Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-2875-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-2875-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Significant improvement of cloud representation in the global climate model MRI-ESM2
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, Japan
Seiji Yukimoto
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, Japan
Tsuyoshi Koshiro
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, Japan
Naga Oshima
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, Japan
Taichu Tanaka
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, Japan
Hiromasa Yoshimura
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, Japan
Ryoji Nagasawa
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, 305-0052, Japan
Related authors
No articles found.
Bosi Sheng, Massimo A. Bollasina, Alexandre S. Gagnon, Laura J. Wilcox, Thomas P. S. Reynolds, Christopher T. S. Beckett, Haolin Wang, Qingxiang Li, Pierre Nabat, Robert J. Allen, Bjørn H. Samset, Joonas Merikanto, Geeta G. Persad, Toshihiko Takemura, Kostas Tsigaridis, Sharar Ahmadi, Maxwell T. Elling, Knut von Salzen, Daniel M. Westervelt, Naga Oshima, and Tsuyoshi Koshiro
EGUsphere, https://doi.org/10.5194/egusphere-2026-2198, https://doi.org/10.5194/egusphere-2026-2198, 2026
Short summary
Short summary
Observational records indicate that austral summer rainfall over southern Africa experienced substantial wetting during the late 20th century, followed by pronounced drying over the past two decades. We show that East Asian sulphate aerosol emissions played a key role in shaping these rainfall trends. These findings have important implications for reducing uncertainties in regional rainfall projections and for understanding their links to ecosystem changes.
David D. Parrish, Charles A. Mims, Richard G. Derwent, Ian C. Faloona, Henry Bowman, Tongwen Wu, Jie Zhang, Makoto Deushi, and Naga Oshima
EGUsphere, https://doi.org/10.5194/egusphere-2026-1939, https://doi.org/10.5194/egusphere-2026-1939, 2026
Preprint archived
Short summary
Short summary
Nearly a century of ozone concentration measurements (the last half continuous) is now available in the northern mid-latitude troposphere. Atmospheric models cannot yet accurately simulate that record. We compare measured and simulated long-term changes and seasonal cycles of background ozone to quantify some model shortcomings, and suggest that their important causes may be inadequate model treatments of ozone precursor evolution and vertical transport out of the planetary boundary layer.
Hiromasa Yoshimura
EGUsphere, https://doi.org/10.5194/egusphere-2025-6120, https://doi.org/10.5194/egusphere-2025-6120, 2026
Short summary
Short summary
We have developed a two-dimensional x–z nonhydrostatic spectral model that achieves high computational efficiency by allowing long timesteps. The model incorporates several improvements that enhance numerical stability and accuracy. The model was tested with various cases, and good results were obtained. The model ran stably even in the case of an extremely steep mountain with an average slope of 63.4°. These improvements will be applied to a global atmospheric model in future work.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael Prather, Alex Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Bjørn H. Samset, Chris Smith, Steven Turnock, Duncan Watson-Parris, and Paul J. Young
Atmos. Chem. Phys., 25, 8289–8328, https://doi.org/10.5194/acp-25-8289-2025, https://doi.org/10.5194/acp-25-8289-2025, 2025
Short summary
Short summary
The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. We review its contribution to AR6 (Sixth Assessment Report of the Intergovernmental Panel on Climate Change) and the wider understanding of the role of these species in climate and climate change. We identify challenges and provide recommendations to improve the utility and uptake of climate model data, detailed summary tables of CMIP6 models, experiments, and emergent diagnostics.
Alkiviadis Kalisoras, Aristeidis K. Georgoulias, Dimitris Akritidis, Robert J. Allen, Vaishali Naik, Chaincy Kuo, Sophie Szopa, Pierre Nabat, Dirk Olivié, Twan van Noije, Philippe Le Sager, David Neubauer, Naga Oshima, Jane Mulcahy, Larry W. Horowitz, and Prodromos Zanis
Atmos. Chem. Phys., 24, 7837–7872, https://doi.org/10.5194/acp-24-7837-2024, https://doi.org/10.5194/acp-24-7837-2024, 2024
Short summary
Short summary
Effective radiative forcing (ERF) is a metric for estimating how human activities and natural agents change the energy flow into and out of the Earth’s climate system. We investigate the anthropogenic aerosol ERF, and we estimate the contribution of individual processes to the total ERF using simulations from Earth system models within the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our findings highlight that aerosol–cloud interactions drive ERF variability during the last 150 years.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Victoria A. Flood, Kimberly Strong, Cynthia H. Whaley, Kaley A. Walker, Thomas Blumenstock, James W. Hannigan, Johan Mellqvist, Justus Notholt, Mathias Palm, Amelie N. Röhling, Stephen Arnold, Stephen Beagley, Rong-You Chien, Jesper Christensen, Makoto Deushi, Srdjan Dobricic, Xinyi Dong, Joshua S. Fu, Michael Gauss, Wanmin Gong, Joakim Langner, Kathy S. Law, Louis Marelle, Tatsuo Onishi, Naga Oshima, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Manu A. Thomas, Svetlana Tsyro, and Steven Turnock
Atmos. Chem. Phys., 24, 1079–1118, https://doi.org/10.5194/acp-24-1079-2024, https://doi.org/10.5194/acp-24-1079-2024, 2024
Short summary
Short summary
It is important to understand the composition of the Arctic atmosphere and how it is changing. Atmospheric models provide simulations that can inform policy. This study examines simulations of CH4, CO, and O3 by 11 models. Model performance is assessed by comparing results matched in space and time to measurements from five high-latitude ground-based infrared spectrometers. This work finds that models generally underpredict the concentrations of these gases in the Arctic troposphere.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
Short summary
Short summary
This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
Flossie Brown, Gerd A. Folberth, Stephen Sitch, Susanne Bauer, Marijn Bauters, Pascal Boeckx, Alexander W. Cheesman, Makoto Deushi, Inês Dos Santos Vieira, Corinne Galy-Lacaux, James Haywood, James Keeble, Lina M. Mercado, Fiona M. O'Connor, Naga Oshima, Kostas Tsigaridis, and Hans Verbeeck
Atmos. Chem. Phys., 22, 12331–12352, https://doi.org/10.5194/acp-22-12331-2022, https://doi.org/10.5194/acp-22-12331-2022, 2022
Short summary
Short summary
Surface ozone can decrease plant productivity and impair human health. In this study, we evaluate the change in surface ozone due to climate change over South America and Africa using Earth system models. We find that if the climate were to change according to the worst-case scenario used here, models predict that forested areas in biomass burning locations and urban populations will be at increasing risk of ozone exposure, but other areas will experience a climate benefit.
Hitoshi Matsui, Tatsuhiro Mori, Sho Ohata, Nobuhiro Moteki, Naga Oshima, Kumiko Goto-Azuma, Makoto Koike, and Yutaka Kondo
Atmos. Chem. Phys., 22, 8989–9009, https://doi.org/10.5194/acp-22-8989-2022, https://doi.org/10.5194/acp-22-8989-2022, 2022
Short summary
Short summary
Using a global aerosol model, we find that the source contributions to radiative effects of black carbon (BC) in the Arctic are quite different from those to mass concentrations and deposition flux of BC in the Arctic. This is because microphysical properties (e.g., mixing state), altitudes, and seasonal variations of BC in the atmosphere differ among emissions sources. These differences need to be considered for accurate simulations of Arctic BC and its source contributions and climate impacts.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
Short summary
Short summary
Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Hiromasa Yoshimura
Geosci. Model Dev., 15, 2561–2597, https://doi.org/10.5194/gmd-15-2561-2022, https://doi.org/10.5194/gmd-15-2561-2022, 2022
Short summary
Short summary
This paper proposes a new double Fourier series (DFS) method on a sphere that improves the numerical stability of a model compared with conventional DFS methods. The shallow-water model and the advection model using the new DFS method give stable results without the appearance of high-wavenumber noise near the poles. The model using the new DFS method is faster than the model using spherical harmonics (especially at high resolutions) and gives almost the same results.
Henry Bowman, Steven Turnock, Susanne E. Bauer, Kostas Tsigaridis, Makoto Deushi, Naga Oshima, Fiona M. O'Connor, Larry Horowitz, Tongwen Wu, Jie Zhang, Dagmar Kubistin, and David D. Parrish
Atmos. Chem. Phys., 22, 3507–3524, https://doi.org/10.5194/acp-22-3507-2022, https://doi.org/10.5194/acp-22-3507-2022, 2022
Short summary
Short summary
A full understanding of ozone in the troposphere requires investigation of its temporal variability over all timescales. Model simulations show that the northern midlatitude ozone seasonal cycle shifted with industrial development (1850–2014), with an increasing magnitude and a later summer peak. That shift reached a maximum in the mid-1980s, followed by a reversal toward the preindustrial cycle. The few available observations, beginning in the 1970s, are consistent with the model simulations.
Sho Ohata, Makoto Koike, Atsushi Yoshida, Nobuhiro Moteki, Kouji Adachi, Naga Oshima, Hitoshi Matsui, Oliver Eppers, Heiko Bozem, Marco Zanatta, and Andreas B. Herber
Atmos. Chem. Phys., 21, 15861–15881, https://doi.org/10.5194/acp-21-15861-2021, https://doi.org/10.5194/acp-21-15861-2021, 2021
Short summary
Short summary
Vertical profiles of black carbon (BC) in the Arctic were measured during the PAMARCMiP aircraft-based experiment in spring 2018 and compared with those observed during previous aircraft campaigns in 2008, 2010, and 2015. Their differences were explained primarily by the year-to-year variation of biomass burning activities in northern midlatitudes over Eurasia. Our observations provide a bases to evaluate numerical model simulations that assess the BC radiative effects in the Arctic spring.
Sho Ohata, Tatsuhiro Mori, Yutaka Kondo, Sangeeta Sharma, Antti Hyvärinen, Elisabeth Andrews, Peter Tunved, Eija Asmi, John Backman, Henri Servomaa, Daniel Veber, Konstantinos Eleftheriadis, Stergios Vratolis, Radovan Krejci, Paul Zieger, Makoto Koike, Yugo Kanaya, Atsushi Yoshida, Nobuhiro Moteki, Yongjing Zhao, Yutaka Tobo, Junji Matsushita, and Naga Oshima
Atmos. Meas. Tech., 14, 6723–6748, https://doi.org/10.5194/amt-14-6723-2021, https://doi.org/10.5194/amt-14-6723-2021, 2021
Short summary
Short summary
Reliable values of mass absorption cross sections (MACs) of black carbon (BC) are required to determine mass concentrations of BC at Arctic sites using different types of filter-based absorption photometers. We successfully estimated MAC values for these instruments through comparison with independent measurements of BC by a continuous soot monitoring system called COSMOS. These MAC values are consistent with each other and applicable to study spatial and temporal variation in BC in the Arctic.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation:
2. Multiple aerosol types, J. Geophys. Res., 105, 6837,
https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak, H., Ghan, S. J., and Rivera-Carpio, C.: A parameterization of
aerosol activation: 1. Single aerosol type, J. Geophys. Res., 103, 6123,
https://doi.org/10.1029/97JD03735, 1998.
Abel, S. J., Walters, D. N., and Allen, G.: Evaluation of stratocumulus cloud
prediction in the Met Office forecast model during VOCALS-REx, Atmos.
Chem. Phys.,
10, 10541–10559, https://doi.org/10.5194/acp-10-10541-2010, 2010.
Betts, A. K.: Diurnal variation of California coastal stratocumulus from two
days of boundary layer soundings, Tellus A, 42, 302–304,
https://doi.org/10.1034/j.1600-0870.1990.t01-1-00007.x, 1990.
Betts, A. K. and Boers, R.: A Cloudiness Transition in a Marine Boundary
Layer, J. Atmos. Sci., 47, 1480–1497,
https://doi.org/10.1175/1520-0469(1990)047<1480:ACTIAM>2.0.CO;2,
1990.
Bigg, E. K.: The supercooling of water, Proc. Phys. Soc. B, 66, 688–694, 1953.
Bodas-Salcedo, A., Williams, K. D., Ringer, M. A., Beau, I., Cole, J. N. S.,
Dufresne, J. L., Koshiro, T., Stevens, B., Wang, Z., and Yokohata, T.:
Origins of the solar radiation biases over the Southern Ocean in CFMIP2
models, J. Climate, 27, 41–56, https://doi.org/10.1175/JCLI-D-13-00169.1, 2014.
Bodas-Salcedo, A., Hill, P. G., Furtado, K., Williams, K. D., Field, P. R.,
Manners, J. C., Hyder, P., and Kato, S.: Large contribution of supercooled
liquid clouds to the solar radiation budget of the Southern Ocean, J. Climate,
29, 4213–4228, https://doi.org/10.1175/JCLI-D-15-0564.1, 2016.
Bony, S., Webb, M., Bretherton, C., Klein, S., Siebesma, P., Tselioudis, G.,
and Zhang, M.: CFMIP: Towards a better evaluation and understanding of
clouds and cloud feedbacks in CMIP5 models, Clivar Exch., 56, 20–24, 2011.
Bretherton, C. S., Wood, R., George, R. C., Leon, D., Allen, G., and Zheng, X.:
Southeast Pacific stratocumulus clouds, precipitation and boundary layer
structure sampled along 20∘ S during VOCALS-REx, Atmos. Chem. Phys., 10,
10639–10654, https://doi.org/10.5194/acp-10-10639-2010, 2010.
Brooks, M. E., Hogan, R. J., and Illingworth, A. J.: Parameterizing the
Difference in Cloud Fraction Defined by Area and by Volume as Observed with
Radar and Lidar, J. Atmos. Sci., 62, 2248–2260, https://doi.org/10.1175/JAS3467.1,
2005.
Bushell, A. C. and Martin, G. M.: The impact of vertical resolution upon GCM
simulations of marine stratocumulus, Clim. Dynam., 15, 293–318,
https://doi.org/10.1007/s003820050283, 1999.
Caldwell, P. and Bretherton, C. S.: Response of a Subtropical
Stratocumulus-Capped Mixed Layer to Climate and Aerosol Changes, J. Climate,
22, 20–38, https://doi.org/10.1175/2008JCLI1967.1, 2009.
Caldwell, P. M., Zhang, Y., and Klein, S. A.: CMIP3 subtropical stratocumulus
cloud feedback interpreted through a mixed-layer model, J. Climate, 26,
1607–1625, https://doi.org/10.1175/JCLI-D-12-00188.1, 2013.
Cesana, G. and Chepfer, H.: Evaluation of the cloud thermodynamic phase in a
climate model using CALIPSO-GOCCP, J. Geophys. Res.-Atmos., 118,
7922–7937, https://doi.org/10.1002/jgrd.50376, 2013.
Cesana, G., Waliser, D. E., Jiang, X., and Li, J. L. F.: Multimodel
evaluation of cloud phase transition using satellite and reanalysis data, J.
Geophys. Res.-Atmos., 120, 7871–7892, https://doi.org/10.1002/2014JD022932, 2015.
Cesana, G., Chepfer, H., Winker, D., Getzewich, B., Cai, X., Jourdan, O.,
Mioche, G., Okamoto, H., Hagihara, Y., Noel, V., and Reverdy, M.: Using in
situ airborne measurements to evaluate three cloud phase products derived
from CALIPSO, J. Geophys. Res.-Atmos., 121, 5788–5808,
https://doi.org/10.1002/2015JD024334, 2016.
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N.,
Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric
Aerosol Optical Thickness from the GOCART Model and Comparisons with
Satellite and Sun Photometer Measurements, J. Atmos. Sci., 59, 461–483,
https://doi.org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2,
2002.
Clarke, A. D., Owens, S. R., and Zhou, J.: An ultrafine sea-salt flux from
breaking waves: Implications for cloud condensation nuclei in the remote
marine atmosphere, J. Geophys. Res., 111, D06202,
https://doi.org/10.1029/2005JD006565, 2006.
Collins, W. D.: Parameterization of Generalized Cloud Overlap for Radiative
Calculations in General Circulation Models, J. Atmos. Sci., 58,
3224–3242, https://doi.org/10.1175/1520-0469(2001)058<3224:POGCOF>2.0.CO;2, 2001.
Cotton, W. R., Tripoli, G. J., Rauber, R. M., and Mulvihill, E. A.: Numerical
Simulation of the Effects of Varying Ice Crystal Nucleation Rates and
Aggregation Processes on Orographic Snowfall, J. Clim. Appl. Meteorol.,
25, 1658–1680, https://doi.org/10.1175/1520-0450(1986)025<1658:NSOTEO>2.0.CO;2, 1986.
Covert, D. S., Kapustin, V. N., Bates, T. S., and Quinn, P. K.: Physical
properties of marine boundary layer aerosol particles of the mid-Pacific in
relation to sources and meteorological transport, J. Geophys. Res.,
101, 6919–6930, https://doi.org/10.1029/95JD03068, 1996.
Deardorff, J. W.: Cloud Top Entrainment Instability, J. Atmos. Sci., 37,
131–147, https://doi.org/10.1175/1520-0469(1980)037<0131:CTEI>2.0.CO;2, 1980.
Duynkerke, P. G. and Teixeira, J.: Comparison of the ECMWF Reanalysis with
FIRE I Observations: Diurnal Variation of Marine Stratocumulus, J. Climate,
14, 1466–1478, https://doi.org/10.1175/1520-0442(2001)014<1466:COTERW>2.0.CO;2, 2001.
ECMWF: Clouds and large-scale precipitation, IFS Documentation, European
Centre for Medium-Range Weather Forecasts, CY25r1, Part IV, Chapter 6, 2002.
ECMWF: Clouds and large-scale precipitation, IFS Documentation, European
Centre for Medium-Range Weather Forecasts, CY43r3, Part IV, Chapter 7, 2017.
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.
Forbes, R. M. and Ahlgrimm, M.: On the Representation of High-Latitude
Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model, Mon. Weather
Rev., 142, 3425–3445, https://doi.org/10.1175/MWR-D-13-00325.1, 2014.
Forbes, R. M., Geer, A., Lonitz, K., and Ahlgrimm, M.: Reducing systematic
errors in cold-air outbreaks, European Centre for
Medium-Range Weather Forecasts, ECMWF Newsletter, No. 146, 17–22, https://doi.org/10.21957/s 41h7q7l,
2016.
Frey, W. R. and Kay, J. E.: The influence of extratropical cloud phase and
amount feedbacks on climate sensitivity, Clim. Dynam., 50, 3097–3116,
https://doi.org/10.1007/s00382-017-3796-5, 2018.
Geleyn, J.-F. and Hollingsworth, A.: An economical analytical method for
the computation of the interaction between scattering and line absorption of
radiation, Beitr. Phys. Atmos., 52, 1–16, 1979.
Guo, Z., Wang, M., Qian, Y., Larson, V. E., Ghan, S., Ovchinnikov, M.,
Bogenschutz, P. A., Zhao, C., Lin, G., and Zhou, T.: A sensitivity analysis
of cloud properties to CLUBB parameters in the single-column Community
Atmosphere Model (SCAM5), J. Adv. Model. Earth Syst., 6, 829–858,
https://doi.org/10.1002/2014MS000315, 2015.
Hahn, C. J. and Warren, S. G.: Extended edited synoptic cloud reports from
ships and land stations over the globe, 1952–1996 (2009 update). NDP-026C,
Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory,
Oak Ridge, TN, 2009.
Heymsfield, A. J.: Precipitation Development in Stratiform Ice Clouds: A
Microphysical and Dynamical Study, J. Atmos. Sci., 34, 367–381,
https://doi.org/10.1175/1520-0469(1977)034<0367:PDISIC>2.0.CO;2, 1977.
Heymsfield, A. J. and Donner, L. J.: A Scheme for Parameterizing Ice-Cloud
Water Content in General Circulation Models, J. Atmos. Sci., 47,
1865–1877, https://doi.org/10.1175/1520-0469(1990)047<1865:ASFPIC>2.0.CO;2, 1990.
Heymsfield, A. J. and Iaquinta, J.: Cirrus Crystal Terminal Velocities, J.
Atmos. Sci., 57, 916–938, https://doi.org/10.1175/1520-0469(2000)057<0916:CCTV>2.0.CO;2, 2000.
Hoose, C., Kristjánsson, J. E., Iversen, T., Kirkevåg, A., Seland,
Ø., and Gettelman, A.: Constraining cloud droplet number concentration in
GCMs suppresses the aerosol indirect effect, Geophys. Res. Lett., 36,
L12807, https://doi.org/10.1029/2009GL038568, 2009.
Hu, Y., Rodier, S., Xu, K. M., Sun, W., Huang, J., Lin, B., Zhai, P., and
Josset, D.: Occurrence, liquid water content, and fraction of supercooled
water clouds from combined CALIOP/IIR/MODIS measurements, J. Geophys. Res., 115,
D00H34, https://doi.org/10.1029/2009JD012384, 2010.
Jakob, C.: The representation of cloud cover in Atmospheric General
Circulation Models, thesis, Fakultat fur Physik der
Ludwig-Maximilians-Universitat, European Centre for Medium-Range Weather
Forecasts, submitted, 2000.
Jiang, J. H., Su, H., Zhai, C., Perun, V. S., Del Genio, A., Nazarenko, L.
S., Donner, L. J., Horowitz, L., Seman, C., Cole, J., Gettelman, A., Ringer,
M. A., Rotstayn, L., Jeffrey, S., Wu, T., Brient, F., Dufresne, J. L.,
Kawai, H., Koshiro, T., Watanabe, M., Lécuyer, T. S., Volodin, E. M.,
Iversen, T., Drange, H., Mesquita, M. D. S., Read, W. G., Waters, J. W.,
Tian, B., Teixeira, J., and Stephens, G. L.: Evaluation of cloud and water
vapor simulations in CMIP5 climate models Using NASA “A-Train” satellite
observations, J. Geophys. Res., 117, D14105, https://doi.org/10.1029/2011JD017237,
2012.
Jones, A., Roberts, D. L., Woodage, M. J., and Johnson, C. E.: Indirect
sulphate aerosol forcing in a climate model with an interactive sulphur
cycle, J. Geophys. Res., 106, 20293–20310,
https://doi.org/10.1029/2000JD000089, 2001.
Kärcher, B. and Lohmann, U.: A Parameterization of cirrus cloud
formation: Homogeneous freezing including effects of aerosol size, J.
Geophys. Res., 107, 4698, https://doi.org/10.1029/2001JD001429,
2002.
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud
formation: Heterogeneous freezing, J. Geophys. Res., 108, 4402,
https://doi.org/10.1029/2002JD003220, 2003.
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based
parameterization of cirrus cloud formation for use in global atmospheric
models, J. Geophys. Res., 111, D01205, https://doi.org/10.1029/2005JD006219, 2006.
Kawai, H.: Improvement of a Cloud Ice Fall Scheme in GCM, CAS/JSC WGNE
Research Activities in Atmospheric and Oceanic Modelling/WMO, 35, 4.11–4.12,
2005.
Kawai, H.: Improvement of a Stratocumulus Scheme for Mid-latitude Marine Low
Clouds, CAS/JSC WGNE Research Activities in Atmospheric and Oceanic
Modelling/WMO, 43, 4.03–4.04, 2013.
Kawai, H. and Inoue, T.: A simple parameterization scheme for subtropical
marine stratocumulus, SOLA, 2, 17–20,
https://doi.org/10.2151/sola.2006-005, 2006.
Kawai, H., Yabu, S., Hagihara, Y., Koshiro, T., and Okamoto, H.:
Characteristics of the cloud top heights of marine boundary layer clouds and
the frequency of marine fog over mid-latitudes, J. Meteorol. Soc. Jpn.,
93, 613–628, https://doi.org/10.2151/jmsj.2015-045, 2015a.
Kawai, H., Koshiro, T., Webb, M., Yukimoto, S., and Tanaka, T.: Cloud
feedbacks in MRI-CGCM3, CAS/JSC WGNE Research Activities in Atmospheric and
Oceanic Modelling/WMO, 45, 7.11–7.12, 2015b.
Kawai, H., Koshiro, T., and Webb, M. J.: Interpretation of factors
controlling low cloud cover and low cloud feedback using a unified
predictive index, J. Climate, 30, 9119–9131, https://doi.org/10.1175/JCLI-D-16-0825.1,
2017.
Kawai, H., Koshiro, T., Endo, H., and Arakawa, O.: Changes in marine fog over
the North Pacific under different climates in CMIP5 multimodel simulations,
J. Geophys. Res.-Atmos., 123, 10911–10924, https://doi.org/10.1029/2018JD028899,
2018.
Kay, J. E., Wall, C., Yettella, V., Medeiros, B., Hannay, C., Caldwell, P.,
and Bitz, C.: Global climate impacts of fixing the Southern Ocean shortwave
radiation bias in the Community Earth System Model (CESM), J. Climate, 29,
4617–4636, https://doi.org/10.1175/JCLI-D-15-0358.1, 2016.
Klein, S. A. and Hartmann, D. L.: The seasonal cycle of low stratiform
clouds, J. Climate, 6, 1587–1606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2,
1993.
Koshiro, T., Shiotani, M., Kawai, H., and Yukimoto, S.: Evaluation of
Relationships between Subtropical Marine Low Stratiform Cloudiness and
Estimated Inversion Strength in CMIP5 Models Using the Satellite Simulator
Package COSP, SOLA, 14, 25–32, https://doi.org/10.2151/sola.2018-005, 2018.
Kuo, H.-C. and Schubert, W. H.: Stability of cloud-topped boundary layers,
Q. J. Roy. Meteor. Soc., 114, 887–916, https://doi.org/10.1002/qj.49711448204,
1988.
Larson, K., Hartmann, D. L., and Klein, S. A.: The Role of Clouds, Water
Vapor, Circulation, and Boundary Layer Structure in the Sensitivity of the
Tropical Climate, J. Climate, 12), 2359–2374,
https://doi.org/10.1175/1520-0442(1999)012<2359:TROCWV>2.0.CO;2,
1999.
Lauer, A. and Hamilton, K.: Simulating Clouds with Global Climate Models: A
Comparison of CMIP5 Results with CMIP3 and Satellite Data, J. Climate, 26,
3823–3845, https://doi.org/10.1175/JCLI-D-12-00451.1, 2013.
Lauer, A., Hamilton, K., Wang, Y., Phillips, V. T. J., and Bennartz, R.: The
Impact of Global Warming on Marine Boundary Layer Clouds over the Eastern
Pacific – A Regional Model Study, J. Climate, 23, 5844–5863,
https://doi.org/10.1175/2010JCLI3666.1, 2010.
Levkov, L., Rockel, B., Kapitza, H., and Raschke, E.: 3D mesoscale numerical
studies of cirrus and stratus clouds by their time and space evolution,
Beitr. Phys. Atmos., 65, 35–58, 1992.
Li, J. L. F., Waliser, D. E., Stephens, G., Lee, S., L'Ecuyer, T., Kato, S.,
Loeb, N., and Ma, H. Y.: Characterizing and understanding radiation budget
biases in CMIP3/CMIP5 GCMs, contemporary GCM, and reanalysis, J. Geophys.
Res.-Atmos., 118, 8166–8184, https://doi.org/10.1002/jgrd.50378, 2013.
Liu, X., Easter, R. C., Ghan, S. J., Zaveri, R., Rasch, P., Shi, X., Lamarque, J.-F.,
Gettelman, A., Morrison, H., Vitt, F., Conley, A., Park, S., Neale, R., Hannay, C.,
Ekman, A. M. L., Hess, P., Mahowald, N., Collins, W., Iacono, M. J., Bretherton, C. S.,
Flanner, M. G., and Mitchell, D.: Toward a minimal representation of aerosols in climate models: description and evaluation in the Community
Atmosphere Model CAM5, Geosci. Model Dev., 5, 709–739, https://doi.org/10.5194/gmd-5-709-2012, 2012.
Lock, A. P.: Factors influencing cloud area at the capping inversion for
shallow cumulus clouds, Q. J. Roy. Meteor. Soc., 135, 941–952,
https://doi.org/10.1002/qj.424, 2009.
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F.,
Kato, S., Manalo-Smith, N., and Wong, T.: Toward Optimal Closure of the
Earth's Top-of-Atmosphere Radiation Budget, J. Climate, 22, 748–766,
https://doi.org/10.1175/2008JCLI2637.1, 2009.
Lohmann, U.: Possible Aerosol Effects on Ice Clouds via Contact Nucleation,
J. Atmos. Sci., 59, 647–656, https://doi.org/10.1175/1520-0469(2001)059<0647:PAEOIC>2.0.CO;2, 2002.
Lohmann, U. and Diehl, K.: Sensitivity Studies of the Importance of Dust Ice
Nuclei for the Indirect Aerosol Effect on Stratiform Mixed-Phase Clouds, J.
Atmos. Sci., 63, 968–982, https://doi.org/10.1175/JAS3662.1, 2006.
Lohmann, U., Stier, P., Hoose, C., Ferrachat, S., Kloster, S., Roeckner, E., and
Zhang, J.: Cloud microphysics and aerosol indirect effects in the global
climate model ECHAM5-HAM, Atmos. Chem. Phys., 7, 3425–3446, https://doi.org/10.5194/acp-7-3425-2007, 2007.
MacVean, M. K.: A Numerical Investigation of the Criterion for Cloud-Top
Entrainment Instability, J. Atmos. Sci., 50, 2481–2495,
https://doi.org/10.1175/1520-0469(1993)050<2481:ANIOTC>2.0.CO;2,
1993.
MacVean, M. K. and Mason, P. J.: Cloud-Top Entrainment Instability through
Small-Scale Mixing and Its Parameterization in Numerical Models, J. Atmos.
Sci., 47, 1012–1030, https://doi.org/10.1175/1520-0469(1990)047<1012:CTEITS>2.0.CO;2, 1990.
Manton, M. J. and Cotton, W. R.: Formulation of approximate equations for
modeling moist deep convection on the mesoscale, Atmospheric Science Paper,
No. 266, Colorado State University, 1977.
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta,
M., Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz,
D., Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a
global model, J. Adv. Model. Earth Syst., 4, M00A01, https://doi.org/10.1029/2012MS000154, 2012.
McCoy, D. T., Hartmann, D. L., Zelinka, M. D., Ceppi, P., and Grosvenor, D.
P.: Mixed-phase cloud physics and Southern Ocean cloud feedback in climate
models, J. Geophys. Res.-Atmos., 120, 9539–9554,
https://doi.org/10.1002/2015JD023603, 2015.
McFarquhar, G. M. and Heymsfield, A. J.: Parameterization of Tropical Cirrus
Ice Crystal Size Distributions and Implications for Radiative Transfer:
Results from CEPEX, J. Atmos. Sci., 54, 2187–2200,
https://doi.org/10.1175/1520-0469(1997)054<2187:POTCIC>2.0.CO;2,
1997.
Medeiros, B., Stevens, B., Held, I. M., Zhao, M., Williamson, D. L., Olson,
J. G., and Bretherton, C. S.: Aquaplanets, Climate Sensitivity, and Low
Clouds, J. Climate, 21, 4974–4991, https://doi.org/10.1175/2008JCLI1995.1, 2008.
Meyers, M. P., DeMott, P. J., and Cotton, W. R.: New Primary Ice-Nucleation
Parameterizations in an Explicit Cloud Model, J. Appl. Meteorol., 31,
708–721, https://doi.org/10.1175/1520-0450(1992)031<0708:NPINPI>2.0.CO;2, 1992.
Miller, R. L.: Tropical Thermostats and Low Cloud Cover, J. Climate, 10,
409–440, https://doi.org/10.1175/1520-0442(1997)010<0409:TTALCC>2.0.CO;2, 1997.
Morrison, H. and Gettelman, A.: A New Two-Moment Bulk Stratiform Cloud
Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3).
Part I: Description and Numerical Tests, J. Climate, 21, 3642–3659,
https://doi.org/10.1175/2008JCLI2105.1, 2008.
Murakami, M.: Numerical Modeling of Dynamical and Microphysical Evolution of
an Isolated Convective Cloud – The 19 July 1981 CCOPE cloud, J. Meteor. Soc.
Jpn., 68, 107–128, https://doi.org/10.2151/jmsj1965.68.2_107,
1990.
Nagasawa, R.: The Problem of Cloud Overlap in the Radiation Process of JMA's
Global NWP Model, CAS/JSC WGNE Research Activities in Atmospheric and
Oceanic Modelling/WMO, 42, 0415-0416, 2012.
Nam, C., Bony, S., Dufresne, J. L., and Chepfer, H.: The 'too few, too bright'
tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39,
L21801, https://doi.org/10.1029/2012GL053421, 2012.
Neubauer, D., Lohmann, U., Hoose, C., and Frontoso, M. G.: Impact of the
representation of marine
stratocumulus clouds on the anthropogenic aerosol effect,
Atmos. Chem. Phys., 14, 11997–12022,
https://doi.org/10.5194/acp-14-11997-2014, 2014.
Nuijens, L., Medeiros, B., Sandu, I., and Ahlgrimm, M.: The behavior of
trade-wind cloudiness in observations and models: The major cloud components
and their variability, J. Adv. Model. Earth Syst., 7, 600–616,
https://doi.org/10.1002/2014MS000390, 2015.
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick
Hofmann, R. J.: Reconciling simulated and observed views of clouds: MODIS,
ISCCP, and the limits of instrument simulators, J. Climate, 25,
4699–4720, https://doi.org/10.1175/JCLI-D-11-00267.1, 2012.
Qu, X., Hall, A., Klein, S. A., and Caldwell, P. M.: On the spread of changes
in marine low cloud cover in climate model simulations of the 21st century,
Clim. Dynam., 42, 2603–2626, https://doi.org/10.1007/s00382-013-1945-z, 2014.
Rahn, D. A. and Garreaud, R.: Marine boundary layer over the subtropical southeast
Pacific during VOCALS-REx -– Part 1: Mean structure and diurnal cycle,
Atmos. Chem. Phys., 10, 4491–4506, https://doi.org/10.5194/acp-10-4491-2010, 2010.
Randall, D. A.: Conditional Instability of the First Kind Upside-Down, J.
Atmos. Sci., 37, 125–130, https://doi.org/10.1175/1520-0469(1980)037<0125:CIOTFK>2.0.CO;2, 1980.
Rossow, W. B. and Schiffer, R. A.: Advances in Understandig Clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287,
https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2,
1999.
Rotstayn, L. D.: A physically based scheme for the treatment of stratiform
clouds and precipitation in large-scale models. I: Description and
evaluation of the microphysical processes, Q. J. Roy. Meteor. Soc.,
123, 1227–1282, https://doi.org/10.1002/qj.49712354106, 1997.
Rotstayn, L. D.: On the “tuning” of autoconversion parameterizations in
climate models, J. Geophys. Res., 105, 15495–15507,
https://doi.org/10.1029/2000JD900129, 2000.
Rutledge, S. A. and Hobbs, P.: The Mesoscale and Microscale Structure and
Organization of Clouds and Precipitation in Midlatitude Cyclones. VIII: A
Model for the “Seeder-Feeder” Process in Warm-Frontal Rainbands, J. Atmos.
Sci., 40, 1185–1206, https://doi.org/10.1175/1520-0469(1983)040<1185:TMAMSA>2.0.CO;2, 1983.
Seinfeld, J. H., and Pandis, S. N.: Atmospheric Chemistry and Physics: From
Air Pollution to Climate Change 2nd ed, John Wiley & Sons, New York, USA,
1203, 2006.
Siebesma, A. P., Jakob, C., Lenderink, G., Neggers, R. A. J., Teixeira, J.,
van Meijgaard, E., Calvo, J., Chlond, A., Grenier, H., Jones, C.,
Köhler, M., Kitagawa, H., Marquet, P., Lock, A. P., Müller, F.,
Olmeda, D. C., and Severijns, C.: Cloud representation in general-circulation
models over the northern Pacific Ocean: A EUROCS intercomparison study, Q.
J. Roy. Meteor. Soc., 130, 3245–3267, https://doi.org/10.1256/qj.03.146, 2004.
Slingo, J. M.: A cloud parametrization scheme derived from GATE data for use
with a numerical model, Q. J. Roy. Meteor. Soc., 106, 747–770,
https://doi.org/10.1002/qj.49710645008, 1980.
Slingo, J. M.: The Development and Verification of A Cloud Prediction Scheme
For the Ecmwf Model, Q. J. Roy. Meteor. Soc., 113, 899–927,
https://doi.org/10.1002/qj.49711347710, 1987.
Smith, R. N. B.: A scheme for predicting layer clouds and their water
content in a general circulation model, Q. J. Roy. Meteor. Soc., 116,
435–460, https://doi.org/10.1002/qj.49711649210, 1990.
Soden, B. J. and Held, I. M.: An Assessment of Climate Feedbacks in Coupled
Ocean – Atmosphere Models, J. Climate, 19, 3354–3360,
https://doi.org/10.1175/JCLI9028.1, 2006.
Soden, B. J., Held, I. M., Colman, R. C., Shell, K. M., Kiehl, J. T., and
Shields, C. A.: Quantifying climate feedbacks using radiative kernels, J.
Climate, 21, 3504–3520, https://doi.org/10.1175/2007JCLI2110.1, 2008.
Su, H., Jiang, J. H., Zhai, C., Perun, V. S., Shen, J. T., Del Genio, A.,
Nazarenko, L. S., Donner, L. J., Horowitz, L., Seman, C., Morcrette, C.,
Petch, J., Ringer, M., Cole, J., Von Salzen, K., Mesquita, M. D. S.,
Iversen, T., Kristjansson, J. E., Gettelman, A., Rotstayn, L., Jeffrey, S.,
Dufresne, J. L., Watanabe, M., Kawai, H., Koshiro, T., Wu, T., Volodin, E.
M., L'Ecuyer, T., Teixeira, J., and Stephens, G. L.: Diagnosis of
regime-dependent cloud simulation errors in CMIP5 models using “a-Train”
satellite observations and reanalysis data, J. Geophys. Res.-Atmos., 118,
2762–2780, https://doi.org/10.1029/2012JD018575, 2013.
Suzuki, K., Stephens, G., Bodas-Salcedo, A., Wang, M., Golaz, J.-C.,
Yokohata, T., and Koshiro, T.: Evaluation of the Warm Rain Formation Process
in Global Models with Satellite Observations, J. Atmos. Sci., 72,
3996–4014, https://doi.org/10.1175/JAS-D-14-0265.1, 2015.
Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y., and Nakajima, T.:
Simulation of climate response to aerosol direct and indirect effects with
aerosol transport-radiation model, J. Geophys. Res., 110, D02202,
https://doi.org/10.1029/2004JD005029, 2005.
Tan, I. and Storelvmo, T.: Sensitivity Study on the Influence of Cloud
Microphysical Parameters on Mixed-Phase Cloud Thermodynamic Phase
Partitioning in CAM5, J. Atmos. Sci., 73, 709–728,
https://doi.org/10.1175/JAS-D-15-0152.1, 2016.
Tan, I., Storelvmo, T., and Zelinka, M. D.: Observational constraints on
mixed-phase clouds imply higher climate sensitivity, Science,
352, 224–227, https://doi.org/10.1126/science.aad5300, 2016.
Tanaka, T. Y., Orito, K., Sekiyama, T. T., Shibata, K., Chiba, M., and
Tanaka, H.: MASINGAR, a global tropospheric aerosol chemical transport model
coupled with MRI/JMA98 GCM: Model description, Pap. Meteorol. Geophys.,
53, 119–138, https://doi.org/10.2467/mripapers.53.119, 2003.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res., 106, 7183–7192,
https://doi.org/10.1029/2000JD900719, 2001.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Teixeira, J.: The impact of increased boundary layer vertical resolution on
the ECMWF forecast system, European Centre for
Medium-Range Weather Forecasts, ECMWF technical memorandum, 268, 1–55, 1999.
Tiedtke, M.: Representation of Clouds in Large-Scale Models, Mon. Weather Rev.,
121, 3040–3061, 1993.
Trenberth, K. E. and Fasullo, J. T.: Simulation of present-day and
twenty-first-century energy budgets of the southern oceans, J. Climate., 23,
440–454, https://doi.org/10.1175/2009JCLI3152.1, 2010.
Tsushima, Y., Emori, S., Ogura, T., Kimoto, M., Webb, M. J., Williams, K.
D., Ringer, M. A., Soden, B. J., Li, B., and Andronova, N.: Importance of the
mixed-phase cloud distribution in the control climate for assessing the
response of clouds to carbon dioxide increase: a multi-model study, Clim.
Dynam., 27, 113–126, https://doi.org/10.1007/s00382-006-0127-7, 2006.
Tsushima, Y., Ringer, M. A., Webb, M. J., and Williams, K. D.: Quantitative
evaluation of the seasonal variations in climate model cloud regimes, Clim.
Dynam., 41, 2679–2696, https://doi.org/10.1007/s00382-012-1609-4, 2013.
Tsushima, Y., Ringer, M. A., Koshiro, T., Kawai, H., Roehrig, R., Cole, J.,
Watanabe, M., Yokohata, T., Bodas-Salcedo, A., Williams, K. D., and Webb, M.
J.: Robustness, uncertainties, and emergent constraints in the radiative
responses of stratocumulus cloud regimes to future warming, Clim. Dynam.,
46, https://doi.org/10.1007/s00382-015-2750-7, 2016.
Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D.
C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A.,
Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E.,
Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. Van De, Bidlot, J.,
Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher,
M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L.,
Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette,
J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K.
E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40
re-analysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012,
https://doi.org/10.1256/qj.04.176, 2005.
Wang, Y., Xu, H. and Xie, S.-P.: Regional Model Simulations of Marine
Boundary Layer Clouds over the Southeast Pacific off South America. Part II:
Sensitivity Experiments, Mon. Weather Rev., 132, 2650–2668,
https://doi.org/10.1175/MWR2812.1, 2004.
Webb, M. J., Lambert, F. H., and Gregory, J. M.: Origins of differences in
climate sensitivity, forcing and feedback in climate models, Clim. Dynam.,
40, 677–707, https://doi.org/10.1007/s00382-012-1336-x, 2013.
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R.,
Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B.,
Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.:
The Cloud Feedback Model Intercomparison Project (CFMIP)
contribution to CMIP6, Geosci. Model Dev., 10, 359–384,
https://doi.org/10.5194/gmd-10-359-2017, 2017.
Williams, K. D., Ringer, M. A., Senior, C. A., Webb, M. J., McAvaney, B. J.,
Andronova, N., Bony, S., Dufresne, J. L., Emori, S., Gudgel, R., Knutson,
T., Li, B., Lo, K., Musat, I., Wegner, J., Slingo, A., and Mitchell, J. F.
B.: Evaluation of a component of the cloud response to climate change in an
intercomparison of climate models, Clim. Dynam., 26, 145–165,
https://doi.org/10.1007/s00382-005-0067-7, 2006.
Wilson, D. R., Smith, R. N. B., Gregory, D., Wilson, C. A., Bushell, A. C.,
and Cusack, S.: The large-scale cloud scheme and saturated specific
humidity, Unified Model documentation paper, 29, Met Office, Exeter, UK,
2007.
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., Morcrette,
C. J., and Bodas-Salcedo, A.: PC2: A prognostic cloud fraction and
condensation scheme. II: Climate model simulations, Q. J. Roy. Meteor. Soc.,
134, 2109–2125, https://doi.org/10.1002/qj.332, 2008.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z.,
Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP
Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323,
https://doi.org/10.1175/2009JTECHA1281.1, 2009.
Wood, R.: Stratocumulus Clouds, Mon. Weather Rev., 140, 2373–2423,
https://doi.org/10.1175/MWR-D-11-00121.1, 2012.
Wood, R. and Bretherton, C. S.: On the relationship between stratiform low
cloud cover and lower-tropospheric stability, J. Climate, 19, 6425–6432,
https://doi.org/10.1175/JCLI3988.1, 2006.
Yamaguchi, T. and Randall, D. A.: Large-Eddy Simulation of Evaporatively
Driven Entrainment in Cloud-Topped Mixed Layers, J. Atmos. Sci., 65,
1481–1504, https://doi.org/10.1175/2007JAS2438.1, 2008.
Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara,
M., Tanaka, T. Y., Deushi, M., Obata, A., Nakano, H., Adachi, Y., Shindo,
E., Yabu, S., Ose, T., and Kitoh, A.: Meteorological Research Institute
Earth System Model Version 1 (MRI-ESM1) – Model Description, Tech. Rep.
of MRI, 64, 83 pp., available at:
http://www.mri-jma.go.jp/Publish/Technical/index_en.html (last access: 4 July 2019),
2011.
Yukimoto, S., Adachi, Y., Hosaka, M., Sakami, T., Yoshimura, H., Hirabara,
M., Tanaka, T. Y., Shindo, E., Tsujino, H., Deushi, M., Mizuta, R., Yabu,
S., Obata, A., Nakano, H., Koshiro, T., Ose, T., and Kitoh, A.: A new global
climate model of the Meteorological Research Institute: MRI-CGCM3 –Model
Description and Basic Performance, J. Meteorol. Soc. Jpn., 90A, 23–64,
https://doi.org/10.2151/jmsj.2012-A02, 2012.
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S.,
Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H.,
Shindo, E., Mizuta, R., Obata, A., Adachi, Y. and Ishii, M.: The
Meteorological Research Institute Earth System Model version 2.0,
MRI-ESM2.0: Description and basic evaluation of the physical component, J.
Meteor. Soc. Jpn., 97, https://doi.org/10.2151/jmsj.2019-051, in press,
2019.
Zhang, Y., Xie, S., Covey, C., Lucas, D. D., Gleckler, P., Klein, S. A.,
Tannahill, J., Doutriaux, C., and Klein, R.: Regional assessment of the
parameter-dependent performance of CAM4 in simulating tropical clouds,
Geophys. Res. Lett., 39, L14708, https://doi.org/10.1029/2012GL052184, 2012.
Zurovac-Jevtić, D. and Zhang, G. J.: Development and Test of a Cirrus
Parameterization Scheme Using NCAR CCM3, J. Atmos. Sci., 60, 1325–1344,
https://doi.org/10.1175/1520-0469(2003)060<1325:DATOAC>2.0.CO;2,
2003.
Short summary
The representation of clouds was significantly improved in the climate model MRI-ESM2. The model is planned for use in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) simulations. In particular, a notorious lack of reflection of solar radiation over the Southern Ocean was drastically improved in the model. The score of the spatial pattern of radiative fluxes for MRI-ESM2 is better than for any CMIP5 model. We present modifications implemented in the various physics schemes.
The representation of clouds was significantly improved in the climate model MRI-ESM2. The model...