CoChat
Sign Up
Cited by 246Climate variability and modelsMeteorological Phenomena and SimulationsGeology and Paleoclimatology Research

Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package

Open access
Planton, Yann Y., Guilyardi, Eric, Wittenberg, Andrew T., Lee, Jiwoo, Gleckler, Peter J., Bayr, Tobias, McGregor, Shayne, McPhaden, Michael J. +4 more·2021·Bulletin of the American Meteorological Society
DOI 10.1175/bams-d-19-0337.1
Open this paper in CoChat — free accountOpen PDF

Abstract

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1175/bams-d-19-0337.1

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • More CMIP6 models simulate realistic ENSO nonlinear feedbacks.

    “Besides some improvements in the representation of the tropical Pacific climate 20 , more CMIP6 models simulate realistic ENSO nonlinear feedbacks 7 .”

    From Increased ENSO sea surface temperature variability under four IPCC emission scenarios (2022) · cites this paper as [20]

    The Summary reports improved ENSO patterns and other metrics in CMIP6, but it does not establish that more models simulate realistic nonlinear feedbacks. The Evaluation of ENSO processes says none of its five feedback-process metrics improved significantly from CMIP5 to CMIP6, and one degraded; it also notes that numerous nonlinear processes were not included in that short metric list. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 81

  1. Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeor., 4, 1147-1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP >2.0.CO;2. · DOI
  2. Ahn, M.-S., D. Kim, K. R. Sperber, I.-S. Kang, E. Maloney, D. Waliser, and H. Hendon, 2017: MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis. Climate Dyn., 49, 4023-4045, https://doi.org/10. 1007/s00382-017-3558-4. · DOI
  3. An, S.-I., E. Tziperman, Y. Okumura, and T. Li, 2020: Irregularity and asymmetry. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 252, Amer. Geophys. Union, 153-172, https://doi.org/10.1002/9781119548164.ch7. · DOI
  4. Annamalai, H., 2020: ENSO Precipitation Anomalies along the Equatorial Pacific: Moist Static Energy Framework Diagnostics. J. Climate, 33, 9103-9127, https://doi.org/10.1175/JCLI-D-19-0374. · DOI
  5. -, P. Liu, and S.-P. Xie, 2005: Southwest Indian Ocean SST variability: Its local effect and remote influence on Asian monsoons. J. Climate, 18, 4150-4167, https://doi.org/10.1175/JCLI3533. · DOI
  6. Atwood, A. R., D. S. Battisti, A. T. Wittenberg, W. H. G. Roberts, and D. J. Vimont, 2017: Characterizing unforced multi-decadal variability of ENSO: A case study with the GFDL CM2.1 coupled GCM. Climate Dyn., 49, 2845-2862, https:// doi.org/10.1007/s00382-016-3477-9. · DOI
  7. Batehup, R., S. McGregor, and A. J. E. Gallant, 2015: The influence of non-sta- tionary teleconnections on paleoclimate reconstructions of ENSO variance using a pseudoproxy framework. Climate Past, 11, 1733-1749, https://doi. org/10.5194/cp-11-1733-2015. · DOI
  8. Bayr, T., M. Latif, D. Dommenget, C. Wengel, J. Harlaß, and W. Park, 2018: Mean-state dependence of ENSO atmospheric feedbacks in climate models. Climate Dyn., 50, 3171-3194, https://doi.org/10.1007/s00382-017-3799-2. · DOI
  9. -, C. Wengel, M. Latif, D. Dommenget, J. Lübbecke, and W. Park, 2019a: Error compensation of ENSO atmospheric feedbacks in climate models and its in- fluence on simulated ENSO dynamics. Climate Dyn., 53, 155-172, https://doi. org/10.1007/s00382-018-4575-7. · DOI
  10. -, D. I. V. Domeisen, and C. Wengel, 2019b: The effect of the equatorial Pacific cold SST bias on simulated ENSO teleconnections to the North Pacific and California. Climate Dyn., 53, 3771-3789, https://doi.org/10.1007/s00382- 019-04746-9. · DOI
  11. Bellenger, H., E. Guilyardi, J. Leloup, M. Lengaigne, and J. Vialard, 2014: ENSO representation in climate models: From CMIP3 to CMIP5. Climate Dyn., 42, 1999-2018, https://doi.org/10.1007/s00382-013-1783-z. · DOI
  12. Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163-172, https://doi.org/10.1175/1520-0493(1969)097 <0163:ATFTEP>2.3.CO;2. · DOI
  13. Boucher, O., and Coauthors, 2020: Presentation and evaluation of the IPSL-CM6A- LR climate model. J. Adv. Model. Earth Sys., 12, e2019MS002010, https://doi. org/10.1029/2019MS002010. · DOI
  14. Brown, J. R., and Coauthors, 2020: Comparison of past and future simulations of ENSO in CMIP5/PMIP3 and CMIP6/PMIP4 models. Climate Past, 16, 1777- 1805, https://doi.org/10.5194/cp-16-1777-2020. · DOI
  15. Cai, W., and Coauthors, 2014: Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Climate Change, 4, 111-116, https://doi. org/10.1038/nclimate2100. · DOI · Paper page
  16. Capotondi, A., and Coauthors, 2015: Understanding ENSO diversity. Bull. Amer. Meteor. Soc., 96, 921-938, https://doi.org/10.1175/BAMS-D-13-00117. · DOI
  17. -, A. T. Wittenberg, J.-S. Kug, K. Takahashi, and M. McPhaden, 2020: ENSO diversity. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 252, Amer. Geophys. Union, 65-86, https://doi. org/10.1002/9781119548164.ch4. · DOI
  18. Carré, M., J. P. Sachs, S. Purca, A. J. Schauer, P. Braconnot, R. A. Falcón, M. Julien, and D. Lavallée, 2014: Holocene history of ENSO variance and asymmetry in the eastern tropical Pacific. Science, 345, 1045-1048, https://doi.org/10.1126/ science.1252220. · DOI
  19. Carton, J. A., G. A. Chepurin, and L. Chen, 2018: SODA3: A new ocean cli- mate reanalysis. J. Climate, 31, 6967-6983, https://doi.org/10.1175/JCLI- D-18-0149. · DOI
  20. Chen, C., M. A. Cane, A. T. Wittenberg, and D. Chen, 2017: ENSO in the CMIP5 simulations: Life cycles, diversity, and responses to climate change. J. Climate, 30, 775-801, https://doi.org/10.1175/JCLI-D-15-0901. · DOI
  21. Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis project. Quart. J. Roy. Meteor. Soc., 137, 1-28, https://doi.org/10.1002/qj.776. · DOI · Paper page
  22. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553-597, https://doi.org/10.1002/qj.828. · DOI · Paper page
  23. Deser, C., I. R. Simpson, K. A. McKinnon, and A. S. Phillips, 2017: 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. · DOI
  24. -, -, A. S. Phillips, and K. A. McKinnon, 2018: How well do we know ENSO's climate impacts over North America, and how do we evaluate mod- els accordingly? J. Climate, 31, 4991-5014, https://doi.org/10.1175/JCLI- D-17-0783. · DOI
  25. -, and Coauthors, 2020: Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Climate Change, 10, 277-286, https://doi.org/10.1038/s41558-020-0731-2. · DOI
  26. Diaz, H. F., M. P. Hoerling, and J. K. Eischeid, 2001: ENSO variability, telecon- nections and climate change. Int. J. Climatol., 21, 1845-1862, https://doi. org/10.1002/joc.631. · DOI
  27. Ding, H., M. Newman, M. A. Alexander, A. T. Wittenberg, 2020: Relating CMIP5 model biases to seasonal forecast skill in the tropical Pacific. Geophys. Res. Lett., 47, e2019GL086765, https://doi.org/10.1029/2019GL086765. · DOI
  28. -, and Coauthors, 2016b: Towards improved and more routine Earth sys- tem model evaluation in CMIP. Earth Syst. Dyn., 7, 813-830, https://doi. org/10.5194/esd-7-813-2016. · DOI
  29. -A community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP. Geosci. Model Dev., 9, 1747-1802, https://doi.org/10.5194/gmd-9- 1747-2016. · DOI
  30. Fedorov, A., S. Hu, A. T. Wittenberg, A. Levine, and C. Deser, 2020: ENSO low-fre- quency modulations and mean state interactions. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 252, Amer. Geophys. Union, 173-198, https://doi.org/10.1002/9781119548164.ch8. · DOI
  31. Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741-866, https://doi.org/10.1017/CBO9781107415324.020. · DOI
  32. 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. · DOI · Paper page
  33. -, 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://doi.org/10.1029/2016EO051663. · DOI
  34. Graham, F. S., A. T. Wittenberg, J. N. Brown, S. J. Marsland, and N. J. Holbrook, 2017: Understanding the double peaked El Niño in coupled GCMs. Climate Dyn., 48, 2045-2063, https://doi.org/10.1007/s00382-016-3189-1. · DOI
  35. Guilyardi, E., 2006: El Niño-mean state-seasonal cycle interactions in a multi- model ensemble. Climate Dyn., 26, 329-348, https://doi.org/10.1007/s00382- 005-0084-6. · DOI
  36. -, A. T. Wittenberg, M. Balmesada, W. Cai, M. Collins, M. J. McPhaden, M. Watanabe, and S.-W. Yeh, 2016: Fourth CLIVAR workshop on the evalu- ation of ENSO processes in climate models: ENSO in a changing climate. Bull. Amer. Meteor. Soc., 96, 921-938, https://doi.org/10.1175/BAMS- D-15-00287. · DOI
  37. -, A. Capotondi, M. Lengaigne, S. Thual, and A. T. Wittenberg, 2020: ENSO modelling: History, progress and challenges. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 252, Amer. Geophys. Union, 199- 226, https://doi.org/10.1002/9781119548164.ch9. · DOI
  38. Hoerling, M. P., A. Kumar, and M. Zhong, 1997: El Niño, La Niña, and the non- linearity of their teleconnections. J. Climate, 10, 1769-1786, https://doi. org/10.1175/1520-0442(1997)010<1769:ENOLNA>2.0.CO;2. · DOI
  39. Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temper- ature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 8179-8205, https://doi.org/10.1175/JCLI-D-16-0836. · DOI
  40. Im, S.-H., S.-I. An, S. T. Kim, and F.-F. Jin, 2015: Feedback processes responsible for El Niño-La Niña amplitude asymmetry. Geophys. Res. Lett., 42, 5556-5563, https://doi.org/10.1002/2015GL064853. · DOI
  41. Izumo, T., J. Vialard, M. Lengaigne, I. Suresh, 2020: Relevance of relative sea sur- face temperature for tropical rainfall interannual variability. Geophys. Res. Lett., 47, e2019GL086182, https://doi.org/10.1029/2019GL086182. · DOI
  42. Jin, F.-F., 1997: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54, 811-829, https://doi.org/10.1175/1520- 0469(1997)054<0811:AEORPF>2.0.CO;2. · DOI
  43. -, S. T. Kim, and L. Bejarano, 2006: A coupled-stability index for ENSO. Geophys. Res. Lett., 33, L23708, https://doi.org/10.1029/2006GL027221. · DOI
  44. Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631-1643, https://doi.org/10.1175/BAMS-83-11-1631. · DOI · Paper page
  45. Klein, S. A., B. J. Soden, and N. Lau, 1999: Remote sea surface temperature vari- ations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917-932, https://doi.org/10.1175/1520-0442(1999)012<0917:RSSTVD >2.0.CO;2. E217 · DOI
  46. Lee, J., K. R. Sperber, P. J. Gleckler, C. J. W. Bonfils, and K. E. Taylor, 2019: Quantify- ing the agreement between observed and simulated extratropical modes of interannual variability. Climate Dyn., 52, 4057-4089, https://doi.org/10.1007/ s00382-018-4355-4. · DOI
  47. Lee, S.-K., P. N. DiNezio, E.-S. Chung, S.-W. Yeh, A. T. Wittenberg, and C. Wang, 2014: Spring persistence, transition and resurgence of El Niño. Geophys. Res. Lett., 41, 8578-8585, https://doi.org/10.1002/2014GL062484. · DOI
  48. Li, J., and Coauthors, 2013: El Niño modulations over the past seven centuries. Nat. Climate Change, 3, 822-826, https://doi.org/10.1038/nclimate1936. · DOI
  49. Liu, Y., and Coauthors, 2017: Recent enhancement of central Pacific El Niño vari- ability relative to last eight centuries. Nat. Commun., 8, 15386, https://doi. org/10.1038/ncomms15386. · DOI
  50. Lloyd, J., E. Guilyardi, and H. Weller, 2012: The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part III: The shortwave flux feedback. J. Climate, 25, 4275-4293, https://doi.org/10.1175/JCLI-D-11-00178. · DOI
  51. Maloney, E. D., and Coauthors, 2019: Process-oriented evaluation of climate and weather forecasting models. Bull. Amer. Meteor. Soc., 100, 1665-1686, https:// doi.org/10.1175/BAMS-D-18-0042. · DOI
  52. McGregor, S., A. Timmermann, M. H. England, O. Elison Timm, and A. T. Wittenberg, 2013: Inferred changes in El Niño-Southern Oscillation variance over the past six centuries. Climate Past, 9, 2269-2284, https://doi.org/10.5194/cp-9-2269- 2013. · DOI
  53. McPhaden, M. J., S. E. Zebiak, and M. H. Glantz, 2006: ENSO as an integrating concept in Earth science. Science, 314, 1740-1745, https://doi.org/10.1126/ science.1132588. · DOI
  54. Oueslati, B., and G. Bellon, 2015: The double ITCZ bias in CMIP5 models: Interac- tion between SST, large-scale circulation and precipitation. Climate Dyn., 44, 585-607, https://doi.org/10.1007/s00382-015-2468-6. · DOI
  55. Perry, S. J., S. McGregor, A. Sen Gupta, and M. H. England, 2017: Future changes to El Niño-Southern Oscillation temperature and precipitation teleconnec- tions. Geophys. Res. Lett., 44, 10 608-10 616, https://doi.org/10.1002/ 2017GL074509. · DOI
  56. -, -, -, -, and N. Maher, 2020: Projected late 21st century changes to the regional impacts of the El Niño-Southern Oscillation. Climate Dyn., 54, 395-412, https://doi.org/10.1007/s00382-019-05006-6. · DOI
  57. Power, S. B., and F. P. Delage, 2018: El Niño-Southern Oscillation and asso- ciated climatic conditions around the world during the latter half of the twenty-first century. J. Climate, 31, 6189-6207, https://doi.org/10.1175/ JCLI-D-18-0138. · DOI
  58. Praveen Kumar, B., J. Vialard, M. Lengaigne, V. S. N. Murty, and M. J. McPhaden, 2012: TropFlux: Air-sea fluxes for the global tropical oceans-Description and evaluation. Climate Dyn., 38, 1521-1543, https://doi.org/10.1007/s00382- 011-1115-0. · DOI
  59. -, -, -, -, -, M. F. Cronin, F. Pinsard, and K. Gopala Reddy, 2013: TropFlux wind stresses over the tropical oceans: Evaluation and comparison with other products. Climate Dyn., 40, 2049-2071, https://doi. org/10.1007/s00382-012-1455-4. · DOI
  60. Ray, S., A. T. Wittenberg, S. M. Griffies, and F. Zeng, 2018a: Understanding the equatorial Pacific cold tongue time-mean heat budget. Part I: Diagnos- tic framework. J. Climate, 31, 9965-9985, https://doi.org/10.1175/JCLI- D-18-0152. · DOI
  61. -, -, -, and -, 2018b: Understanding the equatorial Pacific cold tongue time-mean heat budget. Part II: Evaluation of the GFDL-FLOR coupled GCM. J. Climate, 31, 9987-10 011, https://doi.org/10.1175/JCLI- D-18-0153. · DOI
  62. 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. · DOI
  63. Righi, M., and Coauthors, 2020: ESMValTool v2.0-Technical overview. Geosci. Model Dev., 13, 1179-1199, https://doi.org/10.5194/gmd-13-1179-2020. · DOI
  64. Ropelewski, C. F., and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev., 115, 1606-1626, https://doi.org/10.1175/1520-0493(1987)115<1606:GARS PP>2.0.CO;2. · DOI
  65. Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 3483-3517, https://doi.org/10.1175/JCLI3812. · DOI
  66. Santoso, A., M. J. McPhaden, and W. Cai, 2017: The defining characteristics of ENSO extremes and the strong 2015/2016 El Niño. Rev. Geophys., 55, 1079- 1129, https://doi.org/10.1002/2017RG000560. · DOI
  67. Sillmann, J., V. V. Kharin, X. Zhang, F. W. Zwiers, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part I. Model evaluation in the present climate. J. Geophys. Res. Atmos., 118, 1716-1733, https://doi. org/10.1002/JGRD.50203. · DOI · Paper page
  68. 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, 2711-2744, https://doi.org/10.1007/s00382-012-1607-6. · DOI
  69. Stoner, A. M., K. Hayhoe, and D. J. Wuebbles, 2009: Assessing general circulation model simulations of atmospheric teleconnection patterns. J. Climate, 22, 4348-4372, https://doi.org/10.1175/2009JCLI2577. · DOI
  70. Sun, D.-Z., Y. Yu, and T. Zhang, 2009: Tropical water vapor and cloud feedbacks in climate models: A further assessment using coupled simulations. J. Climate, 22, 1287-1304, https://doi.org/10.1175/2008JCLI2267. · DOI
  71. Timmermann, A., and Coauthors, 2018: El Niño-Southern Oscillation complexity. Nature, 559, 535-545, https://doi.org/10.1038/s41586-018-0252-6. · DOI
  72. Wang, B., and S.-I. An, 2002: A mechanism for decadal changes of ENSO behavior: Roles of background wind changes. Climate Dyn., 18, 475-486, https://doi. org/10.1007/s00382-001-0189-5. · DOI
  73. Wang, C., 2002: Atmospheric circulation cells associated with the El Niño- Southern Oscillation. J. Climate, 15, 399-419, https://doi.org/10.1175/1520- 0442(2002)015<0399:ACCAWT>2.0.CO;2. · DOI
  74. Wengel, C., M. Latif, W. Park, J. Harlaß, and T. Bayr, 2018: Seasonal ENSO phase locking in the Kiel Climate Model: The importance of the equatorial cold sea surface temperature bias. Climate Dyn., 50, 901-919, https://doi.org/10.1007/ s00382-017-3648-3. · DOI
  75. Wittenberg, A. T., 2004: Extended wind stress analyses for ENSO. J. Climate, 17, 2526- 2540, https://doi.org/10.1175/1520-0442(2004)017<2526:EWSAFE>2.0.CO;2. -, 2009: Are historical records sufficient to constrain ENSO simulations? Geophys. Res. Lett., 36, L12702, https://doi.org/10.1029/2009GL038710. · DOI
  76. -, A. Rosati, T. L. Delworth, G. A. Vecchi, and F. Zeng, 2014: ENSO modula- tion: Is it decadally predictable? J. Climate, 27, 2667-2681, https://doi. org/10.1175/JCLI-D-13-00577. · DOI
  77. Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539-2558, https://doi.org/10.1175/1520- 0477(1997)078<2539:GPAYMA>2.0.CO;2. · DOI
  78. Xie, S., H. Annamalai, F. A. Schott, and J. P. McCreary, 2002: Structure and mech- anisms of south Indian Ocean climate variability. J. Climate, 15, 864-878, https://doi.org/10.1175/1520-0442(2002)015<0864:SAMOSI>2.0.CO;2. · DOI
  79. Yeh, S.-W., and B. P. Kirtman, 2004: Tropical Pacific decadal variability and ENSO amplitude modulation in a CGCM. J. Geophys. Res., 109, C11009, https://doi. org/10.1029/2004JC002442. · DOI
  80. -, and Coauthors, 2018: ENSO atmospheric teleconnections and their re- sponse to greenhouse gas forcing. Rev. Geophys., 56, 185-206, https://doi. org/10.1002/2017RG000568. · DOI
  81. Zebiak, S. E., and M. A. Cane, 1987: A model El Niño-Southern Oscillation. Mon. Wea. Rev., 115, 2262-2278, https://doi.org/10.1175/1520-0493(1987)115 <2262:AMENO>2.0.CO;2. · DOI

Cited by

Sorted by citations

Authors
Planton, Y. Y., Guilyardi, E., Wittenberg, A. T., Lee, J., Gleckler, P. J., Bayr, T., McGregor, S., McPhaden, M. J., Power, S., Roehrig, R., Vialard, J., & Voldoire, A.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • LOCEAN-IPSL, CNRS-IRD-MNHN-Sorbonne Université, Paris, France, and National Centre for Atmospheric Science—Climate, University of Reading, Reading, United Kingdom
  • NOAA Geophysical Fluid Dynamics Laboratory (US)
  • Lawrence Livermore National Laboratory (US)
  • GEOMAR Helmholtz Centre for Ocean Research Kiel (DE)
  • Monash University (AU)
  • National Oceanic and Atmospheric Administration (US)
  • Bureau of Meteorology (AU)
ORCID
Venue
Bulletin of the American Meteorological Society
Publisher
American Meteorological Society
Published
2021-02
Volume
102
Issue
2
Pages
E193-E217
ISSN
  • 0003-0007 (ISSN-L)
  • 1520-0477
DOI
10.1175/bams-d-19-0337.1
OpenAlex
W3092213572
Cited by
246
References
2
Citations by year
2026: 22 · 2025: 40 · 2024: 50 · 2023: 62 · 2022: 53 · 2021: 18 · 2020: 1
FWCI
12.47
Type
journal article
Language
en
Open access
Locations
  • Bulletin of the American Meteorological Society (publishedVersion)
  • HAL (submittedVersion, other-oa)
  • Helmholtz Centre for Ocean Research Kiel (GEOMAR) (submittedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • U.S. Department of Energy
  • European Commission (824084, H2020)
  • Deutsche Forschungsgemeinschaft (429334714)
  • Agence Nationale de la Recherche (ANR-18-CE01-0012, ANR18, ANR-15-JCLI-0004)
  • National Oceanic and Atmospheric Administration
  • Office of Science
  • Horizon 2020 Framework Programme (824084)
  • Australian Research Council (FT160100162)
  • Biological and Environmental Research
  • Climate Program Office
  • Lawrence Livermore National Laboratory
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Geology and Paleoclimatology Research
Keywords
ENSO, climate model evaluation, CMIP6, CMIP5, ENSO teleconnections, model bias, interannual climate variability

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review