The effect of atmospheric nudging on the stratospheric residual circulation in chemistry–climate models
Bibliographic record
Abstract
Abstract. We perform the first multi-model intercomparison 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 seven models in which the meteorological fields are nudged towards a reanalysis dataset and compare these with their equivalent free-running simulations and the reanalyses themselves. We show that for the current implementations, 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 models generally show slightly stronger upwelling in the tropical lower stratosphere compared to the free-running versions and exhibit marked differences compared to the directly estimated residual circulation from the reanalysis dataset they are nudged towards. Downward control calculations applied to the nudged simulations reveal substantial differences between the climatological lower-stratospheric tropical upward mass flux (TUMF) computed from the modelled wave forcing and that calculated directly from the residual circulation. This explicitly shows that nudging decouples the wave forcing and the residual circulation so that the divergence of the angular momentum flux due to the mean motion is not balanced by eddy motions, as would typically be expected in the time mean. 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. In contrast, the nudged simulations show a high degree of consistency in the inter-annual variability in the TUMF in the lower stratosphere, which is primarily related to the contribution to variability from the resolved wave forcing. The more consistent inter-annual variability in TUMF in the nudged models also compares more closely with the variability found in the reanalyses, particularly in boreal winter. We apply a multiple linear regression (MLR) model to separate the drivers of inter-annual and long-term variations in the simulated TUMF; this explains up to ∼75 % of the variance in TUMF in the nudged simulations. The MLR model reveals a statistically significant positive trend in TUMF for most models over the period 1980–2009. The TUMF trend magnitude is generally larger in the nudged models compared to their free-running counterparts, but the intermodel range of trends doubles from around a factor of 2 to a factor of 4 due to nudging. Furthermore, the nudged models generally do not match the TUMF trends in the reanalysis they are nudged towards for trends over different periods in the interval 1980–2009. Hence, we conclude that nudging does not strongly constrain long-term trends simulated by the chemistry–climate model (CCM) in the residual circulation. Our findings show that while nudged simulations may, by construction, produce accurate temperatures and realistic representations of fast horizontal transport, this is not typically the case for the slower zonal mean vertical transport in the stratosphere. Consequently, caution is required when using nudged simulations to interpret the behaviour of stratospheric tracers that are affected by the residual circulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".