ALBERT

Description: The ocean carbon cycle is a key player in the climate system through its role in regulating the atmospheric carbon dioxide concentration and other processes that alter the Earth's radiative balance. In the second version of the Norwegian Earth System Model (NorESM2), the oceanic carbon cycle component has gone through numerous updates that include, amongst others, improved process representations, increased interactions with the atmosphere, and additional new tracers. Oceanic dimethyl sulfide (DMS) is now prognostically simulated and its fluxes are directly coupled with the atmospheric component, leading to a direct feedback to the climate. Atmospheric nitrogen deposition and additional riverine inputs of other biogeochemical tracers have recently been included in the model. The implementation of new tracers such as “preformed” and “natural” tracers enables a separation of physical from biogeochemical drivers as well as of internal from external forcings and hence a better diagnostic of the simulated biogeochemical variability. Carbon isotope tracers have been implemented and will be relevant for studying long-term past climate changes. Here, we describe these new model implementations and present an evaluation of the model's performance in simulating the observed climatological states of water-column biogeochemistry and in simulating transient evolution over the historical period. Compared to its predecessor NorESM1, the new model's performance has improved considerably in many aspects. In the interior, the observed spatial patterns of nutrients, oxygen, and carbon chemistry are better reproduced, reducing the overall model biases. A new set of ecosystem parameters and improved mixed layer dynamics improve the representation of upper-ocean processes (biological production and air–sea CO2 fluxes) at seasonal timescale. Transient warming and air–sea CO2 fluxes over the historical period are also in good agreement with observation-based estimates. NorESM2 participates in the Coupled Model Intercomparison Project phase 6 (CMIP6) through DECK (Diagnostic, Evaluation and Characterization of Klima) and several endorsed MIP simulations.

Description: δ13C, the standardised 13C ∕ 12C ratio expressed in per mille, is a widely used ocean tracer to study changes in ocean circulation, water mass ventilation, atmospheric pCO2, and the biological carbon pump on timescales ranging from decades to tens of millions of years. δ13C data derived from ocean sediment core analysis provide information on δ13C of dissolved inorganic carbon and the vertical δ13C gradient (i.e. Δδ13C) in past oceans. In order to correctly interpret δ13C and Δδ13C variations, a good understanding is needed of the influence from ocean circulation, air–sea gas exchange and biological productivity on these variations. The Southern Ocean is a key region for these processes, and we show here that Δδ13C in all ocean basins is sensitive to changes in the biogeochemical state of the Southern Ocean. We conduct a set of idealised sensitivity experiments with the ocean biogeochemistry general circulation model HAMOCC2s to explore the effect of biogeochemical state changes of the Southern and Global Ocean on atmospheric δ13C, pCO2, and marine δ13C and Δδ13C. The experiments cover changes in air–sea gas exchange rates, particulate organic carbon sinking rates, sea ice cover, and nutrient uptake efficiency in an unchanged ocean circulation field. Our experiments show that global mean Δδ13C varies by up to about ±0.35 ‰ around the pre-industrial model reference (1.2 ‰) in response to biogeochemical change. The amplitude of this sensitivity can be larger at smaller scales, as seen from a maximum sensitivity of about −0.6 ‰ on ocean basin scale. The ocean's oldest water (North Pacific) responds most to biological changes, the young deep water (North Atlantic) responds strongly to air–sea gas exchange changes, and the vertically well-mixed water (SO) has a low or even reversed Δδ13C sensitivity compared to the other basins. This local Δδ13C sensitivity depends on the local thermodynamic disequilibrium and the Δδ13C sensitivity to local POC export production changes. The direction of both glacial (intensification of Δδ13C) and interglacial (weakening of Δδ13C) Δδ13C change matches the direction of the sensitivity of biogeochemical processes associated with these periods. This supports the idea that biogeochemistry likely explains part of the reconstructed variations in Δδ13C, in addition to changes in ocean circulation.

Description: The standardized 13C isotope, δ13C, is a widely used ocean tracer to study changes in ocean circulation, water mass ventilation, atmospheric pCO2 and the biological carbon pump on timescales ranging from decades to 10s of millions of years. δ13C data derived from ocean sediment core analysis provide information on δ13C of dissolved inorganic carbon and the vertical δ13C gradient (i.e., Δδ13C) in past oceans. In order to correctly interpret δ13C and Δδ13C variations, a good understanding is needed of the influence from ocean circulation, air-sea gas exchange and biological productivity on these variations. The Southern Ocean is a key region for these processes, and we show here that global mean Δδ13C is sensitive to changes in the biogeochemical state of the Southern Ocean. We conduct four idealised sensitivity experiments with the ocean biogeochemistry general circulation model HAMOCC2s to explore the effect of biogeochemical state changes of the (Southern) Oceans on atmospheric δ13C, pCO2, and marine δ13C and Δδ13C. The experiments cover changes in air-sea gas exchange rates, particulate organic carbon sinking rates, sea ice cover, and nutrient uptake efficiency – in an unchanged ocean circulation field. We conclude that the maximum variation of mean marine Δδ13C in response to (bio)geochemical change is ~ 0.5 ‰, which is about half of the reconstructed variation in Δδ13C over glacial-interglacial timescales. Locally, Δδ13C variations can surpass or even mirror the mean effects on Δδ13C due to the spatial variation in the sensitivity of δ13C to biogeochemical change. The (bio)geochemical environment of a sediment core thus needs to be well constrained in order to be able to interpret reconstructed Δδ13C variations in such a core. The sensitivity of Δδ13C varies spatially depending on the contribution of air-sea gas exchange versus biological export productivity to the local δ13C signature. Interestingly, the direction of both glacial (intensification of Δδ13C) and interglacial (weakening of Δδ13C) Δδ13C change matches biogeochemical processes associated with these periods. This supports the idea that biogeochemistry likely explains part of the reconstructed variations in Δδ13C, and not only ocean circulation.

Description: Model simulations of the Last Glacial Maximum (LGM, ~ 21 000 years before present) can aid the interpretation of proxy records, help to gain an improved mechanistic understanding of the LGM climate system and are valuable for the evaluation of model performance in a different climate state. Ocean-ice only model configurations forced by prescribed atmospheric data (referred to as “forced ocean models”) drastically reduce the computational cost of paleoclimate modelling as compared to fully coupled model frameworks. While feedbacks between the atmosphere and ocean-sea-ice compartments of the Earth system are not present in such model configurations, many scientific questions can be addressed with models of this type. The data presented here are derived from fully coupled paleoclimate simulations of the Palaeoclimate Modelling Intercomparison Project (PMIP3). The data are publicly accessible at the NIRD Research Data Archive at https://doi.org/10.11582/2019.00011 (Morée and Schwinger, 2019). They consist of 2-D anomaly forcing fields suitable for use in ocean models that employ a bulk forcing approach. The data include specific humidity, downwelling longwave and shortwave radiation, precipitation, wind (v and u components), temperature and sea surface salinity (SSS). All fields are provided as climatological mean anomalies between LGM and pre-industrial times. These anomaly data can therefore be added to any pre-industrial ocean forcing data set in order to obtain forcing fields representative of LGM conditions as simulated by PMIP3 models. These forcing data provide a means to simulate the LGM in a computationally efficient way, while still taking advantage of the complexity of fully coupled model set-ups. Furthermore, the dataset can be easily updated to reflect results from upcoming and future paleo model intercomparison activities.

Description: Model simulations of the Last Glacial Maximum (LGM; ∼ 21 000 years before present) can aid the interpretation of proxy records, can help to gain an improved mechanistic understanding of the LGM climate system, and are valuable for the evaluation of model performance in a different climate state. Ocean-ice only model configurations forced by prescribed atmospheric data (referred to as “forced ocean models”) drastically reduce the computational cost of palaeoclimate modelling compared to fully coupled model frameworks. While feedbacks between the atmosphere and ocean and sea-ice compartments of the Earth system are not present in such model configurations, many scientific questions can be addressed with models of this type. Our dataset supports simulations of the LGM in a forced ocean model set-up while still taking advantage of the complexity of fully coupled model set-ups. The data presented here are derived from fully coupled palaeoclimate simulations of the Palaeoclimate Modelling Intercomparison Project phase 3 (PMIP3). The data are publicly accessible at the National Infrastructure for Research Data (NIRD) Research Data Archive at https://doi.org/10.11582/2020.00052 (Morée and Schwinger, 2020). They consist of 2-D anomaly forcing fields suitable for use in ocean models that employ a bulk forcing approach and are optimized for use with CORE forcing fields. The data include specific humidity, downwelling long-wave and short-wave radiation, precipitation, wind (v and u components), temperature, and sea surface salinity (SSS). All fields are provided as climatological mean anomalies between LGM and pre-industrial (PI) simulations. These anomaly data can therefore be added to any pre-industrial ocean forcing dataset in order to obtain forcing fields representative of LGM conditions as simulated by PMIP3 models. Furthermore, the dataset can be easily updated to reflect results from upcoming and future palaeo-model intercomparison activities.

Description: Although both physical and biological marine changes are required to explain the 100 ppm lower atmospheric pCO2 of the Last Glacial Maximum (LGM, ∼21 ka) as compared to preindustrial (PI) times, their exact contributions are debated. Proxies of past marine carbon cycling (such as δ13C) document these changes and thus provide constraints for quantifying the drivers of long-term carbon cycle variability. This modeling study discusses the physical and biological changes in the ocean needed to simulate an LGM ocean in satisfactory agreement with proxy data, here focusing especially on δ13C. We prepared a PI and LGM equilibrium simulation using the ocean model NorESM-OC with full biogeochemistry (including the carbon isotopes δ13C and radiocarbon) and dynamic sea ice. The modeled LGM–PI differences are evaluated against a wide range of physical and biogeochemical proxy data and show agreement for key aspects of the physical ocean state within the data uncertainties. However, the lack of a simulated increase of regenerated nutrients for the LGM indicates that additional biogeochemical changes are required to simulate an LGM ocean in agreement with proxy data. In order to examine these changes, we explore the potential effects of different global mean biological pump efficiencies on the simulated marine biogeochemical tracer distributions. Through estimating which biological pump efficiency reduces LGM model–proxy biases the most, we estimate that the global mean biological pump efficiency increased from 38 % (PI) to up to 75 % (LGM). The drivers of such an increase in the biological pump efficiency may be both biological and related to circulation changes that are incompletely captured by our model – such as stronger isolation of Southern Source Water. Finally, even after considering a 75 % biological pump efficiency in the LGM ocean, a remaining model–proxy error in δ13C exists that is 0.07 ‰ larger than the 0.19 ‰ data uncertainty. This error indicates that additional changes in ocean dynamics are needed to simulate an LGM ocean in agreement with proxy data.