• Adler, R. F., and et al. , 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Amaya, D. J., M. J. DeFlorio, A. J. Miller, and S.-P. Xie, 2017: WES feedback and the Atlantic meridional mode: Observations and CMIP5 comparisons. Climate Dyn., 49, 16651679, https://doi.org/10.1007/s00382-016-3411-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 24932525, https://doi.org/10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechtold, P., 2017: Atmospheric moist convection. ECMWF Meteorological Training Course Lecture Series Doc., 85 pp., https://www.ecmwf.int/node/16953.

  • Biasutti, M., A. Sobel, and Y. Kushnir, 2006: AGCM precipitation biases in the tropical Atlantic. J. Climate, 19, 935958, https://doi.org/10.1175/JCLI3673.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, P., R. Saravanan, L. Ji, and G. C. Hegerl, 2000: The effect of local sea surface temperatures on atmospheric circulation over the tropical Atlantic sector. J. Climate, 13, 21952216, https://doi.org/10.1175/1520-0442(2000)013<2195:TEOLSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, P., L. Ji, and R. Saravanan, 2001: A hybrid coupled model study of tropical Atlantic variability. J. Climate, 14, 361390, https://doi.org/10.1175/1520-0442(2001)013<0361:AHCMSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiang, J. C., and D. J. Vimont, 2004: Analogous Pacific and Atlantic meridional modes of tropical atmosphere–ocean variability. J. Climate, 17, 41434158, https://doi.org/10.1175/JCLI4953.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiang, J. C., Y. Kushnir, and A. Giannini, 2002: Deconstructing Atlantic Intertropical Convergence Zone variability: Influence of the local cross-equatorial sea surface temperature gradient and remote forcing from the eastern equatorial Pacific. J. Geophys. Res., 107, 4004, https://doi.org/10.1029/2000JD000307.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630, https://doi.org/10.1175/JCLI3884.1.

  • Evan, A. T., R. J. Allen, R. Bennartz, and D. J. Vimont, 2013: The modification of sea surface temperature anomaly linear damping time scales by stratocumulus clouds. J. Climate, 26, 36193630, https://doi.org/10.1175/JCLI-D-12-00370.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fiedler, S., and et al. , 2020: Simulated tropical precipitation assessed across three major phases of the Coupled Model Intercomparison Project (CMIP). Mon. Wea. Rev., 148, 36533680, https://doi.org/10.1175/MWR-D-19-0404.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flato, G., and et al. , 2014: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.

  • Foltz, G. R., M. J. McPhaden, and R. Lumpkin, 2012: A strong Atlantic meridional mode event in 2009: The role of mixed layer dynamics. J. Climate, 25, 363380, https://doi.org/10.1175/JCLI-D-11-00150.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giannini, A., R. Saravanan, and P. Chang, 2004: The preconditioning role of tropical Atlantic variability in the development of the ENSO teleconnection: Implications for the prediction of Nordeste rainfall. Climate Dyn., 22, 839855, https://doi.org/10.1007/s00382-004-0420-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hastenrath, S., and L. Greischar, 1993: Circulation mechanisms related to Northeast Brazil rainfall anomalies. J. Geophys. Res., 98, 50935102, https://doi.org/10.1029/92JD02646.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirota, N., Y. N. Takayabu, M. Watanabe, and M. Kimoto, 2011: Precipitation reproducibility over tropical oceans and its relationship to the double ITCZ problem in CMIP3 and MIROC5 climate models. J. Climate, 24, 48594873, https://doi.org/10.1175/2011JCLI4156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., L. Kornblueh, D. Klocke, T. Becker, G. Cioni, J. F. Engels, U. Schulzweida, and B. Stevens, 2020: Climate statistics in global simulations of the atmosphere, from 80 to 2.5 km grid spacing. J. Meteor. Soc. Japan, 98, 7391, https://doi.org/10.2151/jmsj.2020-005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C., S. Woolnough, and G. Lister, 2012: Precipitation distributions for explicit versus parametrized convection in a large-domain high-resolution tropical case study. Quart. J. Roy. Meteor. Soc., 138, 16921708, https://doi.org/10.1002/qj.1903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Z.-Z., and B. Huang, 2006: Physical processes associated with the tropical Atlantic SST meridional gradient. J. Climate, 19, 55005518, https://doi.org/10.1175/JCLI3923.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and et al. , 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, https://doi.org/10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and et al. , 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klocke, D., M. Brueck, C. Hohenegger, and B. Stevens, 2017: Rediscovery of the doldrums in storm-resolving simulations over the tropical Atlantic. Nat. Geosci., 10, 891896, https://doi.org/10.1038/s41561-017-0005-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., and S. Nigam, 1987: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 24182436, https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lucena, D. B., J. Servain, and M. F. Gomes Filho, 2011: Rainfall response in Northeast Brazil from ocean climate variability during the second half of the twentieth century. J. Climate, 24, 61746184, https://doi.org/10.1175/2011JCLI4194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marsham, J. H., N. S. Dixon, L. Garcia-Carreras, G. M. Lister, D. J. Parker, P. Knippertz, and C. E. Birch, 2013: The role of moist convection in the West African monsoon system: Insights from continental-scale convection-permitting simulations. Geophys. Res. Lett., 40, 18431849, https://doi.org/10.1002/grl.50347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martinez-Villalobos, C., and D. J. Vimont, 2016: The role of the mean state in meridional mode structure and growth. J. Climate, 29, 39073921, https://doi.org/10.1175/JCLI-D-15-0542.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Myers, T. A., C. R. Mechoso, and M. J. DeFlorio, 2018: Importance of positive cloud feedback for tropical Atlantic interhemispheric climate variability. Climate Dyn., 51, 17071717, https://doi.org/10.1007/s00382-017-3978-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naumann, A. K., B. Stevens, and C. Hohenegger, 2019: A moist conceptual model for the boundary layer structure and radiatively driven shallow circulations in the trades. J. Atmos. Sci., 76, 12891306, https://doi.org/10.1175/JAS-D-18-0226.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nobre, P., and J. Shukla, 1996: Variations of sea surface temperature, wind stress, and rainfall over the tropical Atlantic and South America. J. Climate, 9, 24642479, https://doi.org/10.1175/1520-0442(1996)009<2464:VOSSTW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oueslati, B., and G. Bellon, 2015: The double ITCZ bias in CMIP5 models: Interaction between SST, large-scale circulation and precipitation. Climate Dyn., 44, 585607, https://doi.org/10.1007/s00382-015-2468-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N., D. E. Parker, E. Horton, C. K. Folland, L. V. Alexander, D. Rowell, E. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richter, I., and S.-P. Xie, 2008: On the origin of equatorial Atlantic biases in coupled general circulation models. Climate Dyn., 31, 587598, https://doi.org/10.1007/s00382-008-0364-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Satoh, M., B. Stevens, F. Judt, M. Khairoutdinov, S.-J. Lin, W. M. Putman, and P. Düben, 2019: Global cloud-resolving models. Curr. Climate Change Rep., 5, 172184, https://doi.org/10.1007/S40641-019-00131-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siongco, A. C., C. Hohenegger, and B. Stevens, 2015: The Atlantic ITCZ bias in CMIP5 models. Climate Dyn., 45, 11691180, https://doi.org/10.1007/s00382-014-2366-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siongco, A. C., C. Hohenegger, and B. Stevens, 2017: Sensitivity of the summertime tropical Atlantic precipitation distribution to convective parameterization and model resolution in ECHAM6. J. Geophys. Res. Atmos., 122, 25792594, https://doi.org/10.1002/2016JD026093.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, B., and et al. , 2019: DYAMOND: The dynamics of the atmospheric general circulation modeled on non-hydrostatic domains. Prog. Earth Planet. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 17791800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., 2010: Transient growth of thermodynamically coupled variations in the tropics under an equatorially symmetric mean state. J. Climate, 23, 57715789, https://doi.org/10.1175/2010JCLI3532.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., and J. A. Carton, 2003: Modeling climate variability in the tropical Atlantic atmosphere. J. Climate, 16, 38583876, https://doi.org/10.1175/1520-0442(2003)016<3858:MCVITT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., and S. G. H. Philander, 1994: A coupled ocean–atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus, 46A, 340350, https://doi.org/10.3402/tellusa.v46i4.15484.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., and J. A. Carton, 2004: Tropical Atlantic variability: Patterns, mechanisms, and impacts. Earth Climate: The Ocean–Atmosphere Interaction, Geophys. Monogr., Vol. 147, Amer. Geophys. Union, 121–142.

    • Crossref
    • Export Citation
  • Zängl, G., D. Reinert, P. Rípodas, and M. Baldauf, 2015: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core. Quart. J. Roy. Meteor. Soc., 141, 563579, https://doi.org/10.1002/qj.2378.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 213 213 76
Full Text Views 45 45 10
PDF Downloads 52 52 15

Explicit versus Parameterized Convection in Response to the Atlantic Meridional Mode

View More View Less
  • 1 Max Planck Institute for Meteorology, Hamburg, Germany
© Get Permissions
Restricted access

Abstract

This study investigates whether the representation of explicit and parameterized convection influences the response to the Atlantic meridional mode (AMM). The main focus is on the precipitation response to the AMM-SST pattern, but possible implications for the atmospheric feedback on SST are also examined by considering differences in the circulation response between explicit and parameterized convection. On the basis of analysis from observations, SST composites are built to represent the positive and negative AMM. These SST patterns, in addition to the March–May climatology, are prescribed to the atmospheric ICON model. High-resolution simulations with explicit convection (E-CON) and coarse-resolution simulations with parameterized convection (P-CON) are used over a nested tropical Atlantic Ocean domain and a global domain, respectively. Our results show that a meridional shift of about 1° in the precipitation climatology explains most of the response to the AMM-SST pattern in simulations both with explicit convection and with parameterized convection. Our results also indicate a linearity in the precipitation response to the positive and negative AMM in E-CON, in contrast to P-CON. Further analysis of the atmospheric response to the AMM reveals that anomalies in the wind-driven enthalpy fluxes are generally stronger in E-CON than in P-CON. This result suggests that SST anomalies would be amplified more strongly in coupled simulations using an explicit representation of convection.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Laura Paccini, laura.paccini@mpimet.mpg.de

Abstract

This study investigates whether the representation of explicit and parameterized convection influences the response to the Atlantic meridional mode (AMM). The main focus is on the precipitation response to the AMM-SST pattern, but possible implications for the atmospheric feedback on SST are also examined by considering differences in the circulation response between explicit and parameterized convection. On the basis of analysis from observations, SST composites are built to represent the positive and negative AMM. These SST patterns, in addition to the March–May climatology, are prescribed to the atmospheric ICON model. High-resolution simulations with explicit convection (E-CON) and coarse-resolution simulations with parameterized convection (P-CON) are used over a nested tropical Atlantic Ocean domain and a global domain, respectively. Our results show that a meridional shift of about 1° in the precipitation climatology explains most of the response to the AMM-SST pattern in simulations both with explicit convection and with parameterized convection. Our results also indicate a linearity in the precipitation response to the positive and negative AMM in E-CON, in contrast to P-CON. Further analysis of the atmospheric response to the AMM reveals that anomalies in the wind-driven enthalpy fluxes are generally stronger in E-CON than in P-CON. This result suggests that SST anomalies would be amplified more strongly in coupled simulations using an explicit representation of convection.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Laura Paccini, laura.paccini@mpimet.mpg.de
Save