lured, D., Jelenak, A., Soerensen, A. M., Gaultier, L., and Her-
lédan, S.: NetCDF Climate and Forecast (CF) Meta-data Con-
ventions V1.10, http://cfconventions.org/Data/cf-conventions/
cf-conventions-1.10/cf-conventions.html (last access: 4 April
2024), 2022.

ESGF LLNL Metagrid: https://esgf-node.llnl.gov/, last access: 8 May 2024.

Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) – 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, 2016a.

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016b.

Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P., Kwiatkowski, L., Lorenz, R., Maloney, E. D., Meehl, G. A., Pendergrass, A. G., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D., Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.: Taking climate model evaluation to the next level, Nat. Clim. Change, 9, 102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019.

Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (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, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, 2020.

Eyring, V., Gillett, N. P., Achuta Rao, K. M., Barimalala, R., Barreiro Parrillo, M., Bellouin, N., Cassou, C., Durack, P. J., Kosaka, Y., McGregor, S. and Min, S., Morgenstern, O., and Sun, Y.: Human Influence on the Climate System, in: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 105, 423–552, https://doi.org/10.1017/9781009157896.005, 2021.

Fasullo, J. T.: Evaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate

Model Assessment Tool (CMATv1), Geosci. Model Dev., 13, 3627–3642, https://doi.org/10.5194/gmd-13-3627-2020, 2020.

Fasullo, J. T., 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.

Ferraro, R., Waliser, D. E., Gleckler, P. J., Taylor, K. E., and Eyring, V.: Evolving OBS4MIPS to support Phase 6 of the Coupled Model Intercomparison Project (CMIP6), B. Am. Meteorol. Soc., 96, ES131–ES133, https://doi.org/10.1175/bams-d-14-00216.1, 2015.

Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., and Forest, C.: Evaluation of climate models, in: Climate change 2013: the physical science basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 741–866, 2014.

Fu, W., Moore, J. K., Primeau, F., Collier, N., Ogunro, O. O., Hoffman, F. M., and Randerson, J. T.: Evaluation of ocean geochemistry and carbon cycling in CMIP earth system models with the international ocean model benchmarking (IOMB) software System. J. Geophys. Res.-Oceans, 127, e2022JC018965, https://doi.org/10.1029/2022JC018965, 2022.

Gates, W. L.: AN AMS continuing series: Global CHANGE-AMIP: The Atmospheric Model Intercomparison Project, B. Am. Meteorol. Soc., 73, 1962–1970, 1992.

Gates, W. L., Henderson-Sellers, A., Boer, G. J., Folland, C. K., Kitoh, A., McAvaney, B. J., Semazzi, F., Smith, N., Weaver, A. J., and Zeng, Q. C.: Climate models – evaluation, Climate Change, 1, 229–284, 1995.

Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M., Drach, R. S., Fiorino, M., Gleckler, P. J., Hnilo, J. J., Marlais, S. M., and Phillips, T. J.: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I), B. Am. Meteorol. Soc., 80, 29–56, 1999.

Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for climate models, J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007jd008972, 2008.

Gleckler, P. J., Ferraro, R., and Waliser, D. E.: Improving use of satellite data in evaluating climate models, Eos T. Am. Geophys. Un., 92, 172, https://doi.org/10.1029/2011eo200005, 2011.

Gleckler, P. J., Doutriaux, C., Durack, P. J., Taylor, K. E., Zhang, Y., Williams, D. N., Mason, E., and Servonnat, J.: A more powerful reality test for climate models, Eos T. Am. Geophys. Un., 97, https://doi.org/10.1029/2016eo051663, 2016.

Golaz, J.-C., Caldwell, P., Van Roekel, L., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G. W., Anantharaj, V., Asay-Davis, X., Bader, D. C., Baldwin, S., Bisht, G., Bogenschutz, P., Branstetter, M. L., Brunke, M. A., Brus, S., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E., Jacob, R., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H. Y., Lin, W., Lipscomb, W. H., Lun, P., Mahajan, S., Maltrud, M., Mametjanov, A., McClean, J. L., McCoy, R., Neale, R., Price, S., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams,
lured, D., Jelenak, A., Soerensen, A. M., Gaultier, L., and Her-
lédan, S.: NetCDF Climate and Forecast (CF) Meta-data Con-
ventions V1.10, http://cfconventions.org/Data/cf-conventions/
cf-conventions-1.10/cf-conventions.html (last access: 4 April
2024), 2022.

ESGF LLNL Metagrid: https://esgf-node.llnl.gov/, last access: 8 May 2024.

Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) – 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, 2016a.

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016b.

Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P., Kwiatkowski, L., Lorenz, R., Maloney, E. D., Meehl, G. A., Pendergrass, A. G., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D., Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.: Taking climate model evaluation to the next level, Nat. Clim. Change, 9, 102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019.

Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (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, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, 2020.

Eyring, V., Gillett, N. P., Achuta Rao, K. M., Barimalala, R., Barreiro Parrillo, M., Bellouin, N., Cassou, C., Durack, P. J., Kosaka, Y., McGregor, S. and Min, S., Morgenstern, O., and Sun, Y.: Human Influence on the Climate System, in: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 105, 423–552, https://doi.org/10.1017/9781009157896.005, 2021.

Fasullo, J. T.: Evaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate

Model Assessment Tool (CMATv1), Geosci. Model Dev., 13, 3627–3642, https://doi.org/10.5194/gmd-13-3627-2020, 2020.

Fasullo, J. T., 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.

Ferraro, R., Waliser, D. E., Gleckler, P. J., Taylor, K. E., and Eyring, V.: Evolving OBS4MIPS to support Phase 6 of the Coupled Model Intercomparison Project (CMIP6), B. Am. Meteorol. Soc., 96, ES131–ES133, https://doi.org/10.1175/bams-d-14-00216.1, 2015.

Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., and Forest, C.: Evaluation of climate models, in: Climate change 2013: the physical science basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 741–866, 2014.

Fu, W., Moore, J. K., Primeau, F., Collier, N., Ogunro, O. O., Hoffman, F. M., and Randerson, J. T.: Evaluation of ocean geochemistry and carbon cycling in CMIP earth system models with the international ocean model benchmarking (IOMB) software System. J. Geophys. Res.-Oceans, 127, e2022JC018965, https://doi.org/10.1029/2022JC018965, 2022.

Gates, W. L.: AN AMS continuing series: Global CHANGE–AMIP: The Atmospheric Model Intercomparison Project, B. Am. Meteorol. Soc., 73, 1962–1970, 1992.

Gates, W. L., Henderson-Sellers, A., Boer, G. J., Folland, C. K., Kitoh, A., McAvaney, B. J., Semazzi, F., Smith, N., Weaver, A. J., and Zeng, Q. C.: Climate models – evaluation, Climate Change, 1, 229–284, 1995.

Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M., Drach, R. S., Fiorino, M., Gleckler, P. J., Hnilo, J. J., Marlais, S. M., and Phillips, T. J.: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I), B. Am. Meteorol. Soc., 80, 29–56, 1999.

Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for climate models, J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007jd008972, 2008.

Gleckler, P. J., Ferraro, R., and Waliser, D. E.: Improving use of satellite data in evaluating climate models, Eos T. Am. Geophys. Un., 92, 172, https://doi.org/10.1029/2011eo200005, 2011.

Gleckler, P. J., Doutriaux, C., Durack, P. J., Taylor, K. E., Zhang, Y., Williams, D. N., Mason, E., and Servonnat, J.: A more powerful reality test for climate models, Eos T. Am. Geophys. Un., 97, https://doi.org/10.1029/2016eo051663, 2016.

Golaz, J.-C., Caldwell, P., Van Roekel, L., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G. W., Anantharaj, V., Asay-Davis, X., Bader, D. C., Baldwin, S., Bisht, G., Bogenschutz, P., Branstetter, M. L., Brunke, M. A., Brus, S., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E., Jacob, R., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H. Y., Lin, W., Lipscomb, W. H., Lun, P., Mahajan, S., Maltrud, M., Mametjanov, A., McClean, J. L., McCoy, R., Neale, R., Price, S., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams,
D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018ms001603, 2019.

Goldenson, N., Leung, L. R., Mearns, L. O., Pierce, D. W., Reed, K. A., Simpson, I. R., Ullrich, P., Krantz, W., Hall, A., Jones, A., and Rahimi, S.: Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling, B. Am. Meteorol. Soc., 104, E1619–E1629, https://doi.org/10.1175/BAMS-D-23-0100.1, 2023.

Guilyardi, E., Wittenberg, A., Fedorov, A., Collins, M., Wang, C., Capotondi, A., Van Oldenborgh, G. J., and Stockdale, T.: Understanding El Niño in ocean-atmosphere general circulation models: Progress and challenges, B. Am. Meteorol. Soc., 90, 325–340, https://doi.org/10.1175/2008BAMS2387.1, 2009.

Guilyardi, E., Capotondi, A., Lengaigne, M., Thual, S., and Wittenberg, A. T.: ENSO modelling: history, progress and challenges, in: El Niño in a changing climate, edited by: McPhaden, M. J., Santoso, A., Cai, W., AGU monograph, ISBN 9781119548164, https://doi.org/10.1002/9781119548164.ch9, 2020.

Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling Experiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.

Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenig, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.

Hannah, W. M., Bradley, A. M., Guba, O., Tang, Q., Golaz, J.-C., and Wolfe, J. D.: Separating physics and dynamics grids for improved computational efficiency in spectral element Earth system models, J. Adv. Model. Earth Sy., 13, e2020MS002419, https://doi.org/10.1029/2020ms002419, 2021.

Hassan, K. A., Rönnberg, N., Forsell, C., Cooper, M., and Johansson, J.: A study on 2D and 3D parallel coordinates for pattern identification in temporal multivariate data, in: 2019 23rd International Conference Information Visualisation (IV), 145–150, https://doi.org/10.1109/IV.2019.00033, 2019.

Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.

Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., and Zelinka, M.: Climate simulations: Recognize the “hot model” problem, Nature, 605, 26–29, https://doi.org/10.1038/d41586-022-01192-2, 2022.

Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T., and coauthors: Structure and performance

of GFDL's CM4.0 climate model, J. Adv. Model. Earth Sy., 11, 3691–3727, https://doi.org/10.1029/2019MS001829, 2019.

Hendon, H. H., Zhang, C., and Glick, J. D.: Interannual Variation of the Madden-Julian Oscillation during Austral Summer, J. Climate, 12, 2538–2550, 1999.

Herger, N., Abramowitz, G., Knutti, R., Angélil, O., Lehmann, K., and Sanderson, B. M.: Selecting a climate model subset to optimise key ensemble properties, Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, 2018.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and coauthors: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.

Hintze, J. L. and Nelson, R. D.: Violin plots: A box plot-density trace synergism, Am. Stat., 52, 181–184, https://doi.org/10.1080/00031305.1998.10480559, 1998.

Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python, J. Open Res. Software, 5, 10, https://doi.org/10.5334/jors.148, 2017.

Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., and Susskind, J.: Global precipitation at one-degree daily resolution from multisatellite observations, J. Hydrometeorol., 2, 36–50, 2001.

Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), Algorithm theoretical basis document (ATBD) version, 4, p. 30, 2015.

Inselberg, A.: Multidimensional detective, in: Proceedings of IEEE Symposium on Information Visualization, 100–107, https://doi.org/10.1109/INFVIS.1997.636793, 1997.

Inselberg, A.: Parallel Coordinates: Visualization, Exploration and Classification of High-Dimensional Data, in: Handbook of Data Visualization, edited by: Chen, C., Härdle, W., and Unwin, A., Springer, Berlin, Heidelberg, Germany, 643–680, https://doi.org/10.1007/978-3-540-33037-0_25, 2008.

Inselberg, A.: Parallel Coordinates, in: Encyclopedia of Database Systems, Springer, edited by: Liu, L., and Özsu, M. T., Springer, New York, NY, U.S.A., https://doi.org/10.1007/978-1-4899-7993-3_262-2, 2016.

Jakob, C., Gettelman, A., and Pitman, A.: The need to operationalize climate modelling, Nat. Clim. Change, 13, 1158–1160, https://doi.org/10.1038/s41558-023-01849-4, 2023.

Johansson, J. and Forsell, C.: Evaluation of parallel coordinates: Overview, categorization and guidelines for future research, IEEE T. Vis. Comput. G. R., 22, 579–588, https://doi.org/10.1109/TVCG.2015.2466992, 2016.

Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5, 487–503, 2004.

Kang, D., Kim, D. H., Ahn, M.-S., Neale, R., Lee, J., and Gleckler, P. J.: The role of the mean state on MJO simulation in CESM2 ensemble simulation, Geophys. Res. Lett., 47, e2020GL089824, https://doi.org/10.1029/2020GL089824, 2020.

Kim, D., Sperber, K. R., Stern, W., Waliser, D. E., Kang, I. S., Maloney, E. D., Wang, W., Weickmann, K. M., Benedict, J. J., Khairoutdinov, M., Lee, M.-I., Neale, R., Suarez, M. J.,
D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018ms001603, 2019.

Goldenson, N., Leung, L. R., Mearns, L. O., Pierce, D. W., Reed, K. A., Simpson, I. R., Ullrich, P., Krantz, W., Hall, A., Jones, A., and Rahimi, S.: Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling, B. Am. Meteorol. Soc., 104, E1619–E1629, https://doi.org/10.1175/BAMS-D-23-0100.1, 2023.

Guilyardi, E., Wittenberg, A., Fedorov, A., Collins, M., Wang, C., Capotondi, A., Van Oldenborgh, G. J., and Stockdale, T.: Understanding El Niño in ocean-atmosphere general circulation models: Progress and challenges, B. Am. Meteorol. Soc., 90, 325–340, https://doi.org/10.1175/2008BAMS2387.1, 2009.

Guilyardi, E., Capotondi, A., Lengaigne, M., Thual, S., and Wittenberg, A. T.: ENSO modelling: history, progress and challenges, in: El Niño in a changing climate, edited by: McPhaden, M. J., Santoso, A., Cai, W., AGU monograph, ISBN 9781119548164, https://doi.org/10.1002/9781119548164.ch9, 2020.

Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling Experiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.

Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenig, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.

Hannah, W. M., Bradley, A. M., Guba, O., Tang, Q., Golaz, J.-C., and Wolfe, J. D.: Separating physics and dynamics grids for improved computational efficiency in spectral element Earth system models, J. Adv. Model. Earth Sy., 13, e2020MS002419, https://doi.org/10.1029/2020ms002419, 2021.

Hassan, K. A., Rönnberg, N., Forsell, C., Cooper, M., and Johansson, J.: A study on 2D and 3D parallel coordinates for pattern identification in temporal multivariate data, in: 2019 23rd International Conference Information Visualisation (IV), 145–150, https://doi.org/10.1109/IV.2019.00033, 2019.

Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.

Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., and Zelinka, M.: Climate simulations: Recognize the “hot model” problem, Nature, 605, 26–29, https://doi.org/10.1038/d41586-022-01192-2, 2022.

Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T., and coauthors: Structure and performance

of GFDL's CM4.0 climate model, J. Adv. Model. Earth Sy., 11, 3691–3727, https://doi.org/10.1029/2019MS001829, 2019.

Hendon, H. H., Zhang, C., and Glick, J. D.: Interannual Variation of the Madden-Julian Oscillation during Austral Summer, J. Climate, 12, 2538–2550, 1999.

Herger, N., Abramowitz, G., Knutti, R., Angélil, O., Lehmann, K., and Sanderson, B. M.: Selecting a climate model subset to optimize key ensemble properties, Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, 2018.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and coauthors: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.

Hintze, J. L. and Nelson, R. D.: Violin plots: A box plot-density trace synergism, Am. Stat., 52, 181–184, https://doi.org/10.1080/00031305.1998.10480559, 1998.

Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python, J. Open Res. Software, 5, 10, https://doi.org/10.5334/jors.148, 2017.

Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., and Susskind, J.: Global precipitation at one-degree daily resolution from multisatellite observations, J. Hydrometeorol., 2, 36–50, 2001.

Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), Algorithm theoretical basis document (ATBD) version, 4, p. 30, 2015.

Inselberg, A.: Multidimensional detective, in: Proceedings of IEEE Symposium on Information Visualization, 100–107, https://doi.org/10.1109/INFVIS.1997.636793, 1997.

Inselberg, A.: Parallel Coordinates: Visualization, Exploration and Classification of High-Dimensional Data, in: Handbook of Data Visualization, edited by: Chen, C., Härdle, W., and Unwin, A., Springer, Berlin, Heidelberg, Germany, 643–680, https://doi.org/10.1007/978-3-540-33037-0_25, 2008.

Inselberg, A.: Parallel Coordinates, in: Encyclopedia of Database Systems, Springer, edited by: Liu, L., and Özsu, M. T., Springer, New York, NY, U.S.A., https://doi.org/10.1007/978-1-4899-7993-3_262-2, 2016.

Jakob, C., Gettelman, A., and Pitman, A.: The need to operationalize climate modelling, Nat. Clim. Change, 13, 1158–1160, https://doi.org/10.1038/s41558-023-01849-4, 2023.

Johansson, J. and Forsell, C.: Evaluation of parallel coordinates: Overview, categorization and guidelines for future research, IEEE T. Vis. Comput. G. R., 22, 579–588, https://doi.org/10.1109/TVCG.2015.2466992, 2016.

Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5, 487–503, 2004.

Kang, D., Kim, D. H., Ahn, M.-S., Neale, R., Lee, J., and Gleckler, P. J.: The role of the mean state on MJO simulation in CESM2 ensemble simulation, Geophys. Res. Lett., 47, e2020GL089824, https://doi.org/10.1029/2020GL089824, 2020.

Kim, D., Sperber, K. R., Stern, W., Waliser, D. E., Kang, I. S., Maloney, E. D., Wang, W., Weickmann, K. M., Benedict, J. J., Khairoutdinov, M., Lee, M.-I., Neale, R., Suarez, M. J.,
Thayer-Calder, K., and Zhang, G.: Application of MJO simulation diagnostics to climate models, J. Climate, 22, 6413–6436, https://doi.org/10.1175/2009jcli3063.1, 2009.

Kim, H., Caron, J. M., Richter, J. H. and Simpson, I. R.: The lack of QBO-MJO connection in CMIP6 models, Geophys. Res. Lett., 47, e2020GL087295, https://doi.org/10.1029/2020GL087295, 2020.

Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and Gleckler, P. J.: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator, J. Geophys. Res.-Atmos., 118, 1329–1342, https://doi.org/10.1002/jgrd.50141, 2013.

Klingaman, N. P., Martin, G. M., and Moise, A.: ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models, Geosci. Model Dev., 10, 57–83, https://doi.org/10.5194/gmd-10-57-2017, 2017.

Knutti, R.: The end of model democracy? Climatic Change, 102, 395–404, https://doi.org/10.1007/s10584-010-9800-2, 2010.

Knutti, R., Abramowitz, G., Collins, M., Eyring, V., Gleckler, P. J., Hewitson, B., and Mearns, L.: Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections, in: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., and Midgley, P. M., IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland, 2010.

Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and Eyring, V.: A climate model projection weighting scheme accounting for performance and interdependence, Geophys. Res. Lett., 44, 1909–1918, https://doi.org/10.1002/2016gl072012, 2017.

Labe, Z. M. and Barnes, E. A.: Comparison of Climate Model Large Ensembles With Observations in the Arctic Using Simple Neural Networks, Earth Space Sci., 9, e2022EA002348, https://doi.org/10.1029/2022EA002348, 2022.

Lambert, S. J. and Boer, G. J.: CMIP1 evaluation and intercomparison of coupled climate models, Clim. Dynam., 17, 83–106, https://doi.org/10.1007/PL00013736, 2001.

Lee, H., Goodman, A., McGibbney, L., Waliser, D. E., Kim, J., Loikith, P. C., Gibson, P. B., and Massoud, E. C.: Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies, Geosci. Model Dev., 11, 4435–4449, https://doi.org/10.5194/gmd-11-4435-2018, 2018.

Lee, J., Gleckler, P., Sperber, K., Doutriaux, C., and Williams, D.: High-dimensional Data Visualization for Climate Model Intercomparison: Application of the Circular Plot, in: Proceedings of the 8th International Workshop on Climate Informatics: CI 2018, NCAR Technical Note NCAR/TN-550+PROC, 12–14, https://doi.org/10.5065/D6BZ64XQ, 2018.

Lee, J., Sperber, K. R., Gleckler, P. J., Bonfils, C., and Taylor, K. E.: Quantifying the agreement between observed and simulated extratropical modes of interannual variability, Clim. Dynam., 52, 4057–4089, https://doi.org/10.1007/s00382-018-4355-4, 2019a.

Lee, J., Xue, Y., De Sales, F., Diallo, I., Marx, L., Ek, M., Sperber, K. R., and Gleckler, P. J.: Evaluation of multi-decadal UCLA-CFSv2 simulation and impact of interactive atmospheric-ocean feedback on global and regional variability, Clim. Dynam., 52, 3683–3707, https://doi.org/10.1007/s00382-018-4351-8, 2019b.

Lee, J., Planton, Y., Gleckler, P. J., Sperber, K. R., Guilyardi, E., Wittenberg, A. T., McPhaden, M. J., and Pallotta, G.: Robust evaluation of ENSO in climate models: How many ensemble members are needed?, Geophys. Res. Lett., 48, e2021GL095041, https://doi.org/10.1029/2021gl095041, 2021a.

Lee, J., Sperber, K. R., Gleckler, P. J., Taylor, K. E., and Bonfils, C.: Benchmarking performance changes in the simulation of extratropical modes of variability across CMIP generations, J. Climate, 34, 6945–6969, https://doi.org/10.1175/jcli-d-20-0832.1, 2021b.

Lee, J., Ahn, M.-S., Ordonez, A., Gleckler, P., and Ullrich, P.: PCMDI/pcmdi_METRICS_results_archive, Zenodo [data set], https://doi.org/10.5281/zenodo.10181201, 2023a.

Lee, J., Gleckler, P., Ordonez, A., Ahn, M.-S., Ullrich, P., Tom, V., Jason, B., Charles, D., Durack, P., Shaheen, Z., Muryanto, L., Painter, J., and Krasting, J.: PCMDI/pcmdi_METRICS: PMP Version 3.1.1, Zenodo [code], https://doi.org/10.5281/zenodo.592790, 2023b.

Leung, L. R., Boos, W. R., Catto, J. L., DeMott, C. A., Martin, G. M., Neelin, J. D., O'Brien, T. A., Xie, S., Feng, Z., Klingaman, N. P. Kuo, Y.-H., Lee, R. W., Martinez-Villalobos, C., Vishnu S., Priestley, M. D. K., Tao, C., and Zhou, Y.: Exploratory precipitation metrics: Spatiotemporal characteristics, process-oriented, and phenomena-based, J. Climate, 35, 3659–3686, https://doi.org/10.1175/JCLI-D-21-0590.1, 2022.

Lin, J.-P., Kiladis, G. N., Mapes, B. E., Weickmann, K. M., Sperber, K. R., Lin, W., Wheeler, M. C., Schubert, S. D., Del Genio, A. D., Donner, L. J., Emori, S., Guérémy, J.-F., Hourdin, F., Rasch, P. J., Roeckner, E., and Scinocca, J.: Tropical intraseasonal variability in 14 IPCC AR4 climate Models. Part I: Convective Signals, J. Climate, 19, 2665–2690, https://doi.org/10.1175/jcli3735.1, 2006.

Lin, Y., Huang, X., Liang, Y., Qin, Y., Xu, S., Huang, W., Xu, F., Liu, L., Wang, Y., Peng, Y., and Wang, L.: Community integrated earth system model (CIESM): Description and evaluation, J. Adv. Model. Earth Sy., 12, e2019MS002036, https://doi.org/10.1029/2019ms002036, 2020.

Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Seiji, K.: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) Edition-4.0 data product, Int. J. Climatol., 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018.

Longmate, J. M., Risser, M. D., and Feldman, D. R.: Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation, Clim. Dynam., 61, 5171–5197, https://doi.org/10.1007/s00382-023-06846-z, 2023.

Lu, L., Wang, W. and Tan, Z.: Double-arc parallel coordinates and its axes re-ordering methods, Mobile Networks and Applications, 25, 1376–1391, https://doi.org/10.1007/s11036-019-01455-9, 2020.

Madden, R. A. and Julian, P.: Detection of a 40–50 day oscillation in the zonal wind in the Tropical Pacific, J. Atmos. Sci., 28, 702–708, https://doi.org/10.1175/1520-0469(1971)028, 1971.

Madden, R. A. and Julian, P.: Description of Global-Scale Circulation Cells in the Tropics with a 40–50 Day Period,
Thayer-Calder, K., and Zhang, G.: Application of MJO simulation diagnostics to climate models, J. Climate, 22, 6413–6436, https://doi.org/10.1175/2009jcli3063.1, 2009.

Kim, H., Caron, J. M., Richter, J. H. and Simpson, I. R.: The lack of QBO-MJO connection in CMIP6 models, Geophys. Res. Lett., 47, e2020GL087295, https://doi.org/10.1029/2020GL087295, 2020.

Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and Gleckler, P. J.: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator, J. Geophys. Res.-Atmos., 118, 1329–1342, https://doi.org/10.1002/jgrd.50141, 2013.

Klingaman, N. P., Martin, G. M., and Moise, A.: ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models, Geosci. Model Dev., 10, 57–83, https://doi.org/10.5194/gmd-10-57-2017, 2017.

Knutti, R.: The end of model democracy? Climatic Change, 102, 395–404, https://doi.org/10.1007/s10584-010-9800-2, 2010.

Knutti, R., Abramowitz, G., Collins, M., Eyring, V., Gleckler, P. J., Hewitson, B., and Mearns, L.: Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections, in: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., and Midgley, P. M., IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland, 2010.

Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and Eyring, V.: A climate model projection weighting scheme accounting for performance and interdependence, Geophys. Res. Lett., 44, 1909–1918, https://doi.org/10.1002/2016gl072012, 2017.

Labe, Z. M. and Barnes, E. A.: Comparison of Climate Model Large Ensembles With Observations in the Arctic Using Simple Neural Networks, Earth Space Sci., 9, e2022EA002348, https://doi.org/10.1029/2022EA002348, 2022.

Lambert, S. J. and Boer, G. J.: CMIP1 evaluation and intercomparison of coupled climate models, Clim. Dynam., 17, 83–106, https://doi.org/10.1007/PL00013736, 2001.

Lee, H., Goodman, A., McGibbney, L., Waliser, D. E., Kim, J., Loikith, P. C., Gibson, P. B., and Massoud, E. C.: Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies, Geosci. Model Dev., 11, 4435–4449, https://doi.org/10.5194/gmd-11-4435-2018, 2018.

Lee, J., Gleckler, P., Sperber, K., Doutriaux, C., and Williams, D.: High-dimensional Data Visualization for Climate Model Intercomparison: Application of the Circular Plot, in: Proceedings of the 8th International Workshop on Climate Informatics: CI 2018, NCAR Technical Note NCAR/TN-550+PROC, 12–14, https://doi.org/10.5065/D6BZ64XQ, 2018.

Lee, J., Sperber, K. R., Gleckler, P. J., Bonfils, C., and Taylor, K. E.: Quantifying the agreement between observed and simulated extratropical modes of interannual variability, Clim. Dynam., 52, 4057–4089, https://doi.org/10.1007/s00382-018-4355-4, 2019a.

Lee, J., Xue, Y., De Sales, F., Diallo, I., Marx, L., Ek, M., Sperber, K. R., and Gleckler, P. J.: Evaluation of multi-decadal UCLA-CFSv2 simulation and impact of interactive atmospheric-ocean

feedback on global and regional variability, Clim. Dynam., 52, 3683–3707, https://doi.org/10.1007/s00382-018-4351-8, 2019b.

Lee, J., Planton, Y., Gleckler, P. J., Sperber, K. R., Guilyardi, E., Wittenberg, A. T., McPhaden, M. J., and Pallotta, G.: Robust evaluation of ENSO in climate models: How many ensemble members are needed?, Geophys. Res. Lett., 48, e2021GL095041, https://doi.org/10.1029/2021gl095041, 2021a.

Lee, J., Sperber, K. R., Gleckler, P. J., Taylor, K. E., and Bonfils, C.: Benchmarking performance changes in the simulation of extratropical modes of variability across CMIP generations, J. Climate, 34, 6945–6969, https://doi.org/10.1175/jcli-d-20-0832.1, 2021b.

Lee, J., Ahn, M.-S., Ordonez, A., Gleckler, P., and Ullrich, P.: PCMDI/pcmdi_METRICS_results_archive, Zenodo [data set], https://doi.org/10.5281/zenodo.10181201, 2023a.

Lee, J., Gleckler, P., Ordonez, A., Ahn, M.-S., Ullrich, P., Tom, V., Jason, B., Charles, D., Durack, P., Shaheen, Z., Muryanto, L., Painter, J., and Krasting, J.: PCMDI/pcmdi_METRICS: PMP Version 3.1.1, Zenodo [code], https://doi.org/10.5281/zenodo.592790, 2023b.

Leung, L. R., Boos, W. R., Catto, J. L., DeMott, C. A., Martin, G. M., Neelin, J. D., O'Brien, T. A., Xie, S., Feng, Z., Klingaman, N. P. Kuo, Y.-H., Lee, R. W., Martinez-Villalobos, C., Vishnu S., Priestley, M. D. K., Tao, C., and Zhou, Y.: Exploratory precipitation metrics: Spatiotemporal characteristics, process-oriented, and phenomena-based, J. Climate, 35, 3659–3686, https://doi.org/10.1175/JCLI-D-21-0590.1, 2022.

Lin, J.-P., Kiladis, G. N., Mapes, B. E., Weickmann, K. M., Sperber, K. R., Lin, W., Wheeler, M. C., Schubert, S. D., Del Genio, A. D., Donner, L. J., Emori, S., Guérémy, J.-F., Hourdin, F., Rasch, P. J., Roeckner, E., and Scinocca, J.: Tropical intraseasonal variability in 14 IPCC AR4 climate Models. Part I: Convective Signals, J. Climate, 19, 2665–2690, https://doi.org/10.1175/jcli3735.1, 2006.

Lin, Y., Huang, X., Liang, Y., Qin, Y., Xu, S., Huang, W., Xu, F., Liu, L., Wang, Y., Peng, Y., and Wang, L.: Community integrated earth system model (CIESM): Description and evaluation, J. Adv. Model. Earth Sy., 12, e2019MS002036, https://doi.org/10.1029/2019ms002036, 2020.

Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Seiji, K.: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) Edition-4.0 data product, Int. J. Climatol., 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018.

Longmate, J. M., Risser, M. D., and Feldman, D. R.: Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation, Clim. Dynam., 61, 5171–5197, https://doi.org/10.1007/s00382-023-06846-z, 2023.

Lu, L., Wang, W. and Tan, Z.: Double-arc parallel coordinates and its axes re-ordering methods, Mobile Networks and Applications, 25, 1376–1391, https://doi.org/10.1007/s11036-019-01455-9, 2020.

Madden, R. A. and Julian, P.: Detection of a 40–50 day oscillation in the zonal wind in the Tropical Pacific, J. Atmos. Sci., 28, 702–708, https://doi.org/10.1175/1520-0469(1971)028, 1971.

Madden, R. A. and Julian, P.: Description of Global-Scale Circulation Cells in the Tropics with a 40–50 Day Period,
J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029, 1972.

Madden, R. A. and Julian, P.: Observations of the 40–50-Day Trop-ical Oscillation – A Review, Mon. Weather Rev., 122, 814–837, https://doi.org/10.1175/1520-0493(1994)122, 1994.

Maloney, E. D., Gettelman, A., Ming, Y., Neelin, J. D., Barrie, D., Mariotti, A., Chen, C., Coleman, D., Kuo, Y. H., Singh, B., An-namalai, H., Berg, A., Booth, J. F., Camargo, S. J., Dai, A., Gon-zalez, A., Hafner, J., Jiang, X., Jing, X., Kim, D. H., Kumar, A., Moon, Y., Naud, C. M., Sobel, A. H., Suzuki, K., Wang, F., Wang, J., Wing, A. A., Xu, X., and Zhao, M.: Process-Oriented evaluation of climate and weather forecasting models, B. Am. Meteorol. Soc., 100, 1665–1686, https://doi.org/10.1175/bams-d-18-0042.1, 2019.

Martin, G. M., Klingaman, N. P., and Moise, A. F.: Connecting spatial and temporal scales of tropical precipitation in observa-tions and the MetUM-GA6, Geosci. Model Dev., 10, 105–126, https://doi.org/10.5194/gmd-10-105-2017, 2017.

McAvaney, B. J., Covey, C., Joussaume, S., Kattsov, V., Kitoh, A., Ogana, W., Pitman, A. J., Weaver, A. J., Wood, R. A., and Zhao, Z. C.: Model evaluation. In Climate Change 2001: The scien-tific basis, Contribution of WG1 to the Third Assessment Re-port of the IPCC (TAR) 471-523, Cambridge University Press, ISBN 0521 80767 0, 2001.

McPhaden, M. J., Zebiak, S. E., and Glantz, M. H.: ENSO as an integrating concept in Earth Science, Science, 314, 1740–1745, https://doi.org/10.1126/science.1132588, 2006.

McPhaden, M. J., Santoso, A., and Cai, W. (Eds.): El Niño Southern oscillation in a changing climate, American Geophysical Union, USA, 528 pp., ISBN 9781119548126, https://doi.org/10.1002/9781119548164, 2020.

Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.: Intercomparison makes for a better climate model, Eos T. Am. Geophys. Un., 78, 445, https://doi.org/10.1029/97eo00276, 1997.

Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.: The Coupled Model Intercomparison Project (CMIP), B. Am. Meteorol. Soc., 81, 313–318, 2000.

Meehl, G. A., Covey, C., Delworth, T. L., Latif, M., McAvaney, B. J., Mitchell, J. F. B., Stouffer, R. J., and Taylor, K. E.: THE WCRP CMIP3 Multimodel Dataset: A new era in cli-mate change research, B. Am. Meteorol. Soc., 88, 1383–1394, https://doi.org/10.1175/bams-88-9-1383, 2007.

Merrifield, A. L., Brunner, L., Lorenz, R., Humphrey, V., and Knutti, R.: Climate model Selection by Indepen-dence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications, Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, 2023.

Neelin, J. D., Krasting, J. P., Radhakrishnan, A., Liptak, J., Jackson, T. J., Ming, Y., Dong, W., Gettelman, A., Coleman, D., Maloney, E. D., Wing, A. A., Kuo, Y. H., Ahmed, F., Ullrich, P. A., Bitz, C. M., Neale, R., Ordonez, A., and Maroon, E.: Process-oriented diagnostics: principles, practice, community development and common standards, B. Am. Meteorol. Soc., 104, E1452–E1468, https://doi.org/10.1175/bams-d-21-0268.1, 2023.

Nowack, P., Runge, J., Eyring, V., and Haigh, J. D.: Causal networks for climate model evaluation and constrained projections, Nat. Commun., 11, 1415, https://doi.org/10.1038/s41467-020-15195-y, 2020.

Orbe, C., Van Roekel, L., Adames, Á. F., Dezfuli, A., Fasullo, J. T., Gleckler, P. J., Lee, J., Li, W., Nazarenko, L., Schmidt, G. A., Sperber, K. R., and Zhao, M.: Representation of modes of variability in six U.S. climate models, J. Climate, 33, 7591–7617, https://doi.org/10.1175/jcli-d-19-0956.1, 2020.

Ordonez, A. C., Klingaman, N. P., and Martin, G.: Analysing scales of precipitation, OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), https://doi.org/10.11578/dc.20211029.5, 2021.

Papalexiou, S. M., Rajulapati, C. R., Clark, M. P., and Lehner, F.: Robustness of CMIP6 historical global mean tempera-ture simulations: Trends, long-term persistence, autocorrelation, and distributional shape, Earth’s Future, 8, e2020EF001667, https://doi.org/10.1029/2020EF001667, 2020.

Pascoe, C., Lawrence, B. N., Guilyardi, E., Juckes, M., and Taylor, K. E.: Documenting numerical experiments in support of the Coupled Model Intercomparison Project Phase 6 (CMIP6), Geosci. Model Dev., 13, 2149–2167, https://doi.org/10.5194/gmd-13-2149-2020, 2020.

PCMDI Simulation Summaries: https://pcmdi.llnl.gov/metrics/, last access: 8 May 2024.

Pendergrass, A. G., Gleckler, P. J., Leung, L. R., and Jakob, C.: Benchmarking simulated precipitation in earth system models, B. Am. Meteorol. Soc., 101, E814–E816, https://doi.org/10.1175/bams-d-19-0318.1, 2020.

Phillips, A. S., Deser, C., and Fasullo, J. T.: Evaluating modes of variability in climate models, Eos T. Am. Geophys. Un., 95, 453–455, https://doi.org/10.1002/2014eo490002, 2014.

Planton, Y., Guilyardi, E., Wittenberg, A. T., Lee, J., Gleckler, P. J., Bayr, T., McGregor, S., McPhaden, M. J., Power, S. B., Roehrig, R., Vialard, J., and Voldoire, A.: Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package, B. Am. Mete-orol. Soc., 102, E193–E217, https://doi.org/10.1175/bams-d-19-0337.1, 2021.

Planton, Y. Y., Lee, J., Wittenberg, A. T. Gleckler, P. J., Guilyardi, E., McGregor, S., and McPhaden, M. J.: Estimating uncertainty in simulated ENSO statistics, J. Adv. Model. Earth Sy., ESS Open Archive [preprint], https://doi.org/10.22541/essoar.170196744.48068128/v1, 2023.

PMP Installation: http://pcmdi.github.io/pcmdimetrics/install.html, last access: 8 May 2024.

Potter, G. L., Bader, D. C., Riches, M., Bamzai, A. and Joseph, R.: Celebrating two decades of the Program for Climate Model Diagnosis and Intercomparison, B. Am. Meteorol. Soc., 92, 629–631, https://doi.org/10.1175/2011BAMS3018.1, 2011.

Qin, Y., Zelinka, M. D., and Klein, S. A.: On the Correspondence Between Atmosphere-Only and Coupled Simulations for Radiative Feedbacks and Forcing From CO₂, J. Geophys. Res.-Atmos., 127, e2021JD035460, https://doi.org/10.1029/2021jd035460, 2022.

Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., and Stouffer, R. J.: Climate models and their evaluation, in: Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR), Cambridge University Press, 589–662, ISBN 978-0-521-88009-1, 2007.

Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Bur-rows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron-
J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029, 1972.

Madden, R. A. and Julian, P.: Observations of the 40–50-Day Tropi- cal Oscillation – A Review, Mon. Weather Rev., 122, 814–837, https://doi.org/10.1175/1520-0493(1994)122, 1994.

Maloney, E. D., Gettelman, A., Ming, Y., Neelin, J. D., Barrie, D., Mariotti, A., Chen, C., Coleman, D., Kuo, Y. H., Singh, B., An- namalai, H., Berg, A., Booth, J. F., Camargo, S. J., Dai, A., Gon- zalez, A., Hafner, J., Jiang, X., Jing, X., Kim, D. H., Kumar, A., Moon, Y., Naud, C. M., Sobel, A. H., Suzuki, K., Wang, F., Wang, J., Wing, A. A., Xu, X., and Zhao, M.: Process-Oriented evaluation of climate and weather forecasting models, B. Am. Meteorol. Soc., 100, 1665–1686, https://doi.org/10.1175/bams- d-18-0042.1, 2019.

Martin, G. M., Klingaman, N. P., and Moise, A. F.: Connecting spatial and temporal scales of tropical precipitation in observa- tions and the MetUM-GA6, Geosci. Model Dev., 10, 105–126, https://doi.org/10.5194/gmd-10-105-2017, 2017.

McAvaney, B. J., Covey, C., Joussaume, S., Kattsov, V., Kitoh, A., Ogana, W., Pitman, A. J., Weaver, A. J., Wood, R. A., and Zhao, Z. C.: Model evaluation. In Climate Change 2001: The scien- tific basis, Contribution of WG1 to the Third Assessment Re- port of the IPCC (TAR) 471-523, Cambridge University Press, ISBN 0521 80767 0, 2001.

McPhaden, M. J., Zebiak, S. E., and Glantz, M. H.: ENSO as an integrating concept in Earth Science, Science, 314, 1740–1745, https://doi.org/10.1126/science.1132588, 2006.

McPhaden, M. J., Santoso, A., and Cai, W. (Eds.): El Niño Southern oscillation in a changing climate, American Geophysical Union, USA, 528 pp., ISBN 9781119548126, https://doi.org/10.1002/9781119548164, 2020.

Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.: Intercomparison makes for a better climate model, Eos T. Am. Geophys. Un., 78, 445, https://doi.org/10.1029/97eo00276, 1997.

Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.: The Coupled Model Intercomparison Project (CMIP), B. Am. Meteorol. Soc., 81, 313–318, 2000.

Meehl, G. A., Covey, C., Delworth, T. L., Latif, M., McAvaney, B. J., Mitchell, J. F. B., Stouffer, R. J., and Taylor, K. E.: THE WCRP CMIP3 Multimodel Dataset: A new era in cli- mate change research, B. Am. Meteorol. Soc., 88, 1383–1394, https://doi.org/10.1175/bams-88-9-1383, 2007.

Merrifield, A. L., Brunner, L., Lorenz, R., Humphrey, V., and Knutti, R.: Climate model Selection by Indepen- dence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications, Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, 2023.

Neelin, J. D., Krasting, J. P., Radhakrishnan, A., Liptak, J., Jackson, T. J., Ming, Y., Dong, W., Gettelman, A., Coleman, D., Maloney, E. D., Wing, A. A., Kuo, Y. H., Ahmed, F., Ullrich, P. A., Bitz, C. M., Neale, R., Ordonez, A., and Maroon, E.: Process-oriented diagnostics: principles, practice, community development and common standards, B. Am. Meteorol. Soc., 104, E1452–E1468, https://doi.org/10.1175/bams-d-21-0268.1, 2023.

Nowack, P., Runge, J., Eyring, V., and Haigh, J. D.: Causal networks for climate model evaluation and constrained projections, Nat. Commun., 11, 1415, https://doi.org/10.1038/s41467-020-15195- y, 2020.

Orbe, C., Van Roekel, L., Adames, Á. F., Dezfuli, A., Fasullo, J. T., Gleckler, P. J., Lee, J., Li, W., Nazarenko, L., Schmidt, G. A., Sperber, K. R., and Zhao, M.: Representation of modes of variability in six U.S. climate models, J. Climate, 33, 7591–7617, https://doi.org/10.1175/jcli-d-19-0956.1, 2020.

Ordonez, A. C., Klingaman, N. P., and Martin, G.: Analysing scales of precipitation, OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), https://doi.org/10.11578/dc.20211029.5, 2021.

Papalexiou, S. M., Rajulapati, C. R., Clark, M. P., and Lehner, F.: Robustness of CMIP6 historical global mean tempera- ture simulations: Trends, long-term persistence, autocorrelation, and distributional shape, Earth’s Future, 8, e2020EF001667, https://doi.org/10.1029/2020EF001667, 2020.

Pascoe, C., Lawrence, B. N., Guilyardi, E., Juckes, M., and Taylor, K. E.: Documenting numerical experiments in support of the Coupled Model Intercomparison Project Phase 6 (CMIP6), Geosci. Model Dev., 13, 2149–2167, https://doi.org/10.5194/gmd-13-2149-2020, 2020.

PCMDI Simulation Summaries: https://pcmdi.llnl.gov/metrics/, last access: 8 May 2024.

Pendergrass, A. G., Gleckler, P. J., Leung, L. R., and Jakob, C.: Benchmarking simulated precipitation in earth system models, B. Am. Meteorol. Soc., 101, E814–E816, https://doi.org/10.1175/bams-d-19-0318.1, 2020.

Phillips, A. S., Deser, C., and Fasullo, J. T.: Evaluating modes of variability in climate models, Eos T. Am. Geophys. Un., 95, 453– 455, https://doi.org/10.1002/2014eo490002, 2014.

Planton, Y., Guilyardi, E., Wittenberg, A. T., Lee, J., Gleckler, P. J., Bayr, T., McGregor, S., McPhaden, M. J., Power, S. B., Roehrig, R., Vialard, J., and Voldoire, A.: Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package, B. Am. Mete- orol. Soc., 102, E193–E217, https://doi.org/10.1175/bams-d-19- 0337.1, 2021.

Planton, Y. Y., Lee, J., Wittenberg, A. T. Gleckler, P. J., Guilyardi, E., McGregor, S., and McPhaden, M. J.: Es- timating uncertainty in simulated ENSO statistics, J. Adv. Model. Earth Sy., ESS Open Archive [preprint], https://doi.org/10.22541/essoar.170196744.48068128/v1, 2023.

PMP Installation: http://pcmdi.github.io/pcmdimetrics/install. html, last access: 8 May 2024.

Potter, G. L., Bader, D. C., Riches, M., Bamzai, A. and Joseph, R.: Celebrating two decades of the Program for Climate Model Diagnosis and Intercomparison, B. Am. Meteorol. Soc., 92, 629– 631, https://doi.org/10.1175/2011BAMS3018.1, 2011.

Qin, Y., Zelinka, M. D., and Klein, S. A.: On the Correspondence Between Atmosphere-Only and Coupled Simulations for Radiative Feedbacks and Forcing From CO₂, J. Geophys. Res.-Atmos., 127, e2021JD035460, https://doi.org/10.1029/2021jd035460, 2022.

Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., and Stouffer, R. J.: Climate models and their evaluation, in: Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR), Cambridge University Press, 589–662, ISBN 978-0-521-88009- 1, 2007.

Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Bur- rows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron-
Smith, P. J., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E. L., Bacmeister, J. T., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M. A., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M. L., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D. C., Flanner, M., Foucar, J. G., Jacob, R., Keen, N. D., Klein, S. A., Liu, X., Salinger, A. G., Shrivastava, M., and Yang, Y.: An overview of the atmospheric component of the Energy Exascale Earth System model, J. Adv. Model. Earth Sy., 11, 2377–2411, https://doi.org/10.1029/2019ms001629, 2019.

Reed, K. A., Goldenson, N., Grotjahn, R., Gutowski, W. J., Jagannathan, K., Jones, A. D., Leung, L. R., McGinnis, S. A., Pryor, S. C., Srivastava, A. K., Ullrich, P. A., and Zarzycki, C. M.: Metrics as tools for bridging climate science and applications, WIREs Climate Change, 13, e799, https://doi.org/10.1002/wcc.799, 2022.

Reichler, T. and Kim, J.: How well do coupled models simulate today's climate?, B. Am. Meteorol. Soc., 89, 303–312, https://doi.org/10.1175/bams-89-3-303, 2008.

Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020.

Sanderson, B. M. and Wehner, M. F.: Weighting strategy for the Fourth National Climate Assessment, in: Climate Science Special Report: Fourth National Climate Assessment, Volume 1, edited by: Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., Dokken, D. J., Stewart, B. C., and Maycock, T. K., U.S. Global Change Research Program, Washington, DC, USA, 436–442, https://doi.org/10.7930/J06T0JS3, 2017.

Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017.

Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G. C., Klein, S. A., Marvel, K., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., Von Der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K., and Zelinka, M. D.: An assessment of Earth's climate sensitivity using multiple lines of evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019rg000678, 2020.

Singh, R., and AchutaRao, K.: Sensitivity of future climate change and uncertainty over India to performance-based model weighting, Climatic Change, 160, 385–406, https://doi.org/10.1007/s10584-019-02643-y, 2020.

Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., and Bronaugh, D.: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate, J. Geophys. Res.-Atmos., 118, 1716–1733, https://doi.org/10.1002/jgrd.50203, 2013.

Sperber, K. R.: Madden-Julian variability in NCAR CAM2.0 and CCSM2.0, Clim. Dynam., 23, 259–278, https://doi.org/10.1007/s00382-004-0447-4, 2004.

Sperber, K. R. and Annamalai, H.: The use of fractional accumulated precipitation for the evaluation of the annual cycle of monsoons, Clim. Dynam., 43, 3219–3244, https://doi.org/10.1007/s00382-014-2099-3, 2014.

Sperber, K. R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner, A., Wang, B., and Zhou, T.: The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulation of the late 20th century, Clim. Dynam., 41, 2711–2744, https://doi.org/10.1007/s00382-012-1607-6, 2013.

Sperber, K. R., Gualdi, S., Legutke, S., and Gayler, V.: The Madden-Julian oscillation in ECHAM4 coupled and uncoupled general circulation models, Clim. Dynam., 25, 117–140, https://doi.org/10.1007/s00382-005-0026-3, 2005.

Srivastava, A., Grotjahn, R., and Ullrich, P. A.: Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions, Weather Climate Extremes, 29, 100268, https://doi.org/10.1016/j.wace.2020.100268, 2020.

Steed, C. A., Shipman, G., Thornton, P., Ricciuto, D., Erickson, D. and Branstetter, M.: Practical application of parallel coordinates for climate model analysis, Procedia Comput. Sci., 9, 877–886, https://doi.org/10.1016/j.procs.2012.04.094, 2012.

Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYNAMICS of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Prog. Earth Planet. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z, 2019.

Stoner, A. M. K., Hayhoe, K., and Wuebbles, D. J.: Assessing general circulation model simulations of atmospheric teleconnection patterns, J. Climate, 22, 4348–4372, https://doi.org/10.1175/2009jcli2577.1, 2009.

Sung, H. M., Kim, J., Shim, S., Seo, J., Kwon, S.-H., Sun, M.-A., Moon, H.-J., Lee, J., Lim, Y. C., Boo, K.-O., Kim, Y., Lee, J., Lee, J., Kim, J.-S., Marzin, C., and Byun, Y.-H.: Climate change projection in the Twenty-First Century simulated by NIMS-KMA CMIP6 model based on new GHGs concentration pathways, Asia-Pac. J. Atmos. Sci., 57, 851–862, https://doi.org/10.1007/s13143-021-00225-6, 2021.

Tang, Q., Prather, M. J., Hsu, J., Ruiz, D. J., Cameron-Smith, P. J., Xie, S., and Golaz, J.-C.: Evaluation of the interactive stratospheric ozone (O3v2) module in the E3SM version 1 Earth system model, Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, 2021.

Tang, S., Fast, J. D., Zhang, K., Hardin, J. C., Varble, A. C., Shilling, J. E., Mei, F., Zawadowicz, M. A., and Ma, P.-L.: Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package, version 1: assessing E3SM aerosol predictions using aircraft, ship, and surface measurements, Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, 2022.

Tang, S., Varble, A. C., Fast, J. D., Zhang, K., Wu, P., Dong, X., Mei, F., Pekour, M., Hardin, J. C., and Ma, P.-L.: Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol-cloud interactions via field campaign and long-term observations, Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, 2023.
Smith, P. J., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E. L., Bacmeister, J. T., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M. A., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M. L., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D. C., Flanner, M., Foucar, J. G., Jacob, R., Keen, N. D., Klein, S. A., Liu, X., Salinger, A. G., Shrivastava, M., and Yang, Y.: An overview of the atmospheric component of the Energy Exascale Earth System model, J. Adv. Model. Earth Sy., 11, 2377–2411, https://doi.org/10.1029/2019ms001629, 2019.

Reed, K. A., Goldenson, N., Grotjahn, R., Gutowski, W. J., Jagannathan, K., Jones, A. D., Leung, L. R., McGinnis, S. A., Pryor, S. C., Srivastava, A. K., Ullrich, P. A., and Zarzycki, C. M.: Metrics as tools for bridging climate science and applications, WIREs Climate Change, 13, e799, https://doi.org/10.1002/wcc.799, 2022.

Reichler, T. and Kim, J.: How well do coupled models simulate today's climate?, B. Am. Meteorol. Soc., 89, 303–312, https://doi.org/10.1175/bams-89-3-303, 2008.

Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020.

Sanderson, B. M. and Wehner, M. F.: Weighting strategy for the Fourth National Climate Assessment, in: Climate Science Special Report: Fourth National Climate Assessment, Volume 1, edited by: Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., Dokken, D. J., Stewart, B. C., and Maycock, T. K., U.S. Global Change Research Program, Washington, DC, USA, 436–442, https://doi.org/10.7930/J06T0JS3, 2017.

Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017.

Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G. C., Klein, S. A., Marvel, K., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., Von Der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K., and Zelinka, M. D.: An assessment of Earth's climate sensitivity using multiple lines of evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019rg000678, 2020.

Singh, R., and AchutaRao, K.: Sensitivity of future climate change and uncertainty over India to performance-based model weighting, Climatic Change, 160, 385–406, https://doi.org/10.1007/s10584-019-02643-y, 2020.

Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., and Bronaugh, D.: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate, J. Geophys. Res.-Atmos., 118, 1716–1733, https://doi.org/10.1002/jgrd.50203, 2013.

Sperber, K. R.: Madden-Julian variability in NCAR CAM2.0 and CCSM2.0, Clim. Dynam., 23, 259–278, https://doi.org/10.1007/s00382-004-0447-4, 2004.

Sperber, K. R. and Annamalai, H.: The use of fractional accumulated precipitation for the evaluation of the annual cycle of monsoons, Clim. Dynam., 43, 3219–3244, https://doi.org/10.1007/s00382-014-2099-3, 2014.

Sperber, K. R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner, A., Wang, B., and Zhou, T.: The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulation of the late 20th century, Clim. Dynam., 41, 2711–2744, https://doi.org/10.1007/s00382-012-1607-6, 2013.

Sperber, K. R., Gualdi, S., Legutke, S., and Gayler, V.: The Madden-Julian oscillation in ECHAM4 coupled and uncoupled general circulation models, Clim. Dynam., 25, 117–140, https://doi.org/10.1007/s00382-005-0026-3, 2005.

Srivastava, A., Grotjahn, R., and Ullrich, P. A.: Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions, Weather Climate Extremes, 29, 100268, https://doi.org/10.1016/j.wace.2020.100268, 2020.

Steed, C. A., Shipman, G., Thornton, P., Ricciuto, D., Erickson, D. and Branstetter, M.: Practical application of parallel coordinates for climate model analysis, Procedia Comput. Sci., 9, 877–886, https://doi.org/10.1016/j.procs.2012.04.094, 2012.

Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYNAMICS of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Prog. Earth Planet. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z, 2019.

Stoner, A. M. K., Hayhoe, K., and Wuebbles, D. J.: Assessing general circulation model simulations of atmospheric teleconnection patterns, J. Climate, 22, 4348–4372, https://doi.org/10.1175/2009jcli2577.1, 2009.

Sung, H. M., Kim, J., Shim, S., Seo, J., Kwon, S.-H., Sun, M.-A., Moon, H.-J., Lee, J., Lim, Y. C., Boo, K.-O., Kim, Y., Lee, J., Lee, J., Kim, J.-S., Marzin, C., and Byun, Y.-H.: Climate change projection in the Twenty-First Century simulated by NIMS-KMA CMIP6 model based on new GHGs concentration pathways, Asia-Pac. J. Atmos. Sci., 57, 851–862, https://doi.org/10.1007/s13143-021-00225-6, 2021.

Tang, Q., Prather, M. J., Hsu, J., Ruiz, D. J., Cameron-Smith, P. J., Xie, S., and Golaz, J.-C.: Evaluation of the interactive stratospheric ozone (O3v2) module in the E3SM version 1 Earth system model, Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, 2021.

Tang, S., Fast, J. D., Zhang, K., Hardin, J. C., Varble, A. C., Shilling, J. E., Mei, F., Zawadowicz, M. A., and Ma, P.-L.: Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package, version 1: assessing E3SM aerosol predictions using aircraft, ship, and surface measurements, Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, 2022.

Tang, S., Varble, A. C., Fast, J. D., Zhang, K., Wu, P., Dong, X., Mei, F., Pekour, M., Hardin, J. C., and Ma, P.-L.: Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol-cloud interactions via field campaign and long-term observations, Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, 2023.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000jd900719, 2001.

Taylor, K. E.: Truly conserving with conservative remapping methods, Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, 2024.

Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/bams-d-11-00094.1, 2012.

Teixeira, J., Waliser, D. E., Ferraro, R., Gleckler, P. J., Lee, T., and Potter, G. L.: Satellite observations for CMIP5: The Genesis of OBS4MIPs, B. Am. Meteorol. Soc., 95, 1329–1334, https://doi.org/10.1175/bams-d-12-00204.1, 2014.

Tian, B. and Dong, X.: The Double-ITCZ Bias in CMIP3, CMIP5, and CMIP6 Models Based on Annual Mean Precipitation, Geophys. Res. Lett., 47, e2020GL087232, https://doi.org/10.1029/2020GL087232, 2020.

Ullrich, P. A. and Zarzycki, C. M.: TempestExtremes: a framework for scale-insensitive pointwise feature tracking on unstructured grids, Geosci. Model Dev., 10, 1069–1090, https://doi.org/10.5194/gmd-10-1069-2017, 2017.

Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., and Reed, K. A.: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets, Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, 2021.

U.S. Department of Energy (DOE): Benchmarking Simulated Precipitation in Earth System Models Workshop Report, DOE/SC-0203, U.S. Department of Energy Office of Science, Biological and Environmental Research (BER) Program, Germantown, Maryland, USA, 2020.

Vo, T., Po-Chedley, P., Boutte, J., Zhang, C., Lee, J., Gleckler, P., Durack, P., Taylor, K., and Golaz, J.-C.: Xarray Climate Data Analysis Tools (xCDAT): A Python Package for Simple and Robust Analysis of Climate Data, The 103rd AMS Annual Meeting, Abstract, 8–12 January, 2023, in Denver, Colorado, 11.3, 412648, 2023.

Waliser, D., Gleckler, P. J., Ferraro, R., Taylor, K. E., Ames, S., Biard, J., Bosilovich, M. G., Brown, O., Chepfer, H., Cinquini, L., Durack, P. J., Eyring, V., Mathieu, P.-P., Lee, T., Pinnock, S., Potter, G. L., Rixen, M., Saunders, R., Schulz, J., Thépaut, J.-N., and Tuma, M.: Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6, Geosci. Model Dev., 13, 2945–2958, https://doi.org/10.5194/gmd-13-2945-2020, 2020.

Waliser, D. E., Sperber, K. R., Hendon, H. H., Kim, D., Maloney, E. D., Wheeler, M. C., Weickmann, K. M., Zhang, C., Donner, L. J., Gottschalck, J., Higgins, W., Kang, I. S., Legler, D. M., Moncrieff, M. W., Schubert, S. D., Stern, W., Vitart, F., Wang, B., Wang, W., and Woolnough, S. J.: MJO Simulation Diagnostics, J. Climate, 22, 3006–3030, https://doi.org/10.1175/2008jcli2731.1, 2009.

Wang, J., Liu, X., Shen, H. W., and Lin, G.: Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots, IEEE T. Vis. Comput. G. R., 23, 81–90, https://doi.org/10.1109/TVCG.2016.2598830, 2017.

Wehner, M., Gleckler, P. J., and Lee, J.: Characterization of long period return values of extreme daily temperature and precipitation in the CMIP6 models: Part 1, model evaluation, Weather Climate Extremes, 30, 100283, https://doi.org/10.1016/j.wace.2020.100283, 2020.

Wehner, M., Lee, J., Risser, M. D., Ullrich, P. A., Gleckler, P. J., and Collins, W. D.: Evaluation of extreme sub-daily precipitation in high-resolution global climate model simulations, Philos. T. R. Soc. A., 379, 20190545, https://doi.org/10.1098/rsta.2019.0545, 2021.

Williams, D. N.: Visualization and analysis tools for ultra-scale climate data, Eos T. Am. Geophys. Un., 95, 377–378, https://doi.org/10.1002/2014eo420002, 2014.

Williams, D. N., Doutriaux, C., Drach, R., and McCoy, R.: The Flexible Climate Data Analysis Tools (CDAT) for Multi-model Climate Simulation Data, IEEE International Conference on Data Mining Workshops, 254–261, https://doi.org/10.1109/icdmw.2009.64, 2009.

Williams, D. N., Balaji, V., Cinquini, L., Denvil, S., Duffy, D. Q., Evans, B., Ferraro, R., Hansen, R., Lautenschlager, M., and Trenham, C.: A global repository for Planet-Sized experiments and observations, B. Am. Meteorol. Soc., 97, 803–816, https://doi.org/10.1175/bams-d-15-00132.1, 2016.

Wong, P. C., Shen, H. W., Leung, R., Hagos, S., Lee, T. Y., Tong, X. and Lu, K.: Visual analytics of large-scale climate model data, in: 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV), 85–92, https://doi.org/10.1109/LDAV.2014.7013208, 2014.

Xie, P., Joyce, R., Wu, S., Yoo, S. H., Yarosh, Y., Sun, F. and Lin, R.: Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998, J. Hydrometeorol., 18, 1617–1641, 2017.

Xue, Z. and Ullrich, P. A.: A Comprehensive Intermediate-Term Drought Evaluation System and Evaluation of Climate Data Products over the Conterminous United States, J. Hydrometeorol., 22, 2311–2337, https://doi.org/10.1175/jhm-d-20-0314.1, 2021.

Young, A. H., Knapp, K. R., Inamdar, A., Hankins, W., and Rossow, W. B.: The International Satellite Cloud Climatology Project H-Series climate data record product, Earth Syst. Sci. Data, 10, 583–593, https://doi.org/10.5194/essd-10-583-2018, 2018.

Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020.

Zelinka, M. D., Klein, S. A., Qin, Y., and Myers, T. A.: Evaluating climate models’ cloud feedbacks against expert judgment, J. Geophys. Res.-Atmos., 127, e2021JD035198, https://doi.org/10.1029/2021jd035198, 2022.

Zhang, C. and Hendon, H. H.: Propagating and standing components of the intraseasonal oscillation in tropical convection, J. Atmos. Sci., 54, 741–752, https://doi.org/10.1175/1520-0469(1997)054, 1997.

Zhang, C., Xie, S., Klein, S. A., Ma, H. Y., Tang, S., Van Weverberg, K., Morcrette, C. J., and Petch, J.: CAUSES: Diagnosis of the summertime warm bias in CMIP5 climate models at the ARM Southern Great Plains site, J. Geophys. Res.-Atmos., 123, 2968–2992, https://doi.org/10.1002/2017JD027200, 2018.

Zhang, C., Xie, S., Tao, C., Tang, S., Emmenegger, T., Neelin, J. D., Schiro, K. A., Lin, W., and Shaheen, Z.: The ARM data-oriented metrics and diagnostics package for climate models: A new tool
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183-7192, https://doi.org/10.1029/2000jd900719, 2001.

Taylor, K. E.: Truly conserving with conservative remapping methods, Geosci. Model Dev., 17, 415-430, https://doi.org/10.5194/gmd-17-415-2024, 2024.

Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485-498, https://doi.org/10.1175/bams-d-11-00094.1, 2012.

Teixeira, J., Waliser, D. E., Ferraro, R., Gleckler, P. J., Lee, T., and Potter, G. L.: Satellite observations for CMIP5: The Genesis of OBS4MIPs, B. Am. Meteorol. Soc., 95, 1329-1334, https://doi.org/10.1175/bams-d-12-00204.1, 2014.

Tian, B. and Dong, X.: The Double-ITCZ Bias in CMIP3, CMIP5, and CMIP6 Models Based on Annual Mean Precipitation, Geophys. Res. Lett., 47, e2020GL087232, https://doi.org/10.1029/2020GL087232, 2020.

Ullrich, P. A. and Zarzycki, C. M.: TempestExtremes: a framework for scale-insensitive pointwise feature tracking on unstructured grids, Geosci. Model Dev., 10, 1069-1090, https://doi.org/10.5194/gmd-10-1069-2017, 2017.

Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., and Reed, K. A.: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets, Geosci. Model Dev., 14, 5023-5048, https://doi.org/10.5194/gmd-14-5023-2021, 2021.

U.S. Department of Energy (DOE): Benchmarking Simulated Precipitation in Earth System Models Workshop Report, DOE/SC-0203, U.S. Department of Energy Office of Science, Biological and Environmental Research (BER) Program, Germantown, Maryland, USA, 2020.

Vo, T., Po-Chedley, P., Boutte, J., Zhang, C., Lee, J., Gleckler, P., Durack, P., Taylor, K., and Golaz, J.-C.: Xarray Climate Data Analysis Tools (xCDAT): A Python Package for Simple and Robust Analysis of Climate Data, The 103rd AMS Annual Meeting, Abstract, 8-12 January, 2023, in Denver, Colorado, 11.3, 412648, 2023.

Waliser, D., Gleckler, P. J., Ferraro, R., Taylor, K. E., Ames, S., Biard, J., Bosilovich, M. G., Brown, O., Chepfer, H., Cinquini, L., Durack, P. J., Eyring, V., Mathieu, P.-P., Lee, T., Pinnock, S., Potter, G. L., Rixen, M., Saunders, R., Schulz, J., Thépaut, J.-N., and Tuma, M.: Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6, Geosci. Model Dev., 13, 2945-2958, https://doi.org/10.5194/gmd-13-2945-2020, 2020.

Waliser, D. E., Sperber, K. R., Hendon, H. H., Kim, D., Maloney, E. D., Wheeler, M. C., Weickmann, K. M., Zhang, C., Donner, L. J., Gottschalck, J., Higgins, W., Kang, I. S., Legler, D. M., Moncrieff, M. W., Schubert, S. D., Stern, W., Vitart, F., Wang, B., Wang, W., and Woolnough, S. J.: MJO Simulation Diagnostics, J. Climate, 22, 3006-3030, https://doi.org/10.1175/2008jcli2731.1, 2009.

Wang, J., Liu, X., Shen, H. W., and Lin, G.: Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots, IEEE T. Vis. Comput. G. R., 23, 81-90, https://doi.org/10.1109/TVCG.2016.2598830, 2017.

Wehner, M., Gleckler, P. J., and Lee, J.: Characterization of long period return values of extreme daily temperature and precipitation in the CMIP6 models: Part 1, model evaluation, Weather Climate Extremes, 30, 100283, https://doi.org/10.1016/j.wace.2020.100283, 2020.

Wehner, M., Lee, J., Risser, M. D., Ullrich, P. A., Gleckler, P. J., and Collins, W. D.: Evaluation of extreme sub-daily precipitation in high-resolution global climate model simulations, Philos. T. R. Soc. A., 379, 20190545, https://doi.org/10.1098/rsta.2019.0545, 2021.

Williams, D. N.: Visualization and analysis tools for ultra-scale climate data, Eos T. Am. Geophys. Un., 95, 377-378, https://doi.org/10.1002/2014eo420002, 2014.

Williams, D. N., Doutriaux, C., Drach, R., and McCoy, R.: The Flexible Climate Data Analysis Tools (CDAT) for Multi-model Climate Simulation Data, IEEE International Conference on Data Mining Workshops, 254-261, https://doi.org/10.1109/icdmw.2009.64, 2009.

Williams, D. N., Balaji, V., Cinquini, L., Denvil, S., Duffy, D. Q., Evans, B., Ferraro, R., Hansen, R., Lautenschlager, M., and Trenham, C.: A global repository for Planet-Sized experiments and observations, B. Am. Meteorol. Soc., 97, 803-816, https://doi.org/10.1175/bams-d-15-00132.1, 2016.

Wong, P. C., Shen, H. W., Leung, R., Hagos, S., Lee, T. Y., Tong, X. and Lu, K.: Visual analytics of large-scale climate model data, in: 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV), 85-92, https://doi.org/10.1109/LDAV.2014.7013208, 2014.

Xie, P., Joyce, R., Wu, S., Yoo, S. H., Yarosh, Y., Sun, F. and Lin, R.: Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998, J. Hydrometeorol., 18, 1617-1641, 2017.

Xue, Z. and Ullrich, P. A.: A Comprehensive Intermediate-Term Drought Evaluation System and Evaluation of Climate Data Products over the Conterminous United States, J. Hydrometeorol., 22, 2311-2337, https://doi.org/10.1175/jhm-d-20-0314.1, 2021.

Young, A. H., Knapp, K. R., Inamdar, A., Hankins, W., and Rossow, W. B.: The International Satellite Cloud Climatology Project H-Series climate data record product, Earth Syst. Sci. Data, 10, 583-593, https://doi.org/10.5194/essd-10-583-2018, 2018.

Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020.

Zelinka, M. D., Klein, S. A., Qin, Y., and Myers, T. A.: Evaluating climate models' cloud feedbacks against expert judgment, J. Geophys. Res.-Atmos., 127, e2021JD035198, https://doi.org/10.1029/2021jd035198, 2022.

Zhang, C. and Hendon, H. H.: Propagating and standing components of the intraseasonal oscillation in tropical convection, J. Atmos. Sci., 54, 741-752, https://doi.org/10.1175/1520-0469(1997)054, 1997.

Zhang, C., Xie, S., Klein, S. A., Ma, H. Y., Tang, S., Van Weverberg, K., Morcrette, C. J., and Petch, J.: CAUSES: Diagnosis of the summertime warm bias in CMIP5 climate models at the ARM Southern Great Plains site, J. Geophys. Res.-Atmos., 123, 2968-2992, https://doi.org/10.1002/2017JD027200, 2018.

Zhang, C., Xie, S., Tao, C., Tang, S., Emmenegger, T., Neelin, J. D., Schiro, K. A., Lin, W., and Shaheen, Z.: The ARM data-oriented metrics and diagnostics package for climate models: A new tool
for evaluating climate models with field data, B. Am. Meteorol. Soc., 101, E1619-E1627, https://doi.org/10.1175/BAMS-D-19-0282.1, 2020.

Zhang, C., Golaz, J.-C., Forsyth, R., Vo, T., Xie, S., Shaheen, Z., Potter, G. L., Asay-Davis, X. S., Zender, C. S., Lin, W., Chen, C.-C., Terai, C. R., Mahajan, S., Zhou, T., Balaguru, K., Tang, Q., Tao, C., Zhang, Y., Emmenegger, T., Burrows, S., and Ullrich, P. A.: The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation, Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, 2022.

Zhao, B., Lin, P., Hu, A., Liu, H., Ding, M., Yu, Z., and Yu, Y.: Uncertainty in Atlantic Multidecadal Oscillation derived from different observed datasets and their possible causes, Front. Mar. Sci., 9, 1007646, https://doi.org/10.3389/fmars.2022.1007646, 2022.

Zhao, M., Golaz, J.-C., Held, I. M., Guo, H., Balaji, V., Benson, R., Chen, J. H., Chen, X., Donner, L. J., Dunne, J., Dunne, K. A., Durachta, J., Fan, S.-M., Freidenreich, S. M., Garner, S. T., Ginoux, P., Harris, L., Horowitz, L. W., Krasting, J. P., Langenhorst, A. R., Zhi, L., Lin, P., Lin, S. J., Malyshev, S., Mason, E., Milly, P. C. D., Ming, Y., Naik, V., Paulot, F., Paynter, D., Phillipps, P. J., Radhakrishnan, A., Ramaswamy, V., Robinson, T., Schwarzkopf, D., Seman, C. J., Shevliakova, E., Shen, Z., Shin, H. H., Silvers, L. G., Wilson, J. R., Winton, M., Wittenberg, A. T., Wyman, B., and Xiang, B.: The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTS, J. Adv. Model. Earth Sy., 10, 691–734, https://doi.org/10.1002/2017ms001208, 2018.
for evaluating climate models with field data, B. Am. Meteorol. Soc., 101, E1619-E1627, https://doi.org/10.1175/BAMS-D-19-0282.1, 2020.

Zhang, C., Golaz, J.-C., Forsyth, R., Vo, T., Xie, S., Shaheen, Z., Potter, G. L., Asay-Davis, X. S., Zender, C. S., Lin, W., Chen, C.-C., Terai, C. R., Mahajan, S., Zhou, T., Balaguru, K., Tang, Q., Tao, C., Zhang, Y., Emmenegger, T., Burrows, S., and Ullrich, P. A.: The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation, Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, 2022.

Zhao, B., Lin, P., Hu, A., Liu, H., Ding, M., Yu, Z., and Yu, Y.: Uncertainty in Atlantic Multidecadal Oscillation derived from different observed datasets and their possible causes, Front. Mar. Sci., 9, 1007646, https://doi.org/10.3389/fmars.2022.1007646, 2022.

Zhao, M., Golaz, J.-C., Held, I. M., Guo, H., Balaji, V., Benson, R., Chen, J. H., Chen, X., Donner, L. J., Dunne, J., Dunne, K. A., Durachta, J., Fan, S.-M., Freidenreich, S. M., Garner, S. T., Ginoux, P., Harris, L., Horowitz, L. W., Krasting, J. P., Langenhorst, A. R., Zhi, L., Lin, P., Lin, S. J., Malyshev, S., Mason, E., Milly, P. C. D., Ming, Y., Naik, V., Paulot, F., Paynter, D., Phillipps, P. J., Radhakrishnan, A., Ramaswamy, V., Robinson, T., Schwarzkopf, D., Seman, C. J., Shevliakova, E., Shen, Z., Shin, H. H., Silvers, L. G., Wilson, J. R., Winton, M., Wittenberg, A. T., Wyman, B., and Xiang, B.: The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTS, J. Adv. Model. Earth Sy., 10, 691–734, https://doi.org/10.1002/2017ms001208, 2018.