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Record W4223503637 · doi:10.1002/essoar.10511063.1

Under what conditions do the global warming responses of drought indices and land-surface models agree?

2022· preprint· en· W4223503637 on OpenAlexaff
Jacob Scheff, Marysa M. Laguë, Sloan Coats

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsUniversity of Saskatchewan
FundersNational Center for Atmospheric Research
KeywordsGlobal warmingEnvironmental scienceMeteorologyGeographyClimate changeGeologyOceanography

Abstract

fetched live from OpenAlex

The regional to global responses of the Standardized Precipitation-Evapotranspiration Index, Palmer Drought Severity Index, and Aridity Index to future global warming tend to be much more pervasively and strongly negative than the responses of comprehensive land model runoff and bulk soil-moisture outputs to the same warming. We term these systematic differences “index-impact gaps.” Some studies have assumed that these gaps arise because land-surface models include water-saving CO2-plant effects that the dryness indices do not, but recently published work makes clear that the gaps largely persist even in model simulations in which these effects are switched off. Thus, the main reason(s) for the index-impact gaps are still unclear, making it difficult to trust either the common dryness indices or the comprehensive land-surface models under climate change. In this study, we are investigating several postulated causes of these index-impact gaps using sensitivity experiments with the state-of-the-art Community Land Model version 5.0. In addition to CO2-plant effects, we are testing the roles of stomatal closure driven by high vapor-pressure deficits, short-term runoff enhancement due to sharper concentration of rain in time with warming, and annual-scale runoff enhancement due to changes in the seasonality of precipitation and/or infiltration with warming. If CLM5.0’s runoff and bulk soil-moisture responses start to agree with the dryness-index responses much more after eliminating these pathways, it will imply that the dryness indices are in fact a useful theoretical baseline for understanding the comprehensive model responses. However, if the index-impact gaps still remain wide, it will imply either that dryness-index responses are fundamentally different from runoff and soil-moisture responses to climate change, or else that CLM5.0’s evapotranspiration is not sensitive enough to rising temperatures. Further experiments will be required in that case.

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)0.009
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.006
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.282
Teacher spread0.259 · 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
Published2022
Admission routes1
Has abstractyes

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