Cloud parameterization for climate modeling: Status and prospects

Randall, David A.

doi:10.1016/0169-8095(89)90025-2

Cited by 36 publications

(24 citation statements)

References 66 publications

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“…Understanding these interactions and capturing them in numerical weather prediction (NWP) models, particularly in the context of climate simulations (e.g., Arakawa 1975), remain at the cutting edge of atmospheric research (e.g., Randall 1989;Ramanathan and Collins 1991;Wallace 1992;Stephens et al 2004). Clouds play an integral role in the radiative, chemical, dynamic, and thermodynamic feedback processes describing the current state of the atmosphere and its short-term evolution (i.e., weather) and long-term averages/trends (i.e., climate).…”

Section: Introductionmentioning

confidence: 99%

Estimating Three-Dimensional Cloud Structure via Statistically Blended Satellite Observations

Miller

Forsythe

Partain

et al. 2014

Journal of Applied Meteorology and Climatology

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The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat's nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or ''curtain,'' of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aqua satellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.

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Section: Introductionmentioning

confidence: 99%

Estimating Three-Dimensional Cloud Structure via Statistically Blended Satellite Observations

Miller

Forsythe

Partain

et al. 2014

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

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“…These may occur at large scales with considerable impacts on temperature and humidity or at small scales within the clouds. The importance of clouds in the regulation of the climate has been demonstrated by various studies [ Vonder Haar , 1982; Roeckner , 1988; Randall , 1989; Twomey , 1991; Pueschel et al , 1997; Rossow et al , 2002; Galli et al , 2004]. The considerable progress made by these studies, has contributed to a better understanding of the effects of clouds on climate change and, the reduction of the uncertainty of these effects in the radiation budget calculations.…”

Section: Introductionmentioning

confidence: 99%

The recent state of the climate: Driving components of cloud-type variability

et al. 2011

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[1] To reduce the Earth's radiation budget uncertainty related to cloud types' changes, and better understand the climate constraints resulting from long-term clouds' variability, frequent and finer (than actually existing) observations are necessary. This is one of the aims of future satellite programs such as the Global Change Observation Mission-Climate (GCOM-C) satellite, to be launched by the Japan Aerospace Exploration Agency (JAXA). To facilitate the transition from past to future observations, the actual state of climate variables (e.g., cloud types) needs to be evaluated. This evaluation is attempted in the present work with the analysis of long-term cloud types' distribution and amounts. The data set used for this study is 25 years of global daytime cloud properties observed by the National Oceanic and Atmospheric Administration-Advanced Very-High-Resolution Radiometer (NOAA-AVHRR) satellites sensors. Though various calibrations have been applied on NOAA-AVHRR data, the effects of the orbit drift experienced by these satellites need to be corrected. A signal processing decomposition method allowing the filtering of the cloud types' amount trend affected by the orbit drift is used to perform the necessary corrections. The results obtained show a quantifiable improvement of the cloud amount estimation and trends of the individual NOAA satellites initial observations, at the global and regional scales. The corrected global cloud amount shows a slight decrease in its linear trend. The driving factors of this trend are the decrease in mid and low clouds overwhelming the increase in high clouds (+0.04% cloud amount/yr). A comparison with other cloud climatology studies such as the International Cloud Satellite Climatology Project (ISCCP) data set shows that the global cloud decrease noticed in NOAA-AVHRR's data is smaller. And, contrary to the NOAA-AVHRR's data, the driving force of the ISCCP linear trend is a sharp decrease in low clouds (−0.20% cloud amount/yr).

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“…Because most clouds have sizes much smaller than those represented by global climate models, a significant part of the effort has been to develop ways of representing or parameterizing the effects of clouds on the larger scales. Randall [1989] reviewed the status of observational and modeling efforts to improve these parameterizations in two areas: modulation of the radiation fields, and involvement in the processes that transport energy, moisture, momentum, chemical species, and water. Some representative recent studies in this area are those of Randall et al .…”

Section: Clouds As Components In the Global Climate Systemmentioning

confidence: 99%

Research in Cloud and Precipitation Physics: Review of U. S. Theoretical and Observational Studies, 1987–1990

Cooper

1991

Reviews of Geophysics

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Cloud parameterization for climate modeling: Status and prospects

Cited by 36 publications

References 66 publications

Estimating Three-Dimensional Cloud Structure via Statistically Blended Satellite Observations

Estimating Three-Dimensional Cloud Structure via Statistically Blended Satellite Observations

The recent state of the climate: Driving components of cloud-type variability

Research in Cloud and Precipitation Physics: Review of U. S. Theoretical and Observational Studies, 1987–1990

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