• Allen, R. J., and C. S. Zender, 2011: Forcing of the Arctic Oscillation by Eurasian snow cover. J. Climate, 24, 65286539, https://doi.org/10.1175/2011JCLI4157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, E. A., 2013: Revisiting the evidence linking Arctic amplification to extreme weather in midlatitudes. Geophys. Res. Lett., 40, 47344739, https://doi.org/10.1002/grl.50880.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baxter, I., and Coauthors, 2019: How tropical Pacific surface cooling contributed to accelerated sea ice melt from 2007 to 2012 as ice is thinned by anthropogenic forcing. J. Climate, 32, 85838602, https://doi.org/10.1175/JCLI-D-18-0783.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chekroun, M. D., E. Simonnet, and M. Ghil, 2011: Stochastic climate dynamics: Random attractors and time-dependent invariant measures. Physica D, 240, 16851700, https://doi.org/10.1016/j.physd.2011.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, W., X. Lan, L. Wang, and Y. Ma, 2013: The combined effects of the ENSO and the Arctic Oscillation on the winter climate anomalies in East Asia. Chin. Sci. Bull., 58, 13551362, https://doi.org/10.1007/s11434-012-5654-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J., J. C. Furtado, M. A. Barlow V. A. Alexeev, and J. E. Cherry, 2012: Arctic warming, increasing snow cover and widespread boreal winter cooling. Environ. Res. Lett., 7, 014007, https://doi.org/10.1088/1748-9326/7/1/014007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J., and Coauthors, 2014: Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci., 7, 627637, https://doi.org/10.1038/ngeo2234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, P., and B. Tan, 2017: The nature of the Arctic Oscillation and diversity of the extreme surface weather anomalies it generates. J. Climate, 30, 55635584, https://doi.org/10.1175/JCLI-D-16-0467.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., 2000: On the teleconnectivity of the “Arctic Oscillation.” Geophys. Res. Lett., 27, 779782, https://doi.org/10.1029/1999GL010945.

  • Deser, C., R. Knutti, S. Solomon, and A. S. Phillips, 2012a: Communication of the role of natural variability in future North American climate. Nat. Climate Change, 2, 775779, https://doi.org/10.1038/nclimate1562.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012b: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527546, https://doi.org/10.1007/s00382-010-0977-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, M. A. Alexander, and B. V. Smoliak, 2014: Projecting North American climate over the next 50 years: Uncertainty due to internal variability. J. Climate, 27, 22712296, https://doi.org/10.1175/JCLI-D-13-00451.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, Q., and Coauthors, 2017: Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Climate Change, 7, 289295, https://doi.org/10.1038/nclimate3241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drótos, G., T. Bódai, and T. Tél, 2015: Probabilistic concepts in a changing climate: A snapshot attractor picture. J. Climate, 28, 32753288, https://doi.org/10.1175/JCLI-D-14-00459.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drótos, G., T. Bódai, and T. Tél, 2016: Quantifying nonergodicity in nonautonomous dissipative dynamical systems: An application to climate change. Phys. Rev. E, 94, 022214, https://doi.org/10.1103/PhysRevE.94.022214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drótos, G., T. Bódai, and T. Tél, 2017: On the importance of the convergence to climate attractors. Eur. Phys. J. Spec. Top., 226, 20312038, https://doi.org/10.1140/epjst/e2017-70045-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Francis, J. A., and S. J. Vavrus, 2012: Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys. Res. Lett., 39, L06801, https://doi.org/10.1029/2012GL051000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Francis, J. A., and N. Skific, 2015: Evidence linking rapid Arctic warming to mid-latitude weather patterns. Philos. Trans. Roy. Soc. London, A373, 20140170, https://doi.org/10.1098/rsta.2014.0170.

    • Search Google Scholar
    • Export Citation
  • Fyfe, J. C., G. J. Boer, and G. M. Flato, 1999: The Arctic and Antarctic oscillations and their projected changes under global warming. Geophys. Res. Lett., 26, 16011604, https://doi.org/10.1029/1999GL900317.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghil, M., M. D. Chekroun, and E. Simonnet, 2008: Climate dynamics and fluid mechanics: Natural variability and related uncertainties. Physica D, 237, 21112126, https://doi.org/10.1016/j.physd.2008.03.036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgetta, M. A., and Coauthors, 2013: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst., 5, 572597, https://doi.org/10.1002/JAME.20038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gong, G., D. Entekhabi, and J. Cohen, 2002: A large-ensemble model study of the wintertime AO–NAO and the role of interannual snow perturbations. J. Climate, 15, 34883499, https://doi.org/10.1175/1520-0442(2002)015<3488:ALEMSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gong, H., L. Wang, W. Chen, X. Chen, and D. Nath, 2017: Biases of the wintertime Arctic Oscillation in CMIP5 models. Environ. Res. Lett., 12, 014001, https://doi.org/10.1088/1748-9326/12/1/014001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gong, H., L. Wang, W. Chen, and D. Nath, 2018: Multidecadal fluctuation of the wintertime Arctic Oscillation pattern and its implication. J. Climate, 31, 55955608, https://doi.org/10.1175/JCLI-D-17-0530.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henderson, G. R., Y. Peings, J. C. Furtado, and P. J. Kushner, 2018: Snow-atmosphere coupling in the Northern Hemisphere. Nat. Climate Change, 8, 954963, https://doi.org/10.1038/s41558-018-0295-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herein, M., J. Márfy, G. Drótos, and T. Tél, 2016: Probabilistic concepts in intermediate-complexity climate models: A snapshot attractor picture. J. Climate, 29, 259272, https://doi.org/10.1175/JCLI-D-15-0353.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herein, M., G. Drótos, T. Haszpra, J. Márfy, and T. Tél, 2017: The theory of parallel climate realizations as a new framework for teleconnection analysis. Sci. Rep., 7, 44529, https://doi.org/10.1038/srep44529.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation. Science, 269, 676679, https://doi.org/10.1126/science.269.5224.676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kutzbach, J. E., 1970: Large-scale features of monthly mean Northern Hemisphere anomaly maps of sea-level pressure. Mon. Wea. Rev., 98, 708716, https://doi.org/10.1175/1520-0493(1970)098<0708:LSFOMM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Labe, Z., Y. Peings, and G. Magnusdottir, 2018: Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss. Geophys. Res. Lett., 45, 56355642, https://doi.org/10.1029/2018GL078158.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamarque, J. F., and Coauthors, 2010: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmos. Chem. Phys., 10, 70177039, https://doi.org/10.5194/acp-10-7017-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leith, C. E., 1978: Predictability of climate. Nature, 276, 352355, https://doi.org/10.1038/276352a0.

  • L’Heureux, M. L., M. K. Tippett, A. Kumar, A. H. Butler, L. M. Ciasto, Q. Ding, K. J. Harnos, and N. C. Johnson, 2017: Strong relations between ENSO and the Arctic Oscillation in the North American multimodel ensemble. Geophys. Res. Lett., 44, 11 65411 662, https://doi.org/10.1002/2017GL074854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lucarini, V., F. Ragone, and F. Lunkeit, 2017: Predicting climate change using response theory: Global averages and spatial patterns. J. Stat. Phys., 166, 10361064, https://doi.org/10.1007/s10955-016-1506-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maher, N., and Coauthors, 2019: The Max Planck Institute Grand ensemble: Enabling the exploration of climate system variability. J. Adv. Model. Earth Syst., 11, 20502069, https://doi.org/10.1029/2019MS001639.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milinski, S., N. Maher, and D. Olonscheck, 2019: How large does a large ensemble need to be? Earth Syst. Dyn. Discuss., https://doi.org/10.5194/ESD-2019-70.

    • 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
  • Overland, J. E., and M. J. Adams, 2001: On the temporal character and regionality of the Arctic Oscillation. Geophys. Res. Lett., 28, 28112814, https://doi.org/10.1029/2000GL011739.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romeiras, F. J., C. Grebogi, and E. Ott, 1990: Multifractal properties of snapshot attractors of random maps. Phys. Rev. A, 41, 784799, https://doi.org/10.1103/PhysRevA.41.784.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., 2017: The missing northern European winter cooling response to Arctic sea ice loss. Nat. Commun., 8, 14603, https://doi.org/10.1038/ncomms14603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2013: Exploring links between Arctic amplification and mid-latitude weather. Geophys. Res. Lett., 40, 959964, https://doi.org/10.1002/grl.50174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and J. A. Francis, 2006: The Arctic amplification debate. Climatic Change, 76, 241264, https://doi.org/10.1007/s10584-005-9017-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, K. L., P. J. Kushner, and J. Cohen, 2011: The role of linear interference in northern annular mode variability associated with Eurasian snow cover extent. J. Climate, 24, 61856202, https://doi.org/10.1175/JCLI-D-11-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2013: Atmospheric component of the MPI-M Earth system model: ECHAM6. J. Adv. Model. Earth Syst., 5, 146172, https://doi.org/10.1002/JAME.20015.

    • 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
  • Tél, T., T. Bódai, G. Drótos, T. Haszpra, M. Herein, B. Kaszás, and M. Vincze, 2020: The theory of parallel climate realizations—A new framework of ensemble methods in a changing climate: An overview. J. Stat. Phys., https://doi.org/10.1007/s10955-019-02445-7, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 12971300, https://doi.org/10.1029/98GL00950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W., and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 10001016, https://doi.org/10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Vuuren, D. P., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 531, https://doi.org/10.1007/s10584-011-0148-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vaughan, D. G., and Coauthors, 2013: Observations: Cryosphere. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 317–382.

  • Vincze, M. I. D. Borcia, and U. Harlander, 2017: Temperature fluctuations in a changing climate: An ensemble-based experimental approach. Sci. Rep., 7, 254, https://doi.org/10.1038/s41598-017-00319-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., and W. Chen, 2010: Downward Arctic Oscillation signal associated with moderate weak stratospheric polar vortex and the cold December 2009. Geophys. Res. Lett., 37, L09707, https://doi.org/10.1029/2010GL042659.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., M. Ting, and P. J. Kushner, 2017: A robust empirical seasonal prediction of winter NAO and surface climate. Sci. Rep., 7, 279, https://doi.org/10.1038/s41598-017-00353-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., A. Deng, and R. Huang, 2019: Wintertime internal climate variability over Eurasia in the CESM large ensemble. Climate Dyn., 52, 67356748, https://doi.org/10.1007/s00382-018-4542-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wettstein, J. J., and L. O. Mearns, 2002: The influence of the North Atlantic–Arctic Oscillation on mean, variance, and extremes of temperature in the northeastern United States and Canada. J. Climate, 15, 35863600, https://doi.org/10.1175/1520-0442(2002)015<3586:TIOTNA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeo, S. R., W. Kim, and K. Y. Kim, 2017: Eurasian snow cover variability in relation to warming trend and Arctic Oscillation. Climate Dyn., 48, 499511, https://doi.org/10.1007/s00382-016-3089-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, B., and H. Lin, 2016: Tropical atmospheric forcing of the wintertime North Atlantic Oscillation. J. Climate, 29, 17551772, https://doi.org/10.1175/JCLI-D-15-0583.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 243 243 85
Full Text Views 36 36 22
PDF Downloads 33 33 18

On the Time Evolution of the Arctic Oscillation and Related Wintertime Phenomena under Different Forcing Scenarios in an Ensemble Approach

View More View Less
  • 1 Institute for Theoretical Physics, and MTA–ELTE Theoretical Physics Research Group, Eötvös Loránd University, Budapest, Hungary
  • 2 Department of Meteorology, Eötvös Loránd University, and Institute for Geological and Geochemical Research, Research Center for Astronomy and Earth Sciences, Budapest, Hungary
  • 3 Institute for Theoretical Physics, and MTA–ELTE Theoretical Physics Research Group, Eötvös Loránd University, Budapest, Hungary
© Get Permissions
Restricted access

Abstract

The Arctic Oscillation (AO) and its related wintertime phenomena are investigated under climate change by 2099 in an ensemble approach using the CESM1 Large Ensemble and the MPI-ESM Grand Ensemble with different RCP scenarios. The loading pattern of the AO is defined as the leading mode of the empirical orthogonal function (EOF) analysis of sea level pressure from 20° to 90°N. It is shown that the traditional AO index (AOI) calculation method, using a base period in a single climate realization, brings subjectivity to the investigation of the AO-related phenomena. Therefore, if an ensemble is available, the changes in the AO and its related phenomena should rather be studied by a reconsidered EOF analysis (snapshot EOF) introduced herein. This novel method is based only on the instantaneous fields of the ensemble, and hence it is capable of monitoring the time evolution of the AO’s pattern and amplitude. Furthermore, the instantaneous correlation coefficient r can objectively be calculated between the AOI and, for example, the surface temperature, and thus the time dependence of the strength of these connections can also be revealed. Results emphasize that both the AO and the related surface temperature pattern are nonstationary and their time evolution depends on the forcing. The AO’s amplitude increases and the Pacific center strengthens considerably in each scenario. Additionally, there exist such regions (e.g., northern Europe or western North America) where r shows remarkable change (0.2–0.4) by 2099. This study emphasizes the importance of the snapshot framework when studying changes in the climate system.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-19-0004.s1.

© 2020 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: Tímea Haszpra, hatimi@caesar.elte.hu

Abstract

The Arctic Oscillation (AO) and its related wintertime phenomena are investigated under climate change by 2099 in an ensemble approach using the CESM1 Large Ensemble and the MPI-ESM Grand Ensemble with different RCP scenarios. The loading pattern of the AO is defined as the leading mode of the empirical orthogonal function (EOF) analysis of sea level pressure from 20° to 90°N. It is shown that the traditional AO index (AOI) calculation method, using a base period in a single climate realization, brings subjectivity to the investigation of the AO-related phenomena. Therefore, if an ensemble is available, the changes in the AO and its related phenomena should rather be studied by a reconsidered EOF analysis (snapshot EOF) introduced herein. This novel method is based only on the instantaneous fields of the ensemble, and hence it is capable of monitoring the time evolution of the AO’s pattern and amplitude. Furthermore, the instantaneous correlation coefficient r can objectively be calculated between the AOI and, for example, the surface temperature, and thus the time dependence of the strength of these connections can also be revealed. Results emphasize that both the AO and the related surface temperature pattern are nonstationary and their time evolution depends on the forcing. The AO’s amplitude increases and the Pacific center strengthens considerably in each scenario. Additionally, there exist such regions (e.g., northern Europe or western North America) where r shows remarkable change (0.2–0.4) by 2099. This study emphasizes the importance of the snapshot framework when studying changes in the climate system.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-19-0004.s1.

© 2020 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: Tímea Haszpra, hatimi@caesar.elte.hu

Supplementary Materials

    • Supplemental Materials (ZIP 25.3 MB)
Save