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Record W2922214047 · doi:10.1088/1748-9326/ab0db3

Land use change impacts on European heat and drought: remote land-atmosphere feedbacks mitigated locally by shallow groundwater

2019· article· en· W2922214047 on OpenAlexaff
Samuel C. Zipper, Jessica Keune, Stefan Kollet

Bibliographic record

VenueEnvironmental Research Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill UniversityUniversity of Victoria
FundersH2020 European Research CouncilBundesministerium für Bildung und Forschung
KeywordsEnvironmental scienceClimate changeShortwave radiationLand coverAtmosphere (unit)Land use, land-use change and forestryMediterranean climateGroundwaterSensible heatLand useClimatologyHydrology (agriculture)Atmospheric sciencesGeologyGeographyMeteorologyOceanography

Abstract

fetched live from OpenAlex

Heat and drought are projected to increase globally but may be mitigated or exacerbated by land use/land cover (LULC) change. Here, we show that remote land-atmosphere feedbacks caused by historical European LULC change led to widespread changes in the energy and water balances, drought, and heat. Using a continental-scale bedrock-to-atmosphere model, we find that LULC change following the Soviet Union collapse and European Union formation may have substantially increased cloud cover and decreased incoming shortwave radiation in western Europe, even in locations where LULC did not change. These changes to the water and energy balances had spatially heterogeneous impacts on drought and heat, including drying in the Mediterranean and Eastern Europe regions. The response of the water and energy balances to remote feedbacks was lessened in areas with shallow groundwater, indicating that local- and continental-scale responses to LULC change are influenced by the coupling between the subsurface, land surface, and atmosphere.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.034
GPT teacher head0.260
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations41
Published2019
Admission routes1
Has abstractyes

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