The role of dynamic sea ice in a simplified general circulation model used for palaeoclimate studies
Bibliographic record
Abstract
AbstractObservational records provide a strong basis for constraining sea ice models within a narrow range of climate conditions. Given current trends away from these conditions, models need to be tested over a wider range of climate states. The past provides many such examples based on paleoclimate data, including abrupt tipping points. However, the millennial-duration of typical paleoclimatesimulations necessitates balancing the inclusion and sophistication of model processes against computational cost. We investigate the impact on climate mean states and variability of introducing sea ice dynamics into the simplified general circulation model PlaSim-LSG [1-3].Considering the technical constraints of PlaSim-LSG, we choose to integrate a modied version of the MITgcm's dynamical sea ice component [4, 5] into the model setup. We adapt the component to the structure and parallelization scheme of PlaSim-LSG, validate the physical consistency and stability of the component, and evaluate the impact of sea ice dynamics onto the simulated climate from decadal to millennial time scales. Specifically, we compare climatologies, variability and scaling of the extended model to control simulations of the preexisting setups, and quantify how additional sea ice dynamics affect well-known climatic biases of the PlaSim model family.With our extended PlaSim-LSG model we aim at capturing the key small-scale sea ice processes that are important to past climate tipping points while maintaining model efficiency for millennial simulations. Sea ice is a key component of coupled atmosphere-ocean processes that led to large-amplitude, abrupt climate variability in the past [6-8]. Therefore, the extended model can be usedto investigate the role of sea ice for such oscillations. This facilitates the understanding of processes that lead to current mismatches between palaeoclimate data and simulations, and that impact thesimulated surface climate variability [9].References[1] K. Fraedrich et al. Meteorol. Z. 14.3 (2005), 299-304. doi: 10.1127/0941-2948/2005/0043.[2] F. Lunkeit et al. Tech. rep. 2011. url: https://www.mi.uni-hamburg.de/en/arbeitsgruppen/theoretische-meteorologie/modelle/sources/psreferencemanual-1.pdf.[3] H. J. Andres et al. Clim. Past 15.4 (2019), 1621-1646. doi: 10.5194/cp-15-1621-2019.[4] J. Zhang et al. J. Geophys. Res. 102.4 (1997), 412-415.[5] M. Losch et al. Ocean Model. 33.1-2 (2010), 129-144. doi: 10.1016/j.ocemod.2009.12.008.[6] T. M. Dokken et al. Paleoceanography 28.3 (2013), 491-502. doi: 10.1002/palo.20042.[7] G. Vettoretti et al. Geophys. Res. Lett. 43.10 (2016), 5336-5344. doi: 10.1002/2016GL068891.[8] C. Li et al. Quat. Sci. Rev. 203 (2019), 1-20. doi: 10.1016/j.quascirev.2018.10.031.[9] N. Weitzel et al. presented at Fall Meeting AGU. 2020. url: https://agu.confex.com/agu/fm20/webprogram/Paper739241.html.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".