Arctic budget study of intermember variability using HIRHAM5 ensemble simulations
Bibliographic record
Abstract
Abstract One of the challenges in evaluating and applying regional climate models (RCMs) is the nonlinear behavior of atmospheric processes, which is still poorly understood. The nonlinearities induce chaos which leads to an internal variability in the model. Therefore, an ensemble of RCM simulations has been run and a budget study for potential temperature has been applied to investigate the internally generated variability. Hence, the physical processes associated with diabatic and dynamical terms inducing the intermember variability have been analyzed. The study is applied over the Arctic on an ensemble of 20 members, differing in their initial conditions, simulated with the RCM HIRHAM5 during summer 2012. This time period is of particular importance because of the melting sea ice and its influence on atmospheric circulation and the resulting effect on the intermember variability. The amplitude of the intermember variability of the simulations fluctuates strongly both temporally and spatially. During the beginning of August 2012 the intermember variability is strongest and coincides with the great Arctic cyclone event. The most important contributions for the intermember variability tendency are the horizontal and vertical “baroclinic” terms. Both terms have largest absolute values along the coastlines of the Arctic Ocean which are associated with the Arctic frontal zone leading to the cyclone maximum over the Arctic Ocean during summer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".