Quantifying the uncertainty introduced by internal climate variability in projections of Canadian crop production
Bibliographic record
Abstract
Abstract Internal climate variability (ICV) is one of the major sources of uncertainty in climate projections, yet it is seldom quantified for projections of crop production. Our study focuses on quantifying the uncertainty due to ICV in projections of crop productions in Canada. We utilize climate scenarios from two large ensembles (LEs, CanESM2-LE and CanRCM4-LE with 25 members each) as inputs to the crop models in the Decision Support System for Agrotechnology Transfer. We simulate crop yields for canola, maize and spring wheat under the future climates of four global warming levels. The coefficient of variation (CV) of the projected crop production across the LE members is used to quantify the uncertainty related to ICV and this is compared with the CVs generated using the 20 GCMs in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Crop production in Canada could increase with global warming, e.g. spring wheat production could increase by up to 21% at the warming level of 3.0 °C. The projections often produce larger uncertainty associated with the GCMs than from ICV at all warming levels above 2.0 °C. The results from an asymptotic test for the equality of CVs show a significant difference in CVs of projections of canola production between CanESM2-LE/CanRCM4-LE and CMIP5 for the warming level of 3.0 °C. However, the test results do not indicate a significant difference among the ensembles at all four warming levels for maize and spring wheat. The uncertainty due to ICV is often comparable to that associated with GCMs at the warming level of 1.5 °C, e.g. a CV of 6.0 and 6.4% for CanESM2-LE and CanRCM4-LE and 6.6% for CMIP5 in the projections of spring wheat production. We conclude there is a need to account for uncertainty related to ICV in projections of Canadian crop production, especially at lower warming levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| 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 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".