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Record W3084268438 · doi:10.5194/gmd-14-7255-2021

Explicit silicate cycling in the Kiel Marine Biogeochemistry Model version 3 (KMBM3) embedded in the UVic ESCM version 2.9

2021· article· en· W3084268438 on OpenAlexfundno aff
Karin Kvale, David P. Keller, Wolfgang Koeve, Katrin J. Meißner, Christopher J. Somes, Wanxuan Yao, Andreas Oschlies

Bibliographic record

VenueGeoscientific model development · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
FundersNOAA Pacific Marine Environmental LaboratoryNational Cancer InstituteMinistry of Business, Innovation and EmploymentAustralian National UniversityGEOMAR Helmholtz-Zentrum für Ozeanforschung KielAustralian Research CouncilNational Oceanic and Atmospheric AdministrationUniversity of New South WalesOregon State UniversityUniversity of Victoria
KeywordsBiogeochemical cycleBiogeochemistryEnvironmental sciencePrimary productionClimatologyCarbon cycleAtmospheric sciencesClimate modelEarth system scienceForcing (mathematics)Marine ecosystemAtmospheric modelOceanographyEcosystemClimate changeGeologyChemistryEcology

Abstract

fetched live from OpenAlex

Abstract. We describe and test a new model of biological marine silicate cycling, implemented in the Kiel Marine Biogeochemical Model version 3 (KMBM3), embedded in the University of Victoria Earth System Climate Model (UVic ESCM) version 2.9. This new model adds diatoms, which are a key component of the biological carbon pump, to an existing ecosystem model. This new model combines previously published parameterizations of a diatom functional type, opal production and export with a novel, temperature-dependent dissolution scheme. Modelled steady-state biogeochemical rates, carbon and nutrient distributions are similar to those found in previous model versions. The new model performs well against independent ocean biogeochemical indicators and captures the large-scale features of the marine silica cycle to a degree comparable to similar Earth system models. Furthermore, it is computationally efficient, allowing both fully coupled, long-timescale transient simulations and “offline” transport matrix spinups. We assess the fully coupled model against modern ocean observations, the historical record starting from 1960 and a business-as-usual atmospheric CO2 forcing to the year 2300. The model simulates a global decline in net primary production (NPP) of 1.4 % having occurred since the 1960s, with the strongest declines in the tropics, northern midlatitudes and Southern Ocean. The simulated global decline in NPP reverses after the year 2100 (forced by the extended RCP8.5 CO2 concentration scenario), and NPP returns to 98 % of the pre-industrial rate by 2300. This recovery is dominated by increasing primary production in the Southern Ocean, mostly by calcifying phytoplankton. Large increases in calcifying phytoplankton in the Southern Ocean offset a decline in the low latitudes, producing a global net calcite export in 2300 that varies only slightly from pre-industrial rates. Diatom distribution moves southward in our simulations, following the receding Antarctic ice front, but diatoms are outcompeted by calcifiers across most of their pre-industrial Southern Ocean habitat. Global opal export production thus drops to 75 % of its pre-industrial value by 2300. Model nutrients such as phosphate, silicate and nitrate build up along the Southern Ocean particle export pathway, but dissolved iron (for which ocean sources are held constant) increases in the upper ocean. This different behaviour of iron is attributed to a reduction of low-latitude NPP (and consequently, a reduction in both uptake and export and particle, including calcite scavenging), an increase in seawater temperatures (raising the solubility of particulate iron) and stratification that “traps” the iron near the surface. These results are meant to serve as a baseline for sensitivity assessments to be undertaken with this model in the future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.205
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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