Articles | Volume 25, issue 4
https://doi.org/10.5194/acp-25-2589-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-2589-2025
© Author(s) 2025. This work is distributed under
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 during boreal winter with multiple-model ensemble simulations
GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148 Kiel, Germany
Tobias Spiegl
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Institute of Meteorology, Freie Universität Berlin, 12165 Berlin, Germany
Sebastian Wahl
GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148 Kiel, Germany
Katja Matthes
GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148 Kiel, Germany
Ulrike Langematz
Institute of Meteorology, Freie Universität Berlin, 12165 Berlin, Germany
Holger Pohlmann
Department Climate Variability, Max Planck Institute for Meteorology, 20146 Hamburg, Germany
Jürgen Kröger
Department Climate Variability, Max Planck Institute for Meteorology, 20146 Hamburg, Germany
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Cited articles
Andrews, M. B., Knight, J. R., and Gray, L. J.: A simulated lagged response of the North Atlantic Oscillation to the solar cycle over the period 1960–2009, Environ. Res. Lett., 10, 054022, https://doi.org/10.1088/1748-9326/10/5/054022, 2015. a, b
Butler, A. H., Perez, A. C., Domeisen, D. I. V., Simpson, I. R., and Sjoberg, J.: Predictability of Northern Hemisphere final stratospheric warmings and their surface impacts, Geophys. Res. Lett., 43, 10578–10588, https://doi.org/10.1029/2019GL083346, 2019. a
Chiodo, G., Marsh, D. R., Garcia-Herrera, R., Calvo, N., and García, J. A.: On the detection of the solar signal in the tropical stratosphere, Atmos. Chem. Phys., 14, 5251–5269, https://doi.org/10.5194/acp-14-5251-2014, 2014. a
Chiodo, G., Oehrlein, J., Polvani, L. M., Fyfe, J. C., and Smith, A. K.: Insignificant influence of the 11-year solar cycle on the North Atlantic Oscillation, Nat. Geosci, 12, 94–99, https://doi.org/10.1038/s41561-018-0293-3, 2019. a, b
Diaconis, P. and Efron, B.: Computer-Intensive Methods in Statistics, Sci. Am., 248, 116–131, 1983. a
Dietmüller, S., Jöckel, P., Tost, H., Kunze, M., Gellhorn, C., Brinkop, S., Frömming, C., Ponater, M., Steil, B., Lauer, A., and Hendricks, J.: A new radiation infrastructure for the Modular Earth Submodel System (MESSy, based on version 2.51), Geosci. Model Dev., 9, 2209–2222, https://doi.org/10.5194/gmd-9-2209-2016, 2016. a
Domeisen, D. I. V., Butler, A. H., Charlton-Perez, A. J., Ayarzagüena, B., Baldwin, M. P., Dunn-Sigouin, E., and Seok-Woo, S.: The role of the stratosphere in subseasonal to seasonal prediction: 1. Predictability of the stratosphere, J. Geophys. Res.-Atmos., 125, e2019JD030920, https://doi.org/10.1029/2019JD030920, 2020a. a
Domeisen, D. I. V., Butler, A. H., Charlton-Perez, A. J., Ayarzaguena, B., Baldwin, M. P., Dunn-Sigouin, E., Furtado, J. C., Garfinkel, C. I., Hitchcock, P., Karpechko, A. Y., Kim, H., Knight, J., Lang, A. L., Lim, E.-P., Marshall, A., Raoff, G., Schwartz, C., Simpson, I. R., Son, S.-W., and Taguchi, M.: The role of stratosphere-troposphere coupling in sub-seasonal to seasonal prediction. 2. Predictability arising from stratosphere-troposphere coupling, J. Geophys. Res., 125, e2019JD030923, https://doi.org/10.1029/2019JD030923, 2020b. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics, J. Geophys. Res.-Oceans, 102, 12609–12646, https://doi.org/10.1029/97JC00480, 1997. a
Funke, B., López-Puertas, M., Stiller, G. P., Versick, S., and von Clarmann, T.: A semi-empirical model for mesospheric and stratospheric NOy produced by energetic particle precipitation, Atmos. Chem. Phys., 16, 8667–8693, https://doi.org/10.5194/acp-16-8667-2016, 2016. a
Giorgetta, M. A. and Bengtsson, L.: Potential role of the quasi‐biennial oscillation in the stratosphere‐troposphere exchange as found in water vapor in general circulation model experiments, J. Geophys. Res.-Atmos., 104, 6003–6019, https://doi.org/10.1029/1998JD200120, 1999. a
Gray, L. J., Beer, J., Geller, M., Haigh, J. D., Lockwood, M., Matthes, K., Cubasch, U., Fleitmann, D., Harrison, G., Hood, L., Luterbacher, J., Meehl, G. A., Shindell, D., van Geel, B., and White, W.: Solar Influences On Climate, Rev. Geophys., 48, RG4001, https://doi.org/10.1029/2009RG000282, 2010. a, b
Gray, L. J., Woollings, T. J., Andrews, M., and Knight, J.: Eleven-year solar cycle signal in the NAO and Atlantic/European blocking, Q. J. Roy. Meteor. Soc., 142, 1890–1903, https://doi.org/10.1002/qj.2782, 2016. a
Gueymard, C. A.: The sun's total and spectral irradiance for solar energy applications and solar radiation models, Sol. Energy, 76, 423–453, https://doi.org/10.1016/j.solener.2003.08.039, 2004. a
Guttu, S., Orsolini, Y., Stordal, F., Otterå, O. H., and Omrani, N.-E.: The 11 year solar cycle UV irradiance effect and its dependency on the Pacific Decadal Oscillation, Environ. Res. Lett., 16, 064030, https://doi.org/10.1088/1748-9326/abfe8b, 2021. a
Hardiman, S. C., Butchart, N., Charlton-Perez, A. J., Shaw, T. A., Akiyoshi, H., Baumgaertner, A., Bekki, S., Braesicke, P., Chipperfield, M., Dameris, M., Garcia, R. R., Michou, M., Pawson, S., Rozanov, E., and Shibata, K.: Improved predictability of the troposphere using stratospheric final warmings, J. Geophys. Res.-Atmos., 116, D18113, https://doi.org/10.1029/2011JD015914, 2011. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N. : ERA5 monthly averaged data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.6860a573, 2023. a
Hood, L., Misios, S., Mitchell, D., Rozanov, E., Gray, L., Tourpali, K., Matthes, K., Schmidt, H., Chiodo, G., Thiéblemont, R., Shindell, D., and Krivolutsky, A.: Solar signals in CMIP-5 simulations: the ozone response, Q. J. Roy. Meteor. Soc., 141, 2670–2689, https://doi.org/10.1002/qj.2553, 2015. a
Hood, L. L. and Soukharev, B. E.: Solar induced variations of odd nitrogen: Multiple regression analysis of UARS HALOE data, Geophys. Res. Lett., 33, L22805, https://doi.org/10.1029/2006GL028122, 2006. a
Huo, W.: Scripts and codes that have been used to produce the figures in Huo et al., 2024, Zenodo, https://doi.org/10.5281/zenodo.14492405, 2024. a
Huo, W., Xiao, Z., and Zhao, L.: Phase-Locked Impact of the 11-Year Solar Cycle on Tropical Pacific Decadal Variability, J. Climate, 36, 421–439, https://doi.org/10.1175/JCLI-D-21-0595.1, 2023. a
Ilyina, T., Six, K. D., Segschneider, J., Maier-Reimer, E., Li, H., and Núñez-Riboni, I.: Global ocean biogeochemistry model HAMOCC: Model architecture and performance as component of the MPI-Earth system model in different CMIP5 experimental realizations, J. Adv. Model. Earth Syst., 5, 287–315, https://doi.org/10.1029/2012MS000178, 2013. a
Jia, L., Yang, X., Vecchi, G., Gudgel, R., Delworth, T., Fueglistaler, S., Lin, P., Scaife, A. A., Underwood, S., and Lin, S.-J.: Seasonal Prediction Skill of Northern Extratropical Surface Temperature Driven by the Stratosphere, J. Climate, 30, 463–4475, https://doi.org/10.1175/JCLI-D-16-0475.1, 2017. a
Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., and Kern, B.: Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geosci. Model Dev., 3, 717–752, https://doi.org/10.5194/gmd-3-717-2010, 2010. a
Jöckel, P., Tost, H., Pozzer, A., Kunze, M., Kirner, O., Brenninkmeijer, C. A. M., Brinkop, S., Cai, D. S., Dyroff, C., Eckstein, J., Frank, F., Garny, H., Gottschaldt, K.-D., Graf, P., Grewe, V., Kerkweg, A., Kern, B., Matthes, S., Mertens, M., Meul, S., Neumaier, M., Nützel, M., Oberländer-Hayn, S., Ruhnke, R., Runde, T., Sander, R., Scharffe, D., and Zahn, A.: Earth System Chemistry integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy) version 2.51, Geosci. Model Dev., 9, 1153–1200, https://doi.org/10.5194/gmd-9-1153-2016, 2016. a
Jungclaus, J. H., Keenlyside, N., Botzet, M., Haak, H., Luo, J.-J., Latif, M., Marotzke, J., Mikolajewicz, U., and Roeckner, E.: Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM, J. Climate, 19, 3952–3972, https://doi.org/10.1175/JCLI3815.1, 2006. a
Jungclaus, J. H., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei, D., Mikolajewicz, U., Notz, D., and Von Storch, J. S.: Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model, J. Adv. Model. Earth Syst., 5, 422–446, https://doi.org/10.1002/jame.20023, 2013. a
Jungclaus, J., Bittner, M., Wieners, K.-H., Wachsmann, F., Schupfner, M., Legutke, S., Giorgetta, M., Reick, C., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Esch, M., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP, Version 20190710, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.741, 2019. a
Kinnison, D. E., Brasseur, G. P., Walters, S., Garcia, R. R., Marsh, D. R., Sassi, F., Harvey, V. L., Randall, C. E., Emmons, L., Lamarque, J. F., Hess, P., Orlando, J. J., Tie, X. X., Randel, W., Pan, L., L., Gettelman, A., Granier, C., Diehl, T., Niemeier, U., and Simmons, A. J.: Sensitivity of chemical tracers to meteorological parameters in the MOZART-3 chemical transport model, J. Geophys. Res.-Atmos., 112, D20302, https://doi.org/10.1029/2006JD007879, 2007. a
Kodera, K. and Kuroda, Y.: A possible mechanism of solar modulation of the spatial structure of the North Atlantic Oscillation, J. Geophys. Res.-Atmos., 110, D02111, https://doi.org/10.1029/2004JD005258, 2005. a
Kuchar, A., Ball, W. T., Rozanov, E. V., Stenke, A., Revell, L., Miksovsky, J., Pisoft, P., and Peter, T.: On the aliasing of the solar cycle in the lower stratospheric tropical temperature, J. Geophys. Res.-Atmos., 122, 9076–9093, https://doi.org/10.1002/2017JD026948, 2017. a
Kunze, M., Godolt, M., Langematz, U., Grenfell, J., Hamann-Reinus, A., and Rauer, H.: Investigating the early Earth faint young Sun problem with a general circulation model, Planet. Space Sci., 98, 77–92, https://doi.org/10.1016/j.pss.2013.09.011, 2014. a
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/10.5194/acp-20-6991-2020, 2020. a, b, c
Kushnir, Y., Scaife, A. A., Arritt, R., Balsamo, G., Boer, G., Doblas-Reyes, F., Hawkins, E., Kimoto, M., Kolli, R. K., Kumar, A., Matei, D., Matthes, K., Müller, W. A., O'Kane, T., Perlwitz, J., Power, S., Raphael, M., Shimpo, A., Smith, D., Tuma, M., and Wu, B.: Towards operational predictions of the near-term climate, Nat. Clim. Change, 9, 94–101, https://doi.org/10.1038/s41558-018-0359-7, 2019. a
Labitzke, K.: On the solar cycle–QBO relationship: a summary, J. Atmos. Sol.-Terr. Phy., 67, 45–54, https://doi.org/10.1016/j.jastp.2004.07.016, 2005. a, b
Labitzke, K. and van Loon, H.: The QBO effect on the solar signal in the global stratosphere in the winter of the Northern Hemisphere, J. Atmos. Sol.-Terr. Phy., 62, 621–628, https://doi.org/10.1016/S1364-6826(00)00047-X, 2000. a
Lawrence, Z. D., Abalos, M., Ayarzagüena, B., Barriopedro, D., Butler, A. H., Calvo, N., de la Cámara, A., Charlton-Perez, A., Domeisen, D. I. V., Dunn-Sigouin, E., García-Serrano, J., Garfinkel, C. I., Hindley, N. P., Jia, L., Jucker, M., Karpechko, A. Y., Kim, H., Lang, A. L., Lee, S. H., Lin, P., Osman, M., Palmeiro, F. M., Perlwitz, J., Polichtchouk, I., Richter, J. H., Schwartz, C., Son, S.-W., Erner, I., Taguchi, M., Tyrrell, N. L., Wright, C. J., and Wu, R. W.-Y.: Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems, Weather Clim. Dynam., 3, 977–1001, https://doi.org/10.5194/wcd-3-977-2022, 2022. a, b
Li, K.-F., Zhang, Q., Tung, K.-K., and Yung, Y. L.: Resolving a long-standing model-observation discrepancy on ozone solar cycle response, Earth Space Sci., 3, 431–440, https://doi.org/10.1002/2016EA000199, 2016. a
Madec, G.: NEMO ocean engine, Note du Pôle modélisation, Inst. Pierre-Simon Laplace, p. 406, ISSN No 1288-1619, 2016. a
Matthes, K., Marsh, D. R., Garcia, R. R., Kinnison, D. E., Sassi, F., and Walters, S.: Role of the QBO in modulating the influence of the 11 year solar cycle on the atmosphere using constant forcings, J. Geophys. Res., 115, D18110, https://doi.org/10.1029/2009JD013020, 2010. a, b
Matthes, K., Kuroda, Y., Kodera, K., and Langematz, U.: Transfer of the solar signal from the stratosphere to the troposphere: Northern winter, J. Geophys. Res., 111, D06108, https://doi.org/10.1029/2005JD006283, 2006. a, b, c, d
Matthes, K., Funke, B., Andersson, M. E., Barnard, L., Beer, J., Charbonneau, P., Clilverd, M. A., Dudok de Wit, T., Haberreiter, M., Hendry, A., Jackman, C. H., Kretzschmar, M., Kruschke, T., Kunze, M., Langematz, U., Marsh, D. R., Maycock, A. C., Misios, S., Rodger, C. J., Scaife, A. A., Seppälä, A., Shangguan, M., Sinnhuber, M., Tourpali, K., Usoskin, I., van de Kamp, M., Verronen, P. T., and Versick, S.: Solar forcing for CMIP6 (v3.2), Geosci. Model Dev., 10, 2247–2302, https://doi.org/10.5194/gmd-10-2247-2017, 2017 (data available at: https://solarisheppa.geomar.de/cmip6, last access: 30 December 2024). a, b, c, d, e, f, g
Matthes, K., Biastoch, A., Wahl, S., Harlaß, J., Martin, T., Brücher, T., Drews, A., Ehlert, D., Getzlaff, K., Krüger, F., Rath, W., Scheinert, M., Schwarzkopf, F. U., Bayr, T., Schmidt, H., and Park, W.: The Flexible Ocean and Climate Infrastructure version 1 (FOCI1): mean state and variability, Geosci. Model Dev., 13, 2533–2568, https://doi.org/10.5194/gmd-13-2533-2020, 2020. a, b
Maycock, A. C., Matthes, K., Tegtmeier, S., Schmidt, H., Thiéblemont, R., Hood, L., Akiyoshi, H., Bekki, S., Deushi, M., Jöckel, P., Kirner, O., Kunze, M., Marchand, M., Marsh, D. R., Michou, M., Plummer, D., Revell, L. E., Rozanov, E., Stenke, A., Yamashita, Y., and Yoshida, K.: The representation of solar cycle signals in stratospheric ozone – Part 2: Analysis of global models, Atmos. Chem. Phys., 18, 11323–11343, https://doi.org/10.5194/acp-18-11323-2018, 2018. a
Mitchell, D., Misios, S., Gray, L., Tourpali, K., Matthes, K., Hood, L., Schmidt, H., Chiodo, G., Thiéblemont, R., Rozanov, E., Shindell, D., and Krivolutsky, A.: Solar signals in CMIP-5 simulations: the stratospheric pathway, Q. J. Roy. Meteor. Soc., 141, 2390–2403, https://doi.org/10.1002/qj.2530, 2015. a, b, c, d
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., Tian, F., and Marotzke, J.: A higher-resolution version of the max planck institute earth system model (MPI-ESM1. 2-HR), J. Adv. Model. Earth Syst., 10, 1383–1413, https://doi.org/10.1029/2017MS001217, 2018. a, b
Nissen, K. M., Matthes, K., Langematz, U., and Mayer, B.: Towards a better representation of the solar cycle in general circulation models, Atmos. Chem. Phys., 7, 5391–5400, https://doi.org/10.5194/acp-7-5391-2007, 2007. a
Pohlmann, H.: SOLCHECK MPI-M MPI-ESM1-2-HR CMIP6 historical simulation without solar and ozone variability, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/SOLCHECK_MPI-ESM-HR_C6_hist, 2021. a
Pyper, B. J. and Peterman, R. M.: Comparison of methods to account for autocorrelation in correlation analyses of fish data, Can. J. Fish. Aquat. Sci., 55, 2127–2140, https://doi.org/10.1139/f98-104, 1998. a
Reick, C. H., Raddatz, T., Brovkin, V., and Gayler, V.: The representation of natural and anthropogenic land cover change in MPI-ESM, J. Adv. Model. Earth Syst., 5, 459–482, https://doi.org/10.1002/jame.20022, 2013. a, b
Roeckner, E., Brokopf, R., Esch, M., Giorgetta, M. A., Hagemann, S., Kornblueh, L., Manzini, E., Schlese, U., and Schulzweida, U.: Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model, J. Climate, 19, 3771–3791, https://doi.org/10.1175/JCLI3824.1, 2006. a
Sander, R., Baumgaertner, A., Gromov, S., Harder, H., Jöckel, P., Kerkweg, A., Kubistin, D., Regelin, E., Riede, H., Sandu, A., Taraborrelli, D., Tost, H., and Xie, Z.-Q.: The atmospheric chemistry box model CAABA/MECCA-3.0, Geosci. Model Dev., 4, 373–380, https://doi.org/10.5194/gmd-4-373-2011, 2011. a
Sander, R., Jöckel, P., Kirner, O., Kunert, A. T., Landgraf, J., and Pozzer, A.: The photolysis module JVAL-14, compatible with the MESSy standard, and the JVal PreProcessor (JVPP), Geosci. Model Dev., 7, 2653–2662, https://doi.org/10.5194/gmd-7-2653-2014, 2014. a
Scaife, A. A., Ineson, S., Knight, J. R., Gray, L., Kodera, K., and Smith, D. M.: A mechanism for lagged North Atlantic climate response to solar variability, Geophys. Res. Lett., 40, 434–439, https://doi.org/10.1002/grl.50099, 2013. a, b
Scaife, A. A., Karpechko, A. Y., Baldwin, M. P., Brookshaw, A., Butler, A. H., Eade, R., Gordon, M., MacLachlan, C., Martin, N., Dunstone, N., and Smith, D.: Seasonal winter forecasts and the stratosphere, Atmos. Sci. Lett., 17, 51–56, https://doi.org/10.1002/asl.598, 2016. a
Scaife, A. A., Baldwin, M. P., Butler, A. H., Charlton-Perez, A. J., Domeisen, D. I. V., Garfinkel, C. I., Hardiman, S. C., Haynes, P., Karpechko, A. Y., Lim, E.-P., Noguchi, S., Perlwitz, J., Polvani, L., Richter, J. H., Scinocca, J., Sigmond, M., Shepherd, T. G., Son, S.-W., and Thompson, D. W. J.: Long-range prediction and the stratosphere, Atmos. Chem. Phys., 22, 2601–2623, https://doi.org/10.5194/acp-22-2601-2022, 2022. a, b, c
Schultz, M. G., Stadtler, S., Schröder, S., Taraborrelli, D., Franco, B., Krefting, J., Henrot, A., Ferrachat, S., Lohmann, U., Neubauer, D., Siegenthaler-Le Drian, C., Wahl, S., Kokkola, H., Kühn, T., Rast, S., Schmidt, H., Stier, P., Kinnison, D., Tyndall, G. S., Orlando, J. J., and Wespes, C.: The chemistry–climate model ECHAM6.3-HAM2.3-MOZ1.0, Geosci. Model Dev., 11, 1695–1723, https://doi.org/10.5194/gmd-11-1695-2018, 2018. a
Soukharev, B. E. and Hood, L. L.: Solar cycle variation of stratospheric ozone: Multiple regression analysis of long-term satellite data sets and comparisons with models, J. Geophys. Res., 111, D20314, https://doi.org/10.1029/2006JD007107, 2006. a
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric component of the MPI-M Earth System Model: ECHAM6, J. Adv. Model. Earth Syst., 5, 146–172, https://doi.org/10.1002/jame.20015, 2013. a, b
Sukhodolov, T., Rozanov, E., Shapiro, A. I., Anet, J., Cagnazzo, C., Peter, T., and Schmutz, W.: Evaluation of the ECHAM family radiation codes performance in the representation of the solar signal, Geosci. Model Dev., 7, 2859–2866, https://doi.org/10.5194/gmd-7-2859-2014, 2014. a, b
Swartz, W. H., Stolarski, R. S., Oman, L. D., Fleming, E. L., and Jackman, C. H.: Middle atmosphere response to different descriptions of the 11-yr solar cycle in spectral irradiance in a chemistry-climate model, Atmos. Chem. Phys., 12, 5937–5948, https://doi.org/10.5194/acp-12-5937-2012, 2012. a
Thejll, P., Christiansen, B., and Gleisner, H.: On correlations between the North Atlantic Oscillation, geopotential heights, and geomagnetic activity, Geophys. Res. Lett., 30, 1347, https://doi.org/10.1029/2002GL016598, 2003. a
Thiéblemont, R., Matthes, K., Omrani, N.-E., Kodera, K., and Hansen, F.: Solar forcing synchronizes decadal North Atlantic climate variability, Nat. Commun., 6, 8268, https://doi.org/10.1038/ncomms9268, 2015. a, b
Tripathi, O. P., Charlton-Perez, A., Sigmond, M., and Vitart, F.: Enhanced long-range forecast skill in boreal winter following stratospheric strong vortex conditions, Environ. Res. Lett., 10, 104007, https://doi.org/10.1088/1748-9326/10/10/104007, 2015. a
Wahl, S., Huo, W., Spiegl, T., Schmidt, F., and Langematz, U.: ROMIC-II-SOLCHECK joint database part I – CMIP6-like ensemble experiments with various solar forcings, DOKU at DKRZ [data set], https://hdl.handle.net/21.14106/8b740ed5795d7e310b74ee1f5e85de5148f213fe (last access: 14 February 2025), 2023. a
Wang, S., Li, K., Zhu, D., Sander, S. P., Yung, Y. L., Pazmino, A., and Querel, R.: Solar 11-Year Cycle Signal in Stratospheric Nitrogen Dioxide – Similarities and Discrepancies Between Model and NDACC Observations, Sol. Phys., 295, 117, https://doi.org/10.1007/s11207-020-01685-1, 2020. a, b
Ward, W., Seppälä, A., Yiğit, E., Nakamura, T., Stolle, C., Laštovička, J., Woods, T. N., Tomikawa, Y., Lübken, F.-J., Solomon, S. C., Marsh, D. R., Funke, B., and Pallamraju, D.: Role Of the Sun and the Middle atmosphere/thermosphere/ionosphere In Climate (ROSMIC): a retrospective and prospective view, Prog. Earth Planet. Sci., 8, 47, https://doi.org/10.1186/s40645-021-00433-8, 2021. a
Short summary
Uncertainties of the solar signals in the middle atmosphere are assessed based on large ensemble simulations with multiple climate models. Our results demonstrate that the 11-year solar signals in the shortwave heating rate, temperature, and ozone anomalies are significant and robust. The simulated dynamical responses are model-dependent, and solar imprints in the polar night jet are influenced by biases in the model used.
Uncertainties of the solar signals in the middle atmosphere are assessed based on large ensemble...
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