the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessment of the 11-year solar cycle signals in the middle atmosphere in multiple-model ensemble simulations
Abstract. To better understand possible reasons for the diverse modeling results and large discrepancies of the detected solar fingerprints, we took one step back and assessed the "initial" solar signals in the middle atmosphere based on large ensemble simulations with multiple climate models — FOCI, EMAC, and MPI-ESM-HR. Consistent with previous work, we find that the 11-year solar cycle signals in the short wave heating rate (SWHR) and ozone anomalies are robust and statistically significant in all three models. These "initial" solar cycle signals in SWHR, ozone, and temperature anomalies are sensitive to the strength of the solar forcing. Correlation coefficients of the solar cycle with the SWHR, ozone, and temperature anomalies linearly increase along with the enhancement of the solar cycle amplitude, and this reliance becomes more complex when the solar cycle amplitude exceeds a certain threshold. In addition, the cold bias in the tropical stratopause of EMAC dampens the subsequent results of the "initial" solar signal. The warm pole bias in MPI-ESM-HR leads to a weak polar night jet (PNJ), which may limit the top-down propagation of the initial solar signal. Although FOCI simulated a so-called top-down response as revealed in previous studies in a period with large solar cycle amplitudes, its warm bias in the tropical upper stratosphere results in a positive bias in PNJ and can lead to a "reversed" response in some extreme cases. We suggest a careful interpretation of the single model result and further re-examination of the solar signal based on more climate models.
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RC1: 'Comment on egusphere-2024-1288', Anonymous Referee #1, 12 Jul 2024
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General comments
This study assesses the 11-year solar cycle signals in the middle atmosphere in multiple-model ensemble simulations. This study starts with initial solar cycle signals in short wave heating rate, ozone, and temperature anomalies and continues with an analysis of whether the top-down mechanism explaining the downward propagation of these initial solar signals can be found in the presented models. I find the study highly relevant for long-term discussion of indirect solar effects and recommend it for publication with minor comments listed below.Specific comments
I am not convinced that three sets of historical-like simulations with 9, 6 and 10 ensemble members, respectively, can be called “large ensembles” (l2+l330) since we do not know how large the large ensemble needs to be (Milinski et al, 2020).Can you specify the threshold in the abstract (l8)?
What does “partly confirmed” (l25) mean?
The authors should elaborate more on the fact that the solar signal may not be stationary (Thejll et al, 2003) related to modulation by QBO and PDO (l30).
I would omit the “controversial” label (l39) even though these studies may reduce the confidence level of the solar-NAO connection as you state.
As shown in previous studies (e.g. Mitchell et al, 2014; Kuchar et al, 2015) the upper stratospheric equatorial temperature anomaly related to the solar cycle has been detected showed a statistically significant signal with structure and amplitude of 1–1.25 K. Temperature response in Fig. 2A maximizes at 0.6 K. I would say that models a bit underestimate the response even with comparison (l167) with Kunze et al (2020). These facts should be discussed and even analyzed more thoroughly in your models.
Based on Fig. 1.c (l159), the authors suggest that a nonlinear response can occur when the solar forcing is strong enough but I would soften these statements given the large spread and not enough samples for high sfu values.
I would omit the publications of Gray et al (2010) which provides a review of the Kodera and Kuroda mechanism and Mitchell et al (2015; CMIP5) which does not show any BDC response (l219) and only highlight the link between weaker BDC and lower-stratospheric temperature induced by the 11-year solar cycle.
How different (l240)?
Would you find relevant to reproduce composite differences between Smax and Smin as in e.g. A8 for ERA5 and assess whether the response of temperature and zonal wind in a reanalyzed dataset also reveals a sensitivity to weak and strong solar epochs?
Using vector figures instead of raster ones may help to improve the quality of your publication.
Due to the extensiveness and unique methodology of the study, I think the whole community would appreciate an adoption of Open Science approaches to allow reproduce the extensive analysis in this study (e.g. Laken, 2016). In particular, I would recommend any kind of willingness of the authors to publish the code allowing to reproduce the figures in the paper. There are multiple ways how to proceed, either to allow the access upon request or via portals allowing to assign Digital Object Identifier (DOI) to the research outputs, e.g. ZENODO. I think it could enhance the quality and reliability of this publication.
I really appreciate the authors’s willingness to use the robust bootstrap method to but why do you use only 1000 samples? Furthermore, this should be used to assess the significance level of the correlation coefficient to secure methodological consistency. Or was the temporal autocorrelation taken into account in your composites? Can you discuss how the inclusion of the effective sample size (see Section 5 in Bretherton et al, 1999) would influence the t-test results? Do your composite samples comply with the t-test assumptions?
Technical comments
Please specify what CCR in your figures stands for
l288 replace EAR with ERA5
l290 replace EAR with ERA5
References
Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., & Bladé, I. (1999). The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field, Journal of Climate, 12(7), 1990-2009. Retrieved Jan 19, 2022, from https://journals.ametsoc.org/view/journals/clim/12/7/1520-0442_1999_012_1990_tenosd_2.0.co_2.xml
Kuchar, A., Sacha, P., Miksovsky, J., & Pišoft, P. (2015). The 11-year solar cycle in current reanalyses: a (non)linear attribution study of the middle atmosphere. Atmospheric Chemistry and Physics, 15(12), 6879–6895. https://doi.org/10.5194/acp-15-6879-2015
Kunze, M., Kruschke, T., Langematz, U., Sinnhuber, M., Reddmann, T., and Matthes, K.: Quantifying uncertainties of climate signals in chemistry climate models related to the 11-year solar cycle – Part 1: Annual mean response in heating rates, temperature, and ozone, Atmos. Chem. Phys., 20, 6991–7019, https://doi.org/https://doi.org/10.5194/acp-20-6991-2020, 2020.
Laken, B. A. (2016). Can Open Science save us from a solar-driven monsoon? Journal of Space Weather and Space Climate, 6, A11. http://doi.org/10.1051/swsc/2016005020.
Milinski, S., Maher, N., and Olonscheck, D.: How large does a large ensemble need to be?, Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020, 2020.
Mitchell, D.M., Gray, L.J., Fujiwara, M., Hibino, T., Anstey, J.A., Ebisuzaki, W., Harada, Y., Long, C., Misios, S., Stott, P.A. and Tan, D. (2015), Signatures of naturally induced variability in the atmosphere using multiple reanalysis datasets. Q.J.R. Meteorol. Soc., 141: 2011-2031. https://doi.org/10.1002/qj.2492Thejll, P., Christiansen, B., and Gleisner, H.: On correlations between the North Atlantic Oscillation, geopotential heights, and geomagnetic activity, Geophys. Res. Lett., 30, https://doi.org/10.1029/2002GL016598, 2003
Citation: https://doi.org/10.5194/egusphere-2024-1288-RC1
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