MétaCan
Menu
← Back to cohort
Record W4408430375 · doi:10.5194/egusphere-egu25-14199

Multi-model intercomparison of northern peatland carbon cycle 

2025· preprint· en· W4408430375 on OpenAlexaff
Xiaoying Shi, Daniel Ricciuto, Yaoping Wang, Paul J. Hanson, Jiafu Mao, Yiqi Luo, Xiaofeng Xu, Dafeng Hui, Hongxing He, Siya Shao, Ayesha Hussain, Qing Sun, Chunjing Qiu, Akihiko Ito, Joe R. Melton, Eleanor Burke, Fortunat Joos, Qianlai Zhuang, Yongjiu Dai, Xingjie Lu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill UniversityEnvironment and Climate Change CanadaNatural Resources Canada
Fundersnot available
KeywordsPeatCoupled model intercomparison projectCarbon cycleEnvironmental scienceCarbon fibersClimatologyAtmospheric sciencesClimate modelOceanographyClimate changeGeographyGeologyEcologyMathematicsEcosystemArchaeology

Abstract

fetched live from OpenAlex

Peatlands cover only 3% of Earth’s land surface but contain about 30% of the global soil carbon pool. Derived predominantly from plant litter and moss accretions, peat deposits are critically sensitive to environmental dynamics such as soil temperature and moisture levels. This vulnerability has raised concerns about potential positive feedback mechanisms in relation to global climate change. However, current global models present disparities in projected emissions, and sensitivities of peatland carbon stocks to changing environments are a major uncertainty in global carbon projections. The Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment is a large‐scale climate change manipulation that focuses on the combined response of multiple levels of warming at both ambient and elevated CO2 concentration (aCO2 and eCO2), which makes it is a valuable testbed for the broader modeling community to improve the diagnosis and attribution of C fluxes in peatland ecosystems. The SPRUCE Multi-model Intercomparison Project (SPRUCE-MIP) aims to evaluate the projections of peatland carbon cycle dynamics and their warming responses of various models by comparing the model outputs to empirical data from SPRUCE. We assessed 12 different models, focusing on their predictions for net ecosystem carbon exchange (NEE) and its components – net primary productivity (NPP), heterotrophic respiration (HR) and methane (CH4) fluxes. These predictions were compared to an extensive on-site carbon cycle dataset across five distinct temperature warming levels and two CO2 concentration scenarios. Our findings revealed significant variability in the model projections, with substantial scatter in the absolute values, warming sensitivities and eCO2 effects. For example, the models’ prediction of carbon take up between 10 and 732 gC m-2 year-1 forthe baseline warming with aCO2 condition, and the warming sensitivity response is about 1 to 126 gC m-2 year-1 ℃-1. In addition, notable increases productivity under eCO2 condition are observed in the ORCHIDEE (138.9%), VISIT (105.2%) and ELM-Microbe (64.4%) models while there is no eCO2 effects for model CoupModel. Experimental measurements showed carbon source even for the baseline warming chamber while most of the model predicted carbon sink, except for model CoupModel, MWM, and PTEM. Furthermore, both the observations and these three models show a significant in C release to atmosphere, making stronger C sources at the extreme +9°C warming level for both aCO2 and eCO2 conditions, and models such as CoLM, ELM-SPRUCE and JULES transition from a C sink to a C source under these conditions. Meanwhile, the DNDC, ORCHIDEE and VIST models switch from a C sink to a C source under aCO2conditions but remain C sinks under eCO2 conditions. In contrast, models like ELM-Microbe, LPX-Bern and TECO predict that the SPRUCE peatland ecosystem continues to function as a C sink even at the +9°C warming level under both CO2 conditions. Integrating models with experimental design will allow targeting of these uncertainties and help to reconcile divergence among models to produce more confident projections of peatland ecosystem responses to global changes.

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)0.002
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 score0.081
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicPeatlands and Wetlands Ecology→French-language works237,207→