Abstract

The annual-mean meridional energy transport in the atmosphere–ocean system (total transport) is estimated using 4-yr mean net radiative fluxes at the top of the atmosphere (TOA) calculated from the International Satellite Cloud Climatology Project cloud datasets. In addition, the net atmospheric and surface radiative fluxes are calculated. When supplemented by a climatology of the surface latent and sensible heat fluxes, these radiative fluxes are used to derive the separate atmospheric and oceanic energy transports using a surface and planetary energy-balance method. Most previous results are based on direct calculations of the atmospheric energy transport from in situ measurements of horizontal wind velocity, temperature, and humidity in the atmosphere and on inference of oceanic heat transports as the difference between the atmospheric transports and the total energy transport (the planetary energy-balance method). Total, atmospheric, and oceanic energy transports from this study are in good agreement with more recent results (within mutual uncertainties). A detailed assessment is made of the uncertainties in the atmospheric and ocean energy transports that arise from uncertainties in the TOA and surface energy fluxes: the largest uncertainties are associated with the surface radiative and latent heat fluxes. Since the errors in the present method are from different sources and have different geographic distributions, the results of this study complement previous estimates of the atmospheric and oceanic energy transports. Assessment of error sources also suggests that improvement of this type of result is more likely in the near future than for the other methods. Because the radiative fluxes are calculated from physical quantities, the authors can characterize the mean effects of clouds on the atmospheric and oceanic energy transports: 1) cloud effects on the TOA radiation budget reduce hemispheric differences introduced by hemispheric differences of surface properties, 2) the cloud effects on the atmospheric and surface radiation budgets induce hemispheric differences in the heating/cooling of the atmosphere and ocean that require cross-equatorial transports in opposite directions by the atmosphere and ocean, and 3) all other factors held constant, clouds tend to reduce oceanic energy transports and increase atmospheric energy transports.

Abstract

Surface temperature and emissivities, as well as atmospheric water vapor and cloud liquid water, have been calculated from Special Sensor Microwave Imager observations for snow-covered land areas using a neural network inversion scheme that includes first-guess information. A learning database to train the neural network is derived from a global collection of coincident surface and atmospheric parameters, extracted from the National Centers for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data, and from microwave emissivity atlases previously calculated. Despite the large space and time variability of the snow microwave response, the surface and atmospheric parameters are retrieved. Water vapor is estimated with a theoretical rms error of approximately 30%, verified against radiosonde measurements, that is almost the same as over snow-free land. The theoretical rms error of the surface skin temperature retrieval is 1.5 and 1.9 K, respectively, for clear and cloudy scenes. The surface skin temperatures are compared with the surface air temperatures measured at meteorological stations to verify that the expected differences are found. The space and time variations of the retrieved surface emissivities are evaluated by comparison with surface parameter variations such as surface air temperature, snow depth, and vegetation cover.

The International Satellite Cloud Climatology Project (ISCCP) has been approved as the first project of the World Climate Research Programme (WCRP) and will begin its operational phase in July 1983. Its basic objective is to collect and analyze satellite radiance data to infer the global distribution of cloud radiative properties in order to improve the modeling of cloud effects on climate. ISCCP has two components, operational and research. The operational component takes advantage of the global coverage provided by the current and planned international array of geostationary and polar-orbiting meteorological satellites during the 1980s to produce a five-year global satellite radiance and cloud data set. The main and most important characteristic of these data will be their globally uniform coverage of various indices of cloud cover. The research component of ISCCP will coordinate studies to validate the climatology, to improve cloud analysis algorithms, to improve modeling of cloud effects in climate models, and to investigate the role of clouds in the atmosphere's radiation budget and hydrologic cycle. Validation will involve comparative measurements at a number of test areas selected as representative of major (or difficult) cloud types and meteorological conditions. Complimentary efforts within the framework of WCRP will promote the use of the resulting ISCCP data sets in climate research.

The operational data-collection phase of the International Satellite Cloud Climatology Project (ISCCP) began in July 1983 as an element of the World Climate Research Program (WCRP). Since then, raw images from an international network of operational geostationary and polar-orbiting meteorological satellites have been routinely processed to develop a global data set of calibrated radiances and derived cloud parameters for climate research. This report outlines the key steps involved in producing the basic ISCCP reduced-resolution global radiance (B3) data set, describes the main features of the data set, and indicates the principal point of contact for obtaining copies of the data tapes. A future paper will focus on the derived cloud properties and their utilization.

Abstract

By identifying individual tropical cloud clusters in eight months of the International Satellite Cloud Climatology Project data, the size distribution, average cloud properties, and their variation with system size in tropical convective systems (CS) is examined. The geographic distribution of CS shows a concentration over land areas in the summer hemisphere with little seasonal variation except for the major shift of location into the summer hemisphere. When the tropics are considered as a whole or a region is considered over a whole season, CS of all sizes (from individual convective towers at 2–20 km to the largest mesoscale systems at 200–2000 km) form a continuous size distribution where the area covered by the clouds in each size range is approximately the same. Land CS show a small excess of the smallest CS and a small deficit of the largest CS in comparison to ocean CS. Average CS cloud properties suggest two major cloud types: one with lower cloud-top pressures and much higher optical thicknesses associated with deep convection, and one with higher cloud-top pressures and lower optical thicknesses associated with the mesoscale stratiform anvil clouds. The anvil cloud properties show some evidence of a further division into optically thicker and thinner parts. The avenge properties of these clouds vary in a correlated fashion such that a larger horizontal extent of the convective system cloud is accompanied by a lower convective cloud-top pressure, larger anvil cloud size, and larger anvil cloud optical thickness. These structural properties and their diurnal variation also suggest that the smallest CS may represent a mixture of the formative and dissipating stages of CS, while the medium and large sizes are, principally, the mature stage. A radiative transfer model is used to evaluate the local radiative effects of CS with average cloud properties. The results imply that the mesoscale anvil cloud reinforces the diabatic heating of the atmosphere by the convection and may help sustain these systems at night. The radiative effects of the convective clouds, while unimportant to the total effect of the CS at the top of the atmosphere, may reinforce the diurnal variation of convection. Evaluating the radiative feedback of tropical cloudiness on climate is shown to be very difficult because of the significant diurnal and geographic variations of convective system cloud properties.

Abstract

Two additional narrowband channels of the Scanner of Radiation Budget (ScaRaB) instrument should improve the Earth Radiation Budget Experiment (ERBE) cloud scene identification. Applying the original International Satellite Cloud Climatology Project (ISCCP) algorithms to the ScaRaB narrowband data gives a clear-sky frequency that is about 5% lower than that given by quasi-simultaneous ISCCP data, an indication that the ISCCP cloud detection is very stable. However, one would expect about 10%–20% smaller clear-sky occurrence for the larger ScaRaB pixels. Adapting the ISCCP algorithms to the ScaRaB spatial resolution and to the different time sampling of the ScaRaB data leads to a reduction of residual cloud contamination. A sensitivity study with time–space-collocated ScaRaB and ISCCP data shows that the clear-sky identification method has a greater effect on the clear-sky frequency and therefore on the statistics than on the zonal mean values of the clear-sky fluxes. The zonal outgoing longwave (LW) fluxes corresponding to ERBE clear sky are in general about 2–10 W m⁻² higher than those from the ScaRaB-adapted ISCCP clear-sky identifications. The latter are close to fluxes corresponding to clear-sky regions from ISCCP data, whereas ScaRaB clear-sky LW fluxes obtained with the original ISCCP identification lie about 1–2 W m⁻² below. Especially in the Tropics, where water vapor abundance is high, the ERBE clear-sky LW fluxes seem to be systematically overestimated by about 4 W m⁻², and shortwave (SW) fluxes are lower by about 5–10 W m⁻². However, another source of uncertainty in the monthly mean zonal cloud radiative effects comes from the low frequency of clear-sky occurrence, when averaging over regions that correspond to the spatial resolution of general circulation models. An additional systematic sampling bias in the clear-sky fluxes appears because the clear-sky regions selected by the different algorithms occur in different geographic regions with different cloud properties.

Abstract

The GISS (Goddard Institute for Space Studies) GCM (general circulation model) predicts stratiform and convective cloud cover and optical thickness at nine atmospheric levels in horizontal grid boxes of 4° lat × 5° long. Until now, the radiative fluxes were calculated once per grid box, assuming clear sky or a complete cloud cover. Here, a refinement of the radiative flux calculation is explored by introducing a horizontal subgrid cloud overlap scheme in which cloud blocks are formed by adjacent cloud layers using maximum overlap. Different cloud blocks are separated by an atmospheric level of clear sky and are assumed to overlap randomly inside the grid box. This subgrid cloud structure allows determination of the occurrence probabilities of columns with different vertical structures inside each horizontal grid box. Then, radiative fluxes are calculated for each of these columns. The radiative fluxes of each horizontal grid box are obtained as the occurrence probability weighted sum of the column fluxes. Compared with the standard GCM version, the horizontal subgrid cloud overlap scheme leads to significant geographical and seasonal changes of the global mean cloud effects on top-of-atmosphere radiative fluxes that are in slightly better agreement with satellite observations. Two extreme assumptions of horizontal cloud size distributions (very small cloud elements or one horizontally continuous cloud) within the cloud blocks are also tested, leading to different column occurrence probabilities. Whereas the global and zonal mean cloud effects on radiative fluxes stay the same, regional differences between the two assumptions (i.e., uncertainties in GCM cloud cover and radiative fluxes produced by a lack of knowledge of subgrid cloud size distributions) can be as large as 15% in cloud cover and 25 (50) W m⁻² in LW (SW) net fluxes.

The implemented cloud overlap scheme is necessary to study radiative effects of different cloud types separately so that one can better understand the discrepancies in cloud radiative effects between observations and model. This study is not possible with the standard version of the GCM because the instantaneous fluxes do not correspond to realistic cloud structures. But by comparing in more detail the radiative effects of high opaque, cirrus, midlevel, and low clouds with help of the new scheme in GCM and in simultaneous Earth Radiation Budget Experiment and International Satellite Cloud Climatology Project observations, one finds out that high opaque clouds in the GCM have a cloud cover that is too small and are too thin over winter hemisphere ocean, whereas cirrus clouds appear with a cloud cover that is too high. Low clouds in the GCM seem to be too low by about 100 hPa.

Abstract

A new 8-year global cloud climatology has been produced by the International Satellite Cloud Climatology Project (ISCCP) that provides information every 3 h at 280-km spatial resolution covering the period from July 1983 through June 1991. If cloud detection errors and differences in area sampling are neglected, individual ISCCP cloud amounts agree with individual surface observations to within 15% rms with biases of only a few percent. When measurements of small-scale, broken clouds are isolated in the comparison, the rms differences between satellite and surface cloud amounts are about 25%, similar to the rms difference between ISCCP and Landsat determinations of cloud amount. For broken clouds, the average ISCCP cloud amounts are about 5% smaller than estimated by surface observers (difference between earth cover and sky cover), but about 5% larger than estimated from very high spatial resolution satellite observations (overestimate due to low spatial resolution offset by underestimate due to finite radiance thresholds). Detection errors, caused by errors in the ISCCP clear-sky radiances or incorrect radiance threshold magnitude are the dominant source of error in monthly average cloud amounts. The ISCCP cloud amounts appear to he too low over land by about 10%, somewhat less in summer and somewhat more in winter, and about right (maybe slightly low) over oceans. In polar regions, ISCCP cloud amounts are probably too low by about 15%–25% in summer and 5%–10% in winter. Comparison of the ISCCP climatology to three other cloud climatologies shows excellent agreement in the geographic distribution and seasonal variation of cloud amounts; there is little agreement about day/night contrasts in cloud amount. Notable results from ISCCP are that the global annual mean cloud amount is about 63%, being about 23% higher over oceans than over land, that it varies by <1% rms from month to month, and that it has varied by about 4% on a time wale ≈2–4 years. The magnitude of interannual variations of local (280-km scale) monthly mean cloud amounts is about 7%–9%. Longitudinal contrasts in cloud amount are just as large as latitudinal contrasts. The largest seasonal variation of cloud amount occurs in the tropics, being larger in summer than in winter; the seasonal variation in middle latitudes has the opposite phase. Polar regions may have little seasonal variability in cloud amount. The ISCCP results show slightly more nighttime than daytime cloud amount over oceans and more daytime than nighttime cloud amount over land.

Abstract

Using GOES-7 ISCCP-B3 satellite data for 1987–88, the authors studied the evolution of the morphological and radiative properties of clouds over the life cycles of deep convective systems (CS) over the Americas at both tropical and middle latitudes. A deep convective cloud system is identified by adjacent satellite image pixels with infrared brightness temperatures, T _IR < 245 K (−28°C), that at some time contain embedded convective clusters that are defined by pixel values of T _IR < 218 K (−55°C). The first part of the analysis computes parameters for each convective system that describe the system areal size, number of convective clusters, fractional convective area, average and maximum size of the convective clusters, shape eccentricity and orientation, mean T _IR, variance of T _IR, T _IR gradient, and the mean, variance, and gradient of collocated visible reflectances (when available). The second part of the analysis searches a 5° × 5° region centered on each convective system, but in the subsequent image (3-h time separation), to locate possible candidates representing the same system at the later time. For each possible candidate, the method calculates the fraction of areal overlap with the target system and the implied speed and direction of propagation of the whole convective system and the largest convective cluster within the candidate system. The authors review previous studies of the sensitivity of CS statistics to the temperature thresholds used for identification and quantify the effects on these statistics produced by different ways of tracking convective systems. Comparisons of the results from several tracking methods explains how they work and why most of the life cycle statistics are not sensitive to tracking method used. The authors confirm that simple coincidence (as used in most previous studies) works as long as the time step between satellite images is smaller than the time required for significant evolution of the CS: since smaller systems evolve more rapidly, getting accurate results for CS smaller than about 50–100 km probably requires time resolution better than 3 h. The whole dataset has been analyzed by a tropical meteorologist who choses the best candidate at each time step by comparing listings of all the calculated parameters and visually examining each satellite image pair. The whole dataset has also been analyzed by a simple automated procedure. Based on about 3200 cases examined by the meteorologist and about 4700 cases obtained by the completely automated method, the statistical behavior of convective systems over the Americas is described: their mean cloud properties as a function of system size and lifetime, the evolution of their cloud properties over their lifetimes, and their motions. The results demonstrate a direct correspondence between the size and lifetime of mesoscale convective systems and exhibit many common features of their growth, maturation, and decay. Some differences between CS over tropical land and tropical ocean are also apparent.

Abstract

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10 W m⁻² each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30 W m⁻² over trade wind cumulus regions, yet smaller CRE by about −30 W m⁻² over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15 W m⁻² smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference.