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Dataset: Elemental and isotopic noble gas ratios and N2/Ar from the North Atlantic: model simulations, This dataset includes model simulations of the following tracers using the Transport Matrix Method (TMM): (i) new observations of heavy noble gas ratios (elemental and isotopic ratios) from the Bermuda Atlantic Time-series (BATS) on cruise 10391 (30 April 2022 - 05 May 2022), and (ii) measurements of Kr/Ar and N2/Ar ratios in stored dissolved gas samples collected in 1981 through the Transient Tracers in the Ocean (TTO) program. Together these measurements and model simulations provide insight into physical processes governing gas exchange in the high-latitude regions of North Atlantic Deep Water formation, and a comparison of physical simulations of N2/Ar ratios to observations in TTO samples reveals excess N2 that arises from benthic denitrification in the deep North Atlantic. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/890293, NSF Division of Ocean Sciences (NSF OCE) OCE-2122427, NSF Division of Ocean Sciences (NSF OCE) OCE-1923915

Dataset: Elemental and isotopic noble gas ratios and N2/Ar from the North Atlantic: TTO data, This dataset includes measurements of Kr/Ar and N2/Ar ratios in stored dissolved gas samples collected in 1981 through the Transient Tracers in the Ocean (TTO) program. These data were used, along with new observations of heavy noble gas ratios (elemental and isotopic ratios) from the Bermuda Atlantic Time-series (BATS) on cruise 10391 (30 April 2022 - 05 May 2022), to model simulations of these tracers using the Transport Matrix Method (TMM). Together these new measurements and model simulations provide insight into physical processes governing gas exchange in the high-latitude regions of North Atlantic Deep Water formation, and a comparison of physical simulations of N2/Ar ratios to observations in TTO samples reveals excess N2 that arises from benthic denitrification in the deep North Atlantic. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/890427, NSF Division of Ocean Sciences (NSF OCE) OCE-2122427, NSF Division of Ocean Sciences (NSF OCE) OCE-1923915

Dataset: Elemental and isotopic noble gas ratios and N2/Ar from the North Atlantic: BATS data, This dataset includes new observations of heavy noble gas ratios (elemental and isotopic ratios) from the Bermuda Atlantic Time-series (BATS) on cruise 10391 on R/V Atlantic Explorer (AE2208) from 30 April 2022 - 05 May 2022. These data were used, along with measurements of Kr/Ar and N2/Ar ratios in stored dissolved gas samples from the Transient Tracers in the Ocean (TTO) program, to model simulations of these tracers using the Transport Matrix Method (TMM). Together these new measurements and model simulations provide insight into physical processes governing gas exchange in the high-latitude regions of North Atlantic Deep Water formation, and a comparison of physical simulations of N2/Ar ratios to observations in TTO samples reveals excess N2 that arises from benthic denitrification in the deep North Atlantic. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/890342, NSF Division of Ocean Sciences (NSF OCE) OCE-2122427, NSF Division of Ocean Sciences (NSF OCE) OCE-1923915

Global mitigation commitments which aim to limit global warming to less than 2ºC require dramatic and rapid reductions in atmospheric carbon dioxide (CO2) over the coming century. Carbon Dioxide Removal (CDR) technologies, whereby CO2 is actively taken out of the atmosphere and ¿durably¿ stored terrestrially, geologically or in the ocean could be employed to help reduce or counter-balance CO2 emissions to meet national net zero and net negative mitigation targets. Weathering processes would naturally draw atmospheric CO2 down towards pre-industrial levels over hundreds of thousands of years. One such CDR approach involves accelerating the uptake of CO2 through ¿enhanced weathering¿ (EW). CDR through EW of silicate minerals such as olivine or carbonate minerals can be achieved, for example, by spreading pulverized rocks on soils or employing mine tailings in specialized reactors to increase the weathering rate and hence carbon sequestration on decadal timescales. Here, we explore the efficiency of using mining waste to achieve CDR through EW. We exploit the results of a recent study by Bullock et al. (Science of the Total Environment, 2022) which produced one of the first comprehensive assessments of the global and country level suitability of mine tailings, accounting for reaction kinetics, and their potential for CO2 drawdown. While the overall CDR from EW of mine tailings is relatively modest, such an approach may still help individual countries meet their net zero goals and it is useful to investigate the broader implications of the deployment of this approach. EW leads to the production of alkalinity and bicarbonate ions (through CDR). We use Bullock et al.¿s estimates of the annual generation of these quantities over the coming century to force an ocean biogeochemical model to investigate the impact of the release of these constituents into the ocean on atmospheric CO2 and ocean chemistry under various emission scenarios. In our simulations, alkalinity and diss

The skill of global ocean biogeochemical models, and the earth system models in which they are embedded, can be improved by systematic calibration of the parameter values against observations. However, such tuning is seldom undertaken as these models are computationally very expensive. Here we investigate the performance of DFO-LS, a local, derivative-free optimisation algorithm which has been designed for computationally expensive models with irregular model–data misfit landscapes typical of biogeochemical models. We use DFO-LS to calibrate six parameters of a relatively complex global ocean biogeochemical model (MOPS) against synthetic dissolved oxygen, phosphate and nitrate “observations” from a reference run of the same model with a known parameter configuration. The performance of DFO-LS is compared with that of CMA-ES, another derivative-free algorithm that was applied in a previous study to the same model in one of the first successful attempts at calibrating a global model of this complexity. We find that DFO-LS successfully recovers five of the six parameters in approximately 40 evaluations of the misfit function (each one requiring a 3000-year run of MOPS to equilibrium), while CMA-ES needs over 1200 evaluations. Moreover, DFO-LS reached a “baseline” misfit, defined by observational noise, in just 11–14 evaluations, whereas CMA-ES required approximately 340 evaluations. We also find that the performance of DFO-LS is not significantly affected by observational sparsity, however fewer parameters were successfully optimised in the presence of observational uncertainty. The results presented here suggest that DFO-LS is sufficiently inexpensive and robust to apply to the calibration of complex, global ocean biogeochemical models.

Biogeochemical model behaviour for micronutrients is typically hard to constrain because of the sparsity of observational data, the difficulty of determining parameters in situ, and uncertainties in observations and models. Here, we assess the influence of data distribution, model uncertainty, and the misfit function on objective parameter optimisation in a model of the oceanic cycle of zinc (Zn), an essential micronutrient for marine phytoplankton with a long whole-ocean residence time. We aim to investigate whether observational constraints are sufficient for reconstruction of biogeochemical model behaviour, given that the Zn data coverage provided by the GEOTRACES Intermediate Data Product 2017 is sparse. Furthermore, we aim to assess how optimisation results are affected by the choice of the misfit function and by confounding factors such as analytical uncertainty in the data or biases in the model related to either seasonal variability or the larger-scale circulation. The model framework applied herein combines a marine Zn cycling model with a state-of-the-art estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES) to optimise the model towards synthetic data in an ensemble of 26 optimisations. Provided with a target field that can be perfectly reproduced by the model, optimisation retrieves parameter values perfectly regardless of data coverage. As differences between the model and the system underlying the target field increase, the choice of the misfit function can greatly impact optimisation results, while limitation of data coverage is in most cases of subordinate significance. In cases where optimisation to full or limited data coverage produces relatively distinct model behaviours, we find that applying a misfit metric that compensates for differences in data coverage between ocean basins considerably improves agreement between optimisation results obtained with the two data situations.

The performance of global ocean biogeochemical models, and the Earth System Models in which they are embedded, can be improved by systematic calibration of the parameter values against observations. However, such tuning is seldom undertaken as these models are computationally very expensive. Here we investigate the performance of DFO-LS, a local, derivative-free optimisation algorithm which has been designed for computationally expensive models with irregular model-data misfit landscapes typical of biogeochemical models. We use DFO-LS to calibrate six parameters of a relatively complex global ocean biogeochemical model (MOPS) against synthetic dissolved oxygen, inorganic phosphate and inorganic nitrate "observations" from a reference run of the same model with a known parameter configuration. The performance of DFO-LS is compared with that of CMA-ES, another derivative-free algorithm that was applied in a previous study to the same model in one of the first successful attempts at calibrating a global model of this complexity. We find that DFO-LS successfully recovers 5 of the 6 parameters in approximately 40 evaluations of the misfit function (each one requiring a 3000 year run of MOPS to equilibrium), while CMA-ES needs over 1200 evaluations. Moreover, DFO-LS reached a "baseline" misfit, defined by observational noise, in just 11–14 evaluations, whereas CMA-ES required approximately 340 evaluations. We also find that the performance of DFO-LS is not significantly affected by observational sparsity, however fewer parameters were successfully optimised in the presence of observational uncertainty. The results presented here suggest that DFO-LS is sufficiently inexpensive and robust to apply to the calibration of complex, global ocean biogeochemical models.

Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 48(18), (2021): e2021GL092621, https://doi.org/10.1029/2021GL092621., In the subsurface ocean, O2 depleted because of organic matter remineralization is generally estimated based on apparent oxygen utilization (AOU). However, AOU is an imperfect measure of oxygen utilization because of O2 air-sea disequilibrium at the site of deepwater formation. Recent methodological and instrumental advances have paved the way to further deconvolve the processes driving the O2 signature. Using numerical model simulations of the global ocean, we show that the measurements of the dissolved O2/Ar ratio, which so far have been confined to the ocean surface, can provide improved estimates of oxygen utilization, especially in regions where the disequilibrium at the site of deepwater formation is associated with physical processes. We discuss applications of this new approach and implications for the current tracers relying on O2 such as remineralization ratios, respiratory quotients, and preformed nutrients. Finally, we propose a new composite geochemical tracer, [O2]bio combining dissolved O2/Ar and phosphate concentration. Being insensitive to photosynthesis and respiration, the change in this new tracer reflects gas exchange at the air-sea interface at the sites of deepwater formation., Nicolas Cassar was supported by the “Laboratoire d'Excellence” LabexMER (ANR-10-LABX-19) and cofunded by a grant from the French government under the program “Investissements d'Avenir.” Samar Khatiwala was supported by UK NERC grant NE/T009357/1. Ellen Cliff acknowledges support from the Rhodes Trust., 2022-03-13

Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many (≈10–≈100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes. We here present a framework for the calibration of global biogeochemical ocean models on short and long time scales. The framework combines an offline approach for transport of biogeochemical tracers with an Estimation of Distribution Algorithm (Covariance Matrix Adaption Evolution Strategy, CMAES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent, to which different setups of the optimization influence model's fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter time scales, are more difficult to determine. In particular the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, l

Conventional integration of Earth system and ocean models can accrue considerable computational expenses, particularly for marine biogeochemical applications. "Offline" numerical schemes in which only the biogeochemical tracers are time stepped and transported using a pre-computed circulation field can substantially reduce the burden and are thus an attractive alternative. One such scheme is the "transport matrix method" (TMM), which represents tracer transport as a sequence of sparse matrix–vector products that can be performed efficiently on distributed-memory computers. While the TMM has been used for a variety of geochemical and biogeochemical studies, to date the resulting solutions have not been comprehensively assessed against their "online" counterparts. Here, we present a detailed comparison of the two. It is based on simulations of the state-of-the-art biogeochemical sub-model embedded within the widely used coarse-resolution University of Victoria Earth System Climate Model (UVic ESCM). The default, non-linear advection scheme was first replaced with a linear, third-order upwind-biased advection scheme to satisfy the linearity requirement of the TMM. Transport matrices were extracted from an equilibrium run of the physical model and subsequently used to integrate the biogeochemical model offline to equilibrium. The identical biogeochemical model was also run online. Our simulations show that offline integration introduces some bias to biogeochemical quantities through the omission of the polar filtering used in UVic ESCM and in the offline application of time-dependent forcing fields, with high latitudes showing the largest differences with respect to the online model. Differences in other regions and in the seasonality of nutrients and phytoplankton distributions are found to be relatively minor, giving confidence that the TMM is a reliable tool for offline integration of complex biogeochemical models. Moreover, while UVic ESCM is a serial code, the TMM

This slide deck was developed to inform broader scientific, as well as general audiences about the role of the ocean in the global carbon cycle, including key sinks and sources of anthropogenic carbon and how they have evolved through time and space.