Globsim v.3 – Improvements to an open-source software library for utilizing atmospheric reanalyses in point-scale land surface simulation
Bibliographic record
Abstract
The lack of long-term and consistent meteorological observations limits the application of land-surface simulators (e.g., of phenomena in hydrology, the cryosphere, ecology) at remote locations. For example, most permafrost areas are remote and lacking consistent meteorological time series, models that describe permafrost change over time cannot be driven for comparison with observations or for impact studies. Reanalysis-derived time series are valuable because they are available with global coverage, for a long time period, and for a broad set of physically consistent variables. Multiple reanalyses can be used to provide estimates of uncertainty. Practically, however, this data is difficult to use for several reasons: grid-scale reanalyses must be downscaled and interpolated horizontally (and vertically within the atmospheric column for mountains regions) to the site‑scale, differences in variables, units, and delivery between reanalyses must be reconciled, and large volumes of data need to be handled. Globsim is an open-source python library (available via GitHub) that was developed to handle these challenges and to facilitate a simulation workflow that takes advantage of the multiple reanalysis products available today. It outputs sub-daily meteorological time series that resemble meteorological stations for any location on the planet. Since the release of the first version of Globsim, we have improved usability, refactored code for maintainability and speed, and fixed a number of bugs. We also added support for ERA5 ensemble data, and added more sophisticated heuristic downscaling algorithms, including TOPOscale for elevation-adjusted radiative fluxes. We use Globsim as a core tool in a multi-model permafrost simulation workflow and, as a future step, we intend to use it as part of a debiasing routine to make predictions of permafrost using climate scenarios. We expect this tool to be broadly applicable to climate change impact modelers and other scientists using climate driven simulations working in (remote) locations that lack meteorological data of sufficient quality and duration for their application.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Software About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Software About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | medium |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".