A Climatology of Midlatitude Continental Clouds from the ARM SGP Central Facility. Part II: Cloud Fraction and Surface Radiative Forcing

Xiquan Dong Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Xiquan Dong in
Current site
Google Scholar
PubMed
Close
,
Baike Xi Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Baike Xi in
Current site
Google Scholar
PubMed
Close
, and
Patrick Minnis NASA Langley Research Center, Hampton, Virginia

Search for other papers by Patrick Minnis in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Data collected at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (SCF) are analyzed to determine the monthly and hourly variations of cloud fraction and radiative forcing between January 1997 and December 2002. Cloud fractions are estimated for total cloud cover and for single-layered low (0–3 km), middle (3–6 km), and high clouds (>6 km) using ARM SCF ground-based paired lidar–radar measurements. Shortwave (SW) and longwave (LW) fluxes are derived from up- and down-looking standard precision spectral pyranometers and precision infrared radiometer measurements with uncertainties of ∼10 W m−2. The annual averages of total and single-layered low-, middle-, and high-cloud fractions are 0.49, 0.11, 0.03, and 0.17, respectively. Both total- and low-cloud amounts peak during January and February and reach a minimum during July and August; high clouds occur more frequently than other types of clouds with a peak in summer. The average annual downwelling surface SW fluxes for total and low clouds (151 and 138 W m−2, respectively) are less than those under middle and high clouds (188 and 201 W m−2, respectively), but the downwelling LW fluxes (349 and 356 W m−2) underneath total and low clouds are greater than those from middle and high clouds (337 and 333 W m−2). Low clouds produce the largest LW warming (55 W m−2) and SW cooling (−91 W m−2) effects with maximum and minimum absolute values in spring and summer, respectively. High clouds have the smallest LW warming (17 W m−2) and SW cooling (−37 W m−2) effects at the surface. All-sky SW cloud radiative forcing (CRF) decreases and LW CRF increases with increasing cloud fraction with mean slopes of −0.984 and 0.616 W m−2 %−1, respectively. Over the entire diurnal cycle, clouds deplete the amount of surface insolation more than they add to the downwelling LW flux. The calculated CRFs do not appear to be significantly affected by uncertainties in data sampling and clear-sky screening. Traditionally, cloud radiative forcing includes not only the radiative impact of the hydrometeors, but also the changes in the environment. Taken together over the ARM SCF, changes in humidity and surface albedo between clear and cloudy conditions offset ∼20% of the NET radiative forcing caused by the cloud hydrometeors alone. Variations in water vapor, on average, account for 10% and 83% of the SW and LW CRFs, respectively, in total cloud cover conditions. The error analysis further reveals that the cloud hydrometeors dominate the SW CRF, while water vapor changes are most important for LW flux changes in cloudy skies. Similar studies over other locales are encouraged where water and surface albedo changes from clear to cloudy conditions may be much different than observed over the ARM SCF.

Corresponding author address: Dr. Xiquan Dong, Department of Atmospheric Sciences, University of North Dakota, 4149 Campus Road, Box 9006, Grand Forks, ND 58202-9006. Email: dong@aero.und.edu

Abstract

Data collected at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (SCF) are analyzed to determine the monthly and hourly variations of cloud fraction and radiative forcing between January 1997 and December 2002. Cloud fractions are estimated for total cloud cover and for single-layered low (0–3 km), middle (3–6 km), and high clouds (>6 km) using ARM SCF ground-based paired lidar–radar measurements. Shortwave (SW) and longwave (LW) fluxes are derived from up- and down-looking standard precision spectral pyranometers and precision infrared radiometer measurements with uncertainties of ∼10 W m−2. The annual averages of total and single-layered low-, middle-, and high-cloud fractions are 0.49, 0.11, 0.03, and 0.17, respectively. Both total- and low-cloud amounts peak during January and February and reach a minimum during July and August; high clouds occur more frequently than other types of clouds with a peak in summer. The average annual downwelling surface SW fluxes for total and low clouds (151 and 138 W m−2, respectively) are less than those under middle and high clouds (188 and 201 W m−2, respectively), but the downwelling LW fluxes (349 and 356 W m−2) underneath total and low clouds are greater than those from middle and high clouds (337 and 333 W m−2). Low clouds produce the largest LW warming (55 W m−2) and SW cooling (−91 W m−2) effects with maximum and minimum absolute values in spring and summer, respectively. High clouds have the smallest LW warming (17 W m−2) and SW cooling (−37 W m−2) effects at the surface. All-sky SW cloud radiative forcing (CRF) decreases and LW CRF increases with increasing cloud fraction with mean slopes of −0.984 and 0.616 W m−2 %−1, respectively. Over the entire diurnal cycle, clouds deplete the amount of surface insolation more than they add to the downwelling LW flux. The calculated CRFs do not appear to be significantly affected by uncertainties in data sampling and clear-sky screening. Traditionally, cloud radiative forcing includes not only the radiative impact of the hydrometeors, but also the changes in the environment. Taken together over the ARM SCF, changes in humidity and surface albedo between clear and cloudy conditions offset ∼20% of the NET radiative forcing caused by the cloud hydrometeors alone. Variations in water vapor, on average, account for 10% and 83% of the SW and LW CRFs, respectively, in total cloud cover conditions. The error analysis further reveals that the cloud hydrometeors dominate the SW CRF, while water vapor changes are most important for LW flux changes in cloudy skies. Similar studies over other locales are encouraged where water and surface albedo changes from clear to cloudy conditions may be much different than observed over the ARM SCF.

Corresponding author address: Dr. Xiquan Dong, Department of Atmospheric Sciences, University of North Dakota, 4149 Campus Road, Box 9006, Grand Forks, ND 58202-9006. Email: dong@aero.und.edu

Save
  • Ackerman, T. P., and G. M. Stokes, 2003: The Atmospheric Radiation Measurement Program. Phys. Today, 56 , 3844.

  • Ackerman, T. P., D. M. Flynn, and R. Marchand, 2003: Quantifying the magnitude of anomalous solar absorption. J. Geophys. Res., 108 .4273, doi:10.1029/2002JD002674.

    • Search Google Scholar
    • Export Citation
  • Barkstrom, B. R., 1984: The Earth Radiation Budget Experiment. Bull. Amer. Meteor. Soc., 65 , 11701185.

  • Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95 , 1660116615.

    • Search Google Scholar
    • Export Citation
  • Cess, R. D., and Coauthors, 1995: Absorption of solar radiation by clouds: Observations versus models. Science, 267 , 496499.

  • Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general circulation models: An update. J. Geophys. Res., 101 , 1279112794.

    • Search Google Scholar
    • Export Citation
  • Charlock, T. P., F. G. Rose, and D. A. Rutan, 2003: Validation of the archived CERES Surface and Atmospheric Radiation Budget (SARB). Proc. 13th ARM Science Team Meeting, Broomfield, CO, Department of Energy, 1–7. [Available online at http://www.arm.gov/publications/proceedings/conf13/index.stm.].

  • Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. A. Miller, and B. E. Martner, 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed (ARM CART) sites. J. Appl. Meteor., 39 , 645665.

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

  • Dong, X., and G. G. Mace, 2003: Arctic stratus cloud properties and radiative forcing derived from ground-based data collected at Barrow, Alaska. J. Climate, 16 , 445461.

    • Search Google Scholar
    • Export Citation
  • Dong, X., P. Minnis, and B. Xi, 2005: A climatology of midlatitude continental clouds from the ARM SGP Central Facility: Part I: Low-level cloud macrophysical, microphysical, and radiative properties. J. Climate, 18 , 13911410.

    • Search Google Scholar
    • Export Citation
  • Gautier, C., and M. Landsfeld, 1997: Surface solar radiation flux and cloud radiative forcing for the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP): A satellite, surface observations, and radiative transfer model study. J. Atmos. Sci., 54 , 12891307.

    • Search Google Scholar
    • Export Citation
  • Gupta, S. K., D. P. Kratz, A. C. Wilber, and L. C. Nguyen, 2004: Validation of the Langley parameterized algorithms used to derive the CERES/TRMM surface radiative fluxes. J. Atmos. Oceanic Technol., 21 , 742752.

    • Search Google Scholar
    • Export Citation
  • Hogan, R. J., and A. J. Illingworth, 2000: Deriving cloud overlap statistics from radar. Quart. J. Roy. Meteor. Soc., 126 , 29032909.

  • Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson, 2001: Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp.

    • Search Google Scholar
    • Export Citation
  • Khaiyer, M. M., A. D. Rapp, P. Minnis, D. R. Doelling, W. L. Smith Jr., L. Nguyen, M. L. Nordeen, and Q. Min, 2002: Evaluation of a 5-year cloud and radiative property dataset derived from GOES-8 data over the Southern Great Plains. Proc. 12th ARM Science Team Meeting, St. Petersburg, FL, Department of Energy, 1–14. [Available online at http://www.arm.gov/publications/proceedings/conf12/index.stm.].

  • Lazarus, S. M., S. K. Krueger, and G. G. Mace, 2000: A cloud climatology of the Southern Great Plains ARM CART. J. Climate, 13 , 17621775.

    • Search Google Scholar
    • Export Citation
  • Li, Z., and H. G. Leighton, 1993: Global climatologies of solar radiation budgets at the surface and in the atmosphere from 5 years of ERBA data. J. Geophys. Res., 98 , 49194930.

    • Search Google Scholar
    • Export Citation
  • Li, Z., and A. P. Trishchenko, 2001: Quantifying uncertainties in determining SW cloud radiative forcing and cloud absorption due to variability in atmospheric conditions. J. Atmos. Sci., 58 , 376389.

    • Search Google Scholar
    • Export Citation
  • Long, C. N., and T. P. Ackerman, 2000: Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects. J. Geophys. Res., 105 , 1560915626.

    • Search Google Scholar
    • Export Citation
  • Matthias, A. D., A. Fimbres, E. E. Sano, D. F. Post, L. Accioly, A. K. Batchily, and L. G. Ferreira, 2000: Surface roughness effects on soil albedo. Soil Sci. Soc. Amer. J., 64 , 10351041.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., and E. F. Harrison, 1984: Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data. Part III: November 1978 radiative parameters. J. Climate Appl. Meteor., 23 , 10321052.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., W. L. Smith Jr., D. P. Garber, J. K. Ayers, and D. R. Doelling, 1995: Cloud properties derived from GOES-7 for the spring 1994 ARM Intensive Observing Period using version 1.0 of the ARM satellite data analysis program. NASA Reference Publication 1366, 59 pp.

  • Moran, K. P., B. E. Martner, M. J. Post, R. A. Kropfli, D. C. Welsh, and K. B. Widener, 1998: An unattended cloud-profiling radar for use in climate research. Bull. Amer. Meteor. Soc., 79 , 443455.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartmann, 1989: Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243 , 5763.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and Y-C. Zhang, 1995: Calculations of surface and top-of-atmosphere radiative fluxes from physical quantities based on ISCCP data sets, 2. Validation and first results. J. Geophys. Res., 100 , 11661197.

    • Search Google Scholar
    • Export Citation
  • Shi, Y., and C. N. Long, 2002: Best Estimate Radiation Flux Value-Added Procedure: Algorithm operational details and explanations. DOE ARM Tech. Rep. TR-08, 51 pp. [Available online at http://www.arm.gov/publications/tech_reports/arm-tr-008.pdf.].

  • Shupe, M., and J. M. Intrieri, 2004: Cloud radiative forcing of the arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle. J. Climate, 17 , 616628.

    • Search Google Scholar
    • Export Citation
  • Tian, B., and V. Ramanathan, 2002: Role of tropical clouds in surface and atmospheric energy budget. J. Climate, 15 , 296305.

  • USCCRI, cited. 2001: The climate change research initiative. [Available online at www.climatescience.gov/about/ccri.htm.].

  • Warren, S. G., C. J. Hahn, J. London, R. M. Chervin, and R. L. Jenne, 1986: Global distribution of total cloud cover and cloud type amounts over land. NCAR Tech. Note NCAR/TN-273+STR, National Center for Atmospheric Research, Boulder, CO, 229 pp.

  • Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to Planet Earth: Role of clouds and radiation in climate. Bull. Amer. Meteor. Soc., 76 , 21252153.

    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., and Coauthors, 1998: Clouds and the Earth’s Radiant Energy System (CERES): Algorithm overview. IEEE Trans. Geosci. Remote Sens., 36 , 11271141.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y-C., W. B. Rossow, and A. A. Lacis, 1995: Calculations of surface and top-of-atmosphere radiative fluxes from physical quantities based on ISCCP data sets, 1. Method and sensitivity to input data uncertainties. J. Geophys. Res., 100 , 11491165.

    • Search Google Scholar
    • Export Citation