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Record W4389306514 · doi:10.1038/s41561-023-01331-y

Local cooling and drying induced by Himalayan glaciers under global warming

2023· article· en· W4389306514 on OpenAlexaff
Franco Salerno, Nicolas Guyennon, Kun Yang, Thomas E. Shaw, Changgui Lin, Nicola Colombo, Emanuele Romano, Stephan Gruber, Tobias Bolch, Andrea Alessandri, Paolo Cristofanelli, Davide Putero, Guglielmina Diolaiuti, Gianni Tartari, Gianpietro Verza, Sudeep Thakuri, Gianpaolo Balsamo, Evan Miles, Francesca Pellicciotti

Bibliographic record

VenueNature Geoscience · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCarleton University
FundersHORIZON EUROPE Framework ProgrammeNepal Academy of Science and TechnologySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean Commission
KeywordsGlacierGlobal warmingClimatologyGlacier mass balanceKatabatic windElevation (ballistics)PrecipitationEnvironmental scienceClimate changeMeltwaterGlacial periodSurgeAtmospheric sciencesGeologyPhysical geographyMeteorologyGeographyOceanographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract Understanding the response of Himalayan glaciers to global warming is vital because of their role as a water source for the Asian subcontinent. However, great uncertainties still exist on the climate drivers of past and present glacier changes across scales. Here, we analyse continuous hourly climate station data from a glacierized elevation (Pyramid station, Mount Everest) since 1994 together with other ground observations and climate reanalysis. We show that a decrease in maximum air temperature and precipitation occurred during the last three decades at Pyramid in response to global warming. Reanalysis data suggest a broader occurrence of this effect in the glacierized areas of the Himalaya. We hypothesize that the counterintuitive cooling is caused by enhanced sensible heat exchange and the associated increase in glacier katabatic wind, which draws cool air downward from higher elevations. The stronger katabatic winds have also lowered the elevation of local wind convergence, thereby diminishing precipitation in glacial areas and negatively affecting glacier mass balance. This local cooling may have partially preserved glaciers from melting and could help protect the periglacial environment.

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)0.000
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 score0.091
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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

Citations57
Published2023
Admission routes1
Has abstractyes

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