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A simulated annealing optimization algorithm can optimize the parameters of ecosystem models. The optimization can figure out the model parameters necessary to reproduce the data observed. The optimization routine is figured out in a general manner and easily changed to include additional information on the desired model output and model parameters. The optimization analysis also gives the means necessary for figuring out the model complexity necessary to model the available data. The hypothetical used demonstrates three different ecosystem model configurations from nitrate, phytoplankton, mesozooplankton and net phyoplankton productivity measurements at a station in the subarctic Pacific.

We detail the system design, model configuration, and data assimilation evaluation for the CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1). CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multiyear climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to the present. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are subsampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via explicit cross-domain covariances between ocean, atmosphere, sea ice, and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean, and sea ice. The system produces 96 climate trajectories (state estimates) over the most recent six decades as well as a complete data archive of initial conditions, potentially enabling individual forecasts for all members each month over the 60-yr period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses. [ABSTRACT FROM AUTHOR]

Ice algae play a fundamental role in shaping polar marine ecosystems and biogeochemistry. This role can be investigated by field observations, however the influence of ice algae at the regional and global scales remains unclear due to limited spatial and temporal coverage of observations, and because ice algae are typically not included in current Earth System Models. To address this knowledge gap, we introduce a new model intercomparison project (MIP), referred to here as the Ice Algae Model Intercomparison Project phase 2 (IAMIP2). IAMIP2 is built upon the experience from its previous phase, and expands its scope to global coverage (both Arctic and Antarctic) and centennial timescales (spanning the mid-twentieth century to the end of the twenty-first century). Participating models are three-dimensional regional and global coupled sea ice–ocean models that incorporate sea-ice ecosystem components. These models are driven by the same initial conditions and atmospheric forcing datasets by incorporating and expanding the protocols of the Ocean Model Intercomparison Project, an endorsed MIP of the Coupled Model Intercomparison Project phase 6 (CMIP6). Doing so provides more robust estimates of model bias and uncertainty, and consequently advances the science of polar marine ecosystems and biogeochemistry. A diagnostic protocol is designed to enhance the reusability of the model data products of IAMIP2. Lastly, the limitations and strengths of IAMIP2 are discussed in the context of prospective research outcomes. [ABSTRACT FROM AUTHOR]

Recent studies have shown that regardless of model configuration, skill in predicting El Niño–Southern Oscillation (ENSO), in terms of target month and forecast lead time, remains largely dependent on the temporal characteristics of the boreal spring predictability barrier. Continuing the 2019 study by O'Kane et al., we compare multiyear ensemble ENSO forecasts from the Climate Analysis Forecast Ensemble (CAFE) to ensemble forecasts from state-of-the-art dynamical coupled models in the North American Multimodel Ensemble (NMME) project. The CAFE initial perturbations are targeted such that they are specific to tropical Pacific thermocline variability. With respect to individual NMME forecasts and multimodel ensemble averages, the CAFE forecasts reveal improvements in skill when predicting ENSO at lead times greater than 6 months, in particular when predictability is most strongly limited by the boreal spring barrier. Initial forecast perturbations generated exclusively as disturbances in the equatorial Pacific thermocline are shown to improve the forecast skill at longer lead times in terms of anomaly correlation and the random walk sign test. Our results indicate that augmenting current initialization methods with initial perturbations targeting instabilities specific to the tropical Pacific thermocline may improve long-range ENSO prediction. [ABSTRACT FROM AUTHOR]

We develop and compare variants of coupled data assimilation (DA) systems based on ensemble optimal interpolation (EnOI) and ensemble transform Kalman filter (ETKF) methods. The assimilation system is first tested on a small paradigm model of the coupled tropical–extratropical climate system, then implemented for a coupled general circulation model (GCM). Strongly coupled DA was employed specifically to assess the impact of assimilating ocean observations [sea surface temperature (SST), sea surface height (SSH), and sea surface salinity (SSS), Argo, XBT, CTD, moorings] on the atmospheric state analysis update via the cross-domain error covariances from the coupled-model background ensemble. We examine the relationship between ensemble spread, analysis increments, and forecast skill in multiyear ENSO prediction experiments with a particular focus on the atmospheric response to tropical ocean perturbations. Initial forecast perturbations generated from bred vectors (BVs) project onto disturbances at and below the thermocline with similar structures to ETKF perturbations. BV error growth leads ENSO SST phasing by 6 months whereupon the dominant mechanism communicating tropical ocean variability to the extratropical atmosphere is via tropical convection modulating the Hadley circulation. We find that bred vectors specific to tropical Pacific thermocline variability were the most effective choices for ensemble initialization and ENSO forecasting. [ABSTRACT FROM AUTHOR]

Atmospheric CO2 levels continue to rise, increasing the risk of severe impacts on the Earth system, and on the ecosystem services that it provides. Artificial Ocean Alkalization (AOA) is capable of reducing atmospheric CO2 concentrations, surface warming and addressing ocean acidification. Here we simulate global and regional responses to alkalinity addition (0.25 PmolAlk/year) using the CSIRO-Mk3L-COAL Earth System Model in the period 2020-2100, under high (RCP8.5) and low (RCP2.6) emissions. While regionally there are large changes associated with locations of AOA, globally we see only a very weak dependence on where and when AOA is applied. We see that under RCP2.6, while the carbon uptake associated with AOA is only ∼ 60 % of the total under RCP8.5, the relative changes in temperature are larger, as are the changes in pH (1.4×) and aragonite saturation (1.7×). The results of this modelling study are significant as they demonstrate that AOA is more effective under lower emissions, and the higher the emissions the more AOA required to achieve the same reduction in global warming and ocean acidification. Finally, our simulations show AOA in the period 2020-2100 is capable of offsetting global warming and ameliorating ocean acidification increases due to low emissions, but regionally the response is more variable. [ABSTRACT FROM AUTHOR]

The Ocean Model Intercomparison Project (OMIP) focuses on the physics and biogeochemistry of the ocean component of Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). OMIP aims to provide standard protocols and diagnostics for ocean models, while offering a forum to promote their common assessment and improvement. It also offers to compare solutions of the same ocean models when forced with reanalysis data (OMIP simulations) vs. when integrated within fully coupled Earth system models (CMIP6). Here we detail simulation protocols and diagnostics for OMIP's biogeochemical and inert chemical tracers. These passive-tracer simulations will be coupled to ocean circulation models, initialized with observational data or output from a model spin-up, and forced by repeating the 1948- 2009 surface fluxes of heat, fresh water, and momentum. These so-called OMIP-BGC simulations include three inert chemical tracers (CFC-11, CFC-12, SF6) and biogeochemical tracers (e.g., dissolved inorganic carbon, carbon isotopes, alkalinity, nutrients, and oxygen). Modelers will use their preferred prognostic BGC model but should follow common guidelines for gas exchange and carbonate chemistry. Simulations include both natural and total carbon tracers. The required forced simulation (omip1) will be initialized with gridded observational climatologies. An optional forced simulation (omip1-spunup) will be initialized instead with BGC fields from a long model spin-up, preferably for 2000 years or more, and forced by repeating the same 62-year meteorological forcing. That optional run will also include abiotic tracers of total dissolved inorganic carbon and radiocarbon, Cabio T and 14Cabio T , to assess deep-ocean ventilation and distinguish the role of physics vs. biology. These simulations will be forced by observed atmospheric histories of the three inert gases and CO2 as well as carbon isotope ratios of CO2. OMIP-BGC simulation protocols are founded on those from previous phases of the Ocean Carbon-Cycle Model Intercomparison Project. They have been merged and updated to reflect improvements concerning gas exchange, carbonate chemistry, and new data for initial conditions and atmospheric gas histories. Code is provided to facilitate their implementation. [ABSTRACT FROM AUTHOR]

Carbon-climate feedbacks have the potential to significantly impact the future climate by altering atmospheric CO2 concentrations (Zaehle et al., 2010). By modifying the future atmospheric CO2 concentrations, the carbon-climate feedbacks will also influence the future trajectory for ocean acidification. Here, we use the CO2 emissions scenarios from 4 Representative Concentration Pathways (RCPs) with an Earth System Model to project the future trajectories of ocean acidification with the inclusion of carbon-climate feedbacks. We show that simulated carbon-climate feedbacks can significantly impact the onset of under-saturated aragonite conditions in the Southern and Arctic Oceans, the suitable habitat for tropical coral and the deepwater saturation states. Under higher emission scenarios (RCP8.5 and RCP6.0), the carbon-climate feedbacks advance the onset of under-saturation conditions and the reduction in suitable coral reef habitat by a decade or more. The impact of the carbon-climate feedback is most significant for the medium (RCP4.5) and low emission (RCP2.6) scenarios. For RCP4.5 scenario by 2100, the carbon-climate feedbacks nearly double the area of surface water under-saturated respect to aragonite and reduce by 50 % the surface water suitable for coral reefs. For RCP2.6 scenario by 2100, the carbon-climate feedbacks reduce the area suitable for coral reefs by 40 % and increase the area of under-saturated surface water by 20 %. The high sensitivity of the impact of ocean acidification to the carbon-climate feedbacks in the low to medium emissions scenarios is important because our recent commitments to reduce CO2 emissions are trying to move us on to such an emissions scenario. The study highlights the need to better characterise the carbon-climate feedbacks to ensure we do not excessively stress the oceans by under-estimating the future impact of ocean acidification. [ABSTRACT FROM AUTHOR]