Modelling framework for asynchronous land-atmosphere coupling using NASA GISS ModelE and LPJ-LMfire: Design, Application and Evaluation for the 2.5ka period
Bibliographic record
Abstract
Abstract. While paleoclimate simulations have been a priority for Earth system modelers over the past three decades, little attention has been paid to the period between the mid-Holocene and the Last Millennium, although this is an important period for the emergence of complex societies. Here, we consider the climate of 2500 BP (550 BCE), a period when compared to late preindustrial time, greenhouse gas concentrations were slightly lower, and orbital forcing led to a stronger seasonal cycle in high latitude insolation. To capture the influence of land cover on climate, we asynchronously coupled the NASA GISS ModelE Earth system model with the LPJ-LMfire dynamic global vegetation model. We simulated global climate and assessed our results in the context of independent paleoclimate reconstructions. We also explored a set of combinations of model performance parameters (bias and variability) and demonstrated their importance for the asynchronous coupling framework. The coupled model system shows substantial vegetation albedo feedback to climate. In the absence of a bias correction, while driving LPJ-LMfire in the coupling process, ModelE drifts towards colder conditions in the high latitudes of the Northern Hemisphere in response to land cover simulated by LPJ-LMfire. A regional precipitation response is also prominent in the various combinations of the coupled model system, with a substantial intensification of the Summer Indian Monsoon and a drying pattern over Europe. Evaluation of the simulated climate against reconstructions of temperature from multiple proxies and the isotopic composition of precipitation (δ18Op) from speleothems demonstrated the skill of ModelE in simulating past climate. A regional analysis of the simulated vegetation-climate response further confirmed the validity of this approach. The coupled model system is sensitive to the representation of shrubs and this land cover type requires particular attention as a potentially important driver of climate in regions where shrubs are abundant. Our results further demonstrate the importance of bias correction in coupled paleoclimate simulations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".