On the impacts of changing data availability on climate trend analysis
Bibliographic record
Abstract
In situ station data records usually have different periods of data coverage, with individual station records starting and stopping at different times. These records may also have different missing data rates and missing data rates may change over time. These changes in data availability (i.e., the inhomogeneous sampling of data), can bias trend estimates using a gridded dataset if left untreated. Reanalysis data, such as the 20CRv3, ERA20C, OCADA, and ERA5 datasets can be used to estimate the effects of changes in data availability. This can be done by comparing results from a spatially and temporally complete version of reanalysis dataset with results that are obtained from an incomplete reanalysis dataset that has the same availability in space and time as the station data. Results obtained from the incomplete data can then be compared with the correct results from the corresponding complete dataset.This study uses reanalysis datasets to estimate the effects of changes in data availability on the gridded precipitation and surface air temperature datasets and their representativeness of the climate and changes therein. The estimated effects are then used to correct sampling biases in the gridded datasets, producing sampling bias-corrected gridded datasets for use to assess climate change in Canada. The results show that sampling biases are larger in precipitation data than in temperature data due to the higher temporal and spatial variability of precipitation and lower precipitation station density. The biases are also larger in the period and regions of sparse observations (such as the early period and northern Canada).
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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 | 0.034 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".