MétaCan
Menu
Back to cohort
Record W2942929945 · doi:10.5194/hess-23-4635-2019

High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach

2019· article· en· W2942929945 on OpenAlex
Yanping Li, Zhenhua Li, Zhe Zhang, Liang Chen, Sopan Kurkute, Lucía Scaff, Xicai Pan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHydrology and earth system sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsWeather Research and Forecasting ModelClimatologyEnvironmental sciencePrecipitationClimate changeForcing (mathematics)Climate modelGlobal warmingOrographyEvapotranspirationMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

Abstract. Climate change poses great risks to western Canada's ecosystem and socioeconomical development. To assess these hydroclimatic risks under high-end emission scenario RCP8.5, this study used the Weather Research Forecasting (WRF) model at a convection-permitting (CP) 4 km resolution to dynamically downscale the mean projection of a 19-member CMIP5 ensemble by the end of the 21st century. The CP simulations include a retrospective simulation (CTL, 2000–2015) for verification forced by ERA-Interim and a pseudo-global warming (PGW) for climate change projection forced with climate change forcing (2071–2100 to 1976–2005) from CMIP5 ensemble added on ERA-Interim. The retrospective WRF-CTL's surface air temperature simulation was evaluated against Canadian daily analysis ANUSPLIN, showing good agreements in the geographical distribution with cold biases east of the Canadian Rockies, especially in spring. WRF-CTL captures the main pattern of observed precipitation distribution from CaPA and ANUSPLIN but shows a wet bias near the British Columbia coast in winter and over the immediate region on the lee side of the Canadian Rockies. The WRF-PGW simulation shows significant warming relative to CTL, especially over the polar region in the northeast during the cold season, and in daily minimum temperature. Precipitation changes in PGW over CTL vary with the seasons: in spring and late autumn precipitation increases in most areas, whereas in summer in the Saskatchewan River basin and southern Canadian Prairies, the precipitation change is negligible or decreased slightly. With almost no increase in precipitation and much more evapotranspiration in the future, the water availability during the growing season will be challenging for the Canadian Prairies. The WRF-PGW projected warming is less than that by the CMIP5 ensemble in all seasons. The CMIP5 ensemble projects a 10 %–20 % decrease in summer precipitation over the Canadian Prairies and generally agrees with WRF-PGW except for regions with significant terrain. This difference may be due to the much higher resolution of WRF being able to more faithfully represent small-scale summer convection and orographic lifting due to steep terrain. WRF-PGW shows an increase in high-intensity precipitation events and shifts the distribution of precipitation events toward more extremely intensive events in all seasons. Due to this shift in precipitation intensity to the higher end in the PGW simulation, the seemingly moderate increase in the total amount of precipitation in summer east of the Canadian Rockies may underestimate the increase in flooding risk and water shortage for agriculture. The change in the probability distribution of precipitation intensity also calls for innovative bias-correction methods to be developed for the application of the dataset when bias correction is required. High-quality meteorological observation over the region is needed for both forcing high-resolution climate simulation and conducting verification. The high-resolution downscaled climate simulations provide abundant opportunities both for investigating local-scale atmospheric dynamics and for studying climate impacts on hydrology, agriculture, and ecosystems.

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.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.084
GPT teacher head0.276
Teacher spread0.192 · 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