Evaluation of land-atmosphere processes of the Polar WRF in the summertime Arctic tundra
Bibliographic record
Abstract
Arctic tundra is undergoing a rapid transition due to global warming and will be exposed to snow-free conditions for longer periods under projected climate scenarios. Regional climate modeling is useful for understanding and predicting climate change in the Arctic tundra, however, the lack of in-situ observations of surface energy fluxes and the planetary boundary layer (PBL) structure hinders accurate predictions of local and regional climate around the Arctic. In this study, we investigate the performance of the Polar-optimized version of the Weather Research and Forecasting model (PWRF) in the Arctic tundra on clear days in summer. Based on simultaneous observations of surface fluxes and the PBL structure in Cambridge Bay, Nunavut, Canada, our validation shows that the PWRF simulates a drier environment, leading to a larger Bowen ratio and a warmer atmosphere compared to observations. Further sensitivity analyses indicate that the model biases are mainly from the uncertainties in physical parameters such as surface albedo and emissivity, the solar constant, and the model top height, rather than structural flaws in the model physics. Importantly, the PWRF reproduces the observations more accurately when the observed soil moisture is fed into the simulation. This indicates that there must be improvements in simulations of the land-atmosphere interaction at the Arctic tundra, not only in the accuracy of the initial soil moisture conditions but also in soil hydraulic properties and drainage processes. The mixing diagram analysis also shows that the entrainment process between the PBL and the overlying atmosphere needs to be improved for better weather and climate simulation. Our findings shed light on modeling studies in the Arctic region by disentangling the model error sources from uncertainties by parameters and physics package options.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".