A Climatology of Midlatitude Continental Cloud Properties and Their Impact on the Surface Radiation Budget

Dong, X., University of Arizona

Cloud Distributions/Characterizations

Cloud Properties

Dong, X., P. Minnis, and B. Xi, 2005: A climatology of midlatitude continental clouds from ARM SGP site. Part I: Low-level Cloud Macrophysical, microphysical and radiative properties. J. Climate. 18, 1391-1410.

Dong, X., B. Xi, and P. Minnis, 2006: A climatology of midlatitude continental clouds from ARM SGP site. Part II: Cloud fraction and surface radiative forcing. J. Climate. 19, 1765-1783.

Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95, 16 601-16 615.

Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general circulation models: An update. J. Geophys. Res., 101, 12 791-12 794.

Lazarus, S.M., S.K. Krueger, and G.G. Mace, 2000: A cloud climatology of the Southern Great Plains ARM CART. J. Climate, 13, 1762-1775.

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, 229 pp., Natl. Cent. for Atmos. Res., Boulder, Colo.


Figure 1. This plot shows the total cloud fractions derived from ground-based radar/lidar (this study), satellite (GOES), and surface human (Warren 1986 and Lazarus 2000) observations over the ACRF SGP site. The annual averaged cloud fractions from different datasets are nearly the same with significantly different field of views, spatial domains, and time periods. Although the monthly means from the various datasets are not exactly the same, they all show that cloud fraction was greatest during winter and spring, least during summer and fall, and dropped significantly from June to July.


Figure 2. This plot shows the monthly means of the LW, SW, and NET CRFs for all cloud types at ACRF SGP site, 1997-2002. The total and low cloud LW CRFs peak during spring and fall, and bottom out during July and August. The LW CRFs for all sky, middle and high clouds, as expected, are generally smaller than those for total and low clouds except the middle cloud LW CRFs, which are slightly larger than those for total clouds during the summer. The variations in SW CRFs almost mirror their LW counterparts. The SW CRFs for all sky, middle and high clouds are much less negative than those for total and low clouds. NET CRFs, the sum of SW and LW CRFs, are primarly determined by SW CRFs throughout most of the year. During winter, however, the negative SW CRFs and positive LW CRFs nearly cancel each other resulting in NET CRFs between -17 and +2 Wm-2.


Figure 1. This plot shows the total cloud fractions derived from ground-based radar/lidar (this study), satellite (GOES), and surface human (Warren 1986 and Lazarus 2000) observations over the ACRF SGP site. The annual averaged cloud fractions from different datasets are nearly the same with significantly different field of views, spatial domains, and time periods. Although the monthly means from the various datasets are not exactly the same, they all show that cloud fraction was greatest during winter and spring, least during summer and fall, and dropped significantly from June to July.

Figure 2. This plot shows the monthly means of the LW, SW, and NET CRFs for all cloud types at ACRF SGP site, 1997-2002. The total and low cloud LW CRFs peak during spring and fall, and bottom out during July and August. The LW CRFs for all sky, middle and high clouds, as expected, are generally smaller than those for total and low clouds except the middle cloud LW CRFs, which are slightly larger than those for total clouds during the summer. The variations in SW CRFs almost mirror their LW counterparts. The SW CRFs for all sky, middle and high clouds are much less negative than those for total and low clouds. NET CRFs, the sum of SW and LW CRFs, are primarly determined by SW CRFs throughout most of the year. During winter, however, the negative SW CRFs and positive LW CRFs nearly cancel each other resulting in NET CRFs between -17 and +2 Wm-2.

The change in Earth's net radiation budget due to clouds is called cloud radiative forcing (CRF), which represents the “bulk effects” of clouds. The bulk effects of clouds are the integrated effects of individual cloud properties, such as cloud fraction, height, and microphysical/optical features. Because the bulk effects of clouds are critically important for validating global climate models (GCMs), they must be accurately parameterized to correctly simulate atmospheric processes. For instance, an intercomparison of 19 GCMs (Cess et al. 1990) produced a variety of cloud feedback results, ranging from modestly negative to strongly positive. These feedbacks were reduced by altering the cloud optical properties in the parameterizations (Cess et al. 1996). For a long-term record of cloud behavior, the ARM Climate Research Facility (ACRF) Southern Great Plains (SGP) site in Oklahoma provides a valuable dataset of continuous ground-based observations for just this purpose. In a two-paper study, published in 2005 and 2006 in the Journal of Climate, researchers funded by the ARM Program compared 6 years of cloud fraction derived from ARM radar-lidar data against those obtained via satellite and human observations. Overall, they concluded that the annual averaged cloud fractions from different datasets are nearly the same, although they have significantly different field of views, spatial domains, and time periods.

As shown in the Dong et al. studies, 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. For total and low clouds, the average annual downwelling surface shortwave (SW) fluxes (151 and 138 Wm-2, respectively) are less than those under middle and high clouds (188 and 201 Wm-2, respectively), but the downwelling longwave (Lw) fluxes (349 and 356 Wm-2) underneath total and low clouds are greater than those from middle and high clouds (337 and 333 Wm-2). Low clouds produce the largest longwave warming (55 Wm-2) and shortwave cooling (-91 Wm-2) effects with maximum and minimum absolute values in spring and summer, respectively. High clouds have the smallest longwave warming (17 Wm-2) and shortwave cooling (-37 Wm-2) effects at the surface. Cloud radiative forcing as traditionally defined includes not only the radiative impact of the hydrometeors, but also the changes in the environment. Taken together over the SGP site, changes in humidity and surface albedo between clear and cloudy conditions offset ~20% of the net radiative forcing caused by the clouds alone. Variations in water vapor, on average, account for 10% and 83% of the shortwave and longwave cloud radiative forcing, respectively, in total cloud cover conditions.

Clouds are the dominant modulators of radiation at the surface, and cloud radiative forcing is a simple but effective means of studying cloud-radiation interactions and diagnosing problems in GCMs. Although the climate modeling community has emphasized the radiative effects of marine stratus/stratocumulus clouds, continental clouds—such as those at the SGP site—are an important part of the overall climate system. Results from this study can serve as a baseline for analyzing the radiation budget at the surface and in the atmosphere when combined with satellite measurements of fluxes at the top of the atmosphere. Similarly, these results can serve as ground truth for validating satellite retrievals over the SGP site. To better understand the geographical variability of surface cloud radiative forcing, similar analyses using datasets collected from additional surface sites, such as the ACRF sites in the Tropical Western Pacific and in Barrow, Alaska, would be very valuable.