Editorial Type:
Article
Article Type:
Research Article

Evaluation of ERA-Interim and MERRA Cloudiness in the Southern Ocean

Catherine M. Naud Applied Physics and Applied Mathematics, Columbia University, New York, New York

Search for other papers by Catherine M. Naud in
Current site
Google Scholar
PubMed Close
,
James F. Booth Applied Physics and Applied Mathematics, Columbia University, New York, New York

Search for other papers by James F. Booth in
Current site
Google Scholar
PubMed Close
, and
Anthony D. Del Genio NASA Goddard Institute for Space Studies, New York, New York

Search for other papers by Anthony D. Del Genio in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Mar 2014
DOI:
https://doi.org/10.1175/JCLI-D-13-00432.1
Page(s):
2109–2124
Restricted access

Abstract

The Southern Ocean cloud cover modeled by the Interim ECMWF Re-Analysis (ERA-Interim) and Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses are compared against Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) observations. ERA-Interim monthly mean cloud amounts match the observations within 5%, while MERRA significantly underestimates the cloud amount. For a compositing analysis of clouds in warm season extratropical cyclones, both reanalyses show a low bias in cloud cover. They display a larger bias to the west of the cyclones in the region of subsidence behind the cold fronts. This low bias is larger for MERRA than for ERA-Interim. Both MODIS and MISR retrievals indicate that the clouds in this sector are at a low altitude, often composed of liquid, and of a broken nature. The combined CloudSat–Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud profiles confirm these passive observations, but they also reveal that low-level clouds in other parts of the cyclones are also not properly represented in the reanalyses. The two reanalyses are in fairly good agreement for the dynamic and thermodynamic characteristics of the cyclones, suggesting that the cloud, convection, or boundary layer schemes are the problem instead. An examination of the lower-tropospheric stability distribution in the cyclones from both reanalyses suggests that the parameterization of shallow cumulus clouds may contribute in a large part to the problem. However, the differences in the cloud schemes and in particular in the precipitation processes, which may also contribute, cannot be excluded.

Denotes Open Access content.

Current affiliation: Earth and Atmospheric Sciences, City College of New York, New York, New York.

Corresponding author address: Catherine M. Naud, Applied Physics and Applied Mathematics, Columbia University, 2880 Broadway, New York, NY 10025. E-mail: cn2140@columbia.edu

Abstract

The Southern Ocean cloud cover modeled by the Interim ECMWF Re-Analysis (ERA-Interim) and Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses are compared against Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) observations. ERA-Interim monthly mean cloud amounts match the observations within 5%, while MERRA significantly underestimates the cloud amount. For a compositing analysis of clouds in warm season extratropical cyclones, both reanalyses show a low bias in cloud cover. They display a larger bias to the west of the cyclones in the region of subsidence behind the cold fronts. This low bias is larger for MERRA than for ERA-Interim. Both MODIS and MISR retrievals indicate that the clouds in this sector are at a low altitude, often composed of liquid, and of a broken nature. The combined CloudSat–Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud profiles confirm these passive observations, but they also reveal that low-level clouds in other parts of the cyclones are also not properly represented in the reanalyses. The two reanalyses are in fairly good agreement for the dynamic and thermodynamic characteristics of the cyclones, suggesting that the cloud, convection, or boundary layer schemes are the problem instead. An examination of the lower-tropospheric stability distribution in the cyclones from both reanalyses suggests that the parameterization of shallow cumulus clouds may contribute in a large part to the problem. However, the differences in the cloud schemes and in particular in the precipitation processes, which may also contribute, cannot be excluded.

Denotes Open Access content.

Current affiliation: Earth and Atmospheric Sciences, City College of New York, New York, New York.

Corresponding author address: Catherine M. Naud, Applied Physics and Applied Mathematics, Columbia University, 2880 Broadway, New York, NY 10025. E-mail: cn2140@columbia.edu
Save
Cover Journal of Climate
Journal of Climate

  • Ackerman, S. A., R. E. Holz, R. Frey, E. W. Eloranta, B. C. Maddux, and M. McGill, 2008: Cloud detection with MODIS. Part II: Validation. J. Atmos. Oceanic Technol., 25, 1073–1086.

    • Search Google Scholar
    • Export Citation

  • Aumann, H. H., and Coauthors, 2003: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products and processing systems. IEEE Trans. Geosci. Remote Sens., 41, 253–264.

    • Search Google Scholar
    • Export Citation

  • Bacmeister, J. T., M. J. Suarez, and F. R. Robertson, 2006: Rain reevaporation, boundary layer–convection interactions, and Pacific rainfall patterns in an AGCM. J. Atmos. Sci., 63, 3383–3403.

    • Search Google Scholar
    • Export Citation

  • Bauer, M., and A. D. Del Genio, 2006: Composite analysis of winter cyclones in a GCM: Influence on climatological humidity. J. Climate, 19, 1652–1672.

    • Search Google Scholar
    • Export Citation

  • Bodas-Salcedo, A., and Coauthors, 2011: COSP satellite simulation software for model assessment. Bull. Amer. Meteor. Soc., 92, 1023–1043.

    • Search Google Scholar
    • Export Citation

  • Bodas-Salcedo, A., K. D. Williams, P. R. Field, and A. P. Lock, 2012: The surface downwelling solar radiation surplus over the Southern Ocean in the Met Office model: The role of midlatitude cyclone clouds. J. Climate, 25, 7467–7486.

    • Search Google Scholar
    • Export Citation

  • Booth, J. F., C. M. Naud, and A. D. Del Genio, 2013: Diagnosing warm frontal cloud formation in a GCM: A novel approach using conditional subsetting. J. Climate, 26, 5827–5845.

    • Search Google Scholar
    • Export Citation

  • Bosilovich, M. G., F. R. Robertson, and J. Chen, 2011: Global energy and water budgets in MERRA. J. Climate, 24, 5721–5739.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation systems. Quart. J. Roy. Meteor. Soc., 137, 553–597.

    • Search Google Scholar
    • Export Citation

  • Di Girolamo L., A. Menzies, G. Zhao, K. Mueller, C. Moroney, and D. J. Diner, 2010: Level 3 cloud fraction by altitude algorithm theoretical basis. JPL Tech. Doc. D-62358, 18 pp.

  • Diner, D. J., and Coauthors, 1998: Multi-Angle Imaging Spectro-radiometer (MISR) instrument description and experiment overview. IEEE Trans. Geosci. Remote Sens., 36, 1072–1087.

    • Search Google Scholar
    • Export Citation

  • Field, P. R., and R. Wood, 2007: Precipitation and cloud structure in midlatitude cyclones. J. Climate, 20, 233–254.

  • Field, P. R., A. Gettelman, R. B. Neale, R. Wood, P. J. Rasch, and H. Morrison, 2008: Midlatitude cyclone compositing to constrain climate model behavior using satellite observations. J. Climate, 21, 5887–5903.

    • Search Google Scholar
    • Export Citation

  • Haynes, J. M., C. Jakob, W. B. Rossow, G. Tselioudis, and J. Brown, 2011: Characteristics of Southern Ocean cloud regimes and their effects on the energy budget. J. Climate, 24, 5061–5080.

    • Search Google Scholar
    • Export Citation

  • Hewson, T. D., 1998: Objective fronts. Meteor. Appl., 5, 37–65.

  • Hodges, K. I., R. W. Lee, and L. Bengtsson, 2011: A comparison of extratropical cyclones in recent reanalyses ERA-interim, NASA MERRA, NCEP CFRS, and JRA-25. J. Climate, 24, 4888–4906.

    • Search Google Scholar
    • Export Citation

  • Kawanishi, T., and Coauthors, 2003: The Adanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA’s contribution to the EOS for global energy and water cycle studies. IEEE Trans. Geosci. Remote Sens., 41, 184–194.

    • Search Google Scholar
    • Export Citation

  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 1588–1606.

  • Köhler, M., M. Ahlgrimm, and A. Beljaars, 2011: Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model. Quart. J. Roy. Meteor. Soc., 137, 43–57.

    • Search Google Scholar
    • Export Citation

  • Lock, A. P., A. R. Brown, M. R. Bush, G. M. Martin, and R. N. B. Smith, 2000: A new boundary layer mixing scheme. Part I: Scheme description and single column model tests. Mon. Wea. Rev., 128, 3187–3199.

    • Search Google Scholar
    • Export Citation

  • Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748–766.

    • Search Google Scholar
    • Export Citation

  • Louis, J., M. Tiedtke, and J. Geleyn, 1982: A short history of the operational PBL-parameterization at ECMWF. Proc. ECMWF Workshop on Planetary Boundary Layer Parameterization, Reading, United Kingdom, ECMWF, 59–79.

  • Mace, G. G., Q. Zhang, M. Vaughan, R. Marchand, G. Stephens, C. Trepte, and D. Winker, 2009: A description of hydrometeor layer occurrence statistics derived from the first year of merged CloudSat and CALIPSO data. J. Geophys. Res., 114, D00A26, doi:10.1029/2007JD008755.

    • Search Google Scholar
    • Export Citation

  • Marchand, R., G. G. Mace, T. Ackerman, and G. Stephens, 2008: Hydrometeor detection using CloudSat—An Earth-orbiting 94-GHz cloud radar. J. Atmos. Oceanic Technol., 25, 519–533.

    • Search Google Scholar
    • Export Citation

  • Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa–Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 978–1002.

    • Search Google Scholar
    • Export Citation

  • Moroney, C., R. Davis, and J.-P. Muller, 2002: Operational retrieval of cloud-top heights using MISR data. IEEE Trans. Geosci. Remote Sens., 40, 1532–1540.

    • Search Google Scholar
    • Export Citation

  • Naud, C. M., A. D. Del Genio, and M. Bauer, 2006: Observational constraints on the cloud thermodynamic phase in midlatitude storms. J. Climate, 19, 5273–5288.

    • Search Google Scholar
    • Export Citation

  • Naud, C. M., A. D. Del Genio, M. Bauer, and W. Kovari, 2010: Cloud vertical distribution across warm and cold fronts in CloudSat–CALIPSO data and a general circulation model. J. Climate, 23, 3397–3415.

    • Search Google Scholar
    • Export Citation

  • Naud, C. M., D. J. Posselt, and S. C. van den Heever, 2012: Observational analysis of cloud and precipitation in midlatitude cyclones: Northern versus Southern Hemisphere warm fronts. J. Climate, 25, 5135–5151.

    • Search Google Scholar
    • Export Citation

  • Naud, C. M., J. F. Booth, D. J. Posselt, and S. C. van den Heever, 2013: Multiple satellite observations of cloud cover in extratropical cyclones. J. Geophys. Res. Atmos., 118, 9982–9996, doi:10.1002/jgrd.50718.

    • Search Google Scholar
    • Export Citation

  • Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41, 459–473.

    • Search Google Scholar
    • Export Citation

  • Posselt, D. J., A. R. Jongeward, C.-Y. Hsu, and G. L. Potter, 2012: Object-based evaluation of MERRA cloud physical properties and radiative fluxes during the 1998 El Niño–La Niña transition. J. Climate, 25, 7313–7327.

    • Search Google Scholar
    • Export Citation

  • Rienecker, M. M., and Coauthors, 2008: The GEOS-5 data assimilation system—Documentation of versions 5.0.1, 5.1.0, and 5.2.0. NASA Tech. Rep. Series on Global Modeling and Data Assimilation NASA/TM-2008-104606, Vol. 27, 118 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/GEOS5_104606-Vol27.pdf.]

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648.

    • Search Google Scholar
    • Export Citation

  • Salomonson, V. V., W. L. Barnes, P. W. Maymon, H. E. Montgomery, and H. Ostrow, 1989: MODIS: Advanced facility instrument for studies of the earth as a system. IEEE Trans. Geosci. Remote Sens., 27, 145–153.

    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train: A new dimension to space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 1771–1790.

    • Search Google Scholar
    • Export Citation

  • Sundqvist, H., 1978: A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Roy. Meteor. Soc., 104, 677–690.

    • Search Google Scholar
    • Export Citation

  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800.

    • Search Google Scholar
    • Export Citation

  • Tompkins, A. M., K. Gierens, and G. Rädel, 2007: Ice supersaturation in the ECMWF integrated forecast system. Quart. J. Roy. Meteor. Soc., 133, 53–63.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., and J. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the Southern Oceans. J. Climate, 23, 440–454.

    • Search Google Scholar
    • Export Citation

  • Wentz, F., and T. Meissner, 2004 AMSR-E/Aqua L2B global swath ocean products derived from Wentz algorithm, version 2. National Snow and Ice Data Center, Boulder, CO, digital media. [Available online at http://nsidc.org/data/ae_ocean.html.]

  • Williams, K. D., and Coauthors, 2013: The transpose-AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models. J. Climate, 26, 3258–3274.

    • Search Google Scholar
    • Export Citation

  • Winker, D. M., M. A. Vaughan, A. H. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 2310–2323.

    • Search Google Scholar
    • Export Citation

  • Yao, M.-S., and Y. Cheng, 2012: Cloud simulations in response to turbulence parameterizations in the GISS Model E GCM. J. Climate, 25, 4963–4974.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1586 863 81
PDF Downloads 617 94 9