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Y. Knyazikhin, R. B. Myneni, A. Marshak, W. J. Wiscombe, M. L. Larsen, and J. V. Martonchik

Abstract

Most cloud radiation models and conventional data processing techniques assume that the mean number of drops of a given radius is proportional to volume. The analysis of microphysical data on liquid water drop sizes shows that, for sufficiently small volumes, this proportionality breaks down; the number of cloud drops of a given radius is instead proportional to the volume raised to a drop size–dependent nonunit power. The coefficient of proportionality, a generalized drop concentration, is a function of the drop size. For abundant small drops the power is unity as assumed in the conventional approach. However, for rarer large drops, it falls increasingly below unity. This empirical fact leads to drop clustering, with the larger drops exhibiting a greater degree of clustering. The generalized drop concentration shows the mean number of drops per cluster, while the power characterizes the occurrence frequency of clusters. With a fixed total number of drops in a cloud, a decrease in frequency of clusters is accompanied by a corresponding increase in the generalized concentration. This initiates a competing process missed in the conventional models: an increase in the number of drops per cluster enhances the impact of rarer large drops on cloud radiation while a decrease in the frequency suppresses it. Because of the nonlinear relationship between the number of clustered drops and the volume, these two opposite tendencies do not necessarily compensate each other. The data analysis suggests that clustered drops likely have a stronger radiative impact compared to their unclustered counterpart; ignoring it results in underestimation of the contribution from large drops to cloud horizontal optical path.

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H. Gerber, J. B. Jensen, A. B. Davis, A. Marshak, and W. J. Wiscombe

Abstract

Aircraft measurements of liquid water content (LWC) made at sampling frequencies of 1 and 2 kHz with a particle volume monitor (PVM) probe from horizontal traverses in stratocumulus clouds during the Southern Ocean Cloud Experiment and cumulus clouds during the Small Cumulus Microphysics Study are described. The spectral density of the LWC measurements is calculated and compared to the −5/3 scaling law. The effect of PVM sampling noise is found to be small in most cases. Most measurements follow approximately the −5/3 law until cloud scales decrease below about 5 m in length. Below this length LWC variance can exceed that predicted by the −5/3 law. It is suggested that the enhanced LWC variance at small scales is related to entrainment of environmental air into the clouds, which changes primarily the droplet concentration.

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C. Prabhakara, D. A. Short, W. Wiscombe, R. S. Fraser, and B. E. Vollmer

Abstract

Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) measurements at five frequencies in the region 6.6 to 37 GHz, at a resolution of 155 km, are analyzed to infer precipitation over the global oceans. The microwave data show, on this spatial scale, that the combined liquid water in the clouds and rain increases the brightness temperature almost linearly with frequency in the 6.6 to 18 GHz region, while at 37 GHz such a simple relationship is not noticed. Further, as the atmospheric water vapor absorption and the effects of scattering by precipitation particles are relatively weak at 6.6 and 10.7 GHz, a technique to remotely sense the liquid water content in the atmosphere is developed based on the brightness measurements at these two frequencies. Seasonal mean patterns of liquid water content in the atmosphere derived from SMMR over global oceans relate closely to climatological patterns of precipitation. Based on this, an empirical relationship is derived to estimate precipitation over the global oceans, with an accuracy of about ±30 percent, on a seasonal basis from satellite measurements made during the three years (1979–81) before the recent El Niño event. The deviations from these three-year means in the precipitation, produced by the 1982–83 El Niño event are then deduced from the SMMR measurements. In the Pacific one notices from these deviations that the precipitation over the ITCZ in the north, the South Pacific Convergence Zone, and the oceans around Indonesia is drastically reduced. At the same time a substantial increase in precipitation is observed over the normally dry central and eastern equatorial Pacific Ocean.

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Y. Knyazikhin, A. Marshak, W. J. Wiscombe, J. V. Martonchik, and R. B. Myneni

Abstract

Most of the existing cloud radiation models treat liquid water drops of a variety of sizes as an ensemble of particles. The ensemble approach assumes that all drop sizes are well represented in an elementary volume, and its scattering and absorbing properties can be accurately specified through the use of the drop size probability density distribution function. The concentration of large drops, however, can be so low that a chance to capture them in the elementary volume is rare. Thus the drop ensemble assumption is not always true, though classical radiative transfer theory uses this assumption to simplify the radiative transfer process, as if scattering takes place from an “average drop” rather than from a particular drop. The theoretical analysis presented in this paper demonstrates that if a cumulative distribution function is used to describe drop size variability with jumps accounting for the probability of finding large drops in the elementary volume, one obtains an extra term, the Green's function, in the solution of the radiative transfer equation. The analysis of data on cloud drop size distribution acquired during the First International Satellite Cloud Climatology Project (ISCCP) Research Experiment (FIRE) field campaign clearly shows jumps in the cumulative drop size distribution; the magnitudes of the jumps are related to the frequencies of large drop occurrence. This discontinuity is primarily responsible for the additional terms that must be added to the solution to properly account for the photon interaction with the large drops. The enhancement of cloud absorption due to accounting for the “missing solution” exhibits a jump-like response to continuous variation in the concentration of large drops and may exceed the enhancement predicted by the ensemble-based models. The results presented here indicate that the missing term might be plausible to explain the enhanced value of the ratio of the shortwave cloud forcing at the surface to the forcing at top of the atmosphere.

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D. D. Turner, A. M. Vogelmann, R. T. Austin, J. C. Barnard, K. Cady-Pereira, J. C. Chiu, S. A. Clough, C. Flynn, M. M. Khaiyer, J. Liljegren, K. Johnson, B. Lin, C. Long, A. Marshak, S. Y. Matrosov, S. A. McFarlane, M. Miller, Q. Min, P. Minimis, W. O'Hirok, Z. Wang, and W. Wiscombe

Many of the clouds important to the Earth's energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and included 18 different algorithms to evaluate their retrieved LWP, optical depth, and effective radii. Surprisingly, evaluation of the simplest case, a single-layer overcast stratocumulus, revealed that huge discrepancies exist among the various techniques, even among different algorithms that are in the same general classification. This suggests that, despite considerable advances that have occurred in the field, much more work must be done, and we discuss potential avenues for future research.)

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