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Record W4319296770 · doi:10.5194/egusphere-2023-167

Evaluating Nitrogen Cycling in Terrestrial Biosphere Models: Implications for the Future Terrestrial Carbon Sink

2023· preprint· en· W4319296770 on OpenAlexaff
Sian Kou‐Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O’Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, Sönke Zaehle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsQueen's UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsBiosphereCyclingTerrestrial ecosystemEnvironmental scienceNitrogen cycleCarbon cycleCarbon sequestrationBiogeochemical cycleBiosphere modelCarbon sinkSink (geography)Temperate climateBiogeochemistryAtmospheric sciencesEcologyEcosystemNitrogenBiologyGeographyChemistryCarbon dioxideGeologyForestry

Abstract

fetched live from OpenAlex

Abstract. Terrestrial carbon (C) sequestration is limited by nitrogen (N), a constraint that could intensify under CO2 fertilisation and future global change. The terrestrial C sink is estimated to currently sequester approximately a third of annual anthropogenic CO2 emissions based on an ensemble of terrestrial biosphere models, which have been evaluated in their ability to reproduce observations of the C, water, and energy cycles. However, their ability to reproduce observations of N cycling and thus the regulation of terrestrial C sequestration by N has been largely unexplored. Here, we evaluate an ensemble of terrestrial biosphere models with coupled C-N cycling and their performance at simulating N cycling, outlining a framework for evaluating N cycling that can be applied across terrestrial biosphere models. We find that models exhibit significant variability across N pools and fluxes, simulating different magnitudes and trends over the historical period, despite their ability to generally reproduce the historical terrestrial C sink. This suggests that the underlying N processes that regulate terrestrial C sequestration operate differently across models and may not be fully captured. Furthermore, models tended to overestimate tropical biological N fixation, vegetation C:N ratio, and soil C:N ratio but underestimate temperate biological N fixation relative to observations. However, there is significant uncertainty associated with measurements of N cycling processes given their scarcity (especially relative to those of C cycling processes) and their high spatiotemporal variability. Overall, our results suggest that terrestrial biosphere models that represent coupled C-N cycling (let alone those without a representation of N cycling) could be overestimating C storage per unit N, which could lead to biases in projections of the future terrestrial C sink under CO2 fertilisation and future global change. More extensive observations of N cycling processes are crucial to evaluate N cycling and its impact on C cycling as well as guide its development in terrestrial biosphere models.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.075
GPT teacher head0.319
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes1
Has abstractyes

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