Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data
Bibliographic record
Abstract
Local land surface modification and variations in data quality affect temperature trends in surface‐measured data. Such effects are considered extraneous for the purpose of measuring climate change, and providers of climate data must develop adjustments to filter them out. If done correctly, temperature trends in climate data should be uncorrelated with socioeconomic variables that determine these extraneous factors. This hypothesis can be tested, which is the main aim of this paper. Using a new database for all available land‐based grid cells around the world we test the null hypothesis that the spatial pattern of temperature trends in a widely used gridded climate data set is independent of socioeconomic determinants of surface processes and data inhomogeneities. The hypothesis is strongly rejected (P = 7.1 × 10−14), indicating that extraneous (nonclimatic) signals contaminate gridded climate data. The patterns of contamination are detectable in both rich and poor countries and are relatively stronger in countries where real income is growing. We apply a battery of model specification tests to rule out spurious correlations and endogeneity bias. We conclude that the data contamination likely leads to an overstatement of actual trends over land. Using the regression model to filter the extraneous, nonclimatic effects reduces the estimated 1980–2002 global average temperature trend over land by about half.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".