Using paleoclimate data to constrain cloud parameterizations in GISS-E2.1
Bibliographic record
Abstract
Much of the inter-model spread in equilibrium climate sensitivity (ECS) estimates is attributed to cloud and convective parameterizations which model cloud and water vapor feedbacks. These parameterizations also directly influence water isotopes, which may be retrieved not only from modern observations, but also a plethora of paleoclimate archives that represent a much broader range of variability than is available in modern measurements. And thus, these water isotope tracers can be used to constrain ECS by flagging unrealistic parts of the parameterization phase space via model biases in a perturbed parameterization ensemble (PPE) of paleoclimate simulations. In this proof-of-concept study, we evaluate a suite of isotope-enabled atmosphere-only GISS-E2.1 simulations, each with varying cloud and convective perturbations, against speleothem and ice core δ18O for the Last Glacial Maximum (LGM, 21000 years ago), mid-Holocene (MH, 6000 years ago) and pre-Industrial periods. The first-order spatial pattern of δ18O of precipitation (δ18Op) is in excellent agreement between proxy data and all parameterizations across all time periods. While the simulations generally capture large scale δ18Op patterns, the magnitude of change is consistently smaller in all simulations than those of the proxies, highlighting uncertainties in both models and proxies. Not a single set of parameterizations worked well in all climate states, indicating that improving future simulations requires determining all plausible parameter combinations critical in refining ECS. Further, it may be that certain parameterization choices represent certain types of variability better than others, and there may be a non-unique solution to ideal clouds/convection parameterization choices that is modulated by the question asked.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".