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Analysis of forced response and internal climate variability in the INMCM Earth system model

  • Maria Buyanova EMAIL logo , Andrei Gavrilov and Dmitry Mukhin
Published/Copyright: April 17, 2025

Abstract

We perform a comparative analysis of the mode composition of forced and internal variability of the INMCM Earth system model for various single realization and ensemble experiments within CMIP5 and CMIP6 projects. For this purpose, we apply Linear Dynamical Mode (LDM) method of data decomposition and its forced/ensemble modifications to surface air temperature data with the main attention paid to the analysis of climate dynamics on interannual, decadal and multidecadal time scales. We compare the results with the mode composition of reanalysis data obtained by the same LDM method. Our main findings classify the representation of the forced response, El Niño – Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation in different versions of the INMCM model, as well as possible contributions of the effects of limited statistics and dynamical forced response.

MSC 2010: 37M10; 62F15; 62P12

Funding statement: Application of the LDM method to INMCM and reanalysis data (Sections 1, 2.1–2.4) was supported by the state assignment of the A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences (Project No. FFUF-2022-0008). Implementation and analysis of the spatial similarity measure (Section 2.5) was supported by the Russian State project FFUF-2023-0004.

Acknowledgment

Support for the Twentieth Century Reanalysis Project version 2c dataset is provided by the U.S. Department of Energy, Office of Science Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. Support for the Twentieth Century Reanalysis Project version 3 dataset is provided by the U.S. Department of Energy, Office of Science Biological and Environmental Research (BER), by the National Oceanic and Atmospheric Administration Climate Program Office, and by the NOAA Earth System Research Laboratory Physical Sciences Laboratory.

References

[1] M. R. Allen and L. A. Smith, Optimal filtering in singular spectrum analysis. Physics Letters A 234 (1997), 419–428.10.1016/S0375-9601(97)00559-8Search in Google Scholar

[2] X. An, B. Wu, T. Zhou, and B. Liu, Atlantic multidecadal oscillation drives interdecadal pacific variability via tropical atmospheric bridge. Journal of Climate 34 (2021), 5543–5553.10.1175/JCLI-D-20-0983.1Search in Google Scholar

[3] D. Aristoff, J. Copperman, N. Mankovich, and A. Davies, Featurizing Koopman mode decomposition for robust forecasting. The Journal of Chemical Physics 161 (2024), 064103.10.1063/5.0220277Search in Google Scholar PubMed PubMed Central

[4] T. DelSole and M. K. Tippett, Average predictability time, part I: Theory. Journal of the Atmospheric Sciences 66 (2009), 1172–1187.10.1175/2008JAS2868.1Search in Google Scholar

[5] T. DelSole and M. K. Tippett, Average predictability time, part II: Seamless diagnoses of predictability on multiple time scales. Journal of the Atmospheric Sciences 66 (2009), 1188–1204.10.1175/2008JAS2869.1Search in Google Scholar

[6] T. DelSole, M. K. Tippett, and J. Shukla, A significant component of unforced multidecadal variability in the recent acceleration of global warming. Journal of Climate 24 (2011), 909–926.10.1175/2010JCLI3659.1Search in Google Scholar

[7] C. Deser, L. Terray, and A. S. Phillips, Forced and internal components of winter air temperature trends over North America during the past 50 years: Mechanisms and implications. Journal of Climate 29 (2016), 2237–2258.10.1175/JCLI-D-15-0304.1Search in Google Scholar

[8] C. Deser et al., Insights from earth system model initial-condition large ensembles and future prospects. Nature Climate Change 10 (2020), 277–286.10.1038/s41558-020-0731-2Search in Google Scholar

[9] D. Dommenget and M. Latif, Generation of hyper climate modes. Geophysical Research Letters 35 (2008), L02706.10.1029/2007GL031087Search in Google Scholar

[10] V. Eyring et al., Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9 (2016), 1937–1958.10.5194/gmd-9-1937-2016Search in Google Scholar

[11] C. Frankignoul, G. Gastineau, and Y.-O. Kwon, Estimation of the SST response to anthropogenic and external forcing and its impact on the atlantic multidecadal oscillation and the pacific decadal oscillation. Journal of Climate 30 (2017), 9871–9895.10.1175/JCLI-D-17-0009.1Search in Google Scholar

[12] A. Gavrilov et al., Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science 26 (2016), 123101.10.1063/1.4968852Search in Google Scholar PubMed

[13] A. Gavrilov et al., Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics 52 (2019), 2199–2216.10.1007/s00382-018-4255-7Search in Google Scholar

[14] A. Gavrilov, S. Kravtsov, and D. Mukhin, Analysis of 20th century surface air temperature using linear dynamical modes. Chaos: An Interdisciplinary Journal of Nonlinear Science 30 (2020), 123110.10.1063/5.0028246Search in Google Scholar PubMed

[15] A. Gavrilov et al., Forced response and internal variability in ensembles of climate simulations: identification and analysis using linear dynamical mode decomposition. Climate Dynamics 62 (2024), 1783–1810.10.1007/s00382-023-06995-1Search in Google Scholar

[16] M. Ghil et al., Advanced spectral methods for climatic time series. Reviews of Geophysics 40 (2002), 1003.10.1029/2000RG000092Search in Google Scholar

[17] A. Hannachi, I. T. Jolliffe, and D. B. Stephenson, Empirical orthogonal functions and related techniques in atmospheric science: A review. International Journal of Climatology 27 (2007), 1119–1152.10.1002/joc.1499Search in Google Scholar

[18] B. J. Henley et al., A tripole index for the interdecadal pacific oscillation. Climate Dynamics 45 (2015), 3077–3090.10.1007/s00382-015-2525-1Search in Google Scholar

[19] H. Jeffreys, Theory of Probability. Clarendon Press, 1998.10.1093/oso/9780198503682.001.0001Search in Google Scholar

[20] I. T. Jolliffe, Principal Component Analysis. 2nd ed., Springer, New York, 1986.10.1007/978-1-4757-1904-8Search in Google Scholar

[21] J. E. Kay et al., The community earth system model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bulletin of the American Meteorological Society 96 (2015), 1333–1349.10.1175/BAMS-D-13-00255.1Search in Google Scholar

[22] S. Kravtsov, Pronounced differences between observed and CMIP5-simulated multidecadal climate variability in the twentieth century. Geophysical Research Letters 44 (2017), 5749–5757.10.1002/2017GL074016Search in Google Scholar

[23] S. Kravtsov, C. Grimm, and S. Gu, Global-scale multidecadal variability missing in state-of-the-art climate models. npj Climate and Atmospheric Science 1 (2018), 34.10.1038/s41612-018-0044-6Search in Google Scholar

[24] S. Kravtsov, A. Gavrilov, M. Buyanova, E. Loskutov, and A. Feigin, Forced signal and predictability in a prototype climate model: Implications for fingerprinting based detection in the presence of multidecadal natural variability. Chaos: An Interdisciplinary Journal of Nonlinear Science 32 (2022), 123130.10.1063/5.0106514Search in Google Scholar PubMed

[25] S. Kravtsov, A. Westgate, and A. Gavrilov, Global-scale multidecadal variability in climate models and observations, part II: The stadium wave. Climate Dynamics 62 (2024), 10281–10306.10.1007/s00382-024-07451-4Search in Google Scholar

[26] N. Maher et al., The Max Planck Institute grand ensemble: Enabling the exploration of climate system variability. Journal of Advances in Modeling Earth Systems 11 (2019), 2050–2069.10.1029/2019MS001639Search in Google Scholar

[27] M. Meinshausen and E. Vogel, input4mips.uom.ghgconcentrations.cmip.uom-cmip-1-2-0 (2016). URL: http://cera-www.dkrz.de/WDCC/meta/CMIP6/input4MIPs.CMIP6.CMIP.UoM.UoM-CMIP-1-2-0.Search in Google Scholar

[28] M. Meinshausen et al., Historical greenhouse gas concentrations for climate modelling (CMIP6). Geoscientific Model Development 10 (2017), 2057–2116.10.5194/gmd-10-2057-2017Search in Google Scholar

[29] I. Mezić, Spectral properties of dynamical systems, model reduction and decompositions. Nonlinear Dynamics 41 (2005), 309–325.10.1007/s11071-005-2824-xSearch in Google Scholar

[30] A. H. Monahan, J. C. Fyfe, M. H. P. Ambaum, D. B. Stephenson, and G. R. North, Empirical orthogonal functions: The medium is the message. Journal of Climate 22 (2009), 6501–6514.10.1175/2009JCLI3062.1Search in Google Scholar

[31] D. Mukhin, A. Gavrilov, A. Feigin, E. Loskutov, and J. Kurths, Principal nonlinear dynamical modes of climate variability. Scientific Reports 5 (2015), 15510.10.1038/srep15510Search in Google Scholar PubMed PubMed Central

[32] D. Mukhin, A. Gavrilov, E. Loskutov, A. Feigin, and J. Kurths, Nonlinear reconstruction of global climate leading modes on decadal scales. Climate Dynamics 51 (2018), 2301–2310.10.1007/s00382-017-4013-2Search in Google Scholar

[33] D. Mukhin, A. Gavrilov, A. Seleznev, and M. Buyanova, An atmospheric signal lowering the spring predictability barrier in statistical ENSO forecasts. Geophysical Research Letters 48 (2021), e2020GL091287.10.1029/2020GL091287Search in Google Scholar

[34] D. Mukhin, S. Safonov, A. Gavrilov, A. Gritsun, and A. Feigin, A new tool for studying seasonality and spatio-temporal structure of ENSO cycles in data and ESM simulations. Russian Journal of Numerical Analysis and Mathematical Modelling 39 (2024), 27–34.10.1515/rnam-2024-0003Search in Google Scholar

[35] Noaa/cires/doe 20th century reanalysis (v2c, v3) data provided by the NOAA Physical Sciences Laboratory. Boulder, Colorado, USA, from their website. URL: https://psl.noaa.gov.Search in Google Scholar

[36] J. L. Proctor, S. L. Brunton, and J. N. Kutz, Dynamic mode decomposition with control. SIAM Journal on Applied Dynamical Systems 15 (2016), 142–161.10.1137/15M1013857Search in Google Scholar

[37] T. Schneider and S. M. Griffies, A conceptual framework for predictability studies. Journal of Climate 12 (1999), 3133–3155.10.1175/1520-0442(1999)012<3133:ACFFPS>2.0.CO;2Search in Google Scholar

[38] T. Schneider and I. M. Held, Discriminants of twentieth-century changes in earth surface temperatures. Journal of Climate 14 (2001), 249–254.10.1175/1520-0442(2001)014<0249:LDOTCC>2.0.CO;2Search in Google Scholar

[39] A. F. Seleznev, A. S. Gavrilov, D. N. Mukhin, A. S. Gritsun, and E. M. Volodin, ENSO phase locking, asymmetry and predictability in the inmcm earth system model. Russian Journal of Numerical Analysis and Mathematical Modelling 39 (2024), 35–46.10.1515/rnam-2024-0004Search in Google Scholar

[40] A. Srivastava and T. DelSole, Decadal predictability without ocean dynamics. Proceedings of the National Academy of Sciences 114 (2017), 2177–2182.10.1073/pnas.1614085114Search in Google Scholar

[41] K. E. Taylor, R. J. Stouffer, and G. A. Meehl, An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93 (2012), 485–498.10.1175/BAMS-D-11-00094.1Search in Google Scholar

[42] E. Volodin and N. Diansky, INMCM4 model output prepared for CMIP5 pre-industrial control, served by ESGF. (2013). URL: http://cera-www.dkrz.de/WDCC/CMIP5/Compact.jsp?acronym=INC4pc.Search in Google Scholar

[43] E. Volodin and N. Diansky, INMCM4 model output prepared for CMIP5 historical, served by ESGF. (2013). URL: http://cera-www.dkrz.de/WDCC/CMIP5/Compact.jsp?acronym=INC4hi.Search in Google Scholar

[44] E. Volodin et al., INM INM-CM4-8 model output prepared for CMIP6 CMIP picontrol. (2019). URL: http://cera-www.dkrz.de/WDCC/meta/CMIP6/CMIP6.CMIP.INM.INM-CM4-8.piControl.Search in Google Scholar

[45] E. Volodin et al., INM INM-CM4-8 model output prepared for CMIP6 PMIP midholocene. (2019). URL: http://cera-www.dkrz.de/WDCC/meta/CMIP6/CMIP6.PMIP.INM.INM-CM4-8.midHolocene.Search in Google Scholar

[46] E. Volodin et al., INM INM-CM5-0 model output prepared for CMIP6 CMIP historical. (2019). URL: http://cera-www.dkrz.de/WDCC/meta/CMIP6/CMIP6.CMIP.INM.INM-CM5-0.historical.Search in Google Scholar

[47] M. O. Williams, I. G. Kevrekidis, and C. W. Rowley, A data-driven approximation of the koopman operator: Extending dynamic mode decomposition. Journal of Nonlinear Science 25 (2015), 1307–1346.10.1007/s00332-015-9258-5Search in Google Scholar

[48] R. C. Wills, T. Schneider, J. M. Wallace, D. S. Battisti, and D. L. Hartmann, Disentangling global warming, multidecadal variability, and El Niño in pacific temperatures. Geophysical Research Letters 45 (2018), 2487–2496.10.1002/2017GL076327Search in Google Scholar

[49] R. C. Wills, D. S. Battisti, K. C. Armour, T. Schneider, and C. Deser, Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations. Journal of Climate 33 (2020), 8693–8719.10.1175/JCLI-D-19-0855.1Search in Google Scholar

[50] R. Zhang et al., A review of the role of the atlantic meridional overturning circulation in atlantic multidecadal variability and associated climate impacts. Reviews of Geophysics 57 (2019), 316–375.10.1029/2019RG000644Search in Google Scholar

Received: 2024-10-28
Revised: 2025-01-16
Accepted: 2025-01-28
Published Online: 2025-04-17
Published in Print: 2025-04-28

© 2025 Walter de Gruyter GmbH, Berlin/Boston, Germany

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