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Record W3154477431 · doi:10.1073/pnas.2015821118

Widespread decline in winds delayed autumn foliar senescence over high latitudes

2021· article· en· W3154477431 on OpenAlexaff
Chaoyang Wu, Jian Wang, Philippe Ciais, Josep Peñuelas, Xiaoyang Zhang, Oliver Sonnentag, Feng Tian, Xiaoyue Wang, Huanjiong Wang, Ronggao Liu, Yongshuo H. Fu, Quansheng Ge

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité de MontréalCenter for Northern Studies
FundersChinese Academy of SciencesNational Natural Science Foundation of ChinaDeutsche Forschungsgemeinschaft
KeywordsLatitudeSenescencePrecipitationEvapotranspirationAtmospheric sciencesClimatologyEnvironmental scienceHigh latitudeFlux (metallurgy)GeographyBiologyMeteorologyEcologyGeologyChemistry

Abstract

fetched live from OpenAlex

The high northern latitudes (>50°) experienced a pronounced surface stilling (i.e., decline in winds) with climate change. As a drying factor, the influences of changes in winds on the date of autumn foliar senescence (DFS) remain largely unknown and are potentially important as a mechanism explaining the interannual variability of autumn phenology. Using 183,448 phenological observations at 2,405 sites, long-term site-scale water vapor and carbon dioxide flux measurements, and 34 y of satellite greenness data, here we show that the decline in winds is significantly associated with extended DFS and could have a relative importance comparable with temperature and precipitation effects in contributing to the DFS trends. We further demonstrate that decline in winds reduces evapotranspiration, which results in less soil water losses and consequently more favorable growth conditions in late autumn. In addition, declining winds also lead to less leaf abscission damage which could delay leaf senescence and to a decreased cooling effect and therefore less frost damage. Our results are potentially useful for carbon flux modeling because an improved algorithm based on these findings projected overall widespread earlier DFS than currently expected by the end of this century, contributing potentially to a positive feedback to climate.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.017
GPT teacher head0.261
Teacher spread0.245 · 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 designObservational
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

Citations105
Published2021
Admission routes1
Has abstractyes

Explore more

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