Abstract. While the Southern Ocean (SO) provides the largest oceanic sink of carbon, some observational studies have suggested that the SO total CO2 (tCO2) uptake exhibited large (∼ 0.3 GtC yr−1) decadal-scale variability over the last 30 years, with a similar SO tCO2 uptake in 2016 as in the early 1990s. Here, using an eddy-rich ocean, sea-ice, carbon cycle model, with a nominal resolution of 0.1∘, we explore the changes in total, natural and anthropogenic SO CO2 fluxes over the period 1980–2021 and the processes leading to the CO2 flux variability. The simulated tCO2 flux exhibits decadal-scale variability with an amplitude of ∼ 0.1 GtC yr−1 globally in phase with observations. Notably, two stagnations in tCO2 uptake are simulated: between 1982 and 2000, and between 2003 and 2011, while re-invigorations are simulated between 2000 and 2003, as well as since 2012. This decadal-scale variability is primarily due to changes in natural CO2 (nCO2) fluxes south of the polar front associated with variability in the Southern Annular Mode (SAM). Positive phases of the SAM, i.e. stronger and poleward shifted southern hemispheric (SH) westerlies, lead to enhanced SO nCO2 outgassing due to higher surface natural dissolved inorganic carbon (DIC) brought about by a combination of Ekman-driven vertical advection and DIC diffusion at the base of the mixed layer. The pattern of the CO2 flux anomalies indicate a dominant control of the interaction between the mean flow south of the polar front and the main topographic features. While positive phases of the SAM also lead to enhanced anthropogenic CO2 (aCO2) uptake south of the polar front, the amplitude of the changes in aCO2 fluxes is only 25 % of the changes in nCO2 fluxes. Due to the larger nCO2 outgassing compared to aCO2 uptake as the SH westerlies strengthen and shift poleward, the SO tCO2 uptake capability thus reduced since 1980 in response to the shift towards positive phases of the SAM. Our results indicate that, even in an eddy-rich ocean model, a strengthening and/or poleward shift of the SH westerlies enhance CO2 outgassing. The projected poleward strengthening of the SH westerlies over the coming century will, thus, reduce the capability of the SO to mitigate the increase in atmospheric CO2.
Abstract. Accurate predictive modeling of the ocean's global carbon and oxygen cycles is challenging because of uncertainties in both biogeochemistry and ocean circulation. Advances over the last decade have made parameter optimization feasible, allowing models to better match observed biogeochemical fields. However, does fitting a biogeochemical model to observed tracers using a circulation with known biases robustly capture the inner workings of the biological pump? Here we embed a mechanistic model of the ocean's coupled nutrient, carbon, and oxygen cycles into two circulations for the current climate. To assess the effects of biases, one circulation (ACCESS-M) is derived from a climate model and the other from data assimilation of observations (OCIM2). We find that parameter optimization compensates for circulation biases at the expense of altering how the biological pump operates. Tracer observations constrain pump strength and regenerated inventories for both circulations, but ACCESS-M export production optimizes to twice that of OCIM2 to compensate for ACCESS-M having lower sequestration efficiencies driven by less efficient particle transfer and shorter residence times. Idealized simulations forcing complete Southern Ocean nutrient utilization show that the response of the optimized system is sensitive to the embedding circulation. In ACCESS-M, Southern Ocean nutrient and dissolved inorganic carbon (DIC) trapping is partially short circuited by unrealistically deep mixed layers. For both circulations, intense Southern Ocean production deoxygenates Southern-Ocean-sourced deep waters, muting the imprint of circulation biases on oxygen. Our findings highlight that the biological pump's plumbing needs careful assessment to predict the biogeochemical response to ecological changes, even when optimally matching observations.
Abstract. Accurate predictive modelling of the ocean's global carbon and oxygen cycles is challenging because of uncertainties in both biogeochemistry and ocean circulation. Advances over the last decade have made parameter optimization feasible, allowing models to better match observed biogeochemical fields. However, does fitting a biogeochemical model to observed tracers using a circulation with known biases robustly capture the inner workings of the biological pump? Here we embed a mechanistic model of the ocean's coupled nutrient, carbon, and oxygen cycles into two circulations for the current climate. To assess the effects of biases, one circulation (ACCESS-M) is derived from a climate model and the other from data assimilation of observations (OCIM2). We find that parameter optimization compensates for circulation biases at the expense of altering how the biological pump operates. Tracer observations constrain pump strength and regenerated inventories for both circulations, but ACCESS-M export production optimizes to twice that of OCIM2 to compensate for ACCESS-M having lower sequestration efficiencies driven by less efficient particle transfer and shorter residence times. Idealized simulations forcing complete Southern Ocean nutrient utilization show that the response of the optimized system is sensitive to the embedding circulation. In ACCESS-M, Southern Ocean nutrient and DIC trapping is partially short-circuited by unrealistically deep mixed layers. For both circulations, intense Southern Ocean production deoxygenates Southern-Ocean-sourced deep waters, muting the imprint of circulation biases on oxygen. Our findings highlight that the biological pump's plumbing needs careful assessment to predict the biogeochemical response to environmental changes, even when optimally matching observations.
Abstract. Over the last decade many climate models have evolved into Earth system models (ESMs), which are able to simulate both physical and biogeochemical processes through the inclusion of additional components such as the carbon cycle. The Australian Community Climate and Earth System Simulator (ACCESS) has been recently extended to include land and ocean carbon cycle components in its ACCESS-ESM1 version. A detailed description of ACCESS-ESM1 components including results from pre-industrial simulations is provided in Part 1. Here, we focus on the evaluation of ACCESS-ESM1 over the historical period (1850–2005) in terms of its capability to reproduce climate and carbon-related variables. Comparisons are performed with observations, if available, but also with other ESMs to highlight common weaknesses. We find that climate variables controlling the exchange of carbon are well reproduced. However, the aerosol forcing in ACCESS-ESM1 is somewhat larger than in other models, which leads to an overly strong cooling response in the land from about 1960 onwards. The land carbon cycle is evaluated for two scenarios: running with a prescribed leaf area index (LAI) and running with a prognostic LAI. We overestimate the seasonal mean (1.7 vs. 1.4) and peak amplitude (2.0 vs. 1.8) of the prognostic LAI at the global scale, which is common amongst CMIP5 ESMs. However, the prognostic LAI is our preferred choice, because it allows for the vegetation feedback through the coupling between LAI and the leaf carbon pool. Our globally integrated land–atmosphere flux over the historical period is 98 PgC for prescribed LAI and 137 PgC for prognostic LAI, which is in line with estimates of land use emissions (ACCESS-ESM1 does not include land use change). The integrated ocean–atmosphere flux is 83 PgC, which is in agreement with a recent estimate of 82 PgC from the Global Carbon Project for the period 1959–2005. The seasonal cycle of simulated atmospheric CO2 is close to the observed seasonal cycle (up to 1 ppm difference for the station at Mace Head and up to 2 ppm for the station at Mauna Loa), but shows a larger amplitude (up to 6 ppm) in the high northern latitudes. Overall, ACCESS-ESM1 performs well over the historical period, making it a useful tool to explore the change in land and oceanic carbon uptake in the future.
Abstract. Anthropogenic climate change leads to ocean warming, acidification, deoxygenation and reductions in near-surface nutrient concentrations, all of which are expected to affect marine ecosystems. Here we assess projections of these drivers of environmental change over the twenty-first century from Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) that were forced under the CMIP6 Shared Socioeconomic Pathways (SSPs). Projections are compared to those from the previous generation (CMIP5) forced under the Representative Concentration Pathways (RCPs). 10 CMIP5 and 13 CMIP6 models are used in the two multi-model ensembles. Under the high-emission scenario SSP5–8.5, the model mean change (2080–2099 mean values relative to 1870–1899) in sea surface temperature, surface pH, subsurface (100–600 m) oxygen concentration and euphotic (0–100 m) nitrate concentration is +3.48 ± 0.78 °C, −0.44 ± 0.005, −13.27 ± 5.28 mmol m−3 and −1.07 ± 0.45 mmol m−3, respectively. Under the low-emission, high-mitigation scenario SSP1–2.6, the corresponding changes are +1.42 ± 0.32 °C, −0.16 ± 0.002, −6.36 ± 2.92 mmol m−3 and −0.53 ± 0.23 mmol m−3. Projected exposure of the marine ecosystem to these drivers of ocean change depends largely on the extent of future emissions, consistent with previous studies. The Earth system models in CMIP6 generally project greater surface warming, acidification, deoxygenation and euphotic nitrate reductions than those from CMIP5 under comparable radiative forcing, with no reduction in inter-model uncertainties. Under the high-emission CMIP5 scenario RCP8.5, the corresponding changes in sea surface temperature, surface pH, subsurface oxygen and euphotic nitrate concentration are +3.04 ± 0.62 °C, −0.38 ± 0.005, −9.51 ± 2.13 mmol m−3 and −0.66 ± 0.49 mmol m−3, respectively. The greater surface acidification in CMIP6 is primarily a consequence of the SSPs having higher associated atmospheric CO2 concentrations than their RCP analogues. The increased projected warming results from a general increase in the climate sensitivity of CMIP6 models relative to those of CMIP5. This enhanced warming results in greater increases in upper ocean stratification in CMIP6 projections, which contributes to greater reductions in euphotic nitrate and subsurface oxygen ventilation.
Abstract. BRAN2020 (2020 version of the Bluelink ReANalysis) is an ocean reanalysis that combines observations with an eddy-resolving, near-global ocean general circulation model to produce a four-dimensional estimate of the ocean state. The data assimilation system employed is ensemble optimal interpolation, implemented with a new multiscale approach that constrains the broad-scale ocean properties and the mesoscale circulation in two steps. There is a separation in the scales that are corrected in the two steps: the high-resolution step corrects the mesoscale dynamics in the same way as previous versions of BRAN, while the extra coarse step is effective at correcting biases that develop at large scales. The reanalysis currently spans January 1993 to December 2019 and assimilates observations of in situ temperature and salinity, as well as of satellite sea-level anomaly and sea surface temperature. BRAN2020 is planned to be updated to within months of real time after this initial release, until an updated version of BRAN is available. Reanalysed fields from BRAN2020 generally show much closer agreement to observations than all previous versions with misfits between reanalysed and observed fields reduced by over 30 % for some variables, for subsurface temperature and salinity in particular. The BRAN2020 dataset is comprised of daily averaged fields of temperature, salinity, velocity, mixed-layer depth and sea level. Reanalysed fields realistically represent all of the major current systems within 75∘ S and 75∘ N, excluding processes relating to sea ice but including boundary currents, equatorial circulation, Southern Ocean variability and mesoscale eddies. BRAN2020 is publicly available at https://doi.org/10.25914/6009627c7af03 (Chamberlain et al., 2021b) and is intended for use by the research community.
Abstract We assess the representation of multiday temperature and rainfall extremes in southeast Australia in three coupled general circulation models (GCMs) of varying resolution. We evaluate the statistics of the modeled extremes in terms of their frequency, duration, and magnitude compared to observations, and the model representation of the midtropospheric circulation (synoptic and large scale) associated with the extremes. We find that the models capture the statistics of observed heatwaves reasonably well, though some models are “too wet” to adequately capture the observed duration of dry spells but not always wet enough to capture the magnitude of extreme wet events. Despite the inability of the models to simulate all extreme event statistics, the process evaluation indicates that the onset and decay of the observed synoptic structures are well simulated in the models, including for wet and dry extremes. We also show that the large-scale wave train structures associated with the observed extremes are reasonably well simulated by the models although their broader onset and decay is not always captured in the models. The results presented here provide some context for, and confidence in, the use of the coupled GCMs in climate prediction and projection studies for regional extremes.
Blue Maps aims to exploit the versatility of an ensemble data assimilation system to deliver gridded estimates of ocean temperature, salinity, and sea-level with the accuracy of an observation-based product. Weekly maps of ocean properties are produced on a 1/10°, near-global grid by combining Argo profiles and satellite observations using ensemble optimal interpolation (EnOI). EnOI is traditionally applied to ocean models for ocean forecasting or reanalysis, and usually uses an ensemble comprised of anomalies for only one spatiotemporal scale (e.g., mesoscale). Here, we implement EnOI using an ensemble that includes anomalies for multiple space- and time-scales: mesoscale, intraseasonal, seasonal, and interannual. The system produces high-quality analyses that produce mis-fits to observations that compare well to other observation-based products and ocean reanalyses. The accuracy of Blue Maps analyses is assessed by comparing background fields and analyses to observations, before and after each analysis is calculated. Blue Maps produces analyses of sea-level with accuracy of about 4 cm; and analyses of upper-ocean (deep) temperature and salinity with accuracy of about 0.45 (0.15) degrees and 0.1 (0.015) practical salinity units, respectively. We show that the system benefits from a diversity of ensemble members with multiple scales, with different types of ensemble members weighted accordingly in different dynamical regions.
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