Numerical models of ocean biogeochemistry are relied upon to make projections about the impact of climate change on marine resources and test hypotheses regarding the drivers of past changes in ...climate and ecosystems. In large areas of the ocean, iron availability regulates the functioning of marine ecosystems and hence the ocean carbon cycle. Accordingly, our ability to quantify the drivers and impacts of fluctuations in ocean ecosystems and carbon cycling in space and time relies on first achieving an appropriate representation of the modern marine iron cycle in models. When the iron distributions from 13 global ocean biogeochemistry models are compared against the latest oceanic sections from the GEOTRACES program, we find that all models struggle to reproduce many aspects of the observed spatial patterns. Models that reflect the emerging evidence for multiple iron sources or subtleties of its internal cycling perform much better in capturing observed features than their simpler contemporaries, particularly in the ocean interior. We show that the substantial uncertainty in the input fluxes of iron results in a very wide range of residence times across models, which has implications for the response of ecosystems and global carbon cycling to perturbations. Given this large uncertainty, iron fertilization experiments based on any single current generation model should be interpreted with caution. Improvements to how such models represent iron scavenging and also biological cycling are needed to raise confidence in their projections of global biogeochemical change in the ocean.
Key Points
First intercomparison of 13 global iron models highlights key challenges in reproducing iron data
Wide uncertainty in iron input fluxes, which results in poorly constrained residence times
Reducing uncertainty in scavenging and biological cycling is a priority
Most dissolved iron in the ocean is bound to organic molecules with strong conditional stability constants, known as ligands that are found at concentrations ranging from 0.2 to more than 10nmolL−1. ...In this work we report the first mechanistic description of ligand dynamics in two three-dimensional models of ocean biogeochemistry and circulation. The model for ligands is based on the concept that ligands are produced both from organic matter remineralization and phytoplankton processes, and that they are lost through bacterial and photochemical degradation, as well as aggregation and to some extent in the process of phytoplankton uptake of ligand-bound iron.
A comparison with a compilation of in-situ measurements shows that the model is able to reproduce some large-scale features of the observations, such as a decrease in ligand concentrations along the conveyor belt circulation in the deep ocean, lower surface and subsurface values in the Southern Ocean, or higher values in the mesopelagic than in the abyssal ocean.
Modeling ligands prognostically (as opposed to assuming a uniform ligand concentration) leads to a more nutrient-like profile of iron that is more in accordance with data. It however, also leads to higher surface concentrations of dissolved iron and negative excess ligand L⁎ in some ocean regions. This is probably an indication that with more realistic and higher ligand concentrations near the surface, as opposed to the traditionally chosen low uniform concentration, iron modelers will have to re-evaluate their assumption of low scavenging rates for iron. Given their sensitivity to environmental conditions, spatio-temporal variations in ligand concentrations have the potential to impact primary production via changes in iron limitation.
•A mechanistic description of the dynamics of iron-binding ligands in the ocean is implemented in a 3-d biogeochemical model.•Ligand concentrations in the ocean vary spatially and the model resolves some of that variability.•Prognostic ligands lead to a more nutrient-like distribution of iron in the water column.•Negative ligand excess values in some surface ocean regions highlight the need to re-evaluate scavenging of uncomplexed iron.
Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO
2
emissions. However, as yet there are no sufficiently accurate material models to effectively ...predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.
Primary production by phytoplankton represents a major pathway whereby atmospheric CO
is sequestered in the ocean, but this requires iron, which is in scarce supply. As over 99% of iron is complexed ...to organic ligands, which increase iron solubility and microbial availability, understanding the processes governing ligand dynamics is of fundamental importance. Ligands within humic-like substances have long been considered important for iron complexation, but their role has never been explained in an oceanographically consistent manner. Here we show iron co-varying with electroactive humic substances at multiple open ocean sites, with the ratio of iron to humics increasing with depth. Our results agree with humic ligands composing a large fraction of the iron-binding ligand pool throughout the water column. We demonstrate how maximum dissolved iron concentrations could be limited by the concentration and binding capacity of humic ligands, and provide a summary of the key processes that could influence these parameters. If this relationship is globally representative, humics could impose a concentration threshold that buffers the deep ocean iron inventory. This study highlights the dearth of humic data, and the immediate need to measure electroactive humics, dissolved iron and iron-binding ligands simultaneously from surface to depth, across different ocean basins.
Low concentrations of iron, an important micronutrient for photosynthetic organisms, limit growth in large parts of the ocean. The solubility and availability of iron is to a large degree determined ...by organic iron-binding molecules, so-called ligands. While ligands come from a variety of sources, many of them are produced in autotrophic or heterotrophic production in the ocean, leading to the possibility of feedbacks between marine primary production and iron availability. The diversity of ligands, reaching from siderophores, small molecules involved in bacterial iron uptake, to breakdown products and long-lived macromolecules like humic substances, means that feedbacks could be both negative and positive or there may even be no feedback at all. Here we investigate first, how the cycling of this ligand pool can be described simplistically in a model such that it reproduces the observed global distribution of dissolved iron and phosphorus as closely as possible. We show that inclusion of a ligand similar to refractory dissolved organic carbon leads to an improved agreement to observations in our model. Inclusion of a second, shorter-lived siderophore-like ligand does not strongly affect this agreement. In a second step we then study how feedbacks affect how iron distribution and oceanic productivity react to changes in external supply of iron. We show that, to be consistent with present-day iron distribution, the dominant feedback is positive, increasing the sensitivity of global biological productivity and hence carbon cycling to changes in iron supply. The strength of the feedback increases with increasing ligand life-time. The negative feedback associated with siderophore-like ligands has the potential to mitigate the positive feedback, especially at the surface and for global export production, but more research on the production and decay of siderophores is needed for a better quantification. Ocean biogeochemical models that assume a constant ligand concentration and hence neglect possible feedbacks may therefore underestimate the reaction of the global carbon cycle to the strong increase in dust deposition under future or glacial climate conditions.
We explore the impact of a latitudinal shift in the westerly wind belt over the Southern Ocean on the Atlantic meridional overturning circulation (AMOC) and on the carbon cycle for Last Glacial ...Maximum background conditions using a state‐of‐the‐art ocean general circulation model. We find that a southward (northward) shift in the westerly winds leads to an intensification (weakening) of no more than 10% of the AMOC. This response of the ocean physics to shifting winds agrees with other studies starting from preindustrial background climate, but the responsible processes are different. In our setup changes in AMOC seemed to be more pulled by upwelling in the south than pushed by downwelling in the north, opposite to what previous studies with different background climate are suggesting. The net effects of the changes in ocean circulation lead to a rise in atmospheric pCO2 of less than 10 μatm for both northward and southward shift in the winds. For northward shifted winds the zone of upwelling of carbon‐ and nutrient‐rich waters in the Southern Ocean is expanded, leading to more CO2outgassing to the atmosphere but also to an enhanced biological pump in the subpolar region. For southward shifted winds the upwelling region contracts around Antarctica, leading to less nutrient export northward and thus a weakening of the biological pump. These model results do not support the idea that shifts in the westerly wind belt play a dominant role in coupling atmospheric CO2 rise and Antarctic temperature during deglaciation suggested by the ice core data.
Key Points
First simulations of shifted westerly winds starting from LGM background climate
Changing westerlies lead to opposing effects on different carbon pumps
Atmospheric CO2 and temperature are not coupled by shifts in westerly winds
This work presents machine learning-inspired data fusion approaches to improve the nondestructive testing of reinforced concrete. The principal effects that are used for data fusion are shown ...theoretically. Their effectiveness is tested in case studies carried out on large-scale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic regression algorithm. It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling. The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.
We present a systematic approach for fusion of multi-sensory nondestructive testing data. Our data set consists of impact-echo, ultrasonic pulse echo and ground penetrating radar data collected on a ...large-scale concrete specimen with built-in honeycombing defects. From each data set, the most significant signatures of honeycombs were extracted in the form of features. We applied two simple data fusion algorithms to the data: Dempster’s rule of combination and the Hadamard product. The performance of the fusion rules versus the single-sensor testing was evaluated. The fusion rules exhibit a slight improvement of false alarm rate over the best single sensor.
•We laid out a novel conceptual framework for multi-sensor defect classification.•No well-established technique for honeycomb detection.•New methods to extract information (features) from each sensor data are presented.•We introduce two conceptually simple yet effective fusion algorithms.•A quantitative evaluation is performed by comparing ROC curves.
Carbon dioxide removal (CDR) approaches are efforts to reduce the atmospheric CO2 concentration. Here we use a marine carbon cycle model to investigate the effects of one CDR technique: the open ...ocean dissolution of the iron-containing mineral olivine. We analyse the maximum CDR potential of an annual dissolution of 3 Pg olivine during the 21st century and focus on the role of the micro-nutrient iron for the biological carbon pump. Distributing the products of olivine dissolution (bicarbonate, silicic acid, iron) uniformly in the global surface ocean has a maximum CDR potential of 0.57 gC/g-olivine mainly due to the alkalinisation of the ocean, with a significant contribution from the fertilisation of phytoplankton with silicic acid and iron. The part of the CDR caused by ocean fertilisation is not permanent, while the CO2 sequestered by alkalinisation would be stored in the ocean as long as alkalinity is not removed from the system. For high CO2 emission scenarios the CDR potential due to the alkalinity input becomes more efficient over time with increasing ocean acidification. The alkalinity-induced CDR potential scales linearly with the amount of olivine, while the iron-induced CDR saturates at 113 PgC per century (on average PgC yr−1) for an iron input rate of 2.3 Tg Fe yr−1 (1% of the iron contained in 3 Pg olivine). The additional iron-related CO2 uptake occurs in the Southern Ocean and in the iron-limited regions of the Pacific. Effects of this approach on surface ocean pH are small .
This data article introduces a dataset comprising 1630 alkali-activated concrete (AAC) mixes, compiled from 106 literature sources. The dataset underwent extensive curation to address feature ...redundancy, transcription errors, and duplicate data, yielding refined data ready for further data-driven science in the field of AAC, where this effort constitutes a novelty. The carbon footprint associated with each material used in the AAC mixes, as well as the corresponding CO
2
footprint of every mix, were approximated using two published articles. Serving as a foundation for future expansions and rigorous data applications, this dataset enables the characterization of AAC properties through machine learning algorithms or as a benchmark for performance comparison among different formulations. In summary, the dataset provides a resource for researchers focusing on AAC and related materials and offers insights into the environmental benefits of substituting traditional Portland concrete with AAC.
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