Articles | Volume 30, issue 1
https://doi.org/10.5194/npg-30-63-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-30-63-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe
Clara Deser
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Boulder, CO, USA
Adam S. Phillips
National Center for Atmospheric Research, Boulder, CO, USA
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Cited articles
Andrews, T., Bodas-Salcedo, A., Gregory, J. M., Dong, Y., Armour, K.
C., Paynter, D., Lin, P., Modak, A., Mauritsen, T., Cole, J. N. S.,
Medeiros, B., Benedict, J. J., Douville, H., Roehrig, R., Koshiro, T.,
Kawai, H., Ogura, T., Dufresne, J.-L., Allan, R. P., and Liu, C.: On the
effect of historical SST patterns on radiative feedback, J. Geophys.
Res.-Atmos., 127, e2022JD036675, https://doi.org/10.1029/2022JD036675, 2022.
Barnes, E. A., Hurrell, J. W., and Uphoff, I. E.: Viewing forced climate
patterns through an AI lens, Geophys. Res. Lett., 46, 13389–13398,
https://doi.org/10.1029/2019GL084944, 2019.
Berkeley Earth: Berkeley Earth's Global Temperature Report for 2022, Berkeley Earth [data set], http://berkeleyearth.org/data/, last access: 10 January 2022.
Beusch, L., Gudmundsson, L., and Seneviratne, S. I.: Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land, Earth Syst. Dynam., 11, 139–159, https://doi.org/10.5194/esd-11-139-2020, 2020.
Bódai, T., Drótos, G., Herein, M., Lunkeit, F., and Lucarini, V.: The
Forced Response of the El Niño–Southern Oscillation–Indian Monsoon
Teleconnection in Ensembles of Earth System Models, J. Climate, 33,
2163–2182, https://doi.org/10.1175/JCLI-D-19-0341.1, 2020.
Bódai, T., Lee, J.-Y., and Sundaresan, A.: Sources of Nonergodicity
for Teleconnections as Cross-Correlations, Geophys. Res. Lett., 49,
e2021GL096587, https://doi.org/10.1029/2021GL096587, 2022.
Bonfils, C. J. W., Santer, B. D., Fyfe, J. C., Marvel, K., Phillips, T. J.,
and Zimmerman, S. R. H.: Human influence on joint changes in temperature,
rainfall and continental aridity, Nat. Clim. Change, 10, 726–731,
https://doi.org/10.1038/s41558-020-0821-1, 2020.
Capotondi, A., Deser, C., Phillips, A., Okumura, Y., and Larson, S.: ENSO and
Pacific DecadalVvariability in the Community Earth System Model Version 2,
J. Adv. Model. Earth Sy., 12, e2019MS002022,
https://doi.org/10.1029/2019MS002022, 2020.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman,
P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M.,
Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff,
S. D., and Worley, S. J.: The twentieth century reanalysis project, Q. J. Roy.
Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011.
Danabasoglu, G., Deser, C., Rodgers, K., and Timmermann, A.: CESM2 Large Ensemble, Climate Data Gateway at NCAR [data set], https://doi.org/10.26024/kgmp-c556, 2020.
Davenport, F. V. and Diffenbaugh, N. S.: Using machine learning to analyze
physical causes of climate change: A case study of U.S. Midwest extreme
precipitation, Geophys. Res. Lett., 48,
e2021GL093787, https://doi.org/10.1029/2021GL093787, 2021.
Deser, C.: Certain uncertainty: The role of internal climate variability in
projections of regional climate change and risk management, Earths
Future, 8, e2020EF001854, https://doi.org/10.1029/2020EF001854, 2020.
Deser, C. and Phillips, A. S.: Defining the internal component of Atlantic
Multidecadal Variability in a changing climate, Geophys. Res. Lett., 48,
e2021GL095023, https://doi.org/10.1029/2021GL095023, 2021.
Deser, C., Phillips, A., Bourdette, V., and Teng, H. Y.: Uncertainty in
climate change projections: The role of internal variability, Clim. Dynam.,
38, 527–546. https://doi.org/10.1007/s00382-010-0977-x, 2012.
Deser, C., Phillips, A., Alexander, M. A., and Smoliak, B. V.: Projecting
North American climate over the next 50 years: Uncertainty due to internal
variability, J. Climate, 27, 2271–2296,
https://doi.org/10.1175/JCLI-D-13-00451.1, 2014.
Deser, C., Terray, L., and Phillips, A. S.: Forced and internal components
of winter air temperature trends over North America during the past 50
years: Mechanisms and implications, J. Climate, 29, 2237–2258,
https://doi.org/10.1175/JCLI-D-15-0304.1, 2016.
Deser, C., Hurrell, J. W., and Phillips, A. S.: The role of the North
Atlantic Oscillation in European Climate Projections, Clim. Dynam., 49,
3141–3157, https://doi.org/10.1007/s00382-016-3502-z, 2017a.
Deser, C., Simpson, I. R., McKinnon K. A., and Phillips, A. S.: The Northern
Hemisphere extra-tropical atmospheric circulation response to ENSO: How well
do we know it and how do we evaluate models accordingly?, J. Climate, 30,
5059–5082, https://doi.org/10.1175/JCLI-D-16-0844.1, 2017b.
Deser, C., Simpson, I. R., Phillips, A. S., and McKinnon, K. A.: How well do
we know ENSO's climate impacts over North America, and how do we evaluate
models accordingly?, J. Climate, 30, 4991–5014,
https://doi.org/10.1175/JCLI-D-17-0783.1, 2018.
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio,
P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E.,
Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S.,
Randerson, J., Screen, J. A, Simpson, I. R., and Ting, M.: Insights from
earth system model initial-condition large ensembles and future
prospects, Nat. Clim. Change, 10, 277–286,
https://doi.org/10.1038/s41558-020-0731-2, 2020a.
Deser, C., Phillips, A. S., Simpson, I. R., Rosenbloom, N., Coleman, D.,
Lehner, F., Pendergrass, A., DiNezio, P., and Stevenson, S.: Isolating the
Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases: A New
CESM1 Large Ensemble Community Resource, J. Climate, 33, 7835–7858,
https://doi.org/10.1175/JCLI-D-20-0123.1, 2020b.
Deutscher Wetterdienst: Global Precipitation Climatology Centre (GPCC) precipitation, Deutscher Wetterdienst [data set], https://www.dwd.de/EN/ourservices/gpcc/gpcc.html, last access: 10 January 2022.
DiNezio, P. N., Deser, C., Okumura, Y., and Karspeck, A.: Predictability of
2-year La Niña events in a coupled general circulation model, Clim. Dynam.,
49, 4237–4261, 2017.
Dong, Y., Armour, K. C., Zelinka, M., Proistosescu, C., Battisti, D., Zhou,
C., and Andrews, T.: Inter-model spread in the pattern effect and its
contribution to climate sensitivity in CMIP5 and CMIP6 models, J.
Climate, 33, 7755–7775, https://doi.org/10.1175/JCLI-D-19-1011.1, 2020.
Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., Hermanson, L.,
and Robinson, N.: Do seasonal-to-decadal climate predictions underestimate
the predictability of the real world?, Geophys. Res. Lett., 41, 5620–5628,
https://doi.org/10.1002/2014GL061146, 2014.
ECMWF: ECMWF Reanalysis v5 (ERA5), ECMWF [data set], https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5, last access: 10 January 2022.
Fasullo, J. T. and Nerem, R. S.: Altimeter-era emergence of the patterns of
forced sea-level rise in climate models and implications for the
future, P. Natl. Acad. Sci. USA, 115, 12944–12949, https://doi.org/10.1073/pnas.1813233115, 2018.
Fasullo, J., Phillips, A. S., and Deser, C.: Evaluation of leading modes of
climate variability in the CMIP Archives, J. Climate, 33, 5527–5545,
https://doi.org/10.1175/JCLI-D-19-1024.1, 2020.
Gordon, E. M. and Barnes, E. A.: Incorporating uncertainty into a regression
neural network enables identification of decadal state-dependent
predictability, Geophys. Res. Lett., 49,
e2022GL098635, https://doi.org/10.1029/2022GL098635, 2022.
Gould, S. J.: Wonderful Life: The burgess shale and the nature of history,
W. W. Norton & Co., ISBN 978-0-393-30700-9, 1989.
Griffies, S. M. and Bryan, K.: Predictability of North Atlantic multidecadal
climate variability, Science, 275, 181–184,
https://doi.org/10.1126/science.275.5297.181, 1997.
Guo, R. X., Deser, C., Terray, L., and Lehner, F.: Human influence on
terrestrial precipitation trends revealed by dynamical adjustment, Geophys.
Res. Lett., 46, 3426–3434, https://doi.org/10.1029/2018GL081316, 2019.
Hegerl, G. C., Zwiers, F. W., Braconnot, P., Gillett, N. P., Luo, Y., Marengo Orsini, J. A., Nicholls, N., Penner, J. E., and Stott, P. A.: Understanding and Attributing Climate Change, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-chapter9-1.pdf (last access: 23 March 2022), 2007.
Hurrell J. W., Kushnir, Y., Ottersen G., and Visbeck M. (Eds.): The North
Atlantic Oscillation: climate significance and environmental impact,
Geophys. Monogr. Ser, 134, AGU, Washington, D.C., 2003.
James, I. N. and James, P. M.: Spatial structure of ultra-low-frequency
variability of the flow in a simple atmospheric circulation model, Q. J.
Roy. Meteor. Soc., 118, 1211–1233, https://doi.org/10.1002/qj.49711850810,
1992.
Jin, E. K., Kinter, J. L., and Wang, B.: Current status of ENSO prediction
skill in coupled ocean–atmosphere models, Clim. Dynam., 31, 647–664,
https://doi.org/10.1007/s00382-008-0397-3, 2008.
Kay, J., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J. -F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) Large Ensemble Project: A community
resource for studying climate change in the presence of internal climate
variability, B. Am. Meteorol. Soc., 96, 1333–1349,
https://doi.org/10.1175/BAMS-D-13-00255.1, 2015.
Klavans, J. M., Cane, M. A., Clement, A. C., and Murphy, L. N.: NAO
predictability from external forcing in the late 20th century, Npj Clim.
Atmos. Sci., 4, 22, https://doi.org/10.1038/s41612-021-00177-8, 2021.
Lehner, F., Schurer, A. P., Hegerl, G. C., Deser, C., and Frölicher, T.
L.: The importance of ENSO phase during volcanic eruptions for detection and
attribution, Geophys. Res. Lett. 43, 2851–2858,
https://doi.org/10.1002/2016GL067935, 2016.
Lehner, F., Deser, C., and Terray, L.: Towards a new estimate of “time of
emergence” of anthropogenic warming: insights from dynamical adjustment and
a large initial-condition model ensemble, J. Climate, 30, 7739–7756,
https://doi.org/10.1175/JCLI-D-16-0792.1, 2017.
Lehner, F., Deser, C., Simpson, I. R., and Terray, L.: Attributing the US
Southwest's recent shift into drier conditions, Geophys. Res. Lett., 45,
6251–6261, https://doi.org/10.1029/2018GL078312, 2018.
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020.
Leith, C. E.: The standard error of time-average estimates of climatic
means, J. Appl. Meteorol. Clim., 12, 1066–1069,
https://doi.org/10.1175/1520-0450(1973)012<1066:TSEOTA>2.0.CO;2,
1973.
Lorenz, E. N.: Deterministic nonperiodic flow, J. Atmos. Sci., 20, 130–141,
https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2, 1963.
Madden, R. A.: Estimates of the natural variability of time-averaged
sea-level pressure, Mon. Weather Rev., 104, 942–952,
https://doi.org/10.1175/1520-0493(1976)104<0942:EOTNVO>2.0.CO;2,
1975.
Maher, N., Matei, D., Milinski, S., and Marotzke, J.: ENSO change in climate
projections: Forced response or internal variability?, Geophys. Res. Lett.,
45, 11390–11398, https://doi.org/10.1029/2018GL079764, 2018.
Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M., Dobrynin, M.,
Kornblueh, L., Kröger, J., Takano, Y., Ghosh, R., Hedemann, C., Li, C.,
Li, H., Manzini, E., Notz, D. Putrasahan, D., Boysen, L., Claussen, M.,
Ilyina, T., Olonscheck, D., Raddatz, T., Stevens, B., and Marotzke, J.: The
Max Planck Institute Grand Ensemble: Enabling the exploration of climate
system variability, J. Adv. Model. Earth Sy., 11, 2050–2069,
https://doi.org/10.1029/2019MS001639, 2019.
McGraw, M. C., Barnes, E. A., and Deser, C.: Reconciling the observed and
modeled southern hemisphere circulation response to volcanic
eruptions, Geophys. Res. Lett., 43, 7259–7266,
https://doi.org/10.1002/2016GL069835, 2016.
McKenna, C. M. and Maycock, A. C.: Sources of uncertainty in multimodel
large ensemble projections of the winter North Atlantic
Oscillation, Geophys. Res. Lett., 48,
e2021GL093258, https://doi.org/10.1029/2021GL093258, 2021.
McKinnon, K.: Observational Large Ensemble, GitHub [code], https://github.com/karenamckinnon/observational_large_ensemble, last access: 21 January 2022.
McKinnon, K.: karenamckinnon/observational_large_ensemble: v1 (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.7636551, 2023.
McKinnon, K. A., Poppick, A., Dunn-Sigouin, E., and Deser, C.: An “Observational Large Ensemble” to compare observed and modeled temperature trend uncertainty due to internal variability, J. Climate, 90, 7585–7598, https://doi.org/10.1175/JCLI-D-16-0905.1, 2017.
McKinnon, K. A. and Deser, C.: Internal variability and regional climate
trends in an Observational Large Ensemble, J. Climate, 31, 6783–6802,
https://doi.org/10.1175/JCLI-D-17-0901.1, 2018.
McKinnon, K. A. and Deser, C.: The inherent uncertainty of precipitation
variability, trends, and extremes due to internal variability, with
implications for Western US water resources, J. Climate, 34, 9605–9622,
https://doi.org/10.1175/JCLI-D-21-0251.1, 2021.
Meehl, G., Hu, A., and Teng, H: Initialized decadal prediction for transition
to positive phase of the Interdecadal Pacific Oscillation, Nat. Commun., 7,
11718, https://doi.org/10.1038/ncomms11718, 2016.
Merrifield, A., Lehner, F., Xie, S.-P., and Deser, C.: Removing circulation
effects to assess Central US land-atmosphere interactions in the CESM Large
Ensemble, Geophys. Res. Lett., 44, 9938–9946,
https://doi.org/10.1002/2017GL074831, 2017.
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.
Newman, M.: Interannual to decadal predictability of tropical and North
Pacific sea surface temperatures, J. Climate, 20, 2333–2356,
https://doi.org/10.1175/JCLI4165.1, 2007.
Newman, M., Alexander, M. A., Ault, T. R., Cobb, K. M., Deser, C., Di
Lorenzo, E., Mantua, N. J., Miller, A. J., Minobe, S., Nakamura, H.,
Schneider, N., Vimont, D. J., Phillips, A. S., Scott, J. D., and Smith, C.
A.: The Pacific decadal oscillation, revisited, J. Climate, 29, 4399–4427,
https://doi.org/10.1175/JCLI-D-15-0508.1, 2016.
O'Brien, J. P. and Deser, C.: Quantifying and understanding forced changes
to unforced modes of atmospheric circulation variability over the North
Pacific in a coupled model large ensemble, J. Climate, 36, 17–35, https://doi.org/10.1175/JCLI-D-22-0101.1, 2023.
Olivarez, H. C., Lovenduski, N. S., Brady, R. X., Fay, A. R., Gehlen,
M., Gregor, L., Landschützer, P., McKinley, G. A., McKinnon, K. A., and
Munro, D. R.: Alternate histories: Synthetic large ensembles of sea-air
CO2 flux, Global Biogeochem. Cy., 36,
e2021GB007174, https://doi.org/10.1029/2021GB007174, 2022.
Persad, G. G. and Caldeira, K.: Divergent global-scale temperature effects
from identical aerosols emitted in different regions, Nat. Commun., 9, 3289,
https://doi.org/10.1038/s41467-018-05838-6, 2018.
Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J.-E., Simpson, I. R., Stein, K., Stuecker, M. F., Yamaguchi, R., Bódai, T., Chung, E.-S., Huang, L., Kim, W. M., Lamarque, J.-F., Lombardozzi, D. L., Wieder, W. R., and Yeager, S. G.: Ubiquity of human-induced changes in climate variability, Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, 2021.
Rohde, R., Muller, R., Jacobsen, R., Perlmutter, S., Rosenfeld, A., Wurtele,
J., Curry, J., Wickham, C., and Mosher, S.: Berkeley Earth temperature
averaging process, Geoinf. Geostat. Overview, 1, 2,
https://doi.org/10.4172/2327-4581.1000103, 2013.
Santer, B., Fyfe, J. C., Solomon, S., Painter, J. F., Bonfils, C., Pallotta,
G., and Zelinka, M. D.: Quantifying stochastic uncertainty in detection time
of human-caused climate signals, P. Natl. Acad. Sci. USA, 116, 19821–19827,
https://doi.org/10.1073/pnas.1904586116, 2019.
Scaife, A. A. and Smith, D.: A signal-to-noise paradox in climate
science, Npj Clim. Atmos. Sci., 1, 28,
https://doi.org/10.1038/s41612-018-0038-4, 2018.
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T.,
Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., Hermanson,
L., Knight, J. R., Lea, D. J., MacLachlan, C., Maidens, A., Martin, M.,
Peterson, A. K., Smith, D., Vellinga, M., Wallace, E., Waters, J., and
Williams, A.: Skillful long-range prediction of European and North American
winters, Geophys. Res. Lett., 41, 2514–2519,
https://doi.org/10.1002/2014GL059637, 2014.
Schneider, D. P., Deser, C., and Fan, T.: Comparing the impacts of tropical
SST variability and polar stratospheric ozone loss on the Southern Ocean
westerly winds, J. Climate, 28, 9350–9372,
https://doi.org/10.1175/JCLI-D-15-0090.1, 2015.
Schneider, U., Fuchs, T., Meyer-Christoffer, A., and Rudolf, B.: GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle, Theor. Appl. Climatol., 115, 15–40, https://doi.org/10.1007/s00704-013-0860-x, 2014.
Shepherd, T.: Atmospheric circulation as a source of uncertainty in climate
change projections, Nat. Geosci., 7, 703–708,
https://doi.org/10.1038/ngeo2253, 2014.
Sippel, S. Meinshausen, N., Merrifield, A., Lehner, F., Pendergrass, A. G.,
Fischer, E., and Knutti, R.: Uncovering the forced climate response from a
single ensemble member using statistical learning, J. Climate, 32,
5677–5699, https://doi.org/10.1175/JCLI-D-18-0882.1, 2019.
Smith, D. M., Scaife, A. A., Eade, R., Athanasiadis, P., Bellucci, A.,
Bethke, I., Bilbao, R., Borchert, L. F., Caron, L. P., Counillon, F.,
Danabasoglu, G., Delworth, T., Doblas-Reyes, F. J., Dunstone, N. J.,
Estella-Perez, V., Flavoni, S., Hermanson, L., Keenlyside, N., Kharin, V.,
Kimoto, M., Merryfield, W. J., Mignot, J., Mochizuki, T., Modali, K.,
Monerie, P. A., Müller, W. A., Nicolí, D., Ortega, P., Pankatz, K.,
Pohlmann, H., Robson, J., Ruggieri, P., Sospedra-Alfonso, R., Swingedouw,
D., Wang, Y., Wild, S., Yeager, S., Yang, X., and Zhang, L.: North Atlantic
climate far more predictable than models imply, Nature, 583, 796–800,
https://doi.org/10.1038/s41586-020-2525-0, 2020.
Smoliak, B. V., Wallace, J. M., Lin, P., and Fu, Q.: Dynamical adjustment of
the Northern Hemisphere surface air temperature field: Methodology and
application to observations, J. Climate, 28, 1613–1629,
https://doi.org/10.1175/JCLI-D-14-00111.1, 2015.
Stevenson, S., Fox-Kemper, B., Jochum, M., Neale, R., Deser, C., and Meehl,
G.: Will there be a significant change to El Nino in the 21st Century?, J.
Climate, 25, 2129–2145, https://doi.org/10.1175/JCLI-D-11-00252.1, 2012.
Strommen, K., Juricke, S., and Cooper, F.: Improved teleconnection between Arctic sea ice and the North Atlantic Oscillation through stochastic process representation, Weather Clim. Dynam., 3, 951–975, https://doi.org/10.5194/wcd-3-951-2022, 2022.
Suarez-Gutierrez, L., Milinski, S., and Maher, N.: Exploiting large
ensembles for a better yet simpler climate model evaluation, Clim.
Dynam., 57, 2557–2580, https://doi.org/10.1007/s00382-021-05821-w,
2021.
Swart, N. C., Fyfe, J. C., Hawkins, E., Kay, J. E., and Jahn A.: Influence
of internal variability on Arctic sea-ice trends, Nat. Clim. Change, 5,
86–89, https://doi.org/10.1038/nclimate2483, 2015.
Tebaldi, C., Dorheim, K., Wehner, M., and Leung, R.: Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates, Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, 2021.
Tél, T., Bódai, T., Drótos, G., Haszpra, T., Herein, M.,
Kaszás, B., and Vincze, M.: The theory of parallel climate realizations,
J. Stat, Phys., 179, 1496–1530, https://doi.org/10.1007/s10955-019-02445-7,
2020.
Teng, H. and Branstator, G.: Initial-value predictability of prominent modes of North Pacific subsurface temperature in a CGCM, Clim. Dynam., 36, 1813–1834, https://doi.org/10.1007/s00382-010-0749-7, 2011.
Terray, L.: A dynamical adjustment perspective on extreme event attribution, Weather Clim. Dynam., 2, 971–989, https://doi.org/10.5194/wcd-2-971-2021, 2021a.
Terray, L.: terrayl/Dynamico: Dynamico version v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.5584777, 2021b.
Thompson, D. W. J., Barnes, E. A., Deser, C., Foust, W. E., and Phillips, A.
S.: Quantifying the role of internal climate variability in future climate
trends, J. Climate, 28, 6443–6456,
https://doi.org/10.1175/JCLI-D-14-00830.1, 2015.
Trenary, L. and DelSole, T.: Does the Atlantic Multidecadal Oscillation Get
Its Predictability from the Atlantic Meridional Overturning Circulation?, J.
Climate, 29, 5267–5280, https://doi.org/10.1175/JCLI-D-16-0030.1, 2016.
Wallace, J. M., Deser, C., Smoliak, B. V., and Phillips, A. S.: Attribution
of climate change in the presence of internal variability, in: Climate
Change: Multidecadal and Beyond, edited by: Chang, C. P., Ghil, M., Latif, M., and Wallace, J.
M., World Scientific Series on Asia-Pacific Weather and Climate, 6,
1–29, https://doi.org/10.1142/9789814579933_0001, 2013.
Wang, C., Deser, C., Yu, J. -Y., DiNezio, P., and Clement, A.: El Nino and
Southern Oscillation (ENSO): A Review, in: Coral Reefs of the Eastern Pacific, edited by: Glymn,
P., Manzello, D. and Enochs, I., Springer Science Publisher, 4,
85–106, https://doi.org/10.1007/978-94-017-7499-4_4, 2017.
Wills, R. C. J., Battisti, D. S., Armour, K. C., Schneider, T., and Deser,
C.: Pattern recognition methods to separate forced responses from internal
variability in climate model ensembles and observations, J. Climate, 33,
8693–8719, https://doi.org/10.1175/JCLI-D-19-0855.1, 2020.
Wittenberg, A. T.: Are historical records sufficient to constrain ENSO
simulations?, Geophys. Res. Lett., 36, L12702,
https://doi.org/10.1029/2009GL038710, 2009.
Wu, X., Okumura, Y. M., Deser, C., and DiNezio, P. N.: Two-year dynamical
predictions of ENSO event duration during 1954–2015, J. Climate, 34,
4069–4087, https://doi.org/10.1175/JCLI-D-20-0619.1, 2021.
Yeager, S. Danabasoglu, D., Rosenbloom, N. A., Strand, W., Bates, S. C.,
Meehl, G. A., Karspeck, A. R., Lindsay, K., Long, M. C., Teng, H., and
Lovenduski, N. S.: Predicting near-term changes in the Earth System: A large
ensemble of initialized decadal prediction simulations using the Community
Earth System Model, B. Am. Meteorol. Soc., 99, 1867–1886,
https://doi.org/10.1175/BAMS-D-17-0098.1, 2018.
Zhang, R., Sutton, R., Danabasoglu, G., Kwon, Y.-O., Marsh, R., Yeager, S.
G., Amrhein, D. E., and Little, C. M.: A review of the role of the Atlantic
Meridional Overturning Circulation in Atlantic Multidecadal Variability and
associated climate impacts, Rev. Geophys., 57, 316–375,
https://doi.org/10.1029/2019RG000644, 2019.
Executive editor
The paper presents a valuable review of state-of-the-art large ensemble methodology, comparing observational-based analogues and model-generated perturbations with the aim of studying the impact of long-term variability on European past, present and future climate. This work is helpful as a theoretical and methodological benchmark for a number of open issues in ensemble modeling, that are currently the object of intense discussions in the research community. Given that this topic very much relates to the work done within the Coupled Model Intercomparison Project, and ultimately to the redaction of the IPCC Assessment Reports, the paper is of major interest for the research community as well as for the broader public.
The paper presents a valuable review of state-of-the-art large ensemble methodology, comparing...
Short summary
Past and future climate change at regional scales is a result of both human influences and natural (internal) variability. Here, we provide an overview of recent advances in climate modeling and physical understanding that has led to new insights into their respective roles, illustrated with original results for the European climate. Our findings highlight the confounding role of internal variability in attribution, climate model evaluation, and accuracy of future projections.
Past and future climate change at regional scales is a result of both human influences and...