Model Assessment of Observed Precipitation Trends over Land Regions: Detectable Human Influences and Possible Low Bias in Model Trends
Bibliographic record
Abstract
Precipitation trends for 1901–2010, 1951–2010, and 1981–2010 over relatively well-observed global land regions are assessed for detectable anthropogenic influences and for consistency with historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CMIP5 historical all-forcing runs are broadly consistent with the observed trend pattern (1901–2010), but with an apparent low trend bias tendency in the simulations. Despite this bias, observed and modeled trends are statistically consistent over 59% of the analyzed area. Over 20% (9%) of the analyzed area, increased (decreased) precipitation is partly attributable to anthropogenic forcing. These inferred human-induced changes include increases over regions of the north-central United States, southern Canada, Europe, and southern South America and decreases over parts of the Mediterranean region and northern tropical Africa. Trends for the shorter periods (1951–2010 and 1981–2010) do not indicate a prominent low trend bias in the models, as found for the 1901–2010 trends. An atmosphere-only model, forced with observed sea surface temperatures and other climate forcing agents, also underpredicts the observed precipitation increase in the Northern Hemisphere extratropics since 1901. The CMIP5 all-forcing ensemble’s low bias in simulated trends since 1901 is a tentative finding that, if borne out in further studies, suggests that precipitation projections using these regions and models could overestimate future drought risk and underestimate future flooding risk, assuming all other factors equal.
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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 | 0.001 | 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 | 0.001 |
| 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".