• Archambault, H. M., L. F. Bosart, D. Keyser, and A. R. Aiyyer, 2008: Influence of large-scale flow regimes on cool-season precipitation in the northeastern United States. Mon. Wea. Rev., 136, 29452963, https://doi.org/10.1175/2007MWR2308.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, E. A., and D. L. Hartmann, 2010: Testing a theory for the effect of latitude on the persistence of eddy-driven jets using CMIP3 simulations. Geophys. Res. Lett., 37, L15801, https://doi.org/10.1029/2010GL044144.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, E. A., and L. Polvani, 2013: Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models. J. Climate, 26, 71177135, https://doi.org/10.1175/JCLI-D-12-00536.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beadling, R. L., and et al. , 2020: Representation of Southern Ocean properties across Coupled Model Intercomparison Project generations: CMIP3 to CMIP6. J. Climate, 33, 65556581, https://doi.org/10.1175/JCLI-D-19-0970.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellenger, H., É. Guilyardi, J. Leloup, M. Lengaigne, and J. Vialard, 2014: ENSO representation in climate models: From CMIP3 to CMIP5. Climate Dyn., 42, 19992018, https://doi.org/10.1007/s00382-013-1783-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bock, L., and et al. , 2020: Quantifying progress across different CMIP phases with the ESMValTool. J. Geophys. Res., 125, e2019JD032321, https://doi.org/10.1029/2019JD032321.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonfils, C., and B. D. Santer, 2011: Investigating the possibility of a human component in various Pacific decadal oscillation indices. Climate Dyn., 37, 14571468, https://doi.org/10.1007/s00382-010-0920-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonfils, C., B. D. Santer, T. J. Phillips, K. Marvel, L. R. Leung, C. Doutriaux, and A. Capotondi, 2015: Relative contributions of mean-state shifts and ENSO-driven variability to precipitation changes in a warming climate. J. Climate, 28, 999710 013, https://doi.org/10.1175/JCLI-D-15-0341.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bracegirdle, T. J., C. R. Holmes, J. S. Hosking, G. J. Marshall, M. Osman, M. Patterson, and T. Rackow, 2020: Improvements in circumpolar Southern Hemisphere extratropical atmospheric circulation in CMIP6 compared to CMIP5. Earth Space Sci., 7, e2019EA001065, https://doi.org/10.1029/2019EA001065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cannon, A. J., 2020: Reductions in daily continental-scale atmospheric circulation biases between generations of global climate models: CMIP5 to CMIP6. Environ. Res. Lett., 15, 064006, https://doi.org/10.1088/1748-9326/ab7e4f.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chylek, P., T. J. Vogelsang, J. D. Klett, N. Hengartner, D. Higdon, G. Lesins, and M. K. Dubey, 2016: Indirect aerosol effect increases CMIP5 models’ projected Arctic warming. J. Climate, 29, 14171428, https://doi.org/10.1175/JCLI-D-15-0362.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compo, G. P., J. S. Whitaker, and P. D. Sardeshmukh, 2006: Feasibility of a 100-year reanalysis using only surface pressure data. Bull. Amer. Meteor. Soc., 87, 175190, https://doi.org/10.1175/BAMS-87-2-175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compo, G. P., and et al. , 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137 (654), 128, https://doi.org/10.1002/qj.776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durkee, J. D., J. D. Frye, C. M. Fuhrmann, M. C. Lacke, H. G. Jeong, and T. L. Mote, 2008: Effects of the North Atlantic Oscillation on precipitation-type frequency and distribution in the eastern United States. Theor. Appl. Climatol., 94, 5165, https://doi.org/10.1007/s00704-007-0345-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., and et al. , 2020: ESMValTool v2. 0—An extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP. Geosci. Model Dev., 13, 33833438, https://doi.org/10.5194/gmd-13-3383-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fasullo, J. T., 2020: Evaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate Model Assessment Tool (CMATv1). Geosci. Model Dev., 13, 36273642, https://doi.org/10.5194/gmd-13-3627-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fasullo, J. T., A. S. Phillips, and C. Deser, 2020: Evaluation of leading modes of climate variability in the CMIP archives. J. Climate, 33, 55275545, https://doi.org/10.1175/JCLI-D-19-1024.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feser, F., M. Barcikowska, O. Krueger, F. Schenk, R. Weisse, and L. Xia, 2015: Storminess over the North Atlantic and northwestern Europe—A review. Quart. J. Roy. Meteor. Soc., 141, 350382, https://doi.org/10.1002/qj.2364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flato, G., and et al. , 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. T. F. Stocker et al., Eds, Cambridge University Press, 741–866.

  • Fogt, R. L., J. Perlwitz, S. Pawson, and M. A. Olsen, 2009: Intra-annual relationships between polar ozone and the SAM. Geophys. Res. Lett., 36, L04707, https://doi.org/10.1029/2008GL036627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, Y., Z. Lin, and T. Wang, 2020: Simulated relationship between wintertime ENSO and East Asian summer rainfall: From CMIP3 to CMIP6. Adv. Atmos. Sci., 38, 221236, https://doi.org/10.1007/s00376-020-0147-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73, 19621970, https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerber, E. P., and P. Martineau, 2018: Quantifying the variability of the annular modes: reanalysis uncertainty vs. sampling uncertainty. Atmos. Chem. Phys., 18, 17 09917 117, https://doi.org/10.5194/acp-18-17099-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerber, E. P., L. M. Polvani, and D. Ancukiewicz, 2008: Annular mode time scales in the Intergovernmental Panel on Climate Change Fourth Assessment Report models. Geophys. Res. Lett., 35, L22707, https://doi.org/10.1029/2008GL035712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007JD008972.

    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., C. Doutriaux, P. J. Durack, K. E. Taylor, Y. Zhang, D. N. Williams, E. Mason, and J. Servonnat, 2016: A more powerful reality test for climate models. Eos, Trans. Amer. Geophys. Union, 97, https://eos.org/science-updates/a-more-powerful-reality-test-for-climate-models.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glen, S., 2020: Z test: Definition & two proportion Z-test. From StatisticsHowTo.com: Elementary Statistics for the rest of us! Accessed 28 September 2020, https://www.statisticshowto.com/z-test/.

  • Gottschalck, J., and et al. , 2010: A framework for assessing operational Madden–Julian oscillation forecasts: A CLIVAR MJO working group project. Bull. Amer. Meteor. Soc., 91, 12471258, https://doi.org/10.1175/2010BAMS2816.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanna, E., J. Cappelen, R. Allan, T. Jónsson, F. Le Blancq, T. Lillington, and K. Hickey, 2008: New insights into North European and North Atlantic surface pressure variability, storminess, and related climatic change since 1830. J. Climate, 21, 67396766, https://doi.org/10.1175/2008JCLI2296.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hannachi, A., I. T. Jolliffe, and D. B. Stephenson, 2007: Empirical orthogonal functions and related techniques in atmospheric science: A review. Int. J. Climatol., 27, 11191152, https://doi.org/10.1002/joc.1499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haszpra, T., D. Topál, and M. Herein, 2020: On the time evolution of the Arctic Oscillation and related wintertime phenomena under different forcing scenarios in an ensemble approach. J. Climate, 33, 31073124, https://doi.org/10.1175/JCLI-D-19-0004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and et al. , 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Hosking, J. S., A. Orr, G. J. Marshall, J. Turner, and T. Phillips, 2013: The influence of the Amundsen–Bellingshausen Seas low on the climate of West Antarctica and its representation in coupled climate model simulations. J. Climate, 26, 66336648, https://doi.org/10.1175/JCLI-D-12-00813.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hosking, J. S., A. Orr, T. J. Bracegirdle, and J. Turner, 2016: Future circulation changes off West Antarctica: Sensitivity of the Amundsen Sea low to projected anthropogenic forcing. Geophys. Res. Lett., 43, 367376, https://doi.org/10.1002/2015GL067143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., and C. Deser, 2010: North Atlantic climate variability: The role of the North Atlantic Oscillation. J. Mar. Syst., 79, 231244, https://doi.org/10.1016/j.jmarsys.2009.11.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ivanciu, I., K. Matthes, S. Wahl, J. Harlaß, and A. Biastoch, 2021: Effects of prescribed CMIP6 ozone on simulating the Southern Hemisphere atmospheric circulation response to ozone depletion. Atmos. Chem. Phys., 21, 57775806, https://doi.org/10.5194/acp-21-5777-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jerez, S., P. Jimenez-Guerrero, J. P. Montávez, and R. M. Trigo, 2013: Impact of the North Atlantic Oscillation on European aerosol ground levels through local processes: A seasonal model-based assessment using fixed anthropogenic emissions. Atmos. Chem. Phys., 13, 11 19511 207, https://doi.org/10.5194/acp-13-11195-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joh, Y., and E. Di Lorenzo, 2017: Increasing coupling between NPGO and PDO leads to prolonged marine heatwaves in the northeast Pacific. Geophys. Res. Lett., 44, 11 66311 671, https://doi.org/10.1002/2017GL075930.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jun, M., R. Knutti, and D. W. Nychka, 2008: Spatial analysis to quantify numerical model bias and dependence: How many climate models are there? J. Amer. Stat. Assoc., 103, 934947, https://doi.org/10.1198/016214507000001265.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jun, S.-Y., J.-H. Kim, J. Choi, S.-J. Kim, B.-M. Kim, and S.-I. An, 2020: The internal origin of the west–east asymmetry of Antarctic climate change. Sci. Adv., 6, eaaz1490, https://doi.org/10.1126/sciadv.aaz1490.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kawatani, Y., K. Hamilton, L. J. Gray, S. M. Osprey, S. Watanabe, and Y. Yamashita, 2019: The effects of a well-resolved stratosphere on the simulated boreal winter circulation in a climate model. J. Atmos. Sci., 76, 12031226, https://doi.org/10.1175/JAS-D-18-0206.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, Y. H., S. K. Min, X. Zhang, J. Sillmann, and M. Sandstad, 2020: Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Wea. Climate Extremes, 29, 100269, https://doi.org/10.1016/j.wace.2020.100269.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett., 40, 11941199, https://doi.org/10.1002/grl.50256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J., K. R. Sperber, P. J. Gleckler, C. J. Bonfils, and K. E. Taylor, 2019a: Quantifying the agreement between observed and simulated extratropical modes of interannual variability. Climate Dyn., 52, 40574089, https://doi.org/10.1007/s00382-018-4355-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J., Y. Xue, F. De Sales, I. Diallo, L. Marx, M. Ek, K. R. Sperber, and P. J. Gleckler, 2019b: Evaluation of multi-decadal UCLA-CFSv2 simulation and impact of interactive atmospheric–ocean feedback on global and regional variability. Climate Dyn., 52, 36833707, https://doi.org/10.1007/s00382-018-4351-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, R., Q. Ding, Z. Wu, I. Baxter, M. Bushuk, Y. Huang, and X. Dong, 2021: Summertime atmosphere–sea ice coupling in the Arctic simulated by CMIP5/6 models: Importance of large-scale circulation. Climate Dyn., 56, 14671485, https://doi.org/10.1007/s00382-020-05543-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 10691080, https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masson, D., and R. Knutti, 2011: Climate model genealogy. Geophys. Res. Lett., 38, L08703, https://doi.org/10.1029/2011GL046864.

  • Meehl, G. A., C. Covey, B. McAvaney, M. Latif, and R. J. Stouffer, 2005: Overview of the Coupled Model Intercomparison Project. Bull. Amer. Meteor. Soc., 86, 8993, https://doi.org/10.1175/BAMS-86-1-89.

    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., J. C. Fyfe, M. H. P. Ambaum, D. B. Stephenson, and G. R. North, 2009: Empirical orthogonal functions: The medium is the message. J. Climate, 22, 65016514, https://doi.org/10.1175/2009JCLI3062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • North, G. R., 1984: Empirical orthogonal functions and normal modes. J. Atmos. Sci., 41, 879887, https://doi.org/10.1175/1520-0469(1984)041<0879:EOFANM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 699706, https://doi.org/10.1175/1520-0493(1982)110<0699:SEITEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orbe, C., and et al. , 2020: Representation of modes of variability in six U.S. climate models. J. Climate, 33, 75917617, https://doi.org/10.1175/JCLI-D-19-0956.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oudar, T., J. Cattiaux, and H. Douville, 2020: Drivers of the northern extratropical eddy-driven jet change in CMIP5 and CMIP6 models. Geophys. Res. Lett., 47, e2019GL086695, https://doi.org/10.1029/2019GL086695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pennell, C., and T. Reichler, 2011: On the effective number of climate models. J. Climate, 24, 23582367, https://doi.org/10.1175/2010JCLI3814.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perlwitz, J., S. Pawson, R. L. Fogt, J. E. Nielsen, and W. D. Neff, 2008: Impact of stratospheric ozone hole recovery on Antarctic climate. Geophys. Res. Lett., 35, L08714, https://doi.org/10.1029/2008GL033317.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perlwitz, J., T. Knutson, and J. Kossin, 2017: Large-scale circulation and climate variability. Climate Science Special Report: Fourth National Climate Assessment, Vol. I, D. J. Wuebbles et al., Eds, U.S. Global Change Research Program, 161–184.

    • Crossref
    • Export Citation
  • Phillips, A. S., C. Deser, and J. Fasullo, 2014: Evaluating modes of variability in climate models. Eos, Trans. Amer. Geophys. Union, 95, 453455, https://doi.org/10.1002/2014EO490002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Planton, Y. Y., and et al. , 2020: Evaluating climate models with the CLIVAR 2020 ENSO metrics package. Bull. Amer. Meteor. Soc., 102, E193E217, https://doi.org/10.1175/BAMS-D-19-0337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pohl, B., and N. Fauchereau, 2012: The southern annular mode seen through weather regimes. J. Climate, 25, 33363354, https://doi.org/10.1175/JCLI-D-11-00160.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poli, P., and et al. , 2016: ERA-20C: An atmospheric reanalysis of the twentieth century. J. Climate, 29, 40834097, https://doi.org/10.1175/JCLI-D-15-0556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Priestley, M. D. K., D. Ackerley, J. L. Catto, K. I. Hodges, R. E. McDonald, and R. W. Lee, 2020: An overview of the extratropical storm tracks in CMIP6 historical simulations. J. Climate, 33, 63156343, https://doi.org/10.1175/JCLI-D-19-0928.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randall, D. A., and et al. , 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis. S. Solomon et al., Eds., Cambridge University Press, 589–662.

  • Rao, J., C. I. Garfinkel, and I. P. White, 2020: How does the quasi-biennial oscillation affect the boreal winter tropospheric circulation in CMIP5/6 models? J. Climate, 33, 89758996, https://doi.org/10.1175/JCLI-D-20-0024.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raphael, M. N., and et al. , 2016: The Amundsen Sea low: Variability, change, and impact on Antarctic climate. Bull. Amer. Meteor. Soc., 97, 111121, https://doi.org/10.1175/BAMS-D-14-00018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reboita, M. S., T. Ambrizzi, and R. P. D. Rocha, 2009: Relationship between the southern annular mode and Southern Hemisphere atmospheric systems. Rev. Bras. Meteor., 24, 4855, https://doi.org/10.1590/S0102-77862009000100005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roach, L. A., and et al. , 2020: Antarctic sea ice area in CMIP6. Geophys. Res. Lett., 47, e2019GL086729, https://doi.org/10.1029/2019GL086729.

  • Santer, B. D., and et al. , 2007: Identification of human-induced changes in atmospheric moisture content. Proc. Natl. Acad. Sci. USA, 104, 15 24815 253, https://doi.org/10.1073/pnas.0702872104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schiemann, R., and et al. , 2020: Northern Hemisphere blocking simulation in current climate models: Evaluating progress from the Climate Model Intercomparison Project Phase 5 to 6 and sensitivity to resolution. Wea. Climate Dyn., 1, 277292, https://doi.org/10.5194/wcd-1-277-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sillmann, J., V. V. Kharin, X. Zhang, F. W. Zwiers, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res., 118, 17161733, https://doi.org/10.1002/jgrd.50203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, I. R., P. Hitchcock, T. G. Shepherd, and J. F. Scinocca, 2013a: Southern annular mode dynamics in observations and models. Part I: The influence of climatological zonal wind biases in a comprehensive GCM. J. Climate, 26, 39533967, https://doi.org/10.1175/JCLI-D-12-00348.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, I. R., T. G. Shepherd, P. Hitchcock, and J. F. Scinocca, 2013b: Southern annular mode dynamics in observations and models. Part II: Eddy feedbacks. J. Climate, 26, 52205241, https://doi.org/10.1175/JCLI-D-12-00495.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, I. R., and et al. , 2020: An evaluation of the large-scale atmospheric circulation and its variability in CESM2 and other CMIP models. J. Geophys. Res. Atmos., 125, e2020JD032835, https://doi.org/10.1029/2020JD032835.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Son, S.-W., and et al. , 2008: The impact of stratospheric ozone recovery on the Southern Hemisphere westerly jet. Science, 320, 14861489, https://doi.org/10.1126/science.1155939.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Son, S.-W., and et al. , 2010: The impact of stratospheric ozone on the Southern Hemisphere circulation changes: A multimodel assessment. J. Geophys. Res., 115, D00M07, https://doi.org/10.1029/2010JD014271.

    • Search Google Scholar
    • Export Citation
  • Son, S.-W., J.-H. Shin, H.-S. Park, and J. Choi, 2021: The relationship between the zonal index and annular mode index in reanalysis and CMIP5 models. Asia Pac. J. Atmos. Sci. https://doi.org/10.1007/s13143-021-00244-3, in press.

    • Crossref
    • Export Citation
  • Sperber, K. R., 2004: Madden–Julian variability in NCAR CAM2.0 and CCSM2.0. Climate Dyn., 23, 259278, https://doi.org/10.1007/s00382-004-0447-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sperber, K. R., and H. Annamalai, 2008: Coupled model simulations of boreal summer intraseasonal (30–50 day) variability, Part 1: Systematic errors and caution on use of metrics. Climate Dyn., 31, 345372, https://doi.org/10.1007/s00382-008-0367-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sperber, K. R., S. Gualdi, S. Legutke, and V. Gayler, 2005: The Madden–Julian oscillation in ECHAM4 coupled and uncoupled general circulation models. Climate Dyn., 25, 117140, https://doi.org/10.1007/s00382-005-0026-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sperber, K. R., H. Annamalai, I. S. Kang, A. Kitoh, A. Moise, A. Turner, B. Wang, and T. Zhou, 2013: The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dyn., 41, 27112744, https://doi.org/10.1007/s00382-012-1607-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoner, A. M. K., K. Hayhoe, and D. J. Wuebbles, 2009: Assessing general circulation model simulations of atmospheric teleconnection patterns. J. Climate, 22, 43484372, https://doi.org/10.1175/2009JCLI2577.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sung, H. M., and et al. , 2021: Evaluation of NIMS/KMA CMIP6 model and future climate change scenarios based on new GHGs concentration pathways. Asia Pac. J. Atmos. Sci. https://doi.org/10.1007/s13143-021-00225-6, in press.

    • Crossref
    • Export Citation
  • Sung, M.-K., S.-I. An, B.-M. Kim, and S.-H. Woo, 2014: A physical mechanism of the precipitation dipole in the western United States based on PDO–storm track relationship. Geophys. Res. Lett., 41, 47194726, https://doi.org/10.1002/2014GL060711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szuwalski, C., W. Cheng, R. Foy, A. Hermann, A. Hollowed, K. Holsman, J. Lee, W. Stockhausen, and J. Zheng, 2020: Climate change and the future productivity and distribution of crab in the Bering Sea. ICES J. Mar. Sci., 2020, fsaa140, https://doi.org/10.1093/icesjms/fsaa140.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, https://doi.org/10.1029/2000JD900719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W., and J. M. Wallace, 2001: Regional climate impacts of the Northern Hemisphere annular mode. Science, 293, 8589, https://doi.org/10.1126/science.1058958.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, J., T. Phillips, J. S. Hosking, G. J. Marshall, and A. Orr, 2012: The Amundsen Sea low. Int. J. Climatol., 33, 18181829, https://doi.org/10.1002/joc.3558.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, D. N., and et al. , 2016: A global repository for planet-sized experiments and observations. Bull. Amer. Meteor. Soc., 97, 803816, https://doi.org/10.1175/BAMS-D-15-00132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, P., Z. Wu, and R. Jin, 2021: How can the winter North Atlantic Oscillation influence the early summer precipitation in Northeast Asia: Effect of the Arctic sea ice. Climate Dyn., 56, 19892005, https://doi.org/10.1007/s00382-020-05570-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Benchmarking Performance Changes in the Simulation of Extratropical Modes of Variability across CMIP Generations

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  • 1 a Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California
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Abstract

We evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the observed northern annular mode (NAM), North Atlantic Oscillation (NAO), Pacific–North America pattern (PNA), southern annular mode (SAM), Pacific decadal oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a common basis function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and high-top vertical coordinate); however, the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the postdominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious postdominant season amplitude errors found in many models can be addressed in an atmospheric-only framework, making it more tractable to address in the model development process.

Kenneth R. Sperber: Retired.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jiwoo Lee, lee1043@llnl.gov

Abstract

We evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the observed northern annular mode (NAM), North Atlantic Oscillation (NAO), Pacific–North America pattern (PNA), southern annular mode (SAM), Pacific decadal oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a common basis function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and high-top vertical coordinate); however, the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the postdominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious postdominant season amplitude errors found in many models can be addressed in an atmospheric-only framework, making it more tractable to address in the model development process.

Kenneth R. Sperber: Retired.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jiwoo Lee, lee1043@llnl.gov

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