Revisiting the relationship between dynamical sensitivity and climate sensitivity in the Southern hemisphere
Bibliographic record
Abstract
The Southern Annular Mode (SAM) is the dominant mode of midlatitude atmospheric circulation variability in the Southern hemisphere. In the future, the SAM trend is expected to be the net result of opposing effects from increasing greenhouse gases (GHG) and ozone recovery. Different greenhouse gas scenarios, which induce different rates of surface and atmospheric temperature change, are therefore associated with different future SAM trends (Barnes et al., 2014). Since the magnitude of warming due to GHGs is an important component of this response, one might expect to find a relationship between equilibrium climate sensitivity (ECS) and future Southern hemisphere circulation trends. In CMIP5, the relationship between the SAM and the level of tropospheric warming across models was found to be strongest in the summer and autumn and could explain around 20% of the intermodel spread (Grise and Polvani, 2014). The spread is more strongly correlated with differences in meridional temperature gradients (Harvey et al., 2014). Many of the latest CMIP6 models show a larger equilibrium climate sensitivity (ECS) of up to ~5.5 K (Forster et al., 2019) compared to a maximum of ~4.7 K in CMIP5. This raises the important question of how a higher level of warming affects projections of the SH midlatitude circulation. In this study, we examine the response of the SAM in CMIP6 models and quantify its relationship to ECS and temperature gradients. Our starting hypothesis is that stronger surface warming will induce a larger increase in tropical free tropospheric temperatures, and hence all being equal, a larger tropics-to-pole temperature gradient and a more positive SAM trend. However, results show that despite the higher level of warming in the CMIP6 models, there is a smaller positive trend in SAM index than in CMIP5 indicating a different relationship between warming and midlatitude circulation trends in CMIP6. We attempt to explain potential reasons for these differences. References: Barnes, E.A., N.W. Barnes, and L.M. Polvani, 2014: Delayed Southern Hemisphere Climate Change Induced by Stratospheric Ozone Recovery, as Projected by the CMIP5 Models. J. Climate, 27, 852–867, https://doi.org/10.1175/JCLI-D-13-00246.1 Forster, P.M., Maycock, A.C., McKenna, C.M. et al. (2019), Latest climate models confirm need for urgent mitigation. Nat. Clim. Chang. (2019) doi:10.1038/s41558-019-0660-0 Grise, K. M., and Polvani, L. M. (2014), Is climate sensitivity related to dynamical sensitivity? A Southern Hemisphere perspective, Geophys. Res. Lett., 41, 534– 540, doi:10.1002/2013GL058466. Harvey, B.J., Shaffrey, L.C. & Woollings, T.J. (2014) Equator-to-pole temperature differences and the extratropical storm track responses of the CMIP5 climate models, Clim Dyn, 43: 1171. https://doi.org/10.1007/s00382-013-1883-9
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".