• Beesley, J. A., , C. S. Bretherton, , C. Jakob, , E. L. Andreas, , J. M. Interieri, , and T. A. Uttal, 2000: A comparison of cloud and boundary layer variables in the ECMWF forecast model with observations at the Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp. J. Geophys. Res., 105 , 1233712349.

    • Search Google Scholar
    • Export Citation
  • Briegleb, B. P., 1992: Delta-eddington approximation for solar radiation in the NCAR Community Climate Model. J. Geophys. Res., 97 , 76037612.

    • Search Google Scholar
    • Export Citation
  • Curry, J. A., , and G. F. Herman, 1985: Relationships between large-scale heat and moisture budgets and the occurrence of Arctic stratus clouds. Mon. Wea. Rev., 113 , 14411457.

    • Search Google Scholar
    • Export Citation
  • Curry, J. A., , W. B. Rossow, , D. Randall, , and J. L. Schramm, 1996: Overview of Arctic cloud and radiation characteristics. J. Climate, 9 , 17311764.

    • Search Google Scholar
    • Export Citation
  • Curry, J. A., and Coauthors, 2000: FIRE Arctic Clouds Experiment. Bull. Amer. Meteor. Soc., 81 , 529.

  • Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121 , 764787.

  • Grell, G. A., , J. Dudhia, , and D. R. Stauffer, 1995: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 138 pp.

    • Search Google Scholar
    • Export Citation
  • Holtslag, A. M., , and B. A. Boville, 1993: Local versus nonlocal boundary-layer diffusion in a global climate model. J. Climate, 6 , 18251842.

    • Search Google Scholar
    • Export Citation
  • Key, J., 2000: The Cloud and Surface Parameter Retrieval (CASPR) System for polar AVHRR data user's guide. Space Science and Engineering Center, University of Wisconsin—Madison, 62 pp.

    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., , A. V. Korolev, , and A. J. Heymsfield, 2002: Profiling cloud ice mass and particle characteristic size from Doppler radar measurements. J. Atmos. Oceanic Technol., 19 , 10031018.

    • 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 , 1666316682.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., , M. D. Shupe, , and J. A. Curry, 2003: Modeling clouds observed at SHEBA using a bulk microphysics parameterizations implemented into a single-column model. J. Geophys. Res.,108, 4255, doi:10.1029/2002JD002229.

    • Search Google Scholar
    • Export Citation
  • Persson, P. O. G., , C. W. Fairall, , E. L. Andreas, , P. S. Guest, , and D. K. Perovich, 2002: Measurements near the Atmospheric Surface Flux Group tower at SHEBA: Near-surface conditions and surface energy budget. J. Geophys. Res.,107, 8045, doi:10.1029/2000JC000705.

    • Search Google Scholar
    • Export Citation
  • Pinto, J. O., , H. C. Morrison, , and J. A. Curry, 2000: Advection profiles inferred from radiosonde data for use in single column model simulations at SHEBA. Preprints, Fifth Int. Symp. on Tropospheric Profiling: Needs and Technology, Adelaide, Australia, 217–219.

    • Search Google Scholar
    • Export Citation
  • Randall, D. A., , and D. G. Cripe, 1999: Alternative methods for specification of observed forcing in single-column models and cloud system models. J. Geophys. Res., 104 , 2452724545.

    • Search Google Scholar
    • Export Citation
  • Schramm, J. L., , M. M. Holland, , J. A. Curry, , and E. E. Ebert, 1997: Modeling the thermodynamics of a sea ice thickness distribution. 1. Sensitivity to ice thickness resolution. J. Geophys. Res., 102 , 2307923091.

    • Search Google Scholar
    • Export Citation
  • Shupe, M. D., , T. Uttal, , S. Y. Matrosov, , and A. S. Frisch, 2001: Cloud water contents and hydrometeor sizes during the FIRE Arctic Clouds Experiment. J. Geophys. Res., 106 , 1501515028.

    • Search Google Scholar
    • Export Citation
  • Stamnes, K., , R. G. Ellingson, , J. A. Curry, , J. E. Walsh, , and B. D. Zak, 1999: Review of science issues and deployment strategies for the North Slope of Alaska/Adjacent Arctic Ocean (NSA/AAO) ARM site. J. Climate, 12 , 4663.

    • Search Google Scholar
    • Export Citation
  • Uttal, T., and Coauthors, 2002: The Surface Heat Budget of the Arctic Ocean. Bull. Amer. Meteor. Soc., 83 , 255275.

  • Westwater, E. R., , Y. Han, , M. D. Shupe, , and S. Y. Matrosov, 2001: Analysis of integrated cloud liquid and precipitable water vapor retrievals from microwave radiometers during SHEBA. J. Geophys. Res., 106 , 3201932030.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., , and J. L. Lin, 1997: Constrained variational analysis of sounding data based on column-integrated budgets of mass, heat, moisture, and momentum: Approach and application to ARM measurements. J. Atmos. Sci., 54 , 15031524.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., , S. Xie, , R. T. Cederwall, , and J. J. Yio, 2001: Description of the ARM operational objective analysis system. Tech. Note ARM TR-005, 19 pp.

    • Search Google Scholar
    • Export Citation
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A New Approach for Obtaining Advection Profiles: Application to the SHEBA Column

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  • 1 Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado
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Abstract

Time-averaged vertically integrated 3D advections are inferred from heat and moisture budgets obtained from observations at the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment for April, May, June, and July. Advection was a source of heat and moisture in the column budgets during the time period, balanced mostly by precipitation and radiative cooling. These inferred advections are used to evaluate and correct the 3D temperature and water vapor advection profiles obtained from operational forecasts of the ECMWF model. Advections from the ECMWF model are generally too warm and moist, particularly in July. These biases lead to overpredictions of temperature and water vapor mixing ratio, often exceeding 12 K and 50%, respectively, in monthlong single-column model simulations. A correction algorithm is developed that constrains the ECMWF advections to the observed column budgets, thereby eliminating a first-order source of error in the advective forcing. The approach described here differs from other constrained analysis techniques since it does not require a spatial network of observed or analyzed fields. Simulations forced with the corrected advections show significant improvements in the modeled temperature and water vapor profiles and precipitation. It is demonstrated that using the new observationally constrained advection profiles allows for a less ambiguous evaluation of the model's physical parameterizations.

Corresponding author address: Hugh Morrison, Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80309. Email: hugh@monsoon.colorado.edu

Abstract

Time-averaged vertically integrated 3D advections are inferred from heat and moisture budgets obtained from observations at the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment for April, May, June, and July. Advection was a source of heat and moisture in the column budgets during the time period, balanced mostly by precipitation and radiative cooling. These inferred advections are used to evaluate and correct the 3D temperature and water vapor advection profiles obtained from operational forecasts of the ECMWF model. Advections from the ECMWF model are generally too warm and moist, particularly in July. These biases lead to overpredictions of temperature and water vapor mixing ratio, often exceeding 12 K and 50%, respectively, in monthlong single-column model simulations. A correction algorithm is developed that constrains the ECMWF advections to the observed column budgets, thereby eliminating a first-order source of error in the advective forcing. The approach described here differs from other constrained analysis techniques since it does not require a spatial network of observed or analyzed fields. Simulations forced with the corrected advections show significant improvements in the modeled temperature and water vapor profiles and precipitation. It is demonstrated that using the new observationally constrained advection profiles allows for a less ambiguous evaluation of the model's physical parameterizations.

Corresponding author address: Hugh Morrison, Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80309. Email: hugh@monsoon.colorado.edu

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