Global Variability of Mesoscale Convective System Anvil Structure from A-train Satellite Data

Yuan, J., Nanjing University

Cloud Distributions/Characterizations

Cloud Life Cycle

Yuan J and RA Houze. 2010. "Global Variability of Mesoscale Convective System Anvil Structure from A-Train Satellite Data." Journal of Climate, 23(21), 10.1175/2010jcli3671.1.

Figure. 1 Annual mean (2007) climatology of anvil clouds associated with (a) small separated MCSs (<12000 km^2, the smallest 25%), (b) large separated MCSs (>40000 km^2, the largest 25%), and (c) connected MCSs. The color indicates percentage of area covered by MCS anvil clouds for each 5°x5° grid.

Figure. 1 Annual mean (2007) climatology of anvil clouds associated with (a) small separated MCSs (<12000 km^2, the smallest 25%), (b) large separated MCSs (>40000 km^2, the largest 25%), and (c) connected MCSs. The color indicates percentage of area covered by MCS anvil clouds for each 5°x5° grid.

In the tropics, upper-level clouds containing ice and mixtures of ice and liquid water strongly affect the transfer of shortwave and longwave radiation and modulate the radiative heating structure through the atmosphere. Such clouds are produced in large quantities by Mesoscale Convective Systems (MCSs), which are also important latent heating agents in the tropics. The latent heating by MCSs has been addressed by TRMM and other programs. The radiative heating profiles associated with MCSs have never been addressed because it is difficult to separate the raining portions of MCSs from the non-raining anvils in satellite datasets. We have broken through this stumbling block by using MODIS, AMSR-E, and CloudSat in a joint analysis scheme to both identify MCSs and separate the raining portions of the MCSs from their non-raining anvil portions.

In this study, we have developed an objective method to identify MCSs and their anvils by combining data from three A-Train satellite instruments: MODIS for cloud top size and coldness, AMSR-E for rain area size and intensity, and CloudSat for horizontal and vertical structures of anvils. The CloudSat data have been verified against ARM vertically pointing radar data. We distinguish two types of MCSs: separated MCSs (SMCSs) and connected MCSs (CMCSs). The latter are those MCSs that share a contiguous rain area.

Mapping of the objectively identified MCSs shows patterns of MCSs that are consistent with previous studies of tropical convection, with separated MCSs dominant over Africa and the Amazon regions and connected (clustering) MCSs favored over the warm pool of the Indian and West Pacific Oceans.

By using MODIS and AMSR-E to separate the non-raining anvil clouds from the raining regions of the identified MCSs, we are able to construct quantitative global maps of anvil coverage (see Figure 1). These maps show that anvil clouds associated with small SMCSs are frequent over land areas, both continents and large islands, but that their total anvil coverage is small (maxima ~0.6%) over Africa and South America. In contrast, anvil clouds from large SMCSs are favored over warm oceans, and they cover several times more area overall than small SMCSs. Anvil clouds from CMCSs are also common over open ocean areas, with most of them occurring over the Indian Ocean, Bay of Bengal, South China Sea, and West Pacific warm pool. In addition to MCSs, we have determined the global pattern of non-MCS anvil clouds. The mapping of anvils accomplished in this study lays a foundation for calculations of radiative effects of these anvil clouds to obtain a more comprehensive global diabatic heating structure associated with MCS.

By using CloudSat CPR data, we show that the modal thickness of MCS anvils is ~4–5 km. Anvil clouds of active MCS are mostly confined to within 1.5–2 times the equivalent radii of the primary rain areas of the MCSs. Over the warm pool they may extend out to four times the rain area radii. The warm ocean MCSs have thicker non-raining and lightly raining anvils near the edges of their actively raining regions, indicating that anvils are generated in and spread out from the primary raining regions of the MCSs. Thicker anvils are nearly absent over continental regions. Oceanic MCSs are more efficient in producing anvil clouds, especially thicker anvil clouds, per the same area of their raining region. Connected MCSs are found to have fewer thinner high-topped clouds compared to separated MCSs, consistent with the clustering nature of connected MCSs.

The morphology and climatological variability of MCS anvils structures determined in this study lay the groundwork for determining the radiative impact of MCSs on the tropical circulation. Further details of the three-dimensional structure can be sought through CloudSat CPR data containing information on the vertical distribution of radar reflectivity within the anvils and from ARM ground-based vertically pointing cloud radars. Using CloudSat data in relation to the full tropical climatology of MCSs derived here, we will be able to determine more comprehensively how the internal structures of MCS anvils vary across the tropics. Then it will be possible to use radiative transfer calculations to calculate heating profiles empirically across the tropics.

The power of the methodology developed in this study is that it can be applied objectively and automatically and therefore take advantage of the massive satellite data sets now being compiled, while ARM sites provide the ground validation. Thus, the method can be applied to instantaneous fields of data and to virtually unlimited sample sizes to obtain a robust and global climatology of tropical MCSs. Such observation-based statistical findings will be useful tools to diagnose/improve models and the more resolved heating structure could be used to drive general circulation models towards better understanding of large-scale mean circulation.