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An Overview of the Atmospheric Component of the Energy Exascale Earth System Model
dc.contributor.authorRasch, P. J.
dc.contributor.authorXie, S.
dc.contributor.authorMa, P.‐l.
dc.contributor.authorLin, W.
dc.contributor.authorWang, H.
dc.contributor.authorTang, Q.
dc.contributor.authorBurrows, S. M.
dc.contributor.authorCaldwell, P.
dc.contributor.authorZhang, K.
dc.contributor.authorEaster, R. C.
dc.contributor.authorCameron‐smith, P.
dc.contributor.authorSingh, B.
dc.contributor.authorWan, H.
dc.contributor.authorGolaz, J.‐c.
dc.contributor.authorHarrop, B. E.
dc.contributor.authorRoesler, E.
dc.contributor.authorBacmeister, J.
dc.contributor.authorLarson, V. E.
dc.contributor.authorEvans, K. J.
dc.contributor.authorQian, Y.
dc.contributor.authorTaylor, M.
dc.contributor.authorLeung, L. R.
dc.contributor.authorZhang, Y.
dc.contributor.authorBrent, L.
dc.contributor.authorBranstetter, M.
dc.contributor.authorHannay, C.
dc.contributor.authorMahajan, S.
dc.contributor.authorMametjanov, A.
dc.contributor.authorNeale, R.
dc.contributor.authorRichter, J. H.
dc.contributor.authorYoon, J.‐h.
dc.contributor.authorZender, C. S.
dc.contributor.authorBader, D.
dc.contributor.authorFlanner, M.
dc.contributor.authorFoucar, J. G.
dc.contributor.authorJacob, R.
dc.contributor.authorKeen, N.
dc.contributor.authorKlein, S. A.
dc.contributor.authorLiu, X.
dc.contributor.authorSalinger, A.G.
dc.contributor.authorShrivastava, M.
dc.contributor.authorYang, Y.
dc.date.accessioned2019-10-30T15:29:18Z
dc.date.availableWITHHELD_11_MONTHS
dc.date.available2019-10-30T15:29:18Z
dc.date.issued2019-08
dc.identifier.citationRasch, P. J.; Xie, S.; Ma, P.‐l. ; Lin, W.; Wang, H.; Tang, Q.; Burrows, S. M.; Caldwell, P.; Zhang, K.; Easter, R. C.; Cameron‐smith, P. ; Singh, B.; Wan, H.; Golaz, J.‐c. ; Harrop, B. E.; Roesler, E.; Bacmeister, J.; Larson, V. E.; Evans, K. J.; Qian, Y.; Taylor, M.; Leung, L. R.; Zhang, Y.; Brent, L.; Branstetter, M.; Hannay, C.; Mahajan, S.; Mametjanov, A.; Neale, R.; Richter, J. H.; Yoon, J.‐h. ; Zender, C. S.; Bader, D.; Flanner, M.; Foucar, J. G.; Jacob, R.; Keen, N.; Klein, S. A.; Liu, X.; Salinger, A.G.; Shrivastava, M.; Yang, Y. (2019). "An Overview of the Atmospheric Component of the Energy Exascale Earth System Model." Journal of Advances in Modeling Earth Systems 11(8): 2377-2411.
dc.identifier.issn1942-2466
dc.identifier.issn1942-2466
dc.identifier.urihttps://hdl.handle.net/2027.42/151811
dc.description.abstractThe Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy’s Energy Exascale Earth System Model is described. The model began as a fork of the wellâ known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of lightâ absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and groundâ based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosolâ cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to â brute forceâ tune the highâ resolution configurations, so shortâ term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models.Plain Language SummaryThis study provides an overview of a new computer model of the Earth’s atmosphere that is used as one component of the Department of Energy’s latest Earth system model. The model can be used to help understand past, present, and future changes in Earth’s behavior as the system responds to changes in atmospheric composition (like pollution and greenhouse gases), land, and water use and to explore how the atmosphere interacts with other components of the Earth system (ocean, land, biology, etc.). Physical, chemical, and biogeochemical processes treated within the atmospheric model are described, and pointers to previous and recent work are listed to provide additional information. The model is compared to presentâ day observations and evaluated for some important tests that provide information about what could happen to clouds and the environment as changes occur. Strengths and weaknesses of the model are listed, as well as opportunities for future work.Key PointsA brief description and evaluation is provided for the atmospheric component of the Department of Energy’s Energy Exascale Earth System ModelModel fidelity has generally improved compared to predecessors and models participating in past international model evaluationsStrengths and weaknesses of the model, as well as opportunities for future work, are described
dc.publisherWiley Periodicals, Inc.
dc.publisherCambridge Univ. Press
dc.subject.otherclimate change
dc.subject.otherEarth system
dc.subject.otheratmospheric model
dc.subject.otherclimate
dc.subject.otherclimate modeling
dc.subject.othergeneral circulation modeling
dc.titleAn Overview of the Atmospheric Component of the Energy Exascale Earth System Model
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelGeological Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/151811/1/jame20932_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/151811/2/jame20932.pdf
dc.identifier.doi10.1029/2019MS001629
dc.identifier.sourceJournal of Advances in Modeling Earth Systems
dc.identifier.citedreferenceRichter, J. H., Solomon, A., & Bacmeister, J. T. ( 2014a ). On the simulation of the quasiâ biennial oscillation in the Community Atmosphere Model, version 5. Journal of Geophysical Research: Atmospheres, 119, 3045 â 3062. https://doi.org/10.1002/2013JD021122
dc.identifier.citedreferenceMcFarlane, N. A. ( 1987 ). The effect of orographically excited wave drag on the general circulation of the lower stratosphere and troposphere. Journal of the Atmospheric Sciences, 44 ( 14 ), 1775 â 1800. https://doi.org/10.1175/1520â 0469(1987)044<1775:TEOOEG>2.0.CO;2
dc.identifier.citedreferenceMitchell, D. L. ( 2002 ). Effective Diameter in Radiation Transfer: General Deï¬ nition, Applications, and Limitations. Journal of the Atmospheric Sciences, 59, 17.
dc.identifier.citedreferenceMlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. ( 1997 ). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlatedâ k model for the longwave. Journal of Geophysical Research, 102 ( D14 ), 16,663 â 16,682. https://doi.org/10.1029/97JD00237
dc.identifier.citedreferenceMote, P. W., Holton, J. R., Russell, J. M., & Boville, B. A. ( 1993 ). A comparison of observed (Haloe) and modeled (CCM2) methane and stratospheric water vapor. Geophysical Research Letters, 20 ( 14 ), 1419 â 1422. https://doi.org/10.1029/93GL01764
dc.identifier.citedreferenceNeale, R. B., Chen, C. C., Gettelman, A., Lauritzen, P. H., Park, S., Williamson, D. L., Conley, A. J., Kinnison, D., Marsh, D., Smith, A. K., Vitt, F., Garcia, R., Lamarque, J.â F., Mills, M., Tilmes, S., Morrison, H., Cameronâ Smith, P., Collins, W. D., Iacono, M. J., Easter, R. C., Liu, X., Ghan, S. J., Rasch, P. J., & Taylor, M. A. ( 2010 ). Description of the NCAR Community Atmosphere Model: CAM5.0. NCAR Technical Note, Boulder, Colorado, USA: National Center for Atmospheric Research.
dc.identifier.citedreferenceNeale, R. B., Richter, J., Park, S., Lauritzen, P. H., Vavrus, S. J., Rasch, P. J., & Zhang, M. ( 2013 ). The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. Journal of Climate, 26, 5150 â 5168. https://doi.org/10.1175/JCLIâ Dâ 12â 00236.1
dc.identifier.citedreferenceNeale, R. B., Richter, J. H., & Jochum, M. ( 2008 ). The Impact of Convection on ENSO: From a Delayed Oscillator to a Series of Events. Journal of Climate, 21 ( 22 ), 5904 â 5924. https://doi.org/10.1175/2008JCLI2244.1
dc.identifier.citedreferenceO’Brien, T. A., Collins, W. D., Kashinath, K., Rübel, O., Byna, S., Gu, J., Krishnan, H., & Ullrich, P. A. ( 2016 ). Resolution dependence of precipitation statistical fidelity in hindcast simulations. Journal of Advances in Modeling Earth Systems, 8, 976 â 990. https://doi.org/10.1002/2016MS000671
dc.identifier.citedreferenceOgunro, O., Burrows, S. M., Elliott, S., Frossard, A. A., Hoffman, F., Letscher, R. T., Moore, J. K., Russell, L. M., Wang, S., & Wingenter, O. W. ( 2015 ). Global distribution and surface activity of macromolecules in offline simulations of marine organic chemistry. Biogeochemistry, 126 ( 1â 2 ), 25 â 56. https://doi.org/10.1007/s10533â 015â 0136â x
dc.identifier.citedreferenceOnogi, K., Tsutsui, J., Koide, H., Sakamoto, M., Kobayashi, S., Hatsushika, H., Matsumoto, T., Yamazaki, N., Kamahori, H., Takahashi, K., Kadokura, S., Wada, K., Kato, K., Oyama, R., Ose, T., Mannoji, N., & Taira, R. ( 2007 ). The JRAâ 25 reanalysis. Journal of the Meteorological Society of Japan. Series II, 85 ( 3 ), 369 â 432. https://doi.org/10.2151/jmsj.85.369
dc.identifier.citedreferenceOrlanski, I. ( 2008 ). Mesoscale dynamics. Eos, Transactions American Geophysical Union, 89 ( 42 ), 408 â 408. https://doi.org/10.1029/2008EO420004
dc.identifier.citedreferencePark, S., Bretherton, C. S., & Rasch, P. J. ( 2014 ). Integrating cloud processes in the Community Atmosphere Model, Version 5. Journal of Climate, 140811131149007. https://doi.org/10.1175/JCLIâ Dâ 14â 00087.1
dc.identifier.citedreferencePhillips, T. J., Potter, G. L., Williamson, D. L., Cederwall, R. T., Boyle, J. S., Fiorino, M., Hnilo, J. J., Olson, J. G., Xie, S., & Yio, J. J. ( 2004 ). Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction. Bulletin of the American Meteorological Society, 85 ( 12 ), 1903 â 1916. https://doi.org/10.1175/BAMSâ 85â 12â 1903
dc.identifier.citedreferencePincus, R., Barker, H. W., & Morcrette, J.â J. ( 2003 ). A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. Journal of Geophysical Research, 108 ( D13 ). https://doi.org/10.1029/2002JD003322
dc.identifier.citedreferenceQian, Y., Wan, H., Yang, B., Golaz, J.â C., Harrop, B., Hou, Z., Larson, V. E., Leung, L. R., Lin, G., Lin, W., Ma, P.â L., Ma, H.â Y., Rasch, P., Singh, B., Wang, H., Xie, S., & Zhang, K. ( 2018 ). Parametric sensitivity and uncertainty quantification in the version 1 of E3SM atmosphere model based on short perturbed parameter ensemble simulations. Journal of Geophysical Research: Atmospheres, 123, 13,046 â 13,073. https://doi.org/10.1029/2018JD028927
dc.identifier.citedreferenceQian, Y., Wang, H., Zhang, R., Flanner, M. G., & Rasch, P. J. ( 2014 ). A sensitivity study on modeling black carbon in snow and its radiative forcing over the Arctic and Northern China. Environmental Research Letters, 9 ( 6 ), 064001. https://doi.org/10.1088/1748â 9326/9/6/064001
dc.identifier.citedreferenceQian, Y., Yan, H., Hou, Z., Johannesson, G., Klein, S., Lucas, D., Neale, R., Rasch, P., Swiler, L., Tannahill, J., Wang, H., Wang, M., & Zhao, C. ( 2015 ). Parametric sensitivity analysis of precipitation at global and local scales in the community atmosphere model CAM5. Journal of Advances in Modeling Earth Systems, 7, 382 â 411. https://doi.org/10.1002/2014ms000354
dc.identifier.citedreferenceRichter, J. H., & Rasch, P. J. ( 2008 ). Effects of convective momentum transport on the atmospheric circulation in the Community Atmosphere Model, Version 3. Journal of Climate, 21 ( 7 ), 1487 â 1499. https://doi.org/10.1175/2007JCLI1789.1
dc.identifier.citedreferenceRichter, J. H., Sassi, F., & Garcia, R. R. ( 2010 ). Toward a physically based gravity wave source parameterization in a general circulation model. Journal of the Atmospheric Sciences, 67, 136 â 156.
dc.identifier.citedreferenceLarson, V. E. ( 2017 ). CLUBBâ SILHS: A parameterization of subgrid variability in the atmosphere. arXiv:1711.03675.
dc.identifier.citedreferenceRichter, J. H., Solomon, A., & Bacmeister, J. T. ( 2014b ). Effects of vertical resolution and nonorographic gravity wave drag on the simulated climate in the Community Atmosphere Model, version 5. Journal of Advances in Modeling Earth Systems, 6, 357 â 383. https://doi.org/10.1002/2013MS000303
dc.identifier.citedreferenceRienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, R., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.â K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., & Woollen, J. ( 2011 ). MERRA: NASA’s Modernâ Era Retrospective Analysis for research and applications. Journal of Climate, 24, 3624 â 3648.
dc.identifier.citedreferenceRinger, M. A., Andrews, T., & Webb, M. J. ( 2014 ). Globalâ mean radiative feedbacks and forcing in atmosphereâ only and coupled atmosphereâ ocean climate change experiments. Geophysical Research Letters, 41, 4035 â 4042. https://doi.org/10.1002/2014GL060347
dc.identifier.citedreferenceRoeckner, E., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kornblueh, L., Manzini, E., Schlese, U., & Schulzweida, U. ( 2006 ). Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. Journal of Climate, 19 ( 16 ), 3771 â 3791. https://doi.org/10.1175/JCLI3824.1
dc.identifier.citedreferenceScinocca, J. F., & McFarlane, N. A. ( 2004 ). The variability of modeled tropical precipitation. Journal of the Atmospheric Sciences, 61 ( 16 ), 1993 â 2015. https://doi.org/10.1175/1520â 0469(2004)061<1993:TVOMTP>2.0.CO;2
dc.identifier.citedreferenceShrivastava, M., Easter, R. C., Liu, X., Zelenyuk, A., Singh, B., Zhang, K., Ma, P. L., Chand, D., Ghan, S., Jimenez, J. L., Zhang, Q., Fast, J., Rasch, P. J., & Tiitta, P. ( 2015 ). Global transformation and fate of SOA: Implications of lowâ volatility SOA and gasâ phase fragmentation reactions. Journal of Geophysical Research: Atmospheres, 120, 4169 â 4195. https://doi.org/10.1002/2014JD022563
dc.identifier.citedreferenceSon, S.â W., Lim, Y., Yoo, C., Hendon, H. H., & Kim, J. ( 2017 ). Stratospheric control of Maddenâ Julian oscillation. Journal of Climate, 30 ( 6 ), 1909 â 1922. https://doi.org/10.1175/JCLIâ Dâ 16â 0620.1
dc.identifier.citedreferenceStephens, G. L., Li, J., Wild, M., Clayson, C. A., Loeb, N., Kato, S., et al. ( 2012 ). An update on Earth’s energy balance in light of the latest global observations. Nature Geoscience, 5 ( 10 ), 691 â 696. https://doi.org/10.1038/ngeo1580
dc.identifier.citedreferenceTan, I., Storelvmo, T., & Zelinka, M. D. ( 2016 ). Observational constraints on mixedâ phase clouds imply higher climate sensitivity. Science, 352 ( 6282 ), 224 â 227. https://doi.org/110.1126/science.aad5300
dc.identifier.citedreferenceTang, Q., Klein, S. A., Xie, S., Lin, W., Golaz, J.â C., Roesler, E. L., Taylor, M. A., Rasch, P. J., Bader, D. C., Berg, L. K., Caldwell, P., Giangrande, S. E., Neale, R. B., Qian, Y., Riihimaki, L. D., Zender, C. S., Zhang, Y., & Zheng, X. ( 2019 ). Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modeling. Geoscientific Model Development, 12, 2679 â 2706. https://doi.org/10.5194/gmdâ 12â 2679â 2019
dc.identifier.citedreferenceTaylor, K. E., Stouffer, R. J., & Meehl, G. A. ( April 2012 ). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93 ( 4 ), 485 â 498. https://doi.org/10.1175/BAMSâ Dâ 11â 00094.1
dc.identifier.citedreferenceTaylor, K. E., Williamson, D., & Zwiers, F. ( 2000 ). The sea surface temperature and seaâ ice concentration boundary conditions of AMIP II simulations. PCMDI Rep. 60, 20 pp. pdf file available at http://wwwâ pcmdi.llnl.gov/publications/pdf/60.pdf
dc.identifier.citedreferenceTaylor, M. A. ( 2011 ). Conservation of mass and energy for the moist atmospheric primitive equations on unstructured grids. In P. H. Lauritzen, et al. (Eds.), Numerical techniques for global atmospheric models, Lecture Notes Comput. Sci. Eng. (Vol. 80, pp. 357 â 380 ). Heidelberg, Germany: Springer. https://doi.org/10.1007/978â 3â 642â 11640â 7_12
dc.identifier.citedreferenceTaylor, M., Tribbia, J., & Iskandarani, M. ( 1997 ). The Spectral Element Method for the Shallow Water Equations on the Sphere. Journal of Computational Physics, 130 ( 1 ), 92 â 108. https://doi.org/10.1006/jcph.1996.5554
dc.identifier.citedreferenceTaylor, M. A., & Fournier, A. ( 2010 ). A compatible and conservative spectral element method on unstructured grids. Journal of Computational Physics, 229 ( 17 ), 5879 â 5895. https://doi.org/10.1016/j.jcp.2010.04.008
dc.identifier.citedreferencevan Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.â L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., & van der Werf, G. R. ( 2017 ). Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750â 2015). Geoscientific Model Development, 10 ( 9 ), 3329 â 3357. https://doi.org/10.5194/gmdâ 10â 3329â 2017
dc.identifier.citedreferencevan Weverberg, K., Morcrette, C. J., Ma, H.â Y., Klein, S. A., & Petch, J. C. ( 2015 ). Using regime analysis to identify the contribution of clouds to surface temperature errors in weather and climate models. Quarterly Journal of the Royal Meteorological Society, 141 ( 693 ), 3190 â 3206. https://doi.org/10.1002/qj.2603
dc.identifier.citedreferenceWan, H., Rasch, P. J., Zhang, K., Qian, Y., Yan, H., & Zhao, C. ( 2014 ). Short ensembles: An efficient method for discerning climateâ relevant sensitivities in atmospheric general circulation models. Geoscientific Model Development, 7, 1961 â 1977. https://doi.org/10.5194/gmdâ 7â 1961â 2014
dc.identifier.citedreferenceWang, B., & Ding, Q. ( 2008 ). Global monsoon: Dominant mode of annual variation in the tropics. Dynamics of Atmospheres and Oceans, 44 ( 3â 4 ), 165 â 183. https://doi.org/10.1016/j.dynatmoce.2007.05.002
dc.identifier.citedreferenceWang, H., Easter, R. C., Rasch, P. J., Wang, M., Liu, X., Ghan, S. J., Qian, Y., Yoon, J.â H., Ma, P.â L., & Vinoj, V. ( 2013 ). Sensitivity of remote aerosol distributions to representation of cloudâ aerosol interactions in a global climate model. Geoscientific Model Development, 6 ( 3 ), 765 â 782. https://doi.org/10.5194/gmdâ 6â 765â 2013
dc.identifier.citedreferenceWang, M., Ghan, S., Liu, X., L’Ecuyer, T. S., Zhang, K., Morrison, H., Ovchinnikov, M., Easter, R., Marchand, R., Chand, D., Qian, Y., & Penner, J. E. ( 2012 ). Constraining cloud lifetime effects of aerosol using Aâ Train satellite observations. Geophysical Research Letters, 39, L15709. https://doi.org/10.1029/2012GL052204
dc.identifier.citedreferenceWang, S., Elliott, S., Maltrud, M., & Cameronâ Smith, P. ( 2015 ). Influence of explicit Phaeocystis parameterizations on the global distribution of marine dimethyl sulfide: Explicit Phaeocystis in a DMS model. Journal of Geophysical Research: Biogeosciences, 120, 2158 â 2177. https://doi.org/10.1002/2015JG003017
dc.identifier.citedreferenceWang, 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, 10 411 â 10 430. https://doi.org/10.5194/acpâ 14â 10411â 2014
dc.identifier.citedreferenceWang, Y.â M., Lean, J. L., & Sheeley, N. R. Jr. ( 2005 ). Modeling the Sun’s Magnetic Field and Irradiance since 1713. The Astrophysical Journal, 625 ( 1 ), 522 â 538. https://doi.org/10.1086/429689
dc.identifier.citedreferenceWehner, M. F., Reed, K. A., Li, F., Prabhat, Bacmeister, J., Chen, C.â T., Paciorek, C., Gleckler, P. J., Sperber, K. R., Collins, W. D., Gettelman, A., & Jablonowski, C. ( 2014 ). The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1. Journal of Advances in Modeling Earth Systems, 6, 980 â 997. https://doi.org/10.1002/2013MS000276
dc.identifier.citedreferenceWheeler, M., & Kiladis, G. N. ( 1999 ). Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumberâ frequency domain. Journal of the Atmospheric Sciences, 56 ( 3 ), 374 â 399. https://doi.org/10.1175/1520â 0469(1999)056<0374:CCEWAO>2.0.CO;2
dc.identifier.citedreferenceWilliams, K. D., Bodasâ Salcedo, A., Deque, M., Fermepin, S., Medeiros, B., Watanabe, M., Jakob, C., Klein, S. A., Senior, C. A., & Williamson, D. L. ( 2013 ). The Transposeâ AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models. Journal of Climate, 26 ( 10 ), 3258 â 3274. https://doi.org/10.1175/JCLIâ Dâ 12â 00429.1
dc.identifier.citedreferenceWiscombe, W. J. ( 1996 ). Mie ScatteringCalculations: Advances in Technique and Fast,Vector-Speed Computer Codes, NCAR/TNâ 140+STR,National Center for Atmospheric Research, Boulder,Colorado. NCAR/TNâ 140+STR Mie Scattering Calculations. https://opensky.ucar.edu/islandora/object/technotes:232/datastream/.../citation.pdf
dc.identifier.citedreferenceWood, R. ( 2005 ). Drizzle in stratiform boundary layer clouds. Part II: Microphysical aspects. Journal of the Atmospheric Sciences, 62 ( 9 ), 3034 â 3050.
dc.identifier.citedreferenceXie, S., Boyle, J., Klein, S. A., Liu, X., & Ghan, S. ( 2008 ). Simulations of Arctic mixedâ phase clouds in forecasts with CAM3 and AM2 for Mâ PACE. Journal of Geophysical Research, 113, D04211. https://doi.org/10.1029/2007JD009225
dc.identifier.citedreferenceXie, S., Lin, W., Rasch, P. J., Ma, P.â L., Neale, R., Larson, V. E., Qian, Y., Bogenschutz, P. A., Caldwell, P., Cameronâ Smith, P., Golaz, J. C., Mahajan, S., Singh, B., Tang, Q., Wang, H., Yoon, J. H., Zhang, K., & Zhang, Y. ( 2018 ). Understanding cloud and convective characteristics in version 1 of the E3SM atmosphere model. Journal of Advances in Modeling Earth Systems, 10, 2618 â 2644. https://doi.org/10.1029/2018MS001350
dc.identifier.citedreferenceXie, S., Liu, X., Zhao, C., & Zhang, Y. ( 2013 ). Sensitivity of CAM5 simulated arctic clouds and radiation to ice nucleation. Journal of Climate, 26 ( 16 ), 5981 â 5999. https://doi.org/10.1175/JCLIâ Dâ 12â 00517.1
dc.identifier.citedreferenceXie, S., Ma, H.â Y., Boyle, J. S., Klein, S. A., & Zhang, Y. ( 2012 ). On the correspondence between shortâ and longâ timeâ scale systematic errors in CAM4/CAM5 for the year of tropical convection. Journal of Climate, 25 ( 22 ), 7937 â 7955. https://doi.org/10.1175/JCLIâ Dâ 12â 00134.1
dc.identifier.citedreferenceXie, S., Wang, Y.â C., Lin, W., Ma, H.â Y., Tang, Q., Tang, S., Zheng, X., Golaz, J.â C., Zhang, G. J., & Zhang, M. ( 2019 ). Improved diurnal cycle of precipitation in E3SM with a revised convective triggering function. Journal of Advances in Modeling Earth Systems, 11. https://doi.org/10.1029/2019MS001702
dc.identifier.citedreferenceXie, S., Zhang, M., Boyle, J. S., Cederwall, R. T., Potter, G. L., & Lin, W. ( 2004 ). Impact of a revised convective triggering mechanism on Community Atmosphere Model, Version 2, simulations: Results from shortâ range weather forecasts. Journal of Geophysical Research, 109, D14102. https://doi.org/10.1029/2004JD004692
dc.identifier.citedreferenceYang, Y., Wang, H., Smith, S. J., Zhang, R., Lou, S., Yun, Q., Ma, P.â L., & Rasch, P. J. ( 2018 ). Recent intensification of winter haze in China linked to foreign emissions and meteorology. Scientific Reports, 8 ( 1 ), 2107. https://doi.org/10.1038/s41598â 018â 20437â 7
dc.identifier.citedreferenceYoo, C., & Son, S.â W. ( 2016 ). Modulation of the boreal wintertime Maddenâ Julian Oscillation by the stratospheric quasiâ biennial oscillation. Geophysical Research Letters, 43, 1392 â 1398. https://doi.org/10.1002/2016GL067762
dc.identifier.citedreferenceZarzycki, C. M., Jablonowski, C., Thatcher, D. R., & Taylor, M. A. ( 2015 ). Effects of localized grid refinement on the general circulation and climatology in the Community Atmosphere Model. Journal of Climate, 28 ( 7 ), 2777 â 2803. https://doi.org/10.1175/JCLIâ Dâ 14â 00599.1
dc.identifier.citedreferenceZhang, G. J., & McFarlane, N. A. ( 1995 ). Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmosphereâ Ocean, 33 ( 3 ), 407 â 446. https://doi.org/10.1080/07055900.1995.9649539
dc.identifier.citedreferenceZhang, K., Rasch, P. J., Taylor, M. A., Wan, H., Leung, R., Ma, P.â L., Golaz, J.â C., Wolfe, J., Lin, W., Singh, B., Burrows, S., Yoon, J.â H., Wang, H., Qian, Y., Tang, Q., Caldwell, P., & Xie, S. ( 2018 ). Impact of numerical choices on water conservation in the E3SM Atmosphere Model version 1 (EAMv1). Geoscientific Model Development, 11 ( 5 ), 1971 â 1988. https://doi.org/10.5194/gmdâ 11â 1971â 2018
dc.identifier.citedreferenceZhang, S., Wang, M., Ghan, S. J., Ding, A., Wang, H., Zhang, K., Neubauer, D., Lohmann, U., Ferrachat, S., Takeamura, T., & Gettelman, A. ( 2016 ). On the characteristics of aerosol indirect effect based on dynamic regimes in global climate models. Atmospheric Chemistry and Physics, 16 ( 5 ), 2765 â 2783. https://doi.org/10.5194/acpâ 16â 2765â 2016
dc.identifier.citedreferenceZhang, Y., Xie, S., Klein, S. A., Marchand, R., Kollias, P., Clothiaux, E. E., Lin, W., Johnson, K., Swales, D., Bodasâ Salcedo, A., Tang, S., Haynes, J. M., Collis, S., Jensen, M., Bharadwaj, N., Hardin, J., & Isom, B. ( 2018 ). The ARM cloud radar simulator for global climate models: Bridging field data and climate models. Bulletin of the American Meteorological Society, 99, 21 â 26. https://doi.org/10.1175/BAMSâ Dâ 16â 0258.1
dc.identifier.citedreferenceZhang, Y., Xie, S., Lin, W., Klein, S. A., Zelinka, M., Ma, P.â L., Rasch, P. J., Qian, Y., Tang, Q., & Ma, H. Y. ( 2019 ). Evaluation of clouds in version 1 of the E3SM atmosphere model with satellite simulators. Journal of Advances in Modeling Earth Systems, 11, 1253 â 1268. https://doi.org/10.1029/2018MS001562
dc.identifier.citedreferenceAbdulâ Razzak, H., & Ghan, S. J. ( 2000 ). A parameterization of aerosol activation: 2. Multiple aerosol types. Journal of Geophysical Research, 105 ( D5 ), 6837 â 6844. https://doi.org/10.1029/1999JD901161
dc.identifier.citedreferenceAdler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., & Arkin, P. ( 2003 ). The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979â Present). Journal of Hydrometeorology, 4 ( 6 ), 1147 â 1167. https://doi.org/10.1175/1525â 7541(2003)004<1147:TVGPCP>2.0.CO;2
dc.identifier.citedreferenceAnstey, J. A., & Shepherd, T. G. ( 2014 ). Highâ latitude influence of the quasiâ biennial oscillation. Quarterly Journal of the Royal Meteorological Society, 140 ( 678 ), 1 â 21. https://doi.org/10.1002/qj.2132
dc.identifier.citedreferenceBacmeister, J. T., Wehner, M. F., Neale, R. B., Gettelman, A., Hannay, C., Lauritzen, P. H., Caron, J. M., & Truesdale, J. E. ( 2013 ). Exploratory highâ resolution climate simulations using the Community Atmosphere Model (CAM). Journal of Climate, 27 ( 9 ), 3073 â 3099. https://doi.org/10.1175/JCLIâ Dâ 13â 00387.1
dc.identifier.citedreferenceBauer, M., & del Genio, A. D. ( 2006 ). Composite analysis of winter cyclones in a GCM: Influence on climatological humidity. Journal of Climate, 19 ( 9 ), 1652 â 1672. https://doi.org/10.1175/JCLI3690.1
dc.identifier.citedreferenceBeheng K. D. ( 1994 ). A parameterization of warm cloud microphysical conversion processes. Atmospheric Research, 33, 193 â 206.
dc.identifier.citedreferenceBeres, J. H., Alexander, M. J., & Holton, J. R. ( 2004 ). A Method of Specifying the Gravity Wave Spectrum above Convection Based on Latent Heating Properties and Background Wind. Journal of the Atmospheric Sciences, 61, 14.
dc.identifier.citedreferenceBodasâ Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J. L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., & John, V. O. ( 2011 ). COSP: Satellite simulation software for model assessment. Bulletin of the American Meteorological Society, 92 ( 8 ), 1023 â 1043. https://doi.org/10.1175/2011BAMS2856.1
dc.identifier.citedreferenceBogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., & Schanen, D. P. ( 2013 ). Higherâ order turbulence closure and its impact on climate simulations in the Community Atmosphere Model. Journal of Climate, 26 ( 23 ), 9655 â 9676. https://doi.org/10.1175/JCLIâ Dâ 13â 00075.1
dc.identifier.citedreferenceBoucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.â M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., & Zhang, X. Y. ( 2013 ). Clouds and aerosols. In T. F. Stocker, et al. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, (pp. 571 â 658 ). Cambridge, and New York: Cambridge Univ. Press. https://doi.org/10.1017/CBO9781107415324.016
dc.identifier.citedreferenceBoyle, J., & Klein, S. A. ( 2010 ). Impact of horizontal resolution on climate model forecasts of tropical convection and diabatic heating for the TWPâ ICE period. Journal of Geophysical Research, 115, D23113. https://doi.org/10.1029/2010JD014262
dc.identifier.citedreferenceBurrows, S. M., Easter, R., Liu, X., Ma, P.â L., Wang, H., Elliott, S. M., Singh, B., Zhang, K., & Rasch, P. J. ( 2018 ). OCEANFILMS seaâ spray organic aerosol emissions; Part 1: Implementation and impacts on clouds. Atmospheric Chemistry and Physics Discussions, 1 â 27. https://doi.org/10.5194/acpâ 2018â 70
dc.identifier.citedreferenceBurrows, S. M., Ogunro, O., Frossard, A. A., Russell, L. M., Rasch, P. J., & Elliott, S. M. ( 2014 ). A physically based framework for modeling the organic fractionation of sea spray aerosol from bubble film Langmuir equilibria. Atmospheric Chemistry and Physics, 14 ( 24 ), 13601 â 13629. https://doi.org/10.5194/acpâ 14â 13601â 2014
dc.identifier.citedreferenceButchart, N., Anstey, J. A., Hamilton, K., Osprey, S., McLandress, C., Bushell, A. C., Kawatani, Y., Kim, Y. H., Lott, F., Scinocca, J., Stockdale, T. N., Andrews, M., Bellprat, O., Braesicke, P., Cagnazzo, C., Chen, C. C., Chun, H. Y., Dobrynin, M., Garcia, R. R., Garciaâ Serrano, J., Gray, L. J., Holt, L., Kerzenmacher, T., Naoe, H., Pohlmann, H., Richter, J. H., Scaife, A. A., Schenzinger, V., Serva, F., Versick, S., Watanabe, S., Yoshida, K., & Yukimoto, S. ( 2018 ). Overview of experiment design and comparison of models participating in phase 1 of the SPARC Quasiâ Biennial Oscillation initiative (QBOi). Geoscientific Model Development, 11 ( 3 ), 1009 â 1032. https://doi.org/10.5194/gmdâ 11â 1009â 2018
dc.identifier.citedreferenceCariolle, D., Lasserrebigorry, A., Royer, J. F., & Geleyn, J. F. ( 1990 ). A general circulation model simulation of the springtime Antarctic Ozone decrease and its impact on midlatitudes. Journal of Geophysical Research, 95 ( D2 ), 1883 â 1898. https://doi.org/10.1029/JD095iD02p01883
dc.identifier.citedreferenceCharron, M., & Manzini, E. ( 2002 ). Gravity waves from fronts: Parameterization and middle atmosphere response in a general circulation model. Journal of the Atmospheric Sciences, 59 ( 5 ), 923 â 941. https://doi.org/10.1175/1520-0469(2002)059<0923:GWFFPA>2.0.CO;2
dc.identifier.citedreferenceCollins, W. D., Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J. R., Williamson, D. L., Briegleb, B. P., Bitz, C. M., Lin, S.â J., & Zhang, M. ( 2006 ). The formulation and atmospheric simulation of the Community Atmosphere Model Version 3 (CAM3). Journal of Climate, 19 ( 11 ), 2144 â 2161. https://doi.org/10.1175/JCLI3760.1
dc.identifier.citedreferenceDee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., KÃ¥llberg, P., Köhler, M., Matricardi, M., McNally, A. P., Mongeâ Sanz, B. M., Morcrette, J.â J., Park, B.â K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.â N., & Vitart, F. ( 2011 ). The ERAâ Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 133 ( 626 ), 1143 â 1157. https://doi.org/10.1002/qj.82
dc.identifier.citedreferenceDennis, J., Edwards, K., Evans, J., Guba, O., Lauritzen, P. H., Mirin, A. A., Stâ Cyr, A., Taylor, M. A., & Worley, P. H. ( 2012 ). CAMâ SE: A scalable spectral element dynamical core for the Community Atmosphere Model. International Journal of High Performance Computing Applications, 26 ( 1 ), 74 â 89.
dc.identifier.citedreferenceDonner, L. J., Wyman, B. L., Hemler, R. S., Horowitz, L. W., Ming, Y., Zhao, M., Golaz, J. C., Ginoux, P., Lin, S. J., Schwarzkopf, M. D., Austin, J., Alaka, G., Cooke, W. F., Delworth, T. L., Freidenreich, S. M., Gordon, C. T., Griffies, S. M., Held, I. M., Hurlin, W. J., Klein, S. A., Knutson, T. R., Langenhorst, A. R., Lee, H. C., Lin, Y., Magi, B. I., Malyshev, S. L., Milly, P. C. D., Naik, V., Nath, M. J., Pincus, R., Ploshay, J. J., Ramaswamy, V., Seman, C. J., Shevliakova, E., Sirutis, J. J., Stern, W. F., Stouffer, R. J., Wilson, R. J., Winton, M., Wittenberg, A. T., & Zeng, F. ( 2011 ). The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL Global Coupled Model CM3. Journal of Climate, 24 ( 13 ), 3484 â 3519. https://doi.org/10.1175/2011JCLI3955.1
dc.identifier.citedreferenceE3SM Project ( 2018 ). DOE. Energy Exascale Earth System Model. Computer Software. https://github.com/E3SMâ Project/E3SM.git. 23 Apr. 2018. Web. https://doi.org/10.11578/E3SM/dc.20180418.36.
dc.identifier.citedreferenceElliott, S. ( 2009 ). Dependence of DMS global seaâ air flux distribution on transfer velocity and concentration field type. Journal of Geophysical Research, 114, G02001. https://doi.org/10.1029/2008JG000710
dc.identifier.citedreferenceEvans, K. J., Lauritzen, P. H., Mishra, S. K., Neale, R., Taylor, M. A., & Tribbia, J. J. ( 2013 ). AMIP Simulation with the CAM4 Spectral Element Dynamical Core. Journal of Climate, 26 ( 3 ), 689 â 709. https://doi.org/10.1175/JCLIâ Dâ 11â 00448.1
dc.identifier.citedreferenceEyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (May 26, 2016 ). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization. Geoscientific Model Development, 9 ( 5 ), 1937 â 1958. https://doi.org/10.5194/gmdâ 9â 1937â 2016
dc.identifier.citedreferenceFlanner, M. G. ( 2013 ). Arctic climate sensitivity to local black carbon. Journal of Geophysical Research: Atmospheres, 118, 1840 â 1851. https://doi.org/10.1002/jgrd.50176
dc.identifier.citedreferenceFlanner, M. G., Liu, X., Zhou, C., Penner, J. E., & Jiao, C. ( 2012 ). Enhanced solar energy absorption by internallyâ mixed black carbon in snow grains. Atmospheric Chemistry and Physics, 12 ( 10 ), 4699 â 4721. https://doi.org/10.5194/acpâ 12â 4699â 2012
dc.identifier.citedreferenceForster, P. M., Andrews, T., Good, P., Gregory, J. M., Jackson, L. S., & Zelinka, M. ( 2013 ). Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. Journal of Geophysical Research: Atmospheres, 118, 1139 â 1150. https://doi.org/10.1002/jgrd.50174
dc.identifier.citedreferenceForster, P. M., Richardson, T., Maycock, A. C., Smith, C. J., Samset, B. H., Myhre, G., Andrews, T., Pincus, R., & Schulz, M. ( 2016 ). Recommendations for diagnosing effective radiative forcing from climate models for CMIP6. Journal of Geophysical Research: Atmospheres, 121, 12,460 â 12,475. https://doi.org/10.1002/2016JD025320
dc.identifier.citedreferenceGelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G. K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., & Zhao, B. ( 2017 ). The Modernâ Era Retrospective Analysis for Research and Applications, Version 2 (MERRAâ 2). Journal of Climate, 30 ( 14 ), 5419 â 5454. https://doi.org/10.1175/JCLIâ Dâ 16â 0758.1
dc.identifier.citedreferenceGeller, M. A., Zhou, T., Shindell, D., Ruedy, R., Aleinov, I., Nazarenko, L., Tausnev, N. L., Kelley, M., Sun, S., Cheng, Y., Field, R. D., & Faluvegi, G. ( 2016 ). Modeling the QBOâ Improvements resulting from higherâ model vertical resolution. Journal of Advances in Modeling Earth Systems, 8, 1092 â 1105. https://doi.org/10.1002/2016MS000699
dc.identifier.citedreferenceGettelman, A., & Morrison, H. ( 2015 ). Advanced twoâ moment bulk microphysics for global models. Part I: Offâ line tests and comparison with other schemes. Journal of Climate, 28 ( 3 ), 1268 â 1287. https://doi.org/10.1175/JCLIâ Dâ 14â 00102.1
dc.identifier.citedreferenceGhan, S., Wang, M., Zhang, S., Ferrachat, S., Gettelman, A., Griesfeller, J., Kipling, Z., Lohmann, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., & Zhang, K. ( 2016 ). Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using presentâ day spatiotemporal variability. Proceedings of the National Academy of Sciences, 113 ( 21 ), 5804 â 5811. www.pnas.org/cgi/doi/10.1073/pnas.1514036113
dc.identifier.citedreferenceGhan, S. & Zaveri, R. A. ( 2007 ). Parameterization of optical properties for hydrated internally mixed aerosol. Journal of Geophysical Research, 112, D10201. https://doi.org/10.1029/2006jd007927
dc.identifier.citedreferenceGhan, S. J. ( 2013 ). Technical note: Estimating aerosol effects on cloud radiative forcing. Atmospheric Chemistry and Physics, 13 ( 19 ), 9971 â 9974. https://doi.org/10.5194/acpâ 13â 9971â 2013
dc.identifier.citedreferenceGleckler, P., Doutriaux, C., Durack, P., Taylor, K., Zhang, Y., Williams, D., Mason, E., & Servonnat, J. ( 2016 ). A more powerful reality test for climate models. Eos, 97. https://doi.org/10.1029/2016eo051663
dc.identifier.citedreferenceGleckler, P. J., Taylor, K. E., & Doutriaux, C. ( 2008 ). Performance metrics for climate models. Journal of Geophysical Research, 113, D06104. https://doi.org/10.1029/2007JD008972
dc.identifier.citedreferenceKhairoutdinov, M., & Kogan, Y. ( 2000 ). A new cloud physics parameterization in a largeâ eddy simulation model of marine stratocumulus. Monthly Weather Review, 128 ( 1 ), 229 â 243. https://doi.org/10.1175/1520â 0493
dc.identifier.citedreferenceKogan, Y. ( 2013 ). A Cumulus Cloud Microphysics Parameterization for Cloud-Resolving Models. Journal of the Atmospheric Sciences, 70 ( 5 ), 1423 â 1436. https://doi.org/10.1175/JASâ Dâ 12â 0183.1
dc.identifier.citedreferenceGolaz, J.â C., Caldwell, P. M., van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asayâ Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameronâ Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.â . Y., Lin, W., Lipscomb, W. H., Ma, P.â . L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A. F., 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.â . H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., & Zhu, Q. ( 2018 ). The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution. Journal of Advances in Modeling Earth Systems, 11. https://doi.org/10.1029/2018MS001603
dc.identifier.citedreferenceGolaz, J.â C., Larson, V. E., & Cotton, W. R. ( 2002 ). A PDFâ based model for boundary layer clouds. Part I: Method and model description. Journal of the Atmospheric Sciences, 59 ( 24 ), 3540 â 3551. https://doi.org/10.1175/1520â 0469(2002)059<3540:APBMFB>2.0.CO;2
dc.identifier.citedreferenceGuba, O., Taylor, M. A., Ullrich, P. A., Overfelt, J. R., & Levy, M. N. ( 2014 ). The spectral element method (SEM) on variable resolution grids: Evaluating grid sensitivity and resolutionâ aware numerical viscosity. Geoscientific Model Development, 7 ( 6 ), 2803 â 2816. https://doi.org/10.5194/gmdâ 7â 2803â 2014
dc.identifier.citedreferenceHack, J., Boville, B., Briegleb, B., Kiehl, J., & Williamson, D. ( 1993 ). Description of the NCAR Community Climate Model (CCM2).â UCAR/NCAR Technical Note. https://doi.org/10.5065/d6qz27xv.
dc.identifier.citedreferenceHarrop, B. E., Ma, P.â L., Rasch, P. J., Neale, R. B., & Hannay, C. ( 2018 ). The role of convective gustiness in reducing seasonal precipitation biases in the Tropical West Pacific. Journal of Advances in Modeling Earth Systems, 10, 961 â 970. https://doi.org/10.1002/2017MS001157
dc.identifier.citedreferenceHaynes, P. H., McIntyre, M. E., Shepherd, T. G., Marks, C. J., & Shine, K. P. ( 1991 ). On the â downward controlâ of extratropical diabatic circulations by eddyâ induced mean zonal forces. Journal of the Atmospheric Sciences, 48 ( 4 ), 651 â 678. https://doi.org/10.1175/1520â 0469(1991)048<0651:OTCOED>2.0.CO;2
dc.identifier.citedreferenceHoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssensâ Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J. I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O&amp;apos;Rourke, P. R., & Zhang, Q. ( 2018 ). Historical (1750â 2014) Anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geoscientific Model Development, 11 ( 1 ), 369 â 408. https://doi.org/10.5194/gmdâ 11â 369â 2018
dc.identifier.citedreferenceHoskins, B. J. ( 1982 ). The mathematical theory of frontogenesis. Annual Review of Fluid Mechanics, 14 ( 1 ), 131 â 151. https://doi.org/10.1146/annurev.fl.14.010182.001023
dc.identifier.citedreferenceHsu, J., & Prather, M. J. ( 2009 ). Stratospheric variability and tropospheric ozone. Journal of Geophysical Research, 114, D06102. https://doi.org/10.1029/2008JD010942
dc.identifier.citedreferenceHuffman, G. J., Adler, R. F., Bolvin, D. T., & Gu, G. ( 2009 ). Improving the global precipitation record: GPCP version 2.1. Geophysical Research Letters, 36, L17808. https://doi.org/10.1029/2009GL040000
dc.identifier.citedreferenceHuffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., & Susskind, J. ( 2001 ). Global precipitation at oneâ degree daily resolution from multisatellite observations. Journal of Hydrometeorology, 2 ( 1 ), 36 â 50. https://doi.org/10.1175/1525â 7541(2001)002<0036:GPAODD>2.0.CO;2
dc.identifier.citedreferenceHurrell, J. W., Hack, J. J., Shea, D., Caron, J. M., & Rosinski, J. ( 2008 ). A new sea surface temperature and sea ice boundary dataset for the community atmosphere model. Journal of Climate, 21 ( 19 ), 5145 â 5153. https://doi.org/10.1175/2008JCLI2292.1
dc.identifier.citedreferenceHurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., et al. ( 2013 ). The Community Earth System Model: A Framework for Collaborative Research. Bulletin of the American Meteorological Society, 94 ( 9 ), 1339 â 1360. https://doi.org/10.1175/BAMSâ Dâ 12â 00121.1
dc.identifier.citedreferenceIacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. ( 2008 ). Radiative forcing by longâ lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13103. https://doi.org/10.1029/2008JD009944
dc.identifier.citedreferenceJung, T., Miller, M. J., Palmer, T. N., Towers, P., Wedi, N., Achuthavarier, D., Adams, J. M., Altshuler, E. L., Cash, B. A., Kinter, J. L. III, Marx, L., Stan, C., & Hodges, K. I. ( 2012 ). Highâ resolution global climate simulations with the ECMWF model in Project Athena: Experimental design, model climate, and seasonal forecast skill. Journal of Climate, 25 ( 9 ), 3155 â 3172. https://doi.org/10.1175/JCLIâ Dâ 11â 00265.1
dc.identifier.citedreferenceKato, S., Loeb, N. G., Rose, F. G., Doelling, D. R., Rutan, D. A., Caldwell, T. E., Yu, L., & Weller, R. A. ( 2013 ). Surface irradiances consistent with CERESâ derived topâ ofâ atmosphere shortwave and longwave irradiances. Journal of Climate, 26 ( 9 ), 2719 â 2740. https://doi.org/10.1175/JCLIâ Dâ 12â 00436.1
dc.identifier.citedreferenceKay, J. E., Medeiros, B., Hwang, Y.â T., Gettelman, A., Perket, J., & Flanner, M. G. ( 2014 ). Processes controlling Southern Ocean shortwave climate feedbacks in CESM. Geophysical Research Letters, 41, 616 â 622. https://doi.org/10.1002/2013GL058315
dc.identifier.citedreferenceLarson, V. E., & Golaz, J. C. ( 2005 ). Using probability density functions to derive consistent closure relationships among higherâ order moments. Monthly Weather Review, 133 ( 4 ), 1023 â 1042. https://doi.org/10.1175/MWR2902.1
dc.identifier.citedreferenceLarson, V. E., Golaz, J. C., & Cotton, W. R. ( 2002 ). Smallâ scale and mesoscale variability in cloudy boundary layers: Joint probability density functions. Journal of the Atmospheric Sciences, 59 ( 24 ), 3519 â 3539. https://doi.org/10.1175/1520â 0469(2002)059<3519:SSAMVI>2.0.CO;2
dc.identifier.citedreferenceLauritzen, P., Ullrich, P. A., Jablonowski, C., Bosler, P. A., Calhoun, D., Conley, A. J., Enomoto, T., Dong, L., Dubey, S., Guba, O., Hansen, A. B., Kaas, E., Kent, J., Lamarque, J.â F., Prather, M. J., Reinert, D., Shashkin, V. V., Skamarock, W. C., Sørensen, B., Taylor, M. A., & Tolstykh, M. A. ( 2014 ). A standard test case suite for twoâ dimensional linear transport on the sphere: Results from a collection of stateâ ofâ theâ art schemes. Geoscientific Model Development, 7 ( 1 ), 105 â 145. https://doi.org/10.5194/gmdâ 7â 105â 2014
dc.identifier.citedreferenceLauritzen, P. H., Bacmeister, J. T., Callaghan, P. F., & Taylor, M. A. ( 2015 ). NCAR_Topo (v1.0): NCAR global model topography generation software for unstructured grids. Geoscientific Model Development, 8 ( 12 ), 3975 â 3986. https://doi.org/10.5194/gmdâ 8â 3975â 2015
dc.identifier.citedreferenceLin, S.â J. ( 2004 ). A â Vertically Lagrangianâ Finiteâ Volume Dynamical Core for Global Models. Monthly Weather Review, 132 ( 10 ), 2293 â 2307. https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2
dc.identifier.citedreferenceLin, Y., Donner, L. J., Petch, J., Bechtold, P., Boyle, J., Klein, S. A., Komori, T., Wapler, K., Willett, M., Xie, X., Zhao, M., Xie, S., MaFarlane, S. A., & Schumacher, C. ( 2012 ). TWPâ ICE global atmospheric model intercomparison: Convection responsiveness and resolution impact. Journal of Geophysical Research, 117, D09111. https://doi.org/10.1029/2011JD017018
dc.identifier.citedreferenceLindzen, R. S., & Foxâ Rabinovitz, M. ( 1989 ). Consistent vertical and horizontal resolution. Monthly Weather Review, 117 ( 11 ), 2575 â 2583. https://doi.org/10.1175/1520â 0493(1989)117<2575:CVAHR>2.0.CO;2
dc.identifier.citedreferenceLiu, X., Ma, P.â L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., & Rasch, P. J. ( 2016 ). Description and evaluation of a new 4â mode version of Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model. Geoscientific Model Development, 9, 505 â 522. https://doi.org/10.5194/gmdâ 9â 505â 2016
dc.identifier.citedreferenceLiu, X., Xie, S., Boyle, J., Klein, S. A., Shi, X., Wang, Z., Lin, W., Ghan, S. J., Earle, M., Liu, P. S. K., & Zelenyuk, A. ( 2011 ). Testing cloud microphysics parameterizations in NCAR CAM5 with ISD AC and Mâ PACE observations. Journal of Geophysical Research, 116, D00T11. https://doi.org/10.1029/2011JD015889
dc.identifier.citedreferenceLoeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F., Kato, S., Manaloâ Smith, N., & Wong, T. ( 2009 ). Toward optimal closure of the Earth’s topâ ofâ atmosphere radiation budget. Journal of Climate, 22 ( 3 ), 748 â 766. https://doi.org/10.1175/2008JCLI2637.1
dc.identifier.citedreferenceLott, F., & Miller, M. J. ( 1997 ). A new subgridâ scale orographic drag parametrization: Its formulation and testing. Quarterly Journal of the Royal Meteorological Society, 123 ( 537 ), 101 â 127. https://doi.org/10.1002/qj.49712353704
dc.identifier.citedreferenceMa, H.â Y., Klein, S. A., Xie, S., Zhang, C., Tang, S., Tang, Q., Morcrette, C. J., van Weverberg, K., Petch, J., Ahlgrimm, M., Berg, L. K., Cheruy, F., Cole, J., Forbes, R., Gustafson, W. I., Huang, M., Liu, Y., Merryfield, W., Qian, Y., Roehrig, R., & Wang, Y.â C. ( 2018 ). CAUSES: On the role of surface energy budget errors to the warm surface air temperature error over the Central United States. Journal of Geophysical Research: Atmospheres, 123, 2888 â 2909. https://doi.org/10.1002/2017JD027194
dc.identifier.citedreferenceMa, H.â Y., Xie, S., Klein, S. A., Williams, K. D., Boyle, J. S., Bony, S., Douville, H., Fermepin, S., Medeiros, B., Tyteca, S., Watanabe, M., & Williamson, D. ( 2014 ). On the correspondence between mean forecast errors and climate errors in CMIP5 models. Journal of Climate, 27 ( 4 ), 1781 â 1798. https://doi.org/10.1175/JCLIâ Dâ 13â 00474.1
dc.identifier.citedreferenceMa, P.â L., Rasch, P. J., Chepfer, H., Winker, D. M., & Ghan, S. J. ( 2018 ). Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations. Nature Communications, 9 ( 1 ), 2640. https://doi.org/10.1038/s41467â 018â 05028â 4
dc.identifier.citedreferenceMa, P.â L., Rasch, P. J., Wang, M., Wang, H., Ghan, S. J., Easter, R. C., Gustafson, W. I. Jr., Liu, X., Zhang, Y., & Ma, H. Y. ( 2015 ). How does increasing horizontal resolution in a global climate model improve the simulation of aerosolâ cloud interactions? Geophysical Research Letters, 42, 5058 â 5065. https://doi.org/10.1002/2015GL064183
dc.identifier.citedreferenceMartin, G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., Hinton, T. J., Jones, C. D., McDonald, R. E., McLaren, A. J., O’Connor, F. M., Roberts, M. J., Rodriguez, J. M., Woodward, S., Best, M. J., Brooks, M. E., Brown, A. R., Butchart, N., Dearden, C., Derbyshire, S. H., Dharssi, I., Doutriauxâ Boucher, M., Edwards, J. M., Falloon, P. D., Gedney, N., Gray, L. J., Hewitt, H. T., Hobson, M., Huddleston, M. R., Hughes, J., Ineson, S., Ingram, W. J., James, P. M., Johns, T. C., Johnson, C. E., Jones, A., Jones, C. P., Joshi, M. M., Keen, A. B., Liddicoat, S., Lock, A. P., Maidens, A. V., Manners, J. C., Milton, S. F., Rae, J. G. L., Ridley, J. K., Sellar, A., Senior, C. A., Totterdell, I. J., Verhoef, A., Vidale, P. L., & Wiltshire, A. ( 2011 ). The HadGEM2 family of Met Office Unified Model climate configurations. Geoscientific Model Development, 4 ( 3 ), 723 â 757. https://doi.org/10.5194/gmdâ 4â 723â 2011
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