LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2
The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CES...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
Wiley Periodicals, Inc.
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/2027.42/172265 https://doi.org/10.1029/2021MS002776 |
| id |
ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/172265 |
|---|---|
| record_format |
openpolar |
| institution |
Open Polar |
| collection |
University of Michigan: Deep Blue |
| op_collection_id |
ftumdeepblue |
| language |
unknown |
| topic |
Community Earth System Model version 2 cloud parameterizations cloud feedback equilibrium climate sensitivity Last Glacial Maximum Geological Sciences Science |
| spellingShingle |
Community Earth System Model version 2 cloud parameterizations cloud feedback equilibrium climate sensitivity Last Glacial Maximum Geological Sciences Science Zhu, Jiang Otto-Bliesner, Bette L. Brady, Esther C. Gettelman, Andrew Bacmeister, Julio T. Neale, Richard B. Poulsen, Christopher J. Shaw, Jonah K. McGraw, Zachary S. Kay, Jennifer E. LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2 |
| topic_facet |
Community Earth System Model version 2 cloud parameterizations cloud feedback equilibrium climate sensitivity Last Glacial Maximum Geological Sciences Science |
| description |
The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time-step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed-phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid-to-high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate-calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol-cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics than CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection.Plain Language SummaryThe Community Earth System Model version 2 (CESM2) shows a much higher equilibrium climate sensitivity (ECS > 5°C) than its predecessor models (≤4°C), which, if true, implies a greater future warming than previously thought and a more severe challenge for climate adaptation and mitigation. It is critical to determine whether the high ... |
| format |
Article in Journal/Newspaper |
| author |
Zhu, Jiang Otto-Bliesner, Bette L. Brady, Esther C. Gettelman, Andrew Bacmeister, Julio T. Neale, Richard B. Poulsen, Christopher J. Shaw, Jonah K. McGraw, Zachary S. Kay, Jennifer E. |
| author_facet |
Zhu, Jiang Otto-Bliesner, Bette L. Brady, Esther C. Gettelman, Andrew Bacmeister, Julio T. Neale, Richard B. Poulsen, Christopher J. Shaw, Jonah K. McGraw, Zachary S. Kay, Jennifer E. |
| author_sort |
Zhu, Jiang |
| title |
LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2 |
| title_short |
LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2 |
| title_full |
LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2 |
| title_fullStr |
LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2 |
| title_full_unstemmed |
LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2 |
| title_sort |
lgm paleoclimate constraints inform cloud parameterizations and equilibrium climate sensitivity in cesm2 |
| publisher |
Wiley Periodicals, Inc. |
| publishDate |
2022 |
| url |
https://hdl.handle.net/2027.42/172265 https://doi.org/10.1029/2021MS002776 |
| genre |
Arctic |
| genre_facet |
Arctic |
| op_relation |
Zhu, Jiang; Otto-Bliesner, Bette L. Brady, Esther C.; Gettelman, Andrew; Bacmeister, Julio T.; Neale, Richard B.; Poulsen, Christopher J.; Shaw, Jonah K.; McGraw, Zachary S.; Kay, Jennifer E. (2022). "LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2." Journal of Advances in Modeling Earth Systems 14(4): n/a-n/a. 1942-2466 https://hdl.handle.net/2027.42/172265 doi:10.1029/2021MS002776 Journal of Advances in Modeling Earth Systems Patnaude, R., Diao, M., Liu, X., & Chu, S. ( 2021 ). Effects of thermodynamics, dynamics and aerosols on cirrus clouds based on in situ observations and NCAR CAM6. Atmospheric Chemistry and Physics, 21 ( 3 ), 1835 – 1859. https://doi.org/10.5194/acp-21-1835-2021 Manabe, S., & Broccoli, A. J. ( 1985 ). A comparison of climate model sensitivity with data from the Last Glacial Maximum. Journal of the Atmospheric Sciences, 42 ( 23 ), 2643 – 2651. https://doi.org/10.1175/1520-0469(1985)042<2643:ACOCMS>2.0.CO;2 Marchand, R., Ackerman, T., Smyth, M., & Rossow, W. B. ( 2010 ). A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS. Journal of Geophysical Research, 115 ( D16 ), D16206. https://doi.org/10.1029/2009JD013422 Meehl, G. A., Arblaster, J. M., Bates, S., Richter, J. H., Tebaldi, C., Gettelman, A., et al. ( 2020 ). Characteristics of future warmer base states in CESM2. Earth and Space Science, 7 ( 9 ), e2020EA001296. https://doi.org/10.1029/2020ea001296 Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J.-F., Stouffer, R. J., et al. ( 2020 ). Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6 ( 26 ), eaba1981. https://doi.org/10.1126/sciadv.aba1981 Mülmenstädt, J., Salzmann, M., Kay, J. E., Zelinka, M. D., Ma, P.-L., Nam, C., et al. ( 2021 ). An underestimated negative cloud feedback from cloud lifetime changes. Nature Climate Change, 11 ( 6 ), 508 – 513. https://doi.org/10.1038/s41558-021-01038-1 Myers, T. A., Scott, R. C., Zelinka, M. D., Klein, S. A., Norris, J. R., & Caldwell, P. M. ( 2021 ). Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity. Nature Climate Change, 11 ( 6 ), 501 – 507. https://doi.org/10.1038/s41558-021-01039-0 Nam, C., Bony, S., Dufresne, J.-L., & Chepfer, H. ( 2012 ). The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models. Geophysical Research Letters, 39 ( 21 ). https://doi.org/10.1029/2012GL053421 Ohgaito, R., Abe-Ouchi, A., O’Ishi, R., Takemura, T., Ito, A., Hajima, T., et al. ( 2018 ). Effect of high dust amount on surface temperature during the Last Glacial Maximum: A modelling study using MIROC-ESM. Climate of the Past, 14 ( 11 ), 1565 – 1581. https://doi.org/10.5194/cp-14-1565-2018 Otto-Bliesner, B. L., Brady, E. C., Clauzet, G., Tomas, R., Levis, S., & Kothavala, Z. ( 2006 ). Last Glacial Maximum and Holocene climate in CCSM3. Journal of Climate, 19 ( 11 ), 2526 – 2544. https://doi.org/10.1175/JCLI3748.1 Peltier, W. R., Argus, D. F., & Drummond, R. ( 2015 ). Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model. Journal of Geophysical Research: Solid Earth, 120 ( 1 ), 450 – 487. https://doi.org/10.1002/2014JB011176 Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., & Patrick Hofmann, R. J. ( 2012 ). Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. Journal of Climate, 25 ( 13 ), 4699 – 4720. https://doi.org/10.1175/jcli-d-11-00267.1 Santos, S. P., Caldwell, P. M., & Bretherton, C. S. ( 2020 ). Numerically relevant timescales in the MG2 microphysics model. Journal of Advances in Modeling Earth Systems, 12 ( 4 ), e2019MS001972. https://doi.org/10.1029/2019MS001972 Schmittner, A., Urban, N. M., Shakun, J. D., Mahowald, N. M., Clark, P. U., Bartlein, P. J., et al. ( 2011 ). Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum. Science, 334 ( 6061 ), 1385 – 1388. https://doi.org/10.1126/science.1203513 Shaw, J. K., McGraw, Z., Bruno, O., Storelvmo, T., & Hofer, S. ( 2022 ). Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds. Geophysical Research Letters, 49 ( 3 ), e2021GL096191. https://doi.org/10.1029/2021GL096191 Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., et al. ( 2020 ). An assessment of Earth’s climate sensitivity using multiple lines of evidence. Reviews of Geophysics, 58 ( 4 ), e2019RG000678. https://doi.org/10.1029/2019RG000678 Shin, S. I., Liu, Z., Otto-Bliesner, B., Brady, E., Kutzbach, J., & Harrison, S. ( 2003 ). A simulation of the Last Glacial Maximum climate using the NCAR-CCSM. Climate Dynamics, 20 ( 2–3 ), 127 – 151. https://doi.org/10.1007/s00382-002-0260-x Swales, D. J., Pincus, R., & Bodas-Salcedo, A. ( 2018 ). The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2. Geoscientific Model Development, 11 ( 1 ), 77 – 81. https://doi.org/10.5194/gmd-11-77-2018 Tan, I., Storelvmo, T., & Zelinka, M. D. ( 2016 ). Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science, 352 ( 6282 ), 224 – 227. https://doi.org/10.1126/science.aad5300 Taylor, K. E. ( 2001 ). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, 106 ( D7 ), 7183 – 7192. https://doi.org/10.1029/2000JD900719 Taylor, K. E., Crucifix, M., Braconnot, P., Hewitt, C. D., Doutriaux, C., Broccoli, A. J., et al. ( 2007 ). Estimating shortwave radiative forcing and response in climate models. Journal of Climate, 20 ( 11 ), 2530 – 2543. https://doi.org/10.1175/JCLI4143.1 Tierney, J. E., Zhu, J., King, J., Malevich, S. B., Hakim, G. J., & Poulsen, C. J. ( 2020 ). Glacial cooling and climate sensitivity revisited. Nature, 584 ( 7822 ), 569 – 573. https://doi.org/10.1038/s41586-020-2617-x Wang, C., Soden, B. J., Yang, W., & Vecchi, G. A. ( 2021 ). Compensation between cloud feedback and aerosol-cloud interaction in CMIP6 models. Geophysical Research Letters, 48 ( 4 ), e2020GL091024. https://doi.org/10.1029/2020GL091024 Wang, Y., Liu, X., Hoose, C., & Wang, B. ( 2014 ). Different contact angle distributions for heterogeneous ice nucleation in the Community Atmospheric Model version 5. Atmospheric Chemistry and Physics, 14 ( 19 ), 10411 – 10430. https://doi.org/10.5194/acp-14-10411-2014 Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al. ( 2020 ). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47 ( 1 ), e2019GL085782. https://doi.org/10.1029/2019GL085782 Zhu, J., Liu, Z., Brady, E., Otto-Bliesner, B., Zhang, J., Noone, D., et al. ( 2017 ). Reduced ENSO variability at the LGM revealed by an isotope-enabled Earth system model. Geophysical Research Letters, 44 ( 13 ), 6984 – 6992. https://doi.org/10.1002/2017GL073406 Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., & DiNezio, P. ( 2021 ). Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum. Geophysical Research Letters, 48 ( 3 ), e2020GL091220. https://doi.org/10.1029/2020GL091220 Zhu, J., & Poulsen, C. J. ( 2020 ). On the increase of climate sensitivity and cloud feedback with warming in the community atmosphere models. Geophysical Research Letters, 47 ( 18 ), e2020GL089143. https://doi.org/10.1029/2020GL089143 Zhu, J., & Poulsen, C. J. ( 2021 ). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. Climate of the Past, 17 ( 1 ), 253 – 267. https://doi.org/10.5194/cp-17-253-2021 Zhu, J., Poulsen, C. J., & Otto-Bliesner, B. L. ( 2020 ). High climate sensitivity in CMIP6 model not supported by paleoclimate. Nature Climate Change, 10 ( 5 ), 378 – 379. https://doi.org/10.1038/s41558-020-0764-6 Zhu, J., Poulsen, C. J., & Tierney, J. E. ( 2019 ). Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. Science Advances, 5 ( 9 ), eaax1874. https://doi.org/10.1126/sciadv.aax1874 Abe-Ouchi, A., Saito, F., Kageyama, M., Braconnot, P., Harrison, S. P., Lambeck, K., et al. ( 2015 ). Ice-sheet configuration in the CMIP5/PMIP3 Last Glacial Maximum experiments. Geoscientific Model Development, 8 ( 11 ), 3621 – 3637. https://doi.org/10.5194/gmd-8-3621-2015 Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin, D., et al. ( 2018 ). The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere, 9 ( 4 ), 138. https://doi.org/10.3390/atmos9040138 Bacmeister, J. T., Hannay, C., Medeiros, B., Gettelman, A., Neale, R., Fredriksen, H. B., et al. ( 2020 ). CO 2 increase experiments using the CESM: Relationship to climate sensitivity and comparison of CESM1 to CESM2. Journal of Advances in Modeling Earth Systems, 12 ( 11 ), e2020MS002120. https://doi.org/10.1029/2020MS002120 Bitz, C. M., Shell, K. M., Gent, P. R., Bailey, D. A., Danabasoglu, G., Armour, K. C., et al. ( 2011 ). Climate sensitivity of the Community Climate System Model, version 4. Journal of Climate, 25 ( 9 ), 3053 – 3070. https://doi.org/10.1175/JCLI-D-11-00290.1 Bjordal, J., Storelvmo, T., Alterskjær, K., & Carlsen, T. ( 2020 ). Equilibrium climate sensitivity above 5°C plausible due to state-dependent cloud feedback. Nature Geoscience, 13 ( 11 ), 718 – 721. https://doi.org/10.1038/s41561-020-00649-1 Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., & Schanen, D. P. ( 2013 ). Higher-order turbulence closure and its impact on climate simulations in the community atmosphere model. Journal of Climate, 26 ( 23 ), 9655 – 9676. https://doi.org/10.1175/JCLI-D-13-00075.1 Brady, E. C., Otto-Bliesner, B. L., Kay, J. E., & Rosenbloom, N. ( 2013 ). Sensitivity to glacial forcing in the CCSM4. Journal of Climate, 26 ( 6 ), 1901 – 1925. https://doi.org/10.1175/JCLI-D-11-00416.1 Ceppi, P., & Nowack, P. ( 2021 ). Observational evidence that cloud feedback amplifies global warming. Proceedings of the National Academy of Sciences, 118 ( 30 ), e2026290118. https://doi.org/10.1073/pnas.2026290118 Cesana, G. V., & Del Genio, A. D. ( 2021 ). Observational constraint on cloud feedbacks suggests moderate climate sensitivity. Nature Climate Change, 11 ( 3 ), 213 – 218. https://doi.org/10.1038/s41558-020-00970-y Charney, J. G., Arakawa, A., Baker, D. J., Bolin, B., Dickinson, R. E., Goody, R. M., et al. ( 1979 ). Carbon dioxide and climate: A scientific assessment. National Academy of Sciences. Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P., et al. ( 2010 ). The GCM-oriented CALIPSO cloud product (CALIPSO-GOCCP). Journal of Geophysical Research, 115 ( D4 ), D00H16. https://doi.org/10.1029/2009JD012251 Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., et al. ( 2020 ). The Community Earth System Model version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12 ( 2 ), e2019MS001916. https://doi.org/10.1029/2019ms001916 DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy, C. H., et al. ( 2010 ). Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proceedings of the National Academy of Sciences, 107 ( 25 ), 11217 – 11222. https://doi.org/10.1073/pnas.0910818107 |
| op_rights |
IndexNoFollow |
| op_doi |
https://doi.org/10.1029/2021MS00277610.1029/2020ea00129610.1126/sciadv.aba198110.1038/s41558-021-01038-110.5194/cp-14-1565-201810.1126/science.120351310.1029/2019RG00067810.1029/2000JD90071910.1175/JCLI4143.110.1029/2019GL08578210.1002/2017GL07340610.5194 |
| _version_ |
1774713615874523136 |
| spelling |
ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/172265 2023-08-20T04:03:12+02:00 LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2 Zhu, Jiang Otto-Bliesner, Bette L. Brady, Esther C. Gettelman, Andrew Bacmeister, Julio T. Neale, Richard B. Poulsen, Christopher J. Shaw, Jonah K. McGraw, Zachary S. Kay, Jennifer E. 2022-04 application/pdf https://hdl.handle.net/2027.42/172265 https://doi.org/10.1029/2021MS002776 unknown Wiley Periodicals, Inc. National Academy of Sciences Zhu, Jiang; Otto-Bliesner, Bette L. Brady, Esther C.; Gettelman, Andrew; Bacmeister, Julio T.; Neale, Richard B.; Poulsen, Christopher J.; Shaw, Jonah K.; McGraw, Zachary S.; Kay, Jennifer E. (2022). "LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2." Journal of Advances in Modeling Earth Systems 14(4): n/a-n/a. 1942-2466 https://hdl.handle.net/2027.42/172265 doi:10.1029/2021MS002776 Journal of Advances in Modeling Earth Systems Patnaude, R., Diao, M., Liu, X., & Chu, S. ( 2021 ). Effects of thermodynamics, dynamics and aerosols on cirrus clouds based on in situ observations and NCAR CAM6. Atmospheric Chemistry and Physics, 21 ( 3 ), 1835 – 1859. https://doi.org/10.5194/acp-21-1835-2021 Manabe, S., & Broccoli, A. J. ( 1985 ). A comparison of climate model sensitivity with data from the Last Glacial Maximum. Journal of the Atmospheric Sciences, 42 ( 23 ), 2643 – 2651. https://doi.org/10.1175/1520-0469(1985)042<2643:ACOCMS>2.0.CO;2 Marchand, R., Ackerman, T., Smyth, M., & Rossow, W. B. ( 2010 ). A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS. Journal of Geophysical Research, 115 ( D16 ), D16206. https://doi.org/10.1029/2009JD013422 Meehl, G. A., Arblaster, J. M., Bates, S., Richter, J. H., Tebaldi, C., Gettelman, A., et al. ( 2020 ). Characteristics of future warmer base states in CESM2. Earth and Space Science, 7 ( 9 ), e2020EA001296. https://doi.org/10.1029/2020ea001296 Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J.-F., Stouffer, R. J., et al. ( 2020 ). Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6 ( 26 ), eaba1981. https://doi.org/10.1126/sciadv.aba1981 Mülmenstädt, J., Salzmann, M., Kay, J. E., Zelinka, M. D., Ma, P.-L., Nam, C., et al. ( 2021 ). An underestimated negative cloud feedback from cloud lifetime changes. Nature Climate Change, 11 ( 6 ), 508 – 513. https://doi.org/10.1038/s41558-021-01038-1 Myers, T. A., Scott, R. C., Zelinka, M. D., Klein, S. A., Norris, J. R., & Caldwell, P. M. ( 2021 ). Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity. Nature Climate Change, 11 ( 6 ), 501 – 507. https://doi.org/10.1038/s41558-021-01039-0 Nam, C., Bony, S., Dufresne, J.-L., & Chepfer, H. ( 2012 ). The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models. Geophysical Research Letters, 39 ( 21 ). https://doi.org/10.1029/2012GL053421 Ohgaito, R., Abe-Ouchi, A., O’Ishi, R., Takemura, T., Ito, A., Hajima, T., et al. ( 2018 ). Effect of high dust amount on surface temperature during the Last Glacial Maximum: A modelling study using MIROC-ESM. Climate of the Past, 14 ( 11 ), 1565 – 1581. https://doi.org/10.5194/cp-14-1565-2018 Otto-Bliesner, B. L., Brady, E. C., Clauzet, G., Tomas, R., Levis, S., & Kothavala, Z. ( 2006 ). Last Glacial Maximum and Holocene climate in CCSM3. Journal of Climate, 19 ( 11 ), 2526 – 2544. https://doi.org/10.1175/JCLI3748.1 Peltier, W. R., Argus, D. F., & Drummond, R. ( 2015 ). Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model. Journal of Geophysical Research: Solid Earth, 120 ( 1 ), 450 – 487. https://doi.org/10.1002/2014JB011176 Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., & Patrick Hofmann, R. J. ( 2012 ). Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. Journal of Climate, 25 ( 13 ), 4699 – 4720. https://doi.org/10.1175/jcli-d-11-00267.1 Santos, S. P., Caldwell, P. M., & Bretherton, C. S. ( 2020 ). Numerically relevant timescales in the MG2 microphysics model. Journal of Advances in Modeling Earth Systems, 12 ( 4 ), e2019MS001972. https://doi.org/10.1029/2019MS001972 Schmittner, A., Urban, N. M., Shakun, J. D., Mahowald, N. M., Clark, P. U., Bartlein, P. J., et al. ( 2011 ). Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum. Science, 334 ( 6061 ), 1385 – 1388. https://doi.org/10.1126/science.1203513 Shaw, J. K., McGraw, Z., Bruno, O., Storelvmo, T., & Hofer, S. ( 2022 ). Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds. Geophysical Research Letters, 49 ( 3 ), e2021GL096191. https://doi.org/10.1029/2021GL096191 Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., et al. ( 2020 ). An assessment of Earth’s climate sensitivity using multiple lines of evidence. Reviews of Geophysics, 58 ( 4 ), e2019RG000678. https://doi.org/10.1029/2019RG000678 Shin, S. I., Liu, Z., Otto-Bliesner, B., Brady, E., Kutzbach, J., & Harrison, S. ( 2003 ). A simulation of the Last Glacial Maximum climate using the NCAR-CCSM. Climate Dynamics, 20 ( 2–3 ), 127 – 151. https://doi.org/10.1007/s00382-002-0260-x Swales, D. J., Pincus, R., & Bodas-Salcedo, A. ( 2018 ). The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2. Geoscientific Model Development, 11 ( 1 ), 77 – 81. https://doi.org/10.5194/gmd-11-77-2018 Tan, I., Storelvmo, T., & Zelinka, M. D. ( 2016 ). Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science, 352 ( 6282 ), 224 – 227. https://doi.org/10.1126/science.aad5300 Taylor, K. E. ( 2001 ). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, 106 ( D7 ), 7183 – 7192. https://doi.org/10.1029/2000JD900719 Taylor, K. E., Crucifix, M., Braconnot, P., Hewitt, C. D., Doutriaux, C., Broccoli, A. J., et al. ( 2007 ). Estimating shortwave radiative forcing and response in climate models. Journal of Climate, 20 ( 11 ), 2530 – 2543. https://doi.org/10.1175/JCLI4143.1 Tierney, J. E., Zhu, J., King, J., Malevich, S. B., Hakim, G. J., & Poulsen, C. J. ( 2020 ). Glacial cooling and climate sensitivity revisited. Nature, 584 ( 7822 ), 569 – 573. https://doi.org/10.1038/s41586-020-2617-x Wang, C., Soden, B. J., Yang, W., & Vecchi, G. A. ( 2021 ). Compensation between cloud feedback and aerosol-cloud interaction in CMIP6 models. Geophysical Research Letters, 48 ( 4 ), e2020GL091024. https://doi.org/10.1029/2020GL091024 Wang, Y., Liu, X., Hoose, C., & Wang, B. ( 2014 ). Different contact angle distributions for heterogeneous ice nucleation in the Community Atmospheric Model version 5. Atmospheric Chemistry and Physics, 14 ( 19 ), 10411 – 10430. https://doi.org/10.5194/acp-14-10411-2014 Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al. ( 2020 ). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47 ( 1 ), e2019GL085782. https://doi.org/10.1029/2019GL085782 Zhu, J., Liu, Z., Brady, E., Otto-Bliesner, B., Zhang, J., Noone, D., et al. ( 2017 ). Reduced ENSO variability at the LGM revealed by an isotope-enabled Earth system model. Geophysical Research Letters, 44 ( 13 ), 6984 – 6992. https://doi.org/10.1002/2017GL073406 Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., & DiNezio, P. ( 2021 ). Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum. Geophysical Research Letters, 48 ( 3 ), e2020GL091220. https://doi.org/10.1029/2020GL091220 Zhu, J., & Poulsen, C. J. ( 2020 ). On the increase of climate sensitivity and cloud feedback with warming in the community atmosphere models. Geophysical Research Letters, 47 ( 18 ), e2020GL089143. https://doi.org/10.1029/2020GL089143 Zhu, J., & Poulsen, C. J. ( 2021 ). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. Climate of the Past, 17 ( 1 ), 253 – 267. https://doi.org/10.5194/cp-17-253-2021 Zhu, J., Poulsen, C. J., & Otto-Bliesner, B. L. ( 2020 ). High climate sensitivity in CMIP6 model not supported by paleoclimate. Nature Climate Change, 10 ( 5 ), 378 – 379. https://doi.org/10.1038/s41558-020-0764-6 Zhu, J., Poulsen, C. J., & Tierney, J. E. ( 2019 ). Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. Science Advances, 5 ( 9 ), eaax1874. https://doi.org/10.1126/sciadv.aax1874 Abe-Ouchi, A., Saito, F., Kageyama, M., Braconnot, P., Harrison, S. P., Lambeck, K., et al. ( 2015 ). Ice-sheet configuration in the CMIP5/PMIP3 Last Glacial Maximum experiments. Geoscientific Model Development, 8 ( 11 ), 3621 – 3637. https://doi.org/10.5194/gmd-8-3621-2015 Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin, D., et al. ( 2018 ). The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere, 9 ( 4 ), 138. https://doi.org/10.3390/atmos9040138 Bacmeister, J. T., Hannay, C., Medeiros, B., Gettelman, A., Neale, R., Fredriksen, H. B., et al. ( 2020 ). CO 2 increase experiments using the CESM: Relationship to climate sensitivity and comparison of CESM1 to CESM2. Journal of Advances in Modeling Earth Systems, 12 ( 11 ), e2020MS002120. https://doi.org/10.1029/2020MS002120 Bitz, C. M., Shell, K. M., Gent, P. R., Bailey, D. A., Danabasoglu, G., Armour, K. C., et al. ( 2011 ). Climate sensitivity of the Community Climate System Model, version 4. Journal of Climate, 25 ( 9 ), 3053 – 3070. https://doi.org/10.1175/JCLI-D-11-00290.1 Bjordal, J., Storelvmo, T., Alterskjær, K., & Carlsen, T. ( 2020 ). Equilibrium climate sensitivity above 5°C plausible due to state-dependent cloud feedback. Nature Geoscience, 13 ( 11 ), 718 – 721. https://doi.org/10.1038/s41561-020-00649-1 Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., & Schanen, D. P. ( 2013 ). Higher-order turbulence closure and its impact on climate simulations in the community atmosphere model. Journal of Climate, 26 ( 23 ), 9655 – 9676. https://doi.org/10.1175/JCLI-D-13-00075.1 Brady, E. C., Otto-Bliesner, B. L., Kay, J. E., & Rosenbloom, N. ( 2013 ). Sensitivity to glacial forcing in the CCSM4. Journal of Climate, 26 ( 6 ), 1901 – 1925. https://doi.org/10.1175/JCLI-D-11-00416.1 Ceppi, P., & Nowack, P. ( 2021 ). Observational evidence that cloud feedback amplifies global warming. Proceedings of the National Academy of Sciences, 118 ( 30 ), e2026290118. https://doi.org/10.1073/pnas.2026290118 Cesana, G. V., & Del Genio, A. D. ( 2021 ). Observational constraint on cloud feedbacks suggests moderate climate sensitivity. Nature Climate Change, 11 ( 3 ), 213 – 218. https://doi.org/10.1038/s41558-020-00970-y Charney, J. G., Arakawa, A., Baker, D. J., Bolin, B., Dickinson, R. E., Goody, R. M., et al. ( 1979 ). Carbon dioxide and climate: A scientific assessment. National Academy of Sciences. Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P., et al. ( 2010 ). The GCM-oriented CALIPSO cloud product (CALIPSO-GOCCP). Journal of Geophysical Research, 115 ( D4 ), D00H16. https://doi.org/10.1029/2009JD012251 Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., et al. ( 2020 ). The Community Earth System Model version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12 ( 2 ), e2019MS001916. https://doi.org/10.1029/2019ms001916 DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy, C. H., et al. ( 2010 ). Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proceedings of the National Academy of Sciences, 107 ( 25 ), 11217 – 11222. https://doi.org/10.1073/pnas.0910818107 IndexNoFollow Community Earth System Model version 2 cloud parameterizations cloud feedback equilibrium climate sensitivity Last Glacial Maximum Geological Sciences Science Article 2022 ftumdeepblue https://doi.org/10.1029/2021MS00277610.1029/2020ea00129610.1126/sciadv.aba198110.1038/s41558-021-01038-110.5194/cp-14-1565-201810.1126/science.120351310.1029/2019RG00067810.1029/2000JD90071910.1175/JCLI4143.110.1029/2019GL08578210.1002/2017GL07340610.5194 2023-07-31T20:31:48Z The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time-step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed-phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid-to-high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate-calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol-cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics than CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection.Plain Language SummaryThe Community Earth System Model version 2 (CESM2) shows a much higher equilibrium climate sensitivity (ECS > 5°C) than its predecessor models (≤4°C), which, if true, implies a greater future warming than previously thought and a more severe challenge for climate adaptation and mitigation. It is critical to determine whether the high ... Article in Journal/Newspaper Arctic University of Michigan: Deep Blue |