a historical transient simulation focusing on N dynamics within the oxygen minimum zone of the Eastern Tropical South Pacific.

Variable fluxes of anthropogenic CO2 emissions into the land and the ocean and the remaining proportion in the atmosphere reflect on the global carbon budget variations and further modulate global climate change. A more accurate reconstruction of the global carbon budget in the past decades and a more reliable prediction of the variations in the next years are crucial for assessing the effectiveness of climate change mitigation policies and supporting global carbon stocktaking and monitoring in compliance with the goals of the Paris Agreement.In this study, we investigate reconstructions and predictions of the CO2 fluxes and atmospheric CO2 growth from ensemble prediction simulations using 5 Earth System Model (ESM) - based decadal prediction systems. These novel prediction systems driven by CO2 emissions with an interactive carbon cycle enable prognostic atmospheric CO2 and represent atmospheric CO2 growth variations in response to the strength of CO2 fluxes into the ocean and the land, which are missing in the conventional concentration-driven decadal prediction systems with prescribed atmospheric CO2 concentration.The reconstructions generated by assimilating physical ocean and atmosphere data products into the prediction systems are able to reproduce the annual mean historical variations of the CO2 fluxes and atmospheric CO2 growth. Multi-model ensemble means best match the assessments of CO2 fluxes and atmospheric CO2 growth rate from the Global Carbon Project with correlations of 0.79, 0.82, and 0.98 for atmospheric CO2 growth rate, air-land CO2 fluxes, and air-sea CO2 fluxes, respectively. The CO2 emission-driven prediction systems with an interactive carbon cycle still maintain the predictive skill of CO2 fluxes and atmospheric CO2 growth as found in conventional concentration-driven prediction systems, i.e., about 2 years for the air-land CO2 fluxes and atmospheric CO2 growth, the air-sea CO2 fluxes have higher skill up to 5 years. The ESM-based prediction systems are capable to reconstruct and predict the variations in the global carbon cycle and hence are powerful tools for supporting carbon budgeting and monitoring, especially in the decarbonization processes. Furthermore, we investigate the contribution of uncertainty in the predictions of CO2 fluxes and atmospheric CO2 growth rate from internal climate variability, different model responses, and emission-forcing reductions to identify the prominent challenge in limiting the skill of CO2 predictions.

We present the CMIP6 version of the Max Planck Institute-Grand Ensemble (MPI-GE CMIP6) with 30 realisations for the historical period and five emission scenarios. The power of MPI-GE CMIP6 goes beyond its predecessor ensemble MPI-GE by providing high-frequency model output, the full range of emission scenarios including the highly policy relevant scenarios SSP1-1.9 and SSP1-2.6, and the opportunity to compare the ensemble to high resolution simulations of the same model version. We demonstrate with six novel application examples how to use the power of MPI-GE CMIP6 to better quantify and understand present and future extreme events in the Earth system, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI-GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heat waves are projected to occur every year in 2071-2100 in high-emission scenarios, that the storm activity in most tropical to mid-latitude oceans is projected to decrease, and that the ensemble is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Increasing computational power enables Earth System Models (ESMs) to be run at higher resolutions than on conventional grids with spacings of O(100km). The new generation of ESMs running at resolutions of O(5-10km) are able to resolve phenomena such as mesoscale eddies in the ocean and convective storms in the atmosphere. Resolving these features can be a step towards reducing uncertainties in carbon cycle modeling as they directly affect the ocean uptake of anthropogenic carbon (Harrison et al. 2018). However, the spin-up of ocean biogeochemistry in globally high resolution ESMs remains computationally challenging. Traditionally, the spin-up duration of ocean biogeochemical components (e.g., in CMIP5/CMIP6 models) ranges from one hundred to several thousand years to avoid model drifts in the euphotic, mesopelagic and even deeper ocean that has an overturning time of O(1000 years). This long spin-up time is, however, not yet computationally affordable in high resolution ESMs despite recent advances in improving their scalability (Linardakis et al. 2022). Therefore, different spin-up strategies are required and need to be explored.We here present our strategy to run the HAMburg Ocean Carbon Cycle model (HAMOCC; Ilyina et al. 2013, Jungclaus et al. 2022) in the high resolution ICON-Sapphire ESM (Hohenegger et al. 2022) configuration. We discuss the steps we take from tuning and spin-up of HAMOCC in a cascade of resolutions and configurations: initially in a 40km ocean only setup, subsequently in a 10km ocean-only and eventually a 5km ESM setup. Furthermore, we examine the possibility of replacing interpolated results (used as initialization for the next higher resolution) with available observations (e.g., nutrients, alkalinity, dissolved inorganic carbon) and its consequence on biogeochemical drifts of key tendencies such as CO2 surface fluxes. Finally, we discuss the scientific questions that can be addressed using this spin-up strategy and its limitations. References:Harrison et al. 2018: https://doi.org/10.1002/2017GB005751Hohenegger et al. 2022: https://doi.org/10.5194/gmd-2022-171Ilyina, T., et al. 2013: https://doi.org/10.1029/2012MS000178.Jungclaus et al. 2022: https://doi.org/10.1029/2021MS002813Linardakis et al. 2022: https://doi.org/10.5194/gmd-2022-214

Atmospheric CO2 concentrations have increased from around 280 parts per million (ppm) in 1800 to over 416 ppm in 2020. This is a direct result of increasing anthropogenic emissions of CO2 since the industrial era. Nearly half of the emitted anthropogenic CO2 is taken up by the ocean and terrestrial ecosystems, while the remaining half remains in the atmosphere, where it is a heat-trapping greenhouse gas. The growth of atmospheric CO2 varies from year to year with inhomogeneous spatial distribution depending on the CO2 uptake by the ocean and land. The CO2 uptake by the natural sinks and atmospheric growth are affected by the climate variations and the long-term changes; in turn, the variations of the carbon cycle also modulate global climate change. The state-of-the-art large ensemble simulations based on Earth System Models (ESMs) prescribe the concentration of atmospheric CO2, but the missing interactive response of atmospheric CO2 changes to the CO2 fluxes into the ocean and the land hinders the investigation of the variability in atmospheric CO2. Furthermore, such simulations will be insufficient to represent the changes in the efficiency of the land and ocean carbon sinks once emissions start to decline. Based on the low-resolution version of the Max Planck Earth System Model v1.2 (MPI-ESM-1.2-LR), we have done a novel set of 30-member ensemble simulations driven by anthropogenic CO2 emissions. In such simulations, atmospheric CO2 concentrations are computed prognostically, modulated by the strength of CO2 fluxes to the land and the ocean. While general trends in atmospheric CO2 concentrations for different Shared Socioeconomic Pathways (SSP) are well known, trends in its global dispersion and variations within the seasons of each year have not been investigated in ESMs with an interactive carbon cycle. In this project, we use MPI-ESM-1.2-LR large ensemble simulations under four SSP scenarios, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5, together with historical runs to analyze changes of atmospheric CO2 concentrations. We focus on seasonal variability and spatial distribution of atmospheric CO2 changes in the presence of internal climate variability. We address two questions: first, what is the temporal evolution of atmospheric CO2 in regard to its seasonal variability by the end of the century following different emission pathways; and second, how does atmospheric CO2 evolve spatially (horizontally across the globe and vertically into the stratosphere) in the historical period and future projections until 2100? This study aims to refine our understanding of the spatial and temporal variations of CO2 in support of activities to monitor and verify decarbonization measures.

The largest mass extinction on Earth with an estimated 90% loss of species occurred at the Permian-Triassic Boundary (~252 Ma). The end-Permian mass extinction coincides with extreme temperature increases and changes in ocean circulation and biogeochemistry. These climate perturbations are associated with carbon emissions linked to Siberian Trap volcanism. Fully-coupled Earth System Models can be applied to investigate the feedbacks and sensitivities of the background latest Permian climate to such carbon emissions. Past studies have focussed on constraining the magnitude of these carbon emissions without examining the sensitivity of palaeo-configured Earth System models designed for modern simulations. We modified a version of the Max Planck Earth System Model v1.2, similar to that used in the 6th-phase of the Coupled Model Intercomparison Project, to simulate the latest Permian climate-carbon system and use geochemical and palaeobiological proxy data to constrain the boundary conditions of the modelled climate state.We first characterise the latest Permian climate state before presenting first results on a sensitivity study of the latest Permian climate-carbon state to CO2 emission pulses. A 100 year global mean 2 m surface air temperature of 17.5°C is simulated, rising up to 34.7°C in the low-latitude continental interior. The continental interior is also largely arid from ~50°N to ~50°S with a total precipitation maximum of 11.1 mm day-1 at the equatorial boundary of the Tethys and Panthalassic Oceans. The prevailing hydrological regime drives woody single-stemmed evergreens and soft-stemmed plant functional groups to dominate in the dynamic vegetation model. The 100 year global mean surface ocean of the latest Permian illustrates a warm-pool across the equatorial boundary between the Tethys and Panthalassic Oceans with a maximum temperature of 30.2°C decreasing to temperatures as low as -1.9°C near the poles. Surface salinities vary broadly across the global oceans with 100 year global mean values ranging from 22.9, in well-flushed regions of strong freshwater flux, to 48.6, in low-latitude regions of restricted exchange. Large-scale seasonal mixing below 60°S in the Panthalassic Ocean dominates the global meridional overturning circulation. These model data fit within the bounds represented by the available proxy data for the Late Permian. The widespread shallow ocean mixed-layer also restricts recirculation of nutrients, driving a high gross primary production with weak seasonality. Furthermore, regions of seasonal deep mixing correlate with seasonal pCO2 patterns at high latitudes. I will also present further analyses of the simulated ocean biogeochemical cycles in the Hamburg Ocean Carbon Cycle model with a focus on the novel extended Nitrogen-cycle processes.

The coastal ocean contributes to regulating atmospheric greenhouse gas concentrations by taking up carbon dioxide (CO2) and releasing nitrous oxide (N2O) and methane (CH4). Major advances have improved our understanding of the coastal air-sea exchanges of these three gasses since the first phase of the Regional Carbon Cycle Assessment and Processes (RECCAP in 2013), but a comprehensive view that integrates the three gasses at the global scale is still lacking. In this second phase (RECCAP2), we quantify global coastal ocean fluxes of CO2, N2O and CH4 using an ensemble of global gap-filled observation-based products and ocean biogeochemical models. The global coastal ocean is a net sink of CO2 in both observational products and models, but the magnitude of the median net global coastal uptake is ~60% larger in models (-0.72 vs. -0.44 PgC/yr, 1998-2018, coastal ocean area of 77 million km2). We attribute most of this model-product difference to the seasonality in sea surface CO2 partial pressure at mid- and high-latitudes, where models simulate stronger winter CO2 uptake. The global coastal ocean is a major source of N2O (+0.70 PgCO2-e /yr in observational product and +0.54 PgCO2-e /yr in model median) and of CH4 (+0.21 PgCO2-e /yr in observational product), which offsets a substantial proportion of the net radiative effect of coastal \co uptake (35-58% in CO2-equivalents). Data products and models need improvement to better resolve the spatio-temporal variability and long term trends in CO2, N2O and CH4 in the global coastal ocean.

Exchange of carbon between the ocean and the atmosphere is a key process that influences past climates via glacial–interglacial variations of the CO2 concentration. The melting of ice sheets during deglaciations induces a sea level rise which leads to the flooding of coastal land areas, resulting in the transfer of terrestrial organic matter to the ocean. However, the consequences of such fluxes on the ocean biogeochemical cycle and on the uptake and release of CO2 are poorly constrained. Moreover, this potentially important exchange of carbon at the land–sea interface is not represented in most Earth system models. We present here the implementation of terrestrial organic matter fluxes into the ocean at the transiently changing land–sea interface in the Max Planck Institute for Meteorology Earth System Model (MPI-ESM) and investigate their effect on the biogeochemistry during the last deglaciation. Our results show that during the deglaciation, most of the terrestrial organic matter inputs to the ocean occurs during Meltwater Pulse 1a (between 15–14 ka) which leads to the transfer of 21.2 Gt C of terrestrial carbon (mostly originating from wood and humus) to the ocean. Although this additional organic matter input is relatively small in comparison to the global ocean inventory (0.06 %) and thus does not have an impact on the global CO2 flux, the terrestrial organic matter fluxes initiate oceanic outgassing in regional hotspots like in Indonesia for a few hundred years. Finally, sensitivity experiments highlight that terrestrial organic matter fluxes are the drivers of oceanic outgassing in flooded coastal regions during Meltwater Pulse 1a. Furthermore, the magnitude of outgassing is rather insensitive to higher carbon-to-nutrient ratios of the terrestrial organic matter. Our results provide a first estimate of the importance of terrestrial organic matter fluxes in a transient deglaciation simulation. Moreover, our model development is an important step towards a fully coupled carbon cycle in an Earth system model applicable to simulations at glacial–interglacial cycles.

The prediction of atmospheric CO2 concentrations is limited by the high interannual variability (IAV) of terrestrial gross primary productivity (GPP). However, there are large uncertainties in the drivers of GPP IAV among Earth system models (ESMs). Here, we evaluate the impact of these uncertainties on the predictability of atmospheric CO2 in six ESMs. We use regression analysis to determine the role of environmental drivers on (i) the patterns of GPP IAV, and (ii) the predictability of GPP. There are large uncertainties in the spatial distribution of GPP IAV. Although all ESMs agree on the high IAV in the tropics, several ESMs have unique hotspots of GPP IAV. The main driver of GPP IAV is temperature in the ESMs using the Community Land Model, and soil moisture in IPSL-CM6A-LR and MPI-ESM-LR, revealing underlying differences in the source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable one year ahead, with four out of six ESMs between 19 % and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, while only 7 % to 13 % of the GPP IAV induced by radiation. The results show that while ESMs are fairly similar in their ability to predict themselves, their predicted contribution to the atmospheric CO2 variability originates from different regions and is caused by different drivers. A higher coherence in atmospheric CO2 predictability could be achieved by reducing uncertainties of GPP sensitivity to soil moisture, and by accurate observational products for GPP IAV.

Ocean alkalinity is critical to the uptake of atmospheric carbon in surface waters and provides buffering capacity towards the associated acidification. However, unlike dissolved inorganic carbon (DIC), alkalinity is not directly impacted by anthropogenic carbon emissions. Within the context of projections of future ocean carbon uptake and potential ecosystem impacts, especially through Coupled Model Intercomparison Projects (CMIPs), the representation of alkalinity and the main driver of its distribution in the ocean interior, the calcium carbonate cycle, have often been overlooked. Here we track the changes from CMIP5 to CMIP6 with respect to the Earth system model (ESM) representation of alkalinity and the carbonate pump which depletes the surface ocean in alkalinity through biological production of calcium carbonate and releases it at depth through export and dissolution. We report an improvement in the representation of alkalinity in CMIP6 ESMs relative to those in CMIP5, with CMIP6 ESMs simulating lower surface alkalinity concentrations, an increased meridional surface gradient and an enhanced global vertical gradient. This improvement can be explained in part by an increase in calcium carbonate (CaCO3) production for some ESMs, which redistributes alkalinity at the surface and strengthens its vertical gradient in the water column. We were able to constrain a particulate inorganic carbon (PIC) export estimate of 44–55 Tmol yr−1 at 100 m for the ESMs to match the observed vertical gradient of alkalinity. Reviewing the representation of the CaCO3 cycle across CMIP5/6, we find a substantial range of parameterizations. While all biogeochemical models currently represent pelagic calcification, they do so implicitly, and they do not represent benthic calcification. In addition, most models simulate marine calcite but not aragonite. In CMIP6, certain model groups have increased the complexity of simulated CaCO3 production, sinking, dissolution and sedimentation. However, this is insufficient to explain the overall improvement in the alkalinity representation, which is therefore likely a result of marine biogeochemistry model tuning or ad hoc parameterizations. Although modellers aim to balance the global alkalinity budget in ESMs in order to limit drift in ocean carbon uptake under pre-industrial conditions, varying assumptions related to the closure of the budget and/or the alkalinity initialization procedure have the potential to influence projections of future carbon uptake. For instance, in many models, carbonate production, dissolution and burial are independent of the seawater saturation state, and when considered, the range of sensitivities is substantial. As such, the future impact of ocean acidification on the carbonate pump, and in turn ocean carbon uptake, is potentially underestimated in current ESMs and is insufficiently constrained.

In the era of exascale computing, machines with unprecedented computing power are available. Making efficient use of these massively parallel machines, with millions of cores, presents a new challenge. Multi-level and multi-dimensional parallelism will be needed to meet this challenge. Coarse-grained component concurrency provides an additional parallelism dimension that complements typically used parallelization methods such as domain decomposition and loop-level shared-memory approaches. While these parallelization methods are data-parallel techniques, and they decompose the data space, component concurrency is a function-parallel technique, and it decomposes the algorithmic space. This additional dimension of parallelism allows us to extend scalability beyond the limits set by established parallelization techniques. It also offers a way to maintain performance (by using more compute power) when the model complexity is increased by adding components, such as biogeochemistry or ice sheet models. Furthermore, concurrency allows each component to run on different hardware, thus leveraging the usage of heterogeneous hardware configurations. In this work we study the characteristics of component concurrency and analyse its behaviour in a general context. The analysis shows that component concurrency increases the “parallel workload”, improving the scalability under certain conditions. These generic considerations are complemented by an analysis of a specific case, namely the coarse-grained concurrency in the multi-level parallelism context of two components of the ICON modelling system: the ICON ocean model ICON-O and the marine biogeochemistry model HAMOCC. The additional computational cost incurred by the biogeochemistry module is about 3 times that of the ICON-O ocean stand alone model, and data parallelization techniques (domain decomposition and loop-level shared-memory parallelization) present a scaling limit that impedes the computational performance of the combined ICON-O–HAMOCC model. Scaling experiments, with and without concurrency, show that component concurrency extends the scaling, in cases doubling the parallel efficiency. The experiments' scaling results are in agreement with the theoretical analysis.

Ocean alkalinity is critical to the uptake of atmospheric carbon in surface waters and provides buffering capacity towards associated acidification. However, unlike dissolved inorganic carbon (DIC), alkalinity is not directly impacted by anthropogenic carbon emissions. Within the context of projections of future ocean carbon uptake and potential ecosystem impacts, especially through Coupled Model Intercomparison Projects (CMIPs), the representation of alkalinity and the main driver of its distribution in the ocean interior, the calcium carbonate cycle, have often been overlooked. Here we track the changes from CMIP5 to CMIP6 with respect to the Earth system model (ESM) representation of alkalinity and the carbonate pump which depletes the surface ocean in alkalinity through biological production of calcium carbonate, and releases it at depth through export and dissolution. We report a significant improvement in the representation of alkalinity in CMIP6 ESMs relative to those in CMIP5. This improvement can be explained in part by an increase in calcium carbonate (CaCO3) production for some ESMs, which redistributes alkalinity at the surface and strengthens its vertical gradient in the water column. We were able to constrain a PIC export estimate of 51–70 Tmol yr-1 at 100 m for the ESMs to match the observed vertical gradient of alkalinity. Biases in the vertical profile of DIC have also significantly decreased, especially with the enhancement of the carbonate pump, but the representation of the saturation horizons has slightly worsened in contrast. Reviewing the representation of the CaCO3 cycle across CMIP5/6, we find a substantial range of parameterizations. While all biogeochemical models currently represent pelagic calcification, they do so implicitly, and they do not represent benthic calcification. In addition, most models simulate marine calcite but not aragonite. In CMIP6 certain model groups have increased the complexity of simulated CaCO3 production, sinking, dissolution and sedimentation. However, this is insufficient to explain the overall improvement in the alkalinity representation, which is therefore likely a result of improved marine biogeochemistry model tuning or ad hoc parameterizations. We find differences in the way ocean alkalinity is initialized that lead to offsets of up to 1 % in the global alkalinity inventory of certain models. These initialization biases should be addressed in future CMIPs by adopting accurate unit conversions. Although modelers aim to balance the global alkalinity budget in ESMs in order to limit drift in ocean carbon uptake under preindustrial conditions, varying assumptions in the closure of the budget have the potential to influence projections of future carbon uptake. For instance, in many models, carbonate production, dissolution and burial are independent of the seawater saturation state, and when considered, the range of sensitivities is substantial. As such, the future impact of ocean acidification on the carbonate pump, and in turn ocean carbon uptake, is potentially underestimated in current ESMs and insufficiently constrained.

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 synthesise data sets and methodology 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), ELUC was 1.1 ± 0.7 GtC yr-1, for a total anthropogenic CO2 emission of 11.1 ± 0.8 GtC yr-1 (40.8 ± 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. total estimated sources too low or sinks 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 +1.1 % (0 % to 1.7 %) globally, and atmospheric CO2 concentration reaching 417.3 ppm, more than 50 % above pre-industrial level. 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 changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, 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 (Friedlingstein et al., 2022a; Friedlingstein et al., 2020; Friedlingstein et al., 2019; Le Quéré et al., 2018b, 2018a, 2016, 2015b, 2015a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

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 methodology 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 first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gases inventories, supporting the assessment of collective countries’ climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1 and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021, suggest a rebound in EFOS relative to 2020 of +4.9 % (4.1 % to 5.7 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, 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 changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra- tropics, 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 (Friedlingstein et al., 2020; Friedlingstein et al., 2019; Le Quéré et al., 2018b, 2018a, 2016, 2015b, 2015a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

The El Niño Southern Oscillation (ENSO) widely modulates the global carbon cycle, in particular, by altering the net uptake of carbon in the tropical ocean. Indeed, over the tropics less carbon is released by oceans during El Niño while it is the opposite for La Niña. Here, the skill of Earth System Models (ESM) from the latest Coupled Model Intercomparison Project (CMIP6) to simulate the observed tropical Pacific CO2 flux variability in response to ENSO is assessed. The temporal amplitude and spatial extent of CO2 flux anomalies vary considerably among models, while the surface temperature signals of El Niño and La Niña phases are generally well represented. Under historical conditions followed by the high warming Shared Socio-economic Pathway (SSP5-8.5) scenarios, about half the ESMs simulate a reversal in ENSO-CO2 flux relationship. This gradual shift, which occurs as early as the first half of the 21st century, is associated with a high CO2-induced increase in Revelle factor that leads to stronger sensitivity of partial pressure of CO2 (pCO2) to changes in surface temperature between ENSO phases. At the same time, uptake of anthropogenic CO2 substantially increases upper ocean dissolved inorganic carbon (DIC) concentrations, reducing its vertical gradient in the thermocline, and weakening the ENSO-modulated surface DIC variability. The response of ENSO-CO2 flux relationship to future climate change is sensitive to the contemporary mean state of the carbonate ion concentration in the tropics. Models that simulate shift in ENSO-CO2 flux relationship simulate positive bias in surface carbonate concentration.

Predicting carbon fluxes and atmospheric CO2 can constrain the expected next-year atmospheric CO2 growth rate and thereby allow to independently monitor total anthropogenic CO2 emission rates. Several studies have established predictive skill in retrospective forecasts of carbon fluxes. These studies are usually backed by perfect-model simulations of single models showing the origins of predictive skill in carbon fluxes and atmospheric CO2 concentration. Yet, a comprehensive multi-model comparison of perfect-model predictions, which can be valuable in explaining differences in retrospective predictions, is still lacking. Moreover, as of now, we don't have sufficient understanding of how well do the models predict their own integrated carbon cycles and how congruent this predictability is across models.Here, we show the predictive skill of land and ocean carbon fluxes as well as atmospheric CO2 concentration in seven Earth-System-Models. Our first results indicate predictive skill of globally aggregated carbon fluxes of 2±1 years and atmospheric CO2 of 3±2 years. However, the regional patterns, hotspots and origins of predictive skill diverge among models. This heterogeneity explains the regional differences found in existing retrospective forecasts and backs the overall consistent predictability time-scales at global scale.

The Southern Ocean plays a major role in both the global oceanic uptake of anthropogenic CO2 and its interannual variations. The size and origin of the interannual variability in the Southern Ocean CO2 fluxes is debated. Observation-based estimates suggest a large variability (+/- 0.11 PgC/yr) while Global Ocean Biogeochemistry Models (GOBMs) simulate almost no variability. Studying the air-sea fluxes of O2 can provide independent information that help resolve this data-model inconsistency. Oceanic O2 is influenced by the same physical and biogeochemical processes as CO2, but unlike CO2, its variability is not masked by a large anthropogenic flux. Here, we used 26 years (1994-2019) of monthly O2 fluxes from 9 GOBMs. These model outputs were compared to air-sea O2 fluxes inferred from an atmospheric inversion of precisely quantified changes in atmospheric O2 and CO2 levels. The 26-year time series of air-sea O2 fluxes from all GOBMs and the atmospheric inversion exhibited similar temporal variations. This could be linked to the Southern Annular Mode and its influence on air-sea heat flux forcing that induced large-scale changes in observed wintertime Mixed Layer Depth (MLD). However, the amplitude of the interannual variability in air-sea O2 fluxes was two times higher in the atmospheric inversion than in GOBMs. It possible that this was induced by the general overestimation of the mean wintertime MLD by the GOBM and subsurface vertical gradients in oxygen saturation lower than observed. Implications of these results for the variability in air-sea fluxes of CO2 will be discussed.

The Late Permian climate is the background state for the climate perturbations which lead to thePermian-Triassic Boundary (~252 Ma). The Permian-Triassic Boundary mass extinction is well established asthe largest of Earth’s mass extinctions with an estimated 90% loss of species. Climate perturbations linked tocarbon emissions from Siberian Trap volcanism are attributed as the drivers of the mass extinction throughextreme temperature increases and changes in ocean circulation and biogeochemistry. Fully-coupled EarthSystem Models are required to investigate the sensitivities and feedbacks of the system to these widespreadclimate perturbations. The Late Permian climate is simulated with a modified version of the Max PlanckEarth System Model v1.2 similar to that used in the 6th -phase of the Coupled Model Intercomparison Project.Geochemical and palaeobiological proxy data are used to constrain the boundary conditions of the modelledclimate state.The simulated Late Permian climate state is characterised by a 100 year global mean 2 m surface airtemperature of 19.7°C, rising up to 37.7°C in the low-latitude continental interior. Prevailing 100 year globalmean total precipitation patterns indicate that the continental interior was largely arid from ~50°N to ~50°S anda rainfall maximum of up to 6.5 mm day-1 is present at the equatorial boundary of the Tethys and PanthalassicOceans. Dynamic terrestrial vegetation in the model is dominated by woody single-stemmed evergreens andsoft-stemmed plant functional groups. The 100 year global mean surface ocean of the Late Permian illustratesa warm-pool across the equatorial boundary between the Tethys and Panthalassic Oceans with a maximumtemperature of 31.7°C decreasing to temperatures as low as -1.9°C near the poles. Surface salinities varybroadly across the global oceans with 100 year global mean values ranging from 21.9, in well flushed regionsof strong freshwater flux, to 49.2, in low-latitude regions of restricted exchange. Large-scale seasonal mixingbelow 60°S in the Panthalassic Ocean dominates the global meridional overturning circulation. These modeldata fit within the bounds represented by the available proxy data for the Late Permian. Additionally, I willpresent first results of the ocean biogeochemical state in the Hamburg Ocean Carbon Cycle model with anextended Nitrogen-cycle. I will also illustrate the results of our investigation into the influence of the LatePermian monsoon variability on the terrestrial vegetation and ocean carbon cycles.