The effect of atmospheric nudging on the stratospheric residualcirculation in chemistry-climate models
Bibliographic record
Abstract
Abstract. We perform the first multi-model comparison of the impact of nudged meteorology on the stratospheric residual circulation using hindcast simulations from the Chemistry Climate Model Initiative (CCMI). We examine simulations over the period 1980–2009 from 5 models in which the meteorological fields are nudged towards reanalysis data and compare with equivalent free-running simulations from 9 models. We show that nudging meteorology does not constrain the mean strength of the stratospheric residual circulation and that the inter-model spread is similar, or even larger, than in the free-running simulations. The nudged simulations also simulate stronger upwelling in the tropical lower stratosphere compared to the residual circulation estimated directly from the reanalyses they are nudged towards. Downward control calculations reveal substantial differences between the mean lower stratospheric tropical upward mass flux (TUMF) computed from the modeled wave forcing and that calculated directly from the residual circulation. Although the mean circulation is poorly constrained, the nudged simulations show a high degree of consistency in the interannual variability of the TUMF in the lower stratosphere, which is related to the contribution to variability from the resolved wave forcing. We apply a multiple linear regression (MLR) model to separate the drivers of interannual and long-term variations in the simulated TUMF. The MLR model explains up to ~ 75 % of the variance in TUMF in the nudged simulations and reveals a statistically significant positive trend for most models in TUMF over the period 1980–2009. Overall, nudging meteorological fields leads to increased inter-model spread for most of the measures of the mean climatological stratospheric residual circulation assessed in this study. Our findings show that while nudged simulations by construction produce accurate temperatures and realistic representations of fast horizontal transport, this is not necessarily the case for the slower zonal mean vertical transport. Consequently, caution is required when using nudged simulations to interpret long-lived stratospheric tracers that are controlled by the residual circulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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".