Using ARM Observations to Evaluate Cloud and Clear-Sky Radiation Processes as Simulated by the Canadian Regional Climate Model GEM
Bibliographic record
Abstract
Abstract The total downwelling shortwave (SWD) and longwave (LWD) radiation and its components are assessed for the limited-area version of the Global Environmental Multiscale Model (GEM-LAM) against Atmospheric Radiation Measurements (ARM) at two sites: the southern Great Plains (SGP) and the North Slope of Alaska (NSA) for the 1998–2005 period. The model and observed SWD and LWD are evaluated as a function of the cloud fraction (CF), that is, for overcast and clear-sky conditions separately, to isolate and analyze different interactions between radiation and 1) atmospheric aerosols and water vapor and 2) cloud liquid water. Through analysis of the mean diurnal cycle and normalized frequency distributions of surface radiation fluxes, the primary radiation error in GEM-LAM is seen to be an excess in SWD in the middle of the day. The SWD bias results from a combination of underestimated CF and clouds, when present, possessing a too-high solar transmissivity, which is particularly the case for optically thin clouds. Concurrent with the SWD bias, a near-surface warm bias develops in GEM-LAM, particularly at the SGP site in the summer. The ultimate cause of this warm bias is difficult to uniquely determine because of the range of complex interactions between the surface, atmospheric, and radiation processes that are involved. Possible feedback loops influencing this warm bias are discussed. The near-surface warm bias is the primary cause of an excess clear-sky LWD. This excess is partially balanced with respect to the all-sky LWD by an underestimated CF, which causes a negative bias in simulated all-sky emissivity. It is shown that there is a strong interaction between all the components influencing the simulated surface radiation fluxes with frequent error compensation, emphasizing the need to evaluate the individual radiation components at high time frequency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".