Uncertainties originating from GCM downscaling and bias correction with application to the MIS-11c Greenland Ice Sheet
Bibliographic record
Abstract
Abstract. The Marine Isotope Stage 11c (MIS-11c) interglacial is an enigmatic period characterized by a long duration of relatively weak insolation forcing, but is thought to have been coincident with a large global sea level rise of 6–13 m. The configuration of the Greenland Ice Sheet during the MIS-11c interglacial highstand is therefore of great interest. Given the limited data constraints, model-based analysis may be of use, but only if model uncertainties are adequately accounted for. A particularly under-addressed issue in coupled climate and ice sheet modeling is the coupling of surface air temperatures to the ice model. Many studies apply a uniform “lapse rate” accounting for the temperature differences at different altitudes over the ice surface, but this uniformity neglects both regional and seasonal differences in near-surface temperature changes. Herein we provide the first such analysis for MIS-11c Greenland that addresses these uncertainties by comparing 1-way coupled CESM and ice sheet model results from several different downscaling methodologies. In our study, a spatially- and temporally-varying temperature downscaling method produced the greatest success rate in matching limited paleodata constraints, and suggests a peak ice volume loss from Greenland during MIS-11c of near 50 % compared to present day (~3.9 m contribution to sea level rise). This result is on the lower bound of existing data- and model-based studies, partly as a consequence of the applied one-way coupling methodology which neglects some feedbacks. Additional uncertainties are examined by comparing two different present-day regional climate analyses for bias correction of temperatures and precipitation, a spread of initialization states and times, and different spatial configurations of precipitation bias corrections. No other factor exhibited greater influence over the simulated Greenland ice sheet than the choice of temperature downscaling scheme.
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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 | 0.004 | 0.009 |
| 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 | 0.001 | 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".