Temperature and water vapor variance scaling in global models: Comparisons to satellite and aircraft data

Journal of the Atmospheric Sciences

Volumn 68, Issue 9, 2011, Pages

  Kahn, Brian H ^a   Teixeira, J ^a   Fetzer, E J ^a   Gettelman, A ^b   Hristova Veleva, S M ^a   Huang, X ^c   Kochanski, A K ^d   Kohler M ^e   Krueger, S K ^d   Wood, R ^f   Zhao, M ^g  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

^d UNIVERSITY OF UTAH   (United States)

^e DEUTSCHER WETTERDIENST   (Germany)

^f UNIVERSITY OF WASHINGTON   (United States)

^g NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

Aircraft observations; Field experiments; Numerical weather prediction forecasting; Satellite observations; Temperature; Water vapor

Indexed keywords

AIRCRAFT DATA; AIRCRAFT OBSERVATIONS; ATMOSPHERIC INFRARED SOUNDERS; CLIMATE MODEL SIMULATIONS; COMMUNITY ATMOSPHERIC MODEL; FIELD EXPERIMENT; GLOBAL MODELS; MODEL ANALYSIS; MODEL EVALUATION; MODEL SIMULATION; MONSOON SYSTEM; NUMERICAL WEATHER PREDICTION; PARAMETERIZATIONS; SATELLITE OBSERVATIONS; SCALE DEPENDENCE; SCALING EXPONENT; SPATIAL RESOLUTION; SUBGRID SCALE;

ACOUSTIC DEVICES; AIRCRAFT MODELS; ATMOSPHERIC TEMPERATURE; CAMS; COMPUTER SIMULATION; EXPERIMENTS; IMAGE RESOLUTION; INFRARED DEVICES; INFRARED INSTRUMENTS; SATELLITES; TEMPERATURE; WATER VAPOR; WEATHER FORECASTING;

CLIMATE MODELS;

AIR TEMPERATURE; CLIMATE MODELING; CLOUD; MONSOON; NUMERICAL MODEL; SATELLITE DATA; SPATIAL RESOLUTION; WATER VAPOR; WEATHER FORECASTING;

EID: 80053142362     PISSN: 00224928     EISSN: None     Source Type: Journal    
DOI: 10.1175/2011JAS3737.1     Document Type: Article

References (57)

1. Anderson, J. L., and Coauthors, 2004: The New GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations. J. Climate, 17, 4641-4673.
2. 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.
3. Bogenschutz, P. A., 2011: Improving the representation of turbulence and clouds in cloud resolving models and general circulation models. Ph.D. dissertation, University of Utah, 320 pp.
4. Bony, S., and J. L. Dufresne, 2005: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., 32, L20806, doi:10.1029/2005GL023851.
5. Boville, B. A., 1991: Sensitivity of simulated climate to model resolution. J. Climate, 4, 469-485.
6. Cahalan, R. F., W. Ridgway, W. J. Wiscombe, T. L. Bell, and J. B. Snider, 1994: The albedo of fractal stratocumulus clouds. J. Atmos. Sci., 51, 2434-2455.
7. Cho, J. Y. N., R. E. Newell, and G. W. Sachse, 2000: Anomalous scaling of mesoscale tropospheric humidity fluctuations. Geophys. Res. Lett., 27, 377-380.
8. Comstock, K. K., C. S. Bretherton, and S. E. Yuter, 2005: Mesoscale variability and drizzle in southeast Pacific stratocumulus. J. Atmos. Sci., 62, 3792-3807.
9. Cusack, S., J. M. Edwards, and R. Kershaw, 1999: Estimating the subgrid variance of saturation, and its parametrization for use in aGCMcloud scheme. Quart. J. Roy. Meteor. Soc., 125, 3057-3076.
10. Davis, A. B., A. Marshak, W. Wiscombe, and R. Cahalan, 1996: Scale invariance of liquid water distributions in marine stratocumulus. Part I: Spectral properties and stationarity issues. J. Atmos. Sci., 53, 1538-1558.
11. Dotzek, N., and K. Gierens, 2008: Instantaneous fluctuations of temperature and moisture in the upper troposphere and tropopause region. Part 2: Structure functions and intermittency. Meteor. Z., 17, 323-337.
12. Fetzer, E. J., B. H. Lambrigtsen, A. Eldering, H. H. Aumann, and M. T. Chahine, 2006: Biases in total precipitable water vapor climatologies from Atmospheric Infrared Sounder and Advanced Microwave Scanning Radiometer. J. Geophys. Res., 111, D09S16, doi:10.1029/2005JD006598.
13. Frehlich, R., and R. Sharman, 2010: Climatology of velocity and temperature turbulence statistics determined from rawinsonde and ACARS/AMDAR data. J. Appl. Meteor. Climatol., 49, 1149-1169.
14. Gettelman, A., and Coauthors, 2010: Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model. J. Geophys. Res., 115, D18216, doi:10.1029/2009JD013797.
15. Hamilton, K., Y. O. Takahashi, and W. Ohfuchi, 2008: Mesoscale spectrum of atmospheric motions investigated in a very fine resolution global general circulation model. J. Geophys. Res., 113, D18110, doi:10.1029/2008JD009785.
16. Jung, T., and Coauthors, 2010: The ECMWF model climate: Recent progress through improved physical parameterizations. Quart. J. Roy. Meteor. Soc., 136, 1145-1160, doi:10.1002/qj.634.
17. Kahn, B. H., and J. Teixeira, 2009: A global climatology of temperature and water vapor variance scaling from the Atmospheric Infrared Sounder. J. Climate, 22, 5558-5576.
18. Kawai, H., and J. Teixeira, 2010: Probability density functions of liquid water path and cloud amount of marine boundary layer clouds: Geographical and seasonal variations and controlling meteorological factors. J. Climate, 23, 2079-2092.
19. Koshyk, J. N., K. Hamilton, and J. D. Mahlman, 1999: Simulation of the k25/3 mesoscale spectral regime in the GFDL SKYHI general circulation model. Geophys. Res. Lett., 26, 843-846.
20. Kuwano-Yoshida, A., T. Enomoto, and W. Ohfuchi, 2010: An improved PDF cloud scheme for climate simulations. Quart. J. Roy. Meteor. Soc., 136, 1583-1597, doi:10.1002/qj.660.
21. Lindborg, E., 2009: Two comments on the surface quasigeostrophic model for the atmospheric energy spectrum. J. Atmos. Sci., 66, 1069-1072.
22. Lindborg, E., K. K. Tung, G. D. Nastrom, J. Y. N. Cho, and K. S. Gage, 2010: Comment on "Reinterpreting aircraft measurement in anisotropic scaling turbulence" by Lovejoy et al. (2009). Atmos. Chem. Phys., 10, 1401-1402.
23. Lovejoy, S., A. F. Tuck, D. Schertzer, and S. J. Hovde, 2009: Reinterpreting aircraft measurements in anisotropic scaling turbulence. Atmos. Chem. Phys., 9, 5007-5025.
24. Maddy, E. S., and C. D. Barnet, 2008: Vertical resolution estimates in version 5 of AIRS operational retrievals. IEEE Trans. Geosci. Remote Sens., 46, 2375-2384.
25. Marchand, R., N. Beagley, and T. P. Ackerman, 2009: Evaluation of hydrometeor occurrence profiles in the Multiscale Modeling Framework climate model using atmospheric classification. J. Climate, 22, 4557-4573.
26. Moeng, C.-H., J. C. McWilliams, R. Rotunno, P. P. Sullivan, and J. Weil, 2004: Investigating 2D modeling of atmospheric convection in the PBL. J. Atmos. Sci., 61, 889-903.
27. Nastrom, G. D., and K. S. Gage, 1985: A climatology of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci., 42, 950-960.
28. Nastrom, G. D., W. H. Jasperson, and K. S. Gage, 1986: Horizontal spectra of atmospheric tracers measured during the global atmospheric sampling program. J. Geophys. Res., 91, 13 201-13 209.
29. Phillips, T. J., and Coauthors, 2004: Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction. Bull. Amer. Meteor. Soc., 85, 1903-1915.
30. Pierce, D. W., T. P. Barnett, E. J. Fetzer, and P. J. Gleckler, 2006: Three-dimensional tropospheric water vapor in coupled climate models compared with observations from the AIRS satellite system. Geophys. Res. Lett., 33, L21701, doi:10.1029/2006GL027060.
31. Pressel, K. G., W. D. Collins, and A. R. Desai, 2010: Variance scaling in water vapor measurements from a tall tower. Extended Abstracts, 13th Conf. on Cloud Physics, Portland, OR, Amer. Meteor. Soc., P1.77. [Available online at http://ams.confex.com/ams/13CldPhy13AtRad/techprogram/paper_171839.htm.]
32. Randall, D. A., and Coauthors, 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589-662.
33. Rasch, P. J., D. B. Coleman, N. Mahowald, D. L. Williamson, S.-J. Lin, B. A. Boville, and P. Hess, 2006: Characteristics of atmospheric transport using three numerical formulations for atmospheric dynamics in a singleGCMframework. J. Climate, 19, 2243-2266.
34. 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. TM-2008-104606, Vol. 27, 118 pp.
35. Rood, R. B., 1987: Numerical advection algorithms and their role in atmospheric transport and chemistry models. Rev. Geophys., 25, 71-100.
36. Schmitt, K. F., C. A. Friehe, and C. H. Gibson, 1979: Structure of marine surface layer turbulence. J. Atmos. Sci., 36, 602-618.
37. Seiffert, R., and J.-S. von Storch, 2008: Impact of atmospheric small-scale fluctuations on climate sensitivity. Geophys. Res. Lett., 35, L10704, doi:10.1029/2008GL033483.
38. Siebesma, A. P., and Coauthors, 2003: A large eddy simulation intercomparison study of shallow cumulus convection. J. Atmos. Sci., 60, 1201-1219.
39. Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019-3032.
40. Smith, K. S., and R.Tulloch, 2009: Reply. J.Atmos. Sci., 66, 1073-1076.
41. Stevens, B., and G. Feingold, 2009: Untangling aerosol effects on clouds and precipitation in a buffered system. Nature, 461, 607-613.
42. Susskind, J., C. D. Barnet, and J. M. Blaisdell, 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41, 390-409.
43. Susskind, J., C. D. Barnet, and J. M. Blaisdell, L. Iredell, F. Keita, L. Kouvaris, G. Molnar, and M. Chahine, 2006: Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover. J. Geophys. Res., 111, D09S17, doi:10.1029/2005JD006272.
44. Takahashi, Y. O., K. Hamilton, and W. Ohfuchi, 2006: Explicit global simulation of the mesoscale spectrum of atmospheric motions. Geophys. Res. Lett., 33, L12812, doi:10.1029/2006GL026429.
45. Tompkins, A. M., 2002: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in largescale models and its use to diagnose cloud cover. J. Atmos. Sci., 59, 1917-1942.
46. Tuck, A. F., 2010: From molecules to meteorology via turbulent scale invariance. Quart. J. Roy. Meteor. Soc., 136, 1125-1144, doi:10.1002/qj.644.
47. Tulloch, R., and K. S. Smith, 2006: A theory for the atmospheric energy spectrum: Depth-limited temperature anomalies at the tropopause. Proc. Natl. Acad. Sci. USA, 103, 14 690-14 694.
48. Tung, K. K., and W. W. Orlando, 2003: The k23 and k25/3 energy spectrum of atmospheric turbulence: Quasigeostrophic twolevel model simulation. J. Atmos. Sci., 60, 824-835.
49. Wang, H., G. Feingold, R. Wood, and J. Kazil, 2010: Modelling microphysical and meteorological controls on precipitation and cloud cellular structures in southeast Pacific stratocumulus. Atmos. Chem. Phys., 10, 6347-6362.
50. Willett, M. R., P. Bechtold, D. L. Williamson, J. C. Petch, S. F. Milton, and S. J. Woolnough, 2008: Modeling suppressed and active convection: Comparisons between three global atmospheric models. Quart. J. Roy. Meteor. Soc., 134, 1881-1896, doi:10.1002/qj.317.
51. Williamson,D. L., 2007: The evolution of dynamical cores for global atmospheric models. J. Meteor. Soc. Japan, 85B, 241-269.
52. Wood, R., and D. L. Hartmann, 2006: Spatial variability of liquid water path in marine low cloud: The importance of mesoscale cellular convection. J. Climate, 19, 1748-1764.
53. Wood, R., and P. R. Field, 2011: The distribution of cloud horizontal sizes. J. Climate, in press.
54. Wood, R., and P. R. Field, and W. R. Cotton, 2002: Autoconversion rate bias in stratiform boundary layer cloud parameterizations. Atmos. Res., 65, 109-128.
55. Wood, R.,andCoauthors, 2011: TheVAMOSOcean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx): Goals, platforms, and field operations. Atmos. Chem. Phys., 11, 627-654.
56. Zhang, J. A., 2010: Spectral characteristics of turbulence in the hurricane boundary layer over the ocean between the outer rain bands. Quart. J. Roy Meteor. Soc., 136, 918-926, doi:10.1002/qj.610.
57. Zhao, M., I. M. Held, S.-J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate, 22, 6653-6678, doi:10.1175/2009JCLI3049.1.