Parameterization and Surface Data Improvements and New Capabilities for the Community Land Model Urban (CLMU)
Bibliographic record
Abstract
Abstract The Community Land Model Urban (CLMU) is an urban parameterization developed to simulate urban climate within a global Earth System Model framework. This paper describes and evaluates parameterization and surface data improvements, and new capabilities that have been implemented since the initial release of CLMU in 2010 as part of version 4 of the Community Land Model (CLM4) and the Community Earth System Model (CESM ® ). These include: 1) an expansion of model capability to simulate multiple urban density classes within each model grid cell; 2) a more sophisticated and realistic building space heating and air conditioning submodel; 3) a revised global dataset of urban morphological, radiative, and thermal properties utilized by the model, including a tool that allows for generating future urban development scenarios, and 4) the inclusion of a module to simulate various heat stress indices. The model and data are evaluated using observed data from five urban flux tower sites and a global anthropogenic heat flux (AHF) dataset. Generally, the new version of the model simulates urban radiative and turbulent fluxes, surface temperatures, and AHF as well or better than the previous version. Significant improvements in the global and regional simulation of AHF are also demonstrated that are primarily due to the new building energy model. The new model is available as part of the public release of CLM5 and CESM2.0.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".