Local Impact of Stochastic Shallow Convection on Clouds and Precipitation in the Tropical Atlantic

Mirjana Sakradzija Max Planck Institute for Meteorology, Hamburg, Germany
Hans Ertel Centre for Weather Research, Deutscher Wetterdienst, Offenbach am Main, Germany

Search for other papers by Mirjana Sakradzija in
Current site
Google Scholar
PubMed
Close
,
Fabian Senf Leibniz-Institut für Troposphärenforschung, Leipzig, Germany

Search for other papers by Fabian Senf in
Current site
Google Scholar
PubMed
Close
,
Leonhard Scheck Ludwig-Maximilian-Universität, Munich, Germany
Hans Ertel Centre for Weather Research, Deutscher Wetterdienst, Offenbach am Main, Germany

Search for other papers by Leonhard Scheck in
Current site
Google Scholar
PubMed
Close
,
Maike Ahlgrimm Deutscher Wetterdienst, Offenbach am Main, Germany

Search for other papers by Maike Ahlgrimm in
Current site
Google Scholar
PubMed
Close
, and
Daniel Klocke Deutscher Wetterdienst, Offenbach am Main, Germany
Hans Ertel Centre for Weather Research, Deutscher Wetterdienst, Offenbach am Main, Germany

Search for other papers by Daniel Klocke in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The local impact of stochastic shallow convection on clouds and precipitation is tested in a case study over the tropical Atlantic on 20 December 2013 using the Icosahedral Nonhydrostatic Model (ICON). ICON is used at a grid resolution of 2.5 km and is tested in several configurations that differ in their treatment of shallow convection. A stochastic shallow convection scheme is compared to the operational deterministic scheme and a case with no representation of shallow convection. The model is evaluated by comparing synthetically generated irradiance data for both visible and infrared wavelengths against actual satellite observations. The experimental approach is designed to distinguish the local effects of parameterized shallow convection (or lack thereof) within the trades versus the ITCZ. The stochastic cases prove to be superior in reproducing low-level cloud cover, deep convection, and its organization, as well as the distribution of precipitation in the tropical Atlantic ITCZ. In these cases, convective heating in the subcloud layer is substantial, and boundary layer depth is increased as a result of the heating, while evaporation is enhanced at the expense of sensible heat flux at the ocean’s surface. The stochastic case where subgrid shallow convection is deactivated below the resolved deep updrafts indicates that local boundary layer convection is crucial for a better representation of deep convection. Based on these results, our study points to a necessity to further develop parameterizations of shallow convection for use at the convection-permitting resolutions and to assuredly include them in weather and climate models even as their imperfect versions.

© 2020 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: Mirjana Sakradzija, mirjana.sakradzija@mpimet.mpg.de

Abstract

The local impact of stochastic shallow convection on clouds and precipitation is tested in a case study over the tropical Atlantic on 20 December 2013 using the Icosahedral Nonhydrostatic Model (ICON). ICON is used at a grid resolution of 2.5 km and is tested in several configurations that differ in their treatment of shallow convection. A stochastic shallow convection scheme is compared to the operational deterministic scheme and a case with no representation of shallow convection. The model is evaluated by comparing synthetically generated irradiance data for both visible and infrared wavelengths against actual satellite observations. The experimental approach is designed to distinguish the local effects of parameterized shallow convection (or lack thereof) within the trades versus the ITCZ. The stochastic cases prove to be superior in reproducing low-level cloud cover, deep convection, and its organization, as well as the distribution of precipitation in the tropical Atlantic ITCZ. In these cases, convective heating in the subcloud layer is substantial, and boundary layer depth is increased as a result of the heating, while evaporation is enhanced at the expense of sensible heat flux at the ocean’s surface. The stochastic case where subgrid shallow convection is deactivated below the resolved deep updrafts indicates that local boundary layer convection is crucial for a better representation of deep convection. Based on these results, our study points to a necessity to further develop parameterizations of shallow convection for use at the convection-permitting resolutions and to assuredly include them in weather and climate models even as their imperfect versions.

© 2020 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: Mirjana Sakradzija, mirjana.sakradzija@mpimet.mpg.de
Save
  • Baldauf, M., A. Seifert, J. Förstner, D. Majewski, M. Raschendorfer, and T. Reinhardt, 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 38873905, https://doi.org/10.1175/MWR-D-10-05013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. J. Rodwell, F. Vitart, and G. Balsamo, 2008: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Quart. J. Roy. Meteor. Soc., 134, 13371351, https://doi.org/10.1002/qj.289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., M. Lebsock, S. Wong, and B. Lambrigtsen, 2012: On the quantification of oceanic rainfall using spaceborne sensors. J. Geophys. Res., 117, D20105, https://doi.org/10.1029/2012JD017979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brisson, E., K. Van Weverberg, M. Demuzere, A. Devis, S. Saeed, M. Stengel, and N. P. M. van Lipzig, 2016: How well can a convection-permitting climate model reproduce decadal statistics of precipitation, temperature and cloud characteristics? Climate Dyn., 47, 30433061, https://doi.org/10.1007/s00382-016-3012-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 23942416, https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ching, J., R. Rotunno, M. LeMone, A. Martilli, B. Kosovic, P. A. Jimenez, and J. Dudhia, 2014: Convectively induced secondary circulations in fine-grid mesoscale numerical weather prediction models. Mon. Wea. Rev., 142, 32843302, https://doi.org/10.1175/MWR-D-13-00318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Craig, G. C., and B. G. Cohen, 2006: Fluctuations in an equilibrium convective ensemble, Part I: Theoretical formulation. J. Atmos. Sci., 63, 19962004, https://doi.org/10.1175/JAS3709.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derrien, M., and H. Le Gléau, 2005: MSG/SEVIRI cloud mask and type from SAFNWC. Int. J. Remote Sens., 26, 47074732, https://doi.org/10.1080/01431160500166128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fosser, G., S. Khodayar, and P. Berg, 2015: Benefit of convection permitting climate model simulations in the representation of convective precipitation. Climate Dyn., 44, 4560, https://doi.org/10.1007/s00382-014-2242-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, Q., 1996: An accurate parameterization of the solar radiative properties of cirrus clouds for climate models. J. Climate, 9, 20582082, https://doi.org/10.1175/1520-0442(1996)009<2058:AAPOTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heinze, R., and Coauthors, 2017: Large-eddy simulations over Germany using ICON: A comprehensive evaluation. Quart. J. Roy. Meteor. Soc., 143, 69100, https://doi.org/10.1002/qj.2947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heise, E., B. Ritter, and E. Schrodin, 2006: Operational implementation of the multilayer soil model TERRA. Tech. Rep., 20 pp.

  • Hentgen, L., N. Ban, N. Kröner, D. Leutwyler, and C. Schär, 2019: Clouds in convection-resolving climate simulations over Europe. J. Geophys. Res. Atmos., 124, 38493870, https://doi.org/10.1029/2018JD030150.

    • 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
  • Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, https://doi.org/10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keil, C., A. Tafferner, and T. Reinhardt, 2006: Synthetic satellite imagery in the Lokal-Modell. Atmos. Res., 82, 1925, https://doi.org/10.1016/j.atmosres.2005.01.008.

    • 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
  • Langhans, W., J. Schmidli, O. Fuhrer, S. Bieri, and C. Schär, 2013: Long-term simulations of thermally driven flows and orographic convection at convection-parameterizing and cloud-resolving resolutions. J. Appl. Meteor. Climatol., 52, 14901510, https://doi.org/10.1175/JAMC-D-12-0167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., and Coauthors, 2017: Continental-scale convection-permitting modeling of the current and future climate of North America. Climate Dyn., 49, 7195, https://doi.org/10.1007/s00382-016-3327-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, G. M., D. W. Johnson, and A. Spice, 1994: The measurement and parameterization of effective radius of droplets in warm stratocumulus clouds. J. Atmos. Sci., 51, 18231842, https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matricardi, M., F. Chevallier, G. Kelly, and J.-N. Thépaut, 2004: An improved general fast radiative transfer model for the assimilation of radiance observations. Quart. J. Roy. Meteor. Soc., 130, 153173, https://doi.org/10.1256/qj.02.181.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., S. Iacobellis, and R. C. J. Somerville, 2003: SCM simulations of tropical ice clouds using observationally based parameterizations of microphysics. J. Climate, 16, 16431664, https://doi.org/10.1175/1520-0442(2003)016<1643:SSOTIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nam, C., S. Bony, J.-L. Dufresne, and H. Chepfer, 2012: The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models. Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neggers, R., B. Stevens, and J. D. Neelin, 2006: A simple equilibrium model for shallow-cumulus-topped mixed layers. Theor. Comput. Fluid Dyn., 20, 305322, https://doi.org/10.1007/s00162-006-0030-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., S. N. Tulich, and J. E. Blanco, 2016: ITCZ structure as determined by parameterized versus explicit convection in aquachannel and aquapatch simulations. J. Adv. Model. Earth Syst., 8, 425452, https://doi.org/10.1002/2015MS000560.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pedruzo-Bagazgoitia, X., P. A. Jiménez, J. Dudhia, and J. Vilà-Guerau de Arellano, 2019: Shallow cumulus representation and its interaction with radiation and surface at the convection gray zone. Mon. Wea. Rev., 147, 24672483, https://doi.org/10.1175/MWR-D-19-0030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petch, J. C., A. R. Brown, and M. E. B. Gray, 2002: The impact of horizontal resolution on the simulations of convective development over land. Quart. J. Roy. Meteor. Soc., 128, 20312044, https://doi.org/10.1256/003590002320603511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., and Coauthors, 2015: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys., 53, 323361, https://doi.org/10.1002/2014RG000475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pscheidt, I., F. Senf, R. Heinze, H. Deneke, S. Trömel, and C. Hohenegger, 2019: How organized is deep convection over Germany? Quart. J. Roy. Meteor. Soc., 145, 23662384, https://doi.org/10.1002/qj.3552.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raschendorfer, M., 2001: The new turbulence parameterization of LM. COSMO Newsletter, No. 1, Consortium for Small-Scale Modeling, Offenbach, Germany, 89–97, http://www.cosmo-model.org/content/model/documentation/newsLetters/newsLetter01/newsLetter_01.pdf.

  • Reinhardt, T., and A. Seifert, 2006: A three-category ice scheme for the LMK. COSMO Newsletter, No. 6, Consortium for Small-Scale Modeling, Offenbach, Germany, 115–120.

  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, https://doi.org/10.1175/2007MWR2123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 22612287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakradzija, M., and C. Hohenegger, 2017: What determines the distribution of shallow convective mass flux through a cloud base? J. Atmos. Sci., 74, 26152632, https://doi.org/10.1175/JAS-D-16-0326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakradzija, M., and D. Klocke, 2018: Physically constrained stochastic shallow convection in realistic kilometer-scale simulations. J. Adv. Model. Earth Syst., 10, 27552776, https://doi.org/10.1029/2018MS001358.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakradzija, M., A. Seifert, and T. Heus, 2015: Fluctuations in a quasi-stationary shallow cumulus cloud ensemble. Nonlinear Processes Geophys., 22, 6585, https://doi.org/10.5194/npg-22-65-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakradzija, M., A. Seifert, and A. Dipankar, 2016: A stochastic scale-aware parameterization of shallow cumulus convection across the convective gray zone. J. Adv. Model. Earth Syst., 8, 786812, https://doi.org/10.1002/2016MS000634.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saunders, R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc., 125, 14071425, https://doi.org/10.1002/qj.1999.49712555615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheck, L., P. Frèrebeau, R. Buras-Schnell, and B. Mayer, 2016: A fast radiative transfer method for the simulation of visible satellite imagery. J. Quant. Spectrosc. Radiat. Transfer, 175, 5467, https://doi.org/10.1016/j.jqsrt.2016.02.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheck, L., M. Weissmann, and B. Mayer, 2018: Efficient methods to account for cloud-top inclination and cloud overlap in synthetic visible satellite images. J. Atmos. Oceanic Technol., 35, 665685, https://doi.org/10.1175/JTECH-D-17-0057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmetz, J., P. Pili, S. Tjemkes, D. Just, J. Kerkmann, S. Rota, and A. Ratier, 2002: An introduction to Meteosat Second Generation (MSG). Bull. Amer. Meteor. Soc., 83, 977992, https://doi.org/10.1175/BAMS-83-7-Schmetz-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seifert, A., 2008: A revised cloud microphysical parameterization for COSMO-LME. COSMO Newsletter, No. 7, Consortium for Small-Scale Modeling, Offenbach, Germany, 25–28.

  • Senf, F., and H. Deneke, 2017: Uncertainties in synthetic Meteosat SEVIRI infrared brightness temperatures in the presence of cirrus clouds and implications for evaluation of cloud microphysics. Atmos. Res., 183, 113129, https://doi.org/10.1016/j.atmosres.2016.08.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Senf, F., D. Klocke, and M. Brueck, 2018: Size-resolved evaluation of simulated deep tropical convection. Mon. Wea. Rev., 146, 21612182, https://doi.org/10.1175/MWR-D-17-0378.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Senf, F., A. Voigt, N. Clerbaux, A. Hünerbein, and H. Deneke, 2020: Increasing resolution and resolving convection improves the simulation of cloud-radiative effects over the North Atlantic. J. Geophys. Res. Atmos., 125, e2020JD032667, https://doi.org/10.1029/2020JD032667.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slingo, J., and Coauthors, 1994: Mean climate and transience in the tropics of the UGAMP GCM: Sensitivity to convective parametrization. Quart. J. Roy. Meteor. Soc., 120, 881922, https://doi.org/10.1002/qj.49712051807.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2019: A high-altitude long-range aircraft configured as a cloud observatory: The NARVAL expeditions. Bull. Amer. Meteor. Soc., 100, 10611077, https://doi.org/10.1175/BAMS-D-18-0198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 1985: A fair-weather cumulus cloud classification scheme for mixed-layer studies. J. Climate Appl. Meteor., 24, 4956, https://doi.org/10.1175/1520-0450(1985)024<0049:AFWCCC>2.0.CO;2.

    • 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
  • Tiedtke, M., W. Heckley, and J. Slingo, 1988: Tropical forecasting at ECMWF: The influence of physical parametrization on the mean structure of forecasts and analyses. Quart. J. Roy. Meteor. Soc., 114, 639664, https://doi.org/10.1002/qj.49711448106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • TRMM, 2011: TRMM (TMPA) rainfall estimate L3 3 hour 0.25 degree × 0.25 degree V7. Accessed 27 August 2019, https://doi.org/10.5067/TRMM/TMPA/3H/7.

    • 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 379 0 0
Full Text Views 15520 13842 485
PDF Downloads 540 120 6