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ISCCP continues to quantify the global distribution and diurnal-to-interannual variations of cloud properties in a revised version. This paper summarizes assessments of the previous version, describes refinements of the analysis and enhanced features of the product design, discusses the few notable changes in the results, and illustrates the long-term variations of global mean cloud properties and differing high cloud changes associated with ENSO. The new product design includes a global, pixel-level product on a 0.1° grid, all other gridded products at 1.0°-equivalent equal area, separate satellite products with ancillary data for regional studies, more detailed, embedded quality information, and all gridded products in netCDF format. All the data products including all input data, expanded documentation, the processing code, and an operations guide are available online. Notable changes are 1) a lowered ice–liquid temperature threshold, 2) a treatment of the radiative effects of aerosols and surface temperature inversions, 3) refined specification of the assumed cloud microphysics, and 4) interpolation of the main daytime cloud information overnight. The changes very slightly increase the global monthly mean cloud amount with a little more high cloud and a little less middle and low cloud. Over the whole period, total cloud amount slowly decreases caused by decreases in cumulus/altocumulus; consequently, average cloud-top temperature and optical thickness have increased. The diurnal and seasonal cloud variations are very similar to earlier versions. Analysis of the whole record shows that high cloud variations, but not low clouds, exhibit different patterns in different ENSO events.

The current heterogeneity of the existing global collection of measuring assets, satellite and surface based, is a major obstacle to creating a truly integrated, globally uniform information system. Many surveys of Earth science needs over the last 40+ years mention research-to-operations (R2O) actions that are needed to develop such a system but focus mainly on making and collecting measurements with little discussion of the processing system and the integrated team of talented scientists needed to turn raw observations into usable information or the archival system needed to make reliable information readily and widely accessible. We discuss an example of addressing the problems in producing globally uniform information from such observations: the creation in 1982–83 of the data collection, processing, and archival system for the International Satellite Cloud Climatology Project (ISCCP). ISCCP was originally built in a research environment for climate studies, but has now transitioned to a fully operational environment to extend the length of the data record for climate research. Transforming multiple satellite observations into a uniform, global set of physical information about clouds that is readily accessible was and is challenging for several reasons. In this short commentary, we reflect on the experiences and lessons learned in building the ISCCP observation–processing–archival system to address these challenges and discuss the ISCCP R2O process to serve as a pathfinder for building a global observing and information system.

This study investigates the accuracy of the ISCCP FD and AIRS surface air and skin temperatures, as well as the ISCCP FD and MODIS surface albedos and aerosol optical depths. The comparisons showed that the AIRS air and skin temperatures were slightly more accurate compared to the surface measurements than those in ISCCP FD. The MODIS surface albedos differed from the ISCCP FD values by no more than 0.02–0.07, but because these differences are mostly at longer wavelengths, they did not change the net solar radiation very much. Therefore, to obtain the best estimate of surface net radiation with the best combination of spatial and temporal resolution, we developed a method to adjust the ISCCP FD surface longwave fluxes using the AIRS surface air and skin temperatures to obtain the higher spatial resolution of the latter, while retaining the 3-hr time intervals of the former.

Recent work using observational data from the International Satellite Cloud Climatology Project (ISCCP) and reanalysis products suggests that African easterly waves (AEWs) form in association with a “transition” process from smaller and scattered convection into larger and organized mesoscale convective activity. However, the transition process is unclear and how mesoscale convection initiates AEWs is not well understood. Analysis based on 25 years of ISCCP and reanalysis datasets show that increasing intradiurnal activity, atmospheric instability, and specific humidity precede the development of well-organized convection over the Ethiopian highlands. Atmospheric instability favors a high frequency of scattered, isolated convection to the east of the Ethiopian highlands, first, followed by a continuing and large increase in instability and increasing humidity, which supports well-organized larger-scale convection. The timing of the changes of thermodynamic variables shows that the dominant transition process is scattered, with weakly organized convection transitioning into the well-organized mesoscale convection, and this initiates the AEWs. Slightly before the mesoscale convection peaks over the Ethiopian highlands, low-level moist westerlies, low- to midlevel wind shear, and positive relative vorticity increase over the region. Evidence shows that the large-scale and local environment enables the scattered and less well-organized convection to merge and form larger and well-organized convection. The dynamic processes suggest that the dominant pathway for AEW initiation is scattered convection transitioning to large and well-organized convection over the Ethiopian highlands and this initiates AEWs westward of the Ethiopian highlands.

Although Tropical Rainfall Measuring Mission (TRMM) and CloudSat/CALIPSO fly in different orbits, they frequently cross each other so that for the period between 2006 and 2010, a total of 15,986 intersect lines occurred within 20 min of each other from 30°S to 30°N, providing a rare opportunity to study tropical cloud and precipitation regimes and their internal vertical structure from near-simultaneous measurements by these active sensors. A k-means cluster analysis of TRMM and CloudSat matchups identifies three tropical cloud and precipitation regimes: the first two regimes correspond to, respectively, organized deep convection with heavy rain and cirrus anvils with moderate rain; the third regime is a convectively suppressed regime that can be further divided into three subregimes, which correspond to, respectively, stratocumulus clouds with drizzle, cirrus overlying low clouds, and nonprecipitating cumulus. Inclusion of CALIPSO data adds to the dynamic range of cloud properties and identifies one more cluster; subcluster analysis further identifies a thin, midlevel cloud regime associated with tropical mountain ranges. The radar-lidar cloud regimes are compared with the International Satellite Cloud Climatology Project (ISCCP) weather states (WSs) for the extended tropics. Focus is placed on the four convectively active WSs, namely, WS1–WS4. ISCCP WS1 and WS2 are found to be counterparts of Regime 1 and Regime 2 in radar-lidar observations, respectively. ISCCP WS3 and WS4, which are mainly isolated convection and broken, detached cirrus, do not have a strong association with any individual radar and lidar regimes, a likely effect of the different sampling strategies between ISCCP and active sensors and patchy cloudiness of these WSs.

Clouds associated with extratropical cyclones complicate the well-developed theory of dry baroclinic waves through feedback on their dynamics by precipitation and cloud-altered radiative heating. The relationships between cyclone characteristics and the diabatic heating associated with cloud radiative effects (CREs) and latent heat release remain unclear. A cyclone tracking algorithm [NASA’s Modeling, Analysis, and Prediction (MAP) Climatology of Midlatitude Storminess (MCMS)] is used to identify over 106 cyclones in 33 years of the ERA-Interim and collect the properties of each disturbance. Considering storm intensity as related to wind speeds, which depend on the pressure gradient, the distribution of cyclone properties is investigated using groups defined by their depth (local pressure anomaly) and the radius of the region within closed pressure contours to investigate variations with longitude (especially ocean and land), hemisphere, and season. Using global data products of cloud radiative effects on in-atmosphere net radiation [the ISCCP radiative flux profile dataset (ISCCP-FD)] and precipitation (GPCP), composites are assembled for each cyclone group and for “nonstormy” locations. On average, the precipitation rate and the CRE are approximately the same among all cyclone groups and do not strongly differ from nonstormy conditions. The variance of both precipitation and CRE increases with cyclone size and depth. In larger, deeper storms, maximum precipitation and CRE increase, but so do the amounts of nonprecipitating and clear-sky conditions.

The International Satellite Cloud Climatology Project (ISCCP)’s Convection Tracking (CT) database was designed to track the evolution of deep convective cloud systems throughout their life cycle. T...

Analysis of multiple global satellite products identifies distinctive weather states of the atmosphere from the mesoscale pattern of cloud properties and quantifies the associated diabatic heating/cooling by radiative flux divergence, precipitation, and surface sensible heat flux. The results show that the forcing for the atmospheric general circulation is a very dynamic process, varying strongly at weather space–time scales, comprising relatively infrequent, strong heating events by “stormy” weather and more nearly continuous, weak cooling by “fair” weather. Such behavior undercuts the value of analyses of time-averaged energy exchanges in observations or numerical models. It is proposed that an analysis of the joint time-related variations of the global weather states and the general circulation on weather space–time scales might be used to establish useful “feedback like” relationships between cloud processes and the large-scale circulation.

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.

The primary product of the Global Aerosol Climatology Project (GACP) is a continuous record of the aerosol optical thickness (AOT) over the oceans. It is based on channel-1 and -2 radiance data from the Advanced Very High Resolution Radiometer (AVHRR) instruments flown on successive National Oceanic and Atmospheric Administration (NOAA) platforms. We extend the previous GACP dataset by four years through the end of 2009 using NOAA-17 and -18 AVHRR radiances recalibrated against MODerate resolution Imaging Spectroradiometer (MODIS) radiance data, thereby making the GACP record almost three decades long. The temporal overlap of over three years of the new NOAA-17 and the previous NOAA-16 record reveals an excellent agreement of the corresponding global monthly mean AOT values, thereby confirming the robustness of the vicarious radiance calibration used in the original GACP product. The temporal overlap of the NOAA-17 and -18 instruments is used to introduce a small additive adjustment to the channel-2 calibration of the latter resulting in a consistent record with increased data density. The Principal Component Analysis (PCA) of the newly extended GACP record shows that most of the volcanic AOT variability can be isolated into one mode responsible for ~12% of the total variance. This conclusion is confirmed by a combined PCA analysis of the GACP, MODIS, andMulti-angle Imaging SpectroRadiometer (MISR) AOTs during the volcano-free period fromFebruary 2000 to December 2009.We show that the modes responsible for the tropospheric AOT variability in the three datasets agree well in terms of correlation and spatial patterns. A previously identified negative AOT trend which started in the late 1980s and continued into the early 2000s is confirmed. Its magnitude and duration indicate that it was caused by changes in tropospheric aerosols. The latest multi-satellite segment of the GACP record shows that this trend tapered off, with no noticeable AOT change after 2002. This result is consistent with the MODIS andMISR AOT records as well as with the recent gradual reversal frombrightening to dimming revealed by surface flux measurements in many aerosol producing regions. Thus the robustness of the GACP record is confirmed, increasing our confidence in the validity of the negative trend. Although the nominal negative GACP AOT trend could partially be an artifact of increasing aerosol absorption, we argue that the time dependence of the GACP record, including the latest flat period, is more consistent with the actual decrease in the tropospheric AOT.

Because of the importance of clouds in modulating Earth’s energy budget, it is critical to understand their variability in space and time for climate and modeling studies. This study examines the consistency of the spatiotemporal variability of cloud amount (CA) and cloud-top pressure (CTP) represented by five 7-yr satellite datasets from the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project, and total cloud fraction observation from the Extended Edited Cloud Reports Archive (EECRA). Two spectral analysis techniques, namely combined maximum covariance analysis (CMCA) and combined principal component analysis (CPCA), are used to extract the dominant modes of variability from the combined datasets, and the resulting spatial patterns are compared in parallel. The results indicate that the datasets achieve overall excellent agreement on both seasonal and interannual scales of variability, with the correlations between the spatial patterns mostly above 0.6 and often above 0.8. For seasonal variability, the largest differences are found in the Northern Hemisphere high latitudes and near the South African coast for CA and in the Sahel region for CTP, where some differences in the phase and strength of the seasonal cycle are found. On interannual scales, global cloud variability is mostly associated with major climate modes, including El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Indian Ocean dipole mode (IODM), and the datasets also agree reasonably well. The good agreement across the datasets supports the conclusion that they are describing cloud variations with these climate modes.

Two systematic calibrations have been compiled for the visible radiances measured by the series of AVHRR instruments flown on the NOAA operational polar weather satellites: one by the International Satellite Cloud Climatology Project (ISCCP), anchored on NASA ER-2 underflights in the 1980s and early 1990s and covering the period 1981–2009, and one by the PATMOS-x project, anchored on comparisons to the MODIS instruments on the Aqua and Terra satellites in the 2000s and covering the period 1979–2010 (this result also includes calibration for the near-IR channels). Both methods have had to extend their anchor calibrations over a long series of instruments using different vicarious approaches, so a comparison provides an opportunity to evaluate how well this extension works by cross-checking the results at the anchor points. The basic result of this comparison is that for the “afternoon” series of AVHRRs, the calibrations agree to within their mutual uncertainties. However, this retrospective evaluation also shows that the representation of the time variations can be simplified. The ISCCP procedure had much more difficulty extending the calibration to the “morning” series of AVHRRs with the calibrations for NOAA-15 and NOAA-17 exceeding the estimated uncertainties. Given the general agreement, a new calibration for all AVHRR visible radiances (except TIROS-N, NOAA-6, NOAA-19, and MetOp-A) is proposed that is based on the average of the best linear fits to the two time records. The estimated uncertainty of these calibrations is ±3% absolute (scaled radiance units).

An increase in the frequency of organized deep convection—essentially a large aggregation of heavily precipitating and largely stratiform clouds—is behind most of the recent increases in tropical precipitation. There is a broad consensus that tropical precipitation will increase in a warming climate, but the processes driving the increase are uncertain, and thus so too are the magnitude and location of possible increases. Jackson Tan and colleagues now use satellite observations to show that an increase in organized deep convection — essentially a large aggregation of heavily precipitating and largely stratiform clouds — is behind most of the recent increases in tropical precipitation. Shifts in organized deep convection also largely explain the spatial patterns of tropical precipitation change. Increasing global precipitation has been associated with a warming climate resulting from a strengthening of the hydrological cycle1. This increase, however, is not spatially uniform. Observations and models have found that changes in rainfall show patterns characterized as ‘wet-gets-wetter’1,2,3,4,5,6,7 and ‘warmer-gets-wetter’5,8,9. These changes in precipitation are largely located in the tropics and hence are probably associated with convection. However, the underlying physical processes for the observed changes are not entirely clear. Here we show from observations that most of the regional increase in tropical precipitation is associated with changes in the frequency of organized deep convection. By assessing the contributions of various convective regimes to precipitation, we find that the spatial patterns of change in the frequency of organized deep convection are strongly correlated with observed change in rainfall, both positive and negative (correlation of 0.69), and can explain most of the patterns of increase in rainfall. In contrast, changes in less organized forms of deep convection or changes in precipitation within organized deep convection contribute less to changes in precipitation. Our results identify organized deep convection as the link between changes in rainfall and in the dynamics of the tropical atmosphere, thus providing a framework for obtaining a better understanding of changes in rainfall. Given the lack of a distinction between the different degrees of organization of convection in climate models10, our results highlight an area of priority for future climate model development in order to achieve accurate rainfall projections in a warming climate.

This study investigates the accuracy of the ISCCP FD and AIRS surface air and skin temperatures, as well as the ISCCP FD and MODIS surface albedos and aerosol optical depths. The comparisons showed that the AIRS air and skin temperatures were slightly more accurate compared to the surface measurements than those in ISCCP FD. The MODIS surface albedos differed from the ISCCP FD values by no more than 0.02–0.07, but because these differences are mostly at longer wavelengths, they did not change the net solar radiation very much. Therefore, to obtain the best estimate of surface net radiation with the best combination of spatial and temporal resolution, we developed a method to adjust the ISCCP FD surface longwave fluxes using the AIRS surface air and skin temperatures to obtain the higher spatial resolution of the latter, while retaining the 3-hr time intervals of the former.

The objective of this paper is to better isolate the snow signature in microwave signals to be able to explore the ability of satellite microwave measurements to determine snowpack properties. The surface microwave effective emissivities used in this study are derived from SSM/I passive microwave observations by removing the contributions of the cloud and atmosphere and then separating out the surface temperature variations using ancillary atmospheric, cloud and surface data. The sensitivity of the effective emissivity to the presence/absence of snow is evaluated for the Northern Hemisphere. The effect of the presence of snow, the variation of land types, and temperature on the emissivities have been examined by observing the temporal and spatial variability of these measurements between 19 and 85 GHz over the Northern Hemisphere. The time-anomaly of differences between effective emissivity at 19 V and 85 V enabled the constant effects of land surface vegetation properties to be removed to isolate the snow signature. The resulting 12-year snow signal combined with skin temperature data can detect the existence of snow cover over the Northern Hemisphere on daily basis. The results of this method compared with the operational NOAA weekly snow cover maps agree at 90% of locations and times. Most of the disagreements could be explained by rapid evolution of snow emissivities associated with freeze–melt–refreeze cycles and precipitation (snowfall), and some of them by the space–time resolution differences of the microwave and operational snow cover determinations. These results compared with the NISE, NOAA IMS, CMC, and MODIS, and snow products agree within 78% to 92%.

The generation of zonal and eddy available potential energy (Gz and Ge) as formulated by Lorenz are computed on a global-, daily-, and synoptic-scale basis to consider the contribution of each diabatic heating component separately and in combination. Using global, mostly satellite-derived datasets for the diabatic heating components and the temperature enables us to obtain Gz and, especially, Ge from observations for the first time and at higher temporal and spatial resolution than previously possible. The role of clouds in maintaining G is investigated. The global annual mean Gz is 1.52 W m−2. Values reach a minimum of 0.63 W m−2 in the Northern Hemisphere during spring and a maximum of 2.27 W m−2 in the Southern Hemisphere during winter. The largest contributors to Gz are latent heating in the tropical upper troposphere, associated with the intertropical convergence zone in the summer hemisphere and surface sensible heat fluxes in the winter pole. Diabatic cooling by radiative fluxes (mostly longwave) generally destroys Gz. The value of Ge is negative and is about an order of magnitude smaller than Gz, with a global annual mean of −0.29 W m−2. However, the small value of Ge results from the cancellation of the contributions from the individual diabatic heating terms, which are actually roughly similar in magnitude to their Gz contributions. The results presented herein suggest that the large-scale dynamics of the atmosphere organize the spatial and temporal distribution of clouds and precipitation in such a way as to increase the energy available to drive the circulation, a kind of positive feedback.

The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during winter 2011 at the research site of the National Weather Service office, Caribou ME, USA. In this ground experiment, dual polarized microwave (37 and 89 GHz) observations are conducted along with detailed synchronous observations of snowpack properties. The objective of this long term field experiment is to improve our understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations in the microwave spectral range. In this paper, we presented the overview of field experiment and preliminary analysis of the microwave observations for the first year of experiment along with support observations of the snowpack properties obtained during the 2011 winter season. SNTHERM and HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures respectively. Simulated brightness temperatures were compared with observed brightness temperature from radiometer under different snow conditions. On the time series, large difference in the brightness temperature were observed for fresh compared to aged snow even under the same snow depth, suggesting a substantial impact of other parameters such as: snow grain size and density on microwave observations. A large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization factor was observed due to cold nights and warm days, which caused a cycling between wet snow and ice-over-snow states during the early spring. Scattering analysis of microwave brightness temperatures from radiometers were performed to distinguished different snow conditions developed through the winter season.

Classifying tropical deep convective systems by the mesoscale distribution of their cloud properties and sorting matching precipitation measurements over an 11-yr period reveals that the whole distribution of instantaneous precipitation intensity and daily average accumulation rate is composed of (at least) two separate distributions representing distinctly different types of deep convection associated with different meteorological conditions (the distributions of non-deep-convective situations are also shown for completeness). The two types of deep convection produce very different precipitation intensities and occur with very different frequencies of occurrence. Several previous studies have shown that the interaction of the large-scale tropical circulation with deep convection causes switching between these two types, leading to a substantial increase of precipitation. In particular, the extreme portion of the tropical precipitation intensity distribution, above 2 mm h−1, is produced by 40% of the larger, longer-lived mesoscale-organized type of convection with only about 10% of the ordinary convection occurrences producing such intensities. When average precipitation accumulation rates are considered, essentially all of the values above 2 mm h−1 are produced by the mesoscale systems. Yet today’s atmospheric models do not represent mesoscale-organized deep convective systems that are generally larger than current-day circulation model grid cell sizes but smaller than the resolved dynamical scales and last longer than the typical physics time steps. Thus, model-based arguments for how the extreme part of the tropical precipitation distribution might change in a warming climate are suspect.

The authors examine the daytime precipitation characteristics of the International Satellite Cloud Climatology Project (ISCCP) weather states in the extended tropics (35°S–35°N) for a 10-yr period. The main precipitation dataset used is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis operational product 3B42 dataset, but Global Precipitation Climatology Project daily data are also used for comparison. It is found that the most convectively active ISCCP weather state (WS1), despite an occurrence frequency below 10%, is the most dominant state with regard to surface precipitation, producing both the largest mean precipitation rates when present and the largest percent contribution to the total precipitation of the tropics; yet, even this weather state appears to not precipitate about half the time, although this may be to some extent an artifact of detection and spatiotemporal matching limitations of the precipitation dataset. WS1 exhibits a modest annual cycle of the domain-average precipitation rate, but notable seasonal shifts in its geographic distribution. The precipitation rates of the other weather states appear to be stronger when occurring before or after WS1. The precipitation rates of the various weather states are different between ocean and land, with WS1 producing higher daytime rates on average over ocean than land, likely because of the larger size and more persistent nature of oceanic WS1s. The results of this study, in addition to advancing the understanding of tropical hydrology, can serve as higher-order diagnostics for evaluating the realism of tropical precipitation distributions in large-scale models.

In this study, the authors apply a clustering algorithm to International Satellite Cloud Climatology Project (ISCCP) cloud optical thickness–cloud top pressure histograms in order to derive weather states (WSs) for the global domain. The cloud property distribution within each WS is examined and the geographical variability of each WS is mapped. Once the global WSs are derived, a combination of CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical cloud structure retrievals is used to derive the vertical distribution of the cloud field within each WS. Finally, the dynamic environment and the radiative signature of the WSs are derived and their variability is examined. The cluster analysis produces a comprehensive description of global atmospheric conditions through the derivation of 11 WSs, each representing a distinct cloud structure characterized by the horizontal distribution of cloud optical depth and cloud top pressure. Matching those distinct WSs with cloud vertical profiles derived from CloudSat and CALIPSO retrievals shows that the ISCCP WSs exhibit unique distributions of vertical layering that correspond well to the horizontal structure of cloud properties. Matching the derived WSs with vertical velocity measurements shows a normal progression in dynamic regime when moving from the most convective to the least convective WS. Time trend analysis of the WSs shows a sharp increase of the fair-weather WS in the 1990s and a flattening of that increase in the 2000s. The fact that the fair-weather WS is the one with the lowest cloud radiative cooling capability implies that this behavior has contributed excess radiative warming to the global radiative budget during the 1990s.

To retrieve microwave land emissivity, infrared surface skin temperatures have been used as surface physical temperature since there is no global information on physical vegetation/soil temperature profiles. However, passive microwave emissions originate from deeper layers with respect to the skin temperature. So, this inconsistency in sensitivity depths between skin temperatures and microwave temperatures may introduce a discrepancy in the determined emissivity. Previous studies showed that this inconsistency can lead to significant differences between day and night retrievals of land emissivity which can exceed 10%. This study proposes an approach to address this inconsistency and improve the retrieval of land emissivity using microwave observations from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E). The diurnal cycle of the microwave brightness temperature ( Tb ) was constructed over the globe for different frequencies/polarizations using a constellation of satellites. Principal component analysis (PCA) was conducted to evaluate the spatial variation of the Tb diurnal cycle. The diurnal amplitudes of microwave temperatures observed in desert areas were not consistent with the larger amplitudes of the diurnal cycle of skin temperature. Densely vegetated areas with more moisture have shown smaller amplitudes. A lookup table of effective temperature ( T eff ) anomalies is constructed based on the Tb diurnal cycle to resolve the inconsistencies between infrared and Tb diurnal variation. This lookup table of T eff anomalies is a weighted average over the layers contributing to the microwave signal, for each channel and month. The integration of this T eff in the retrieval of land emissivity reduced the differences between day and night retrieved emissivities to less than 0.01 for AMSR-E observations.

Microwave observations at low frequencies exhibit more sensitivity to surface and subsurface properties with little interference from the atmosphere. The objective of this study is to develop a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and to investigate its sensitivity to land surface properties. The developed product complements existing land emissivity products from SSM/I and AMSU by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer. Ancillary data used in the analysis, such as surface skin temperature and cloud mask, are obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties are obtained from the TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. A sensitivity test confirms the small effect of the atmosphere but shows that skin temperature accuracy can significantly affect emissivity estimates. Retrieved emissivities at C- and X-bands and their polarization differences exhibit similar patterns of variation with changes in land cover type, soil moisture, and vegetation density as seen at SSM/I-like frequencies (Ka and Ku bands). The emissivity maps from AMSR-E at these higher frequencies agree reasonably well with the existing SSM/I-based product. The inherent discrepancy introduced by the difference between SSM/I and AMSR-E frequencies, incidence angles, and calibration has been assessed. Significantly greater standard deviation of estimated emissivities compared to SSM/I land emissivity product was found over desert regions. Large differences between emissivity estimates from ascending and descending overpasses were found at lower frequencies due to the inconsistency between thermal IR skin temperatures and passive microwave brightness temperatures which can originate from below the surface. The mismatch between day and night AMSR-E emissivities is greater than ascending and descending differences of SSM/I emissivity. This is because of unique orbit time of AMSR-E (01:30 a.m./p.m. LT) while other microwave sensors have orbit time of 06:00 to 09:00 (a.m./p.m.). This highlights the importance of considering the penetration depth of the microwave signal and diurnal variability of the temperature in emissivity retrieval. The effect of these factors is greater for AMSR-E observations than SSM/I observations, as AMSR-E observations exhibit a greater difference between day and night measures. This issue must be addressed in future studies to improve the accuracy of the emissivity estimates especially at AMSR-E lower frequencies.

A model evaluation approach is proposed in which weather and climate prediction models are analyzed along a Pacific Ocean cross section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade winds, to the deep convection regions of the ITCZ—the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/WGNE) Pacific Cross-Section Intercomparison (GPCI). The main goal of GPCI is to evaluate and help understand and improve the representation of tropical and subtropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross section from the subtropics to the tropics for the season June–July–August of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the 40-yr ECMWF Re-Analysis (ERA-40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical cross sections of cloud properties (in particular), vertical velocity, and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA-40 in the stratocumulus regions [as compared to the first International Satellite Cloud Climatology Project (ISCCP)] is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade wind Lagrangian trajectory. Histograms of cloud cover along the cross section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.

Clouds over the Southern Ocean are often poorly represented by climate models, but they make a significant contribution to the top-of-atmosphere (TOA) radiation balance, particularly in the shortwave portion of the energy spectrum. This study seeks to better quantify the organization and structure of Southern Hemisphere midlatitude clouds by combining measurements from active and passive satellite-based datasets. Geostationary and polar-orbiter satellite data from the International Satellite Cloud Climatology Project (ISCCP) are used to quantify large-scale, recurring modes of cloudiness, and active observations from CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used to examine vertical structure, radiative heating rates, and precipitation associated with these clouds. It is found that cloud systems are organized into eight distinct regimes and that ISCCP overestimates the midlevel cloudiness of these regimes. All regimes contain a relatively high occurrence of low cloud, with 79% of all cloud layers observed having tops below 3 km, but multiple-layered clouds systems are present in approximately 34% of observed cloud profiles. The spatial distribution of regimes varies according to season, with cloud systems being geometrically thicker, on average, during the austral winter. Those regimes found to be most closely associated with midlatitude cyclones produce precipitation the most frequently, although drizzle is extremely common in low-cloud regimes. The regimes associated with cyclones have the highest in-regime shortwave cloud radiative effect at the TOA, but the low-cloud regimes, by virtue of their high frequency of occurrence over the oceans, dominate both TOA and surface shortwave effects in this region as a whole.

The interaction between deep convection and easterly waves over tropical North Africa is studied using a weather state (WS) dataset from the International Cloud Climatology Project (ISCCP) and reanalysis products from the European Centre for Medium-Range Weather Forecast, as well as radiative fluxes from ISCCP and a precipitation dataset from the Global Precipitation Climatology Project. Composite analysis based on 21 yr of data shows that stronger latent and radiative heating of the atmosphere are associated with stronger, more organized, convective activity than with weaker, less organized, convective activity, implying that any transition from less to more organized and stronger convection increases atmospheric heating. Regression composites based on a meridional wind predictor reveal coherent westward propagation of WS and large-scale wind anomalies from the Arabian Sea into East Africa and through West Africa. The analysis shows that enhanced, but unorganized, convective activity, which develops over the Arabian Sea and western Indian Ocean, switches to organized convective activity prior to the appearance of the African easterly wave (AEW) signature. The results also suggest that low-level moisture flux convergence and the upper-tropospheric wind divergence facilitate this change. Thus, the upper-level easterly waves, propagating into East Africa from the Indian Ocean, enhance one form of convection, which interacts with the Ethiopian highlands to trigger another, more organized, form of convection that, in turn, initiates the low-level AEWs.

The dense network of the Surface Heat Budget of the Arctic (SHEBA) observations is used to assess relationships between winter surface and atmospheric variables as the SHEBA site came under the influence of cyclonic and anticyclonic atmospheric circulation systems. Two distinct and preferred states of subsurface, surface, atmosphere, and clouds occur during the SHEBA winter, extending from the oceanic mixed layer through the troposphere and preceded by same-sign variations in the stratosphere. These states are apparent in distributions of surface temperature, sensible heat and longwave radiation fluxes, ocean heat conduction, cloud-base height and temperature, and in the atmospheric humidity and temperature structure. Surface and atmosphere are in radiative–turbulent–conductive near-equilibrium during a warm opaquely cloudy-sky state, which persists up to 10 days and usually occurs during the low surface pressure phase of a baroclinic wave, although occasionally occurs during the high surface pressure phase because of low, scattered clouds. Clouds occurring in this state have near-unity emissivity and the lowest bases in the vicinity of, or below, the temperature inversion peak. A cold radiatively clear-sky state persists up to two weeks, and occurs only in the high surface pressure phase of a baroclinic wave. The radiatively clear state has clouds that are too tenuous when surface based or, irrespective of opacity, located too far aloft to contribute significantly to the surface energy budget. There is a 13-K surface temperature difference between the two states, and atmospheric inversion peak temperatures are linearly related to the surface temperature in both states. The snow–sea ice interface temperature oscillates over the course of the winter season, as it cools during the radiatively clear state and is warmed from atmospheric emission above and ocean heat conduction from below during the opaquely cloudy state. Analysis of satellite data over the Arctic from 70°–90°N indicates that the radiatively clear and opaquely cloudy states observed at SHEBA may be representative of the entire Arctic basin. The results suggest that model formulation inadequacies should be easier to diagnose if modeled energy transfers are compared with observations using process-based metrics that acknowledge the bimodal nature of the Arctic ocean–ice–snow–atmosphere column, rather than monthly and regionally averaged quantities. Climate change projections of thinner Arctic sea ice and larger advective water vapor influxes into the Arctic could yield different frequencies of occupation of the radiatively clear and opaquely cloudy states and higher wintertime temperatures of SHEBA ocean, ice, snow, atmosphere, and clouds—in particular, a wintertime warming of the snow–sea ice interface temperature.

The recent analysis of Rossow et al. used a clustering technique to derive six tropical weather states (WS) based on mesoscale cloud-type patterns and documented the spatial distribution of those WS and the modes of variability of the convective WS in the tropical western Pacific. In this study, the global tropics are separated into 30° × 30° regions, and a clustering algorithm is applied to the regional WS frequency distributions to derive the dominant modes of weather state variability (or the climate state variability) in each region. The results show that the whole tropical atmosphere oscillates between a convectively active and a convectively suppressed regime with the exception of the eastern parts of the two ocean basins, where the oscillation is between a stratocumulus and a trade cumulus regime. The dominant mode of both those oscillations is the seasonal cycle with the exception of the eastern Indian and western–central Pacific region, where El Niño frequencies dominate. The transitions between the convectively active and suppressed regimes produce longwave (LW) and shortwave (SW) top-of-atmosphere (TOA) radiative differences that are of opposite sign and of similar magnitude, being of order 20–30 W m−2 over ocean and 10–20 W m−2 over land and thus producing an overall balance in the TOA radiative budget. The precipitation differences between the convectively active and suppressed regimes are found to be of order 2.5–3 mm day−1 over ocean and 1–2.4 mm day−1 over land. Finally, the transitions between the stratocumulus and shallow cumulus regimes produce noticeable TOA SW differences of order 10–20 W m−2 and very small TOA LW and precipitation differences. The potential climate feedback implications of the regime radiation and precipitation differences are discussed.

A model of the three-dimensional distribution of clouds was developed from the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and the statistics of cloud vertical structure (CVS) from an analysis of radiosonde humidity profiles. The CVS model associates each cloud type, defined by cloud-top pressure of the topmost cloud layer and total column optical thickness, with a particular CVS. The advent of satellite cloud radar (CloudSat) and lidar [Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements (together C&C) of CVS allows for a quantitative evaluation of this statistical model. The zonal monthly-mean cloud layer distribution from the ISCCP CVS agrees with that from C&C to within 10% (when normalized to the same total cloud amount). The largest differences are an overestimate of middle-level cloudiness in winter polar regions, an overestimate of cloud-top pressures of the highest-level clouds, especially in the tropics, and an underestimate of low-level cloud amounts over southern midlatitude oceans. A more severe test of the hypothesized relationship is made by comparing CVS for individual satellite pixels. The agreement of CVS is good for isolated low-level clouds and reasonably good when the uppermost cloud layer is a high-level cloud; however, the agreement is not good when the uppermost cloud layer is a middle-level cloud, even when ISCCP correctly locates cloud top. An improved CVS model combining C&C and ISCCP may require classification at spatial scales larger than individual satellite pixels.

Temporal variations of surface water volume over inundated areas of the Lower Ob' Basin in Siberia, one of the largest contributor of freshwater to the Arctic Ocean, are estimated using combined observations from a multisatellite inundation dataset and water levels over rivers and floodplains derived from the TOPEX/POSEIDON (T/P) radar altimetry. We computed time-series of monthly maps of surface water volume over the common period of available T/P and multisatellite data (1993–2004). The results exhibit interannual variabilities similar to precipitation estimates and river discharge observations. This study also presents monthly estimates of groundwater and permafrost mass anomalies during 2003–2004 based on a synergistic analysis of multisatellite observations and hydrological models. Water stored in the soil is isolated from the total water storage measured by GRACE when removing the contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the snow estimated by inversion of GRACE measurements. The time variations of groundwater and permafrost are then obtained when removing the water content of the root zone reservoir simulated by hydrological models.

To better understand the interaction between tropical deep convection and the Madden–Julian oscillation (MJO), tropical cloud regimes are defined by cluster analysis of International Satellite Cloud Climatology Project (ISCCP) cloud-top pressure—optical thickness joint distributions from the D1 dataset covering 21.5 yr. An MJO index based solely on upper-level wind anomalies is used to study variations of the tropical cloud regimes. The MJO index shows that MJO events are present almost all the time; instead of the MJO event being associated with “on or off” deep convection, it is associated with weaker or stronger mesoscale organization of deep convection. Atmospheric winds and humidity from NCEP–NCAR reanalysis 1 are used to characterize the large-scale dynamics of the MJO; the results show that the large-scale motions initiate an MJO event by moistening the lower troposphere by horizontal advection. Increasingly strong convection transports moisture into the upper troposphere, suggesting a reinforcement of the convection itself. The change of convection organization shown by the cloud regimes indicates a strong interaction between the large-scale circulation and deep convection. The analysis is extended to the complete atmospheric diabatic heating by precipitation, radiation, and surface fluxes. The wave organizes stronger convective heating of the tropical atmosphere, which results in stronger winds, while there is only a passive response of the surface, directly linked to cloud radiative effects. Overall, the results suggest that an MJO event is an amplification of large-scale wave motions by stronger convective heating, which results from a dynamic reorganization of scattered deep convection into more intense mesoscale systems.

A number of passive satellite instruments have been used to develop global climatologies of terrestrial tropospheric aerosols by analyzing the properties of sunlight reflected by the atmosphere–surface system. The outcome of these efforts are several climatologies which all purport to represent the same aerosol characteristics such as optical thickness and size. However, the quantitative differences between these climatologies have been found to far exceed the corresponding individual uncertainty claims. The magnitude of these differences is alarming and necessitates a detailed critical assessment and integrated analysis that would go far beyond simple intercomparisons of various satellite products and comparisons of satellite aerosol optical thickness results with ground-based sun-photometer data. This paper outlines the framework for a global long-term satellite climatology of aerosol properties based on a consistent combination of previous, current, and near-future satellite retrievals. We also discuss potential future strategies for deriving a much improved aerosol climatology from Earth-orbiting satellites.

This study presents a first attempt to estimate the extent and seasonality of northern wetlands using radar altimeter satellite observations. The sensitivity of the Topex-Poseidon dual-frequency radar altimeter to detect inundation is investigated and compared with passive and active microwave satellite measurements along with a land surface climatology database. The C band backscatter altimeter signal clearly tracks passive microwave emissivity observations of wetlands and is able to detect small flooded areas. Because of the nadir incidence angle, the radar altimeter also shows more capability to detect wetlands than the C band scatterometer. Monthly flooded areas are calculated by estimating flooded pixel fractional coverage using the altimeter C band backscatter magnitude and a linear mixing model with dual-frequency altimeter backscatter difference, C–Ku, to account for vegetation effects. Because of the Topex-Poseidon satellite spatial coverage, the results are given only from 40u N to 66u N. This region nevertheless represents more than 30% of world’s inundated surfaces during the summer. A direct validation of the inundated extent is unfortunately impossible on a large scale, due to the scarcity of quantitative observations. As a consequence, the results are evaluated by comparison with other existing estimates. Radar altimetry estimates, comprising natural wetlands and river/lakes, indicate a maximum inundated area of 1.86610 6 km 2 for July 1993 and 1994 as compared with 1.31610 6 km 2 from passive microwave technique and ,2.10610 6 km 2 from climatology dataset. The wetland seasonal variability derived from the altimeter and passive microwave techniques agrees well. These promising results could soon be applied to the ENVISAT dualfrequency radar altimeter that will provide a better survey of global inundated surfaces thanks to its much more complete spatial coverage.

Microwave land surface emissivities have been calculated over the globe for ~10 yr between 19 and 85 GHz at 53° incidence angle for both orthogonal polarizations, using satellite observations from the Special Sensor Microwave Imager (SSM/I). Ancillary data (IR satellite observations and meteorological reanalysis) help remove the contribution from the atmosphere, clouds, and rain from the measured satellite signal and separate surface temperature from emissivity variations. The method to calculate the emissivity is general and can be applied to other sensors. The monthly mean emissivities are available for the community, with a 0.25° × 0.25° spatial resolution. The emissivities are sensitive to variations of the vegetation density, the soil moisture, the presence of standing water at the surface, or the snow behavior, and can help characterize the land surface properties. These emissivities (not illustrated in this paper) also allow for improved atmospheric retrieval over land and can help evaluate land surface emissivity models at global scales.

In the first part of the paper, a high space–time resolution (1° latitude/longitude and daily) dataset of the turbulent fluxes at the ocean surface is used to estimate and study the seasonal to annual near-global maps of the decorrelation scales of the latent and sensible heat fluxes. The decorrelation scales describe the temporal and spatial patterns that dominate the flux fields (within a bandpass window) and hence reveal the dominant variability in the air–sea interaction. Regional comparison to the decorrelation scales of the flux-related variables such as the wind stress, the humidity difference, and the SST identifies the main mechanism responsible for the variability in each flux field. In the second part of the paper, the decorrelation scales are used to develop a method for filling missing values in the dataset that result from the incomplete satellite coverage. Weight coefficients in a linear regression function are determined from the spatial and temporal decorrelations and are functions of zonal and meridional distance and time. Therefore they account for all spatial and temporal patterns on scales greater than 1 day and 1° latitude/longitude and less than 1 yr and the ocean basin scale. The method is evaluated by simulating the missing-value distribution of the Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF2) dataset in the NCEP SST, the International Satellite Climatology Project (ISCCP)-FD (fluxes calculated using D1 series) surface radiation, and the Global Precipitation Climatology Project (GPCP) datasets and by comparing the filled datasets to the original ones. Main advantages of the method are that the decorrelation scales are unrestricted functions of space and time; only information internal to the flux field is used in the interpolation scheme, and the computation cost of the method is low enough to facilitate its use in similar large datasets.

To diagnose how cloud processes feed back on weather- and climate-scale variations of the atmosphere requires determining the changes that clouds produce in the atmospheric diabatic heating by radiation and precipitation at the same scales of variation. In particular, not only the magnitude of these changes must be quantified but also their correlation with atmospheric temperature variations; hence, the space–time resolution of the cloud perturbations must be sufficient to account for the majority of these variations. Although extensive new global cloud and radiative flux datasets have recently become available, the vertical profiles of clouds and consequent radiative flux divergence have not been systematically measured covering weather-scale variations from about 100 km, 3 h up to climate-scale variations of 10 000 km, decadal inclusive. By combining the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and an analysis of radiosonde humidity profiles, a statistical model has been developed that associates each cloud type, recognizable from satellite measurements, with a particular cloud vertical structure. Application of this model to the ISCCP cloud layer amounts produces estimates of low-level cloud amounts and average cloud-base pressures that are quantitatively closer to observations based on surface weather observations, capturing the variations with latitude and season and land and ocean (results are less good in the polar regions). The main advantage of this statistical model is that the correlations of cloud vertical structure with meteorology are qualitatively similar to “classical” information relating cloud properties to weather. These results can be evaluated and improved with the advent of satellites that can directly probe cloud vertical structures over the globe, providing statistics with changing meteorological conditions.

Trends in cirrus cloud cover have been estimated based on 16 years of data from ISCCP (International Satellite Cloud Climatology Project). The results have been spatially correlated with aircraft density data to determine the changes in cirrus cloud cover due to aircraft traffic. The correlations are only moderate, as many other factors have also contributed to changes in cirrus. Still we regard the results to be indicative of an impact of aircraft on cirrus amount. The main emphasis of our study is on the area covered by the METEOSAT satellite to avoid trends in the ISCCP data resulting from changing satellite viewing geometry. In Europe, which is within the METEOSAT region, we find indications of a trend of about 1-2% cloud cover per decade due to aircraft, in reasonable agreement with previous studies. The positive trend in cirrus in areas of high aircraft traffic contrasts with a general negative trend in cirrus. Extrapolation in time to cover the entire period of aircraft operations and in space to cover the global scale yields a mean estimate of 0.03 Wm-2 (lower limit 0.01, upper limit 0.08 Wm-2) for the radiative forcing due to aircraft induced cirrus. The mean is close to the value given by IPCC (1999) as an upper limit.

International Satellite Cloud Climatology Project (ISCCP DX) and microwave sensor data collected by the Tropical Rainfall Measuring Mission (TRMM) are used to identify and describe structural characteristics of convective systems (CSs) over continental South America (SA) related to cold-frontal incursions in a 3-yr period. An austral wet-season climatology for CS events of the three most important types of front–tropical convection interaction is built by applying latitude–time diagrams and a cloud-tracking method to DX data. Type 1 is characterized by the penetration of a cold front over subtropical SA that interacts with convection and moves with it into lower tropical latitudes. Type 2 refers to Amazon convection and its enhancement in a quasi-stationary northwest–southeast-oriented band extending from the Amazon to subtropical SA along with the passage of a cold front in the subtropics and characterizes the synoptic formation of the South Atlantic convergence zone. A quasi-stationary cold front over subtropical SA that has only weak interaction with tropical convection corresponds to type 3. Results show that the three types of front–tropical convection interaction strongly modulate deep convection over SA, producing mesoscale CSs with significant fractions of deep convective clouds and rain at their mature phase. Type 2 CSs (type 1 CSs) are constituted of larger deep convective cloud fractions with weaker (stronger) vertical development compared to type 1 CSs (type 3 CSs) in the Tropics (subtropics), resulting in larger rain fractions and less (more) presence of convective rain. Type 1 CSs have larger fractions of deep convective clouds and rain but with weaker vertical development in the subtropics than in the Tropics, showing that cold fronts organize convection more in area in the subtropics, but more in vertical extent in the Tropics. Life cycle variations of CS cloud and rain properties show tropical CSs with a more intense initial development and similar structural differences between the CS types and those found at their mature phase.