Articles | Volume 24, issue 14
https://doi.org/10.5194/acp-24-8105-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-8105-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using historical temperature to constrain the climate sensitivity, the transient climate response, and aerosol-induced cooling
Olaf Morgenstern
CORRESPONDING AUTHOR
National Institute of Water and Atmospheric Research (NIWA), Wellington, Aotearoa / New Zealand
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa / New Zealand
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Preprint withdrawn
Short summary
Short summary
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Vidya Varma, Olaf Morgenstern, Kalli Furtado, Paul Field, and Jonny Williams
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-438, https://doi.org/10.5194/acp-2021-438, 2021
Revised manuscript not accepted
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Short summary
We introduce a simple parametrisation whereby the immersion freezing temperature in the model is linked to the mineral dust distribution through a diagnostic function, thus invoking regional differences in the nucleation temperatures instead of the global default value of −10 °C. This provides a functionality to mimic the role of Ice Nucleating Particles in the atmosphere on influencing the short-wave radiation over the Southern Ocean region by impacting the cloud phase.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
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Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Chaim I. Garfinkel, Ohad Harari, Shlomi Ziskin Ziv, Jian Rao, Olaf Morgenstern, Guang Zeng, Simone Tilmes, Douglas Kinnison, Fiona M. O'Connor, Neal Butchart, Makoto Deushi, Patrick Jöckel, Andrea Pozzer, and Sean Davis
Atmos. Chem. Phys., 21, 3725–3740, https://doi.org/10.5194/acp-21-3725-2021, https://doi.org/10.5194/acp-21-3725-2021, 2021
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Fiona M. O'Connor, N. Luke Abraham, Mohit Dalvi, Gerd A. Folberth, Paul T. Griffiths, Catherine Hardacre, Ben T. Johnson, Ron Kahana, James Keeble, Byeonghyeon Kim, Olaf Morgenstern, Jane P. Mulcahy, Mark Richardson, Eddy Robertson, Jeongbyn Seo, Sungbo Shim, João C. Teixeira, Steven T. Turnock, Jonny Williams, Andrew J. Wiltshire, Stephanie Woodward, and Guang Zeng
Atmos. Chem. Phys., 21, 1211–1243, https://doi.org/10.5194/acp-21-1211-2021, https://doi.org/10.5194/acp-21-1211-2021, 2021
Short summary
Short summary
This paper calculates how changes in emissions and/or concentrations of different atmospheric constituents since the pre-industrial era have altered the Earth's energy budget at the present day using a metric called effective radiative forcing. The impact of land use change is also assessed. We find that individual contributions do not add linearly, and different Earth system interactions can affect the magnitude of the calculated effective radiative forcing.
Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Richard Querel, Israel Silber, and Connor J. Flynn
Geosci. Model Dev., 14, 43–72, https://doi.org/10.5194/gmd-14-43-2021, https://doi.org/10.5194/gmd-14-43-2021, 2021
Cited articles
Allen, M. and Tett, S.: Checking for model consistency in optimal fingerprinting, Clim. Dynam., 15, 419–434, https://doi.org/10.1007/s003820050291, 1999. a
Andreae, M., Jones, C., and Cox, P.: Strong present-day aerosol cooling implies a hot future, Nature, 435, 1187–1190, https://doi.org/10.1038/nature03671, 2005. a
Andrews, M. B., Ridley, J. K., Wood, R. A., Andrews, T., Blockley, E. W., Booth, B., Burke, E., Dittus, A. J., Florek, P., Gray, L. J., Haddad, S., Hardiman, S. C., Hermanson, L., Hodson, D., Hogan, E., Jones, G. S., Knight, J. R., Kuhlbrodt, T., Misios, S., Mizielinski, M. S., Ringer, M. A., Robson, J., and Sutton, R. T.: Historical simulations with HadGEM3-GC3.1 for CMIP6, J. Adv. Model. Earth Sy., 12, e2019MS001995, https://doi.org/10.1029/2019MS001995, 2020. a, b
Arrhenius, S.: Nature's heat usage, Nord. Tidsk., 14, 121–130, 1896. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a, b
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation (ESGF) [data set], https://doi.org/10.22033/ESGF/CMIP6.5195, 2018a. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., and Gastineau, G.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.13825, 2018b. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., and Gastineau, G.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.13827, 2018c. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., and Gastineau, G.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.13831, 2018d. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.5264, 2019. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7627, 2019a. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7604, 2019b. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7609, 2019c. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7605, 2020. a
Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah, C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A., Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M., Bodman, R., Dias, F. B., Domingues, C. M., Hannah, N., Heerdegen, A., Savita, A., Wales, S., Allen, C., Druken, K., Evans, B., Richards, C., Ridzwan, S. M., Roberts, D., Smillie, J., Snow, K., Ward, M., and Yang, R.: CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4271, 2019a. a
Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah, C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A., Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M., Bodman, R., Dias, F. B., Domingues, C. M., Hannah, N., Heerdegen, A., Savita, A., Wales, S., Allen, C., Druken, K., Evans, B., Richards, C., Ridzwan, S. M., Roberts, D., Smillie, J., Snow, K., Ward, M., and Yang, R.: CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4321, 2019b. a
Dix, M., Mackallah, C., Bi, D., Bodman, R., Marsland, S., Rashid, H., Woodhouse, M., and Druken, K.: CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.14365, 2020a. a
Dix, M., Mackallah, C., Bi, D., Bodman, R., Marsland, S., Rashid, H., Woodhouse, M., and Druken, K.: CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.14369, 2020b. a
Dix, M., Mackallah, C., Bi, D., Bodman, R., Marsland, S., Rashid, H., Woodhouse, M., and Druken, K.: CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.14377, 2020c. a
E3SM: E3SM-Project E3SM2.0 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.16953, 2022a. a
E3SM: E3SM-Project E3SM2.0 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.17024, 2022b. a
E3SM: E3SM-Project E3SM2.0 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.17026, 2022c. a
427 E3SM: E3SM-Project E3SM2.0 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.17030, 2022d. a
E3SM: The DOE E3SM model version 2: Overview of the physical model and initial model evaluation, EESM, https://climatemodeling.
science.energy.gov/news/doe-e3sm-model-version-2-overview-
physical-model-and-initial-model-evaluation (last access: 9 May 2024), 2022e. a
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, 2016. a, b
Flynn, C. M. and Mauritsen, T.: On the climate sensitivity and historical warming evolution in recent coupled model ensembles, Atmos. Chem. Phys., 20, 7829–7842, https://doi.org/10.5194/acp-20-7829-2020, 2020. a, b
Flynn, C. M., Huusko, L., Modak, A., and Mauritsen, T.: Strong aerosol cooling alone does not explain cold-biased mid-century temperatures in CMIP6 models, Atmos. Chem. Phys., 23, 15121–15133, https://doi.org/10.5194/acp-23-15121-2023, 2023. a, b, c, d
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity, 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, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m
Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019. a
Gillett, N., Kirchmeier-Young, M., Ribes, A., Shiogama, H., Hegerl, G. C., Knutti, R., Gastineau, G., John, J. G., Li, L., Nazarenko, L., Rosenbloom, N., Seland, O., Wu, T., Yukimoto, S., and Ziehn, T.: Constraining human contributions to observed warming since the pre-industrial period, Nat. Clim. Change, 11, 207–212, https://doi.org/10.1038/s41558-020-00965-9, 2021. a, b, c, d, e, f, g, h, i
Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., Santer, B. D., Stone, D., and Tebaldi, C.: The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6, Geosci. Model Dev., 9, 3685–3697, https://doi.org/10.5194/gmd-9-3685-2016, 2016. a, b, c
Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P.-L., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N., Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Di Vittorio, A. V., Dang, C., Conlon, L. M., Chen, C.-C.-J., Brunke, M. A., Bisht, G., Benedict, J. J., Asay-Davis, X. S., Zhang, Y., Zhang, M., Zeng, X., Xie, S., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Reeves Eyre, J. E. J., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM model version 2: Overview of the physical model and initial model evaluation, J. Adv. Model. Earth Sy., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022. a, b
Good, P.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.10851, 2019. a
Hasselmann, K.: Optimal fingerprints for the detection of time-dependent climate change, J. Climate, 6, 1957–1971, https://doi.org/10.1175/1520-0442(1993)006%3C1957:OFFTDO%3E2.0.CO;2, 1993. a
Hegerl, G., Hasselmann, K., Cubasch, U., Mitchell, J. F. B., Roeckner, E., Voss, R., and Waszkewitz, J.: Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change, Clim. Dynam., 13, 613–634, https://doi.org/10.1007/s003820050186, 1997. a
Hodnebrog, Ø., Myhre, G., Jouan, C., Andrews, T., Forster, P. M., Jia, H., Quaas, J., Loeb, N. G., Olivié, D. J. L., Schulz, M., and Paynter, D.: Recent reductions in aerosol emissions have increased Earth's energy imbalance, Communications Earth and Environment, 5, 166, https://doi.org/10.1038/s43247-024-01324-8, 2024. a, b
Horowitz, L. W., John, J. G., Blanton, C., McHugh, C., Radhakrishnan, A., Rand, K., Vahlenkamp, H., Zadeh, N. T., Wilson, C., Dunne, J. P., Ploshay, J., Winton, M., and Zeng, Y.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.8570, 2018a. a
Horowitz, L. W., John, J. G., Blanton, C., McHugh, C., Radhakrishnan, A., Rand, K., Vahlenkamp, H., Zadeh, N. T., Wilson, C., Dunne, J. P., Ploshay, J., Winton, M., and Zeng, Y.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.8571, 2018b. a
Horowitz, L. W., John, J. G., Blanton, C., McHugh, C., Radhakrishnan, A., Rand, K., Vahlenkamp, H., Zadeh, N. T., Wilson, C., Dunne, J. P., Ploshay, J., Winton, M., and Zeng, Y.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.8575, 2018c. a
John, J. G., Blanton, C., McHugh, C., Radhakrishnan, A., Rand, K., Vahlenkamp, H., Wilson, C., Zadeh, N. T., Dunne, J. P., Dussin, R., Horowitz, L. W., Krasting, J. P., Lin, P., Malyshev, S., Naik, V., Ploshay, J., Shevliakova, E., Silvers, L., Stock, C., Winton, M., and Zeng, Y.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.8686, 2018. a
Jones, G.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6051, 2019a. a
Jones, G.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6052, 2019b. a
Jones, G.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6059, 2019c. a
Knutti, R., Rugenstein, M., and Hegerl, G.: Beyond equilibrium climate sensitivity, Nat. Geosci., 10, 727–736, https://doi.org/10.1038/ngeo3017, 2017. a
Krasting, J. P., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Dunne, K. A., Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Silvers, L., Wyman, B., Zeng, Y., Adcroft, A., Dunne, J. P., Dussin, R., Guo, H., He, J., Held, I. M., Horowitz, L. W., Lin, P., Milly, P., Shevliakova, E., Stock, C., Winton, M., Wittenberg, A. T., Xie, Y., and Zhao, M.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.8597, 2018. a
Lee, J.-Y., Marotzke, J., Bala, G., Cao, L., Corti, S., Dunne, J., Engelbrecht, F., Fischer, E., Fyfe, J., Jones, C., Maycock, A., Mutemi, J., Ndiaye, O., Panickal, S., and Zhou, T.: Future Global Climate: Scenario-Based Projections and Near- Term Information, 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, Chap. 4, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 553–672, https://doi.org/10.1017/9781009157896.006, 2021. a, b, c
Li, L.: CAS FGOALS-g3 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3356, 2019. a
Li, L.: CAS FGOALS-g3 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3321, 2020a. a
Li, L.: CAS FGOALS-g3 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3323, 2020b. a
Li, L.: CAS FGOALS-g3 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3330, 2020c. a
Li, L.: CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3469, 2020d. a
Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J.-F., Stouffer, R. J., Taylor, K. E., and Schlund, M.: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models, Science Advances, 6, eaba1981, https://doi.org/10.1126/sciadv.aba1981, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Morgenstern, O.: Scripts and data for “Using historical temperature to constrain the climate sensitivity, the transient climate response, and aerosol-induced cooling”, to appear in Atmos. Chem. Phys., Zenodo [code and data set], https://doi.org/10.5281/zenodo.11366923, 2024. a
Morice, C.: HadCRUT5, Met Office Hadley Centre [data set], http://www.metoffice.gov.uk/hadobs/hadcrut5 (last access: 31 July 2023), 2022. a
Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick, R. E., Dunn, R. J. H., Osborn, T. J., Jones, P. D., and Simpson, I. R.: An updated assessment of near-surface temperature change from 1850: The HadCRUT5 data set, J. Geophys. Res.-Atmos., 126, e2019JD032361, https://doi.org/10.1029/2019JD032361, 2021. a, b, c
Müller, W., Ilyina, T., Li, H., Timmreck, C., Gayler, V., Wieners, K.-H., Botzet, M., Brovkin, V., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.15022, 2019a. a
Müller, W., Ilyina, T., Li, H., Timmreck, C., Gayler, V., Wieners, K.-H., Botzet, M., Brovkin, V., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.15024, 2019b. a
Müller, W., Ilyina, T., Li, H., Timmreck, C., Gayler, V., Wieners, K.-H., Botzet, M., Brovkin, V., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-sol, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.15030, 2019c. a
Müller, W., Ilyina, T., Li, H., Timmreck, C., Gayler, V., Wieners, K.-H., Botzet, M., Brovkin, V., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-volc, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.15033, 2019d. a
NASA/GISS: NASA-GISS GISS-E2.1G model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7127, 2018a. a
NASA/GISS: NASA-GISS GISS-E2.1G model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7079, 2018b. a
NASA/GISS: NASA-GISS GISS-E2.1G model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7081, 2018c. a
NASA/GISS: NASA-GISS GISS-E2.1G model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7415, 2018d. a
National Archives: Open Government Licence for public sector information, http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3, last access: 9 July 2024. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6109, 2019. a
Scafetta, N.: CMIP6 GCM Validation Based on ECS and TCR Ranking for 21st Century Temperature Projections and Risk Assessment, Atmosphere, 14, 345, https://doi.org/10.3390/atmos14020345, 2023. a
Schurer, A., Hegerl, G., Ribes, A., Polson, D., Morice, C., and Tett, S.: Estimating the Transient Climate Response from Observed Warming, J. Climate, 31, 8645–8663, https://doi.org/10.1175/JCLI-D-17-0717.1, 2018. a, b, c, d
Seland, Ø., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.8036, 2019a. a
Seland, Ø., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7966, 2019b. a
Seland, Ø., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7969, 2019c. a
Seland, Ø., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.7979, 2019d. a
Seland, Ø., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.8253, 2019e. a
Shiogama, H.: MIROC MIROC6 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.5578, 2019a. a
Shiogama, H.: MIROC MIROC6 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.5579, 2019b. a
Shiogama, H.: MIROC MIROC6 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.5583, 2019c. a
Shiogama, H., Abe, M., and Tatebe, H.: MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.5746, 2019. a
Smith, C. J., Harris, G. R., Palmer, M. D., Bellouin, N., Collins, W., Myhre, G., Schulz, M., Golaz, J.-C., Ringer, M., Storelvmo, T., and Forster, P. M.: Energy Budget Constraints on the Time History of Aerosol Forcing and Climate Sensitivity, J. Geophys. Res.-Atmos., 126, e2020JD033622, https://doi.org/10.1029/2020JD033622, 2021. a, b
Stone, D. A., Allen, M. R., Selten, F., Kliphuis, M., and Stott, P. A.: The detection and attribution of climate change Using an ensemble of opportunity, J. Climate, 20, 504–516, https://doi.org/10.1175/JCLI3966.1, 2007a. a
Stone, D. A., Allen, M. R., and Stott, P. A.: A multimodel update on the detection and attribution of global surface warming, J. Climate, 20, 517–530, https://doi.org/10.1175/JCLI3964.1, 2007b. a
Storelvmo, T., Leirvik, T., Lohmann, U., Phillips, P., and Wild, M.: Disentangling greenhouse warming and aerosol cooling to reveal Earth's climate sensitivity, Nat. Geosci., 9, 286–289, https://doi.org/10.1038/ngeo2670, 2016. a, b
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3610, 2019a. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3596, 2019b. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3597, 2019c. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3601, 2019d. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3685, 2019e. a
Szopa, S., Naik, V., Adhikary, B., Artaxo, P., Berntsen, T., Collins, W., Fuzzi, S., Gallardo, L., Kiendler-Scharr, A., Klimont, Z., Liao, H., Unger, N., and Zanis, P.: Short-Lived Climate Forcers, 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, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 817–922, https://doi.org/10.1017/9781009157896.008, 2021. a, b
Tatebe, H. and Watanabe, M.: MIROC MIROC6 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.5603, 2018. a
Voldoire, A.: CMIP6 simulations of the CNRM-CERFACS based on CNRM-CM6-1 model for CMIP experiment historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4066, 2018. a
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4043, 2019a. a
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4044, 2019b. a
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4048, 2019c. a
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4189, 2019d. a
Watson-Parris, D. and Smith, C.: Large uncertainty in future warming due to aerosol forcing, Nat. Clim. Change, 12, 1111–1113, https://doi.org/10.1038/s41558-022-01516-0, 2022. a
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6595, 2019. a
Wu, T., Chu, M., Dong, M., Fang, Y., Jie, W., Li, J., Li, W., Liu, Q., Shi, X., Xin, X., Yan, J., Zhang, F., Zhang, J., Zhang, L., and Zhang, Y.: BCC BCC-CSM2MR model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.2948, 2018. a
Wu, T., Chu, M., Dong, M., Fang, Y., Jie, W., Li, J., Li, W., Liu, Q., Shi, X., Xin, X., Yan, J., Zhang, F., Zhang, J., Zhang, L., and Zhang, Y.: BCC BCC-CSM2MR model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.2924, 2019a. a
Wu, T., Chu, M., Dong, M., Fang, Y., Jie, W., Li, J., Li, W., Liu, Q., Shi, X., Xin, X., Yan, J., Zhang, F., Zhang, J., Zhang, L., and Zhang, Y.: BCC BCC-CSM2MR model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.2925, 2019b. a
Wu, T., Chu, M., Dong, M., Fang, Y., Jie, W., Li, J., Li, W., Liu, Q., Shi, X., Xin, X., Yan, J., Zhang, F., Zhang, J., Zhang, L., and Zhang, Y.: BCC BCC-CSM2MR model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.2929, 2019c. a
Xin, X., Wu, T., Shi, X., Zhang, F., Li, J., Chu, M., Liu, Q., Yan, J., Ma, Q., and Wei, M.: BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.3030, 2019. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6842, 2019a. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6820, 2019b. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6821, 2019c. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6825, 2019d. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.6910, 2019e. a
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O'Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP historical, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4272, 2019a. a
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O'Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP ssp245, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.4322, 2019b. a
Ziehn, T., Dix, M., Mackallah, C., Chamberlain, M., Lenton, A., Law, R., Druken, K., and Ridzwan, S. M.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 DAMIP hist-GHG, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.14366, 2020a. a
Ziehn, T., Dix, M., Mackallah, C., Chamberlain, M., Lenton, A., Law, R., Druken, K., and Ridzwan, S. M.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 DAMIP hist-aer, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.14370, 2020b. a
Ziehn, T., Dix, M., Mackallah, C., Chamberlain, M., Lenton, A., Law, R., Druken, K., and Ridzwan, S. M.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 DAMIP hist-nat, ESGF [data set], https://doi.org/10.22033/ESGF/CMIP6.14378, 2020c. a
Executive editor
The question of the climate sensitivity of CMIP models (used for IPCC) is a central question regarding the reliability of climate projections. Aerosol aspects are central here
The question of the climate sensitivity of CMIP models (used for IPCC) is a central question...
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I use errors in climate model simulations to derive correction factors for the impacts of greenhouse gases and particles that bring these simulated temperature fields into agreement with an observational reconstruction of the Earth's temperature. On average across eight models, a reduction by about one-half of the particle-induced cooling would be required, causing only 0.24 K of cooling since 1850–1899. The greenhouse gas warming simulated by several highly sensitive models would also reduce.
I use errors in climate model simulations to derive correction factors for the impacts of...
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