ALBERT

Description: The Clouds and the Earth’s Radiant Energy System (CERES) project provides observations of Earth’s radiation budget using measurements from CERES instruments on board the Terra, Aqua, Suomi National Polar-orbiting Partnership (S-NPP), and NOAA-20 satellites. The CERES top-of-atmosphere (TOA) fluxes are produced by converting radiance measurements using empirical angular distribution models, which are functions of cloud properties that are retrieved from imagers flying with the CERES instruments. As the objective is to create a long-term climate data record, not only calibration consistency of the six CERES instruments needs to be maintained for the entire time period, it is also important to maintain the consistency of other input data sets used to produce this climate data record. In this paper, we address aspects that could potentially affect the CERES TOA flux data quality. Discontinuities in imager calibration can affect cloud retrieval which can lead to erroneous flux trends. When imposing an artificial 0.6 per decade decreasing trend to cloud optical depth, which is similar to the trend difference between CERES Edition 2 and Edition 4 cloud retrievals, the decadal SW flux trend changed from − 0.3 5 ± 0.18 Wm − 2 to 0.61 ± 0.18 Wm − 2 . This indicates that a 13% change in cloud optical depth results in about 1% change in the SW flux. Furthermore, different CERES instruments provide valid fluxes at different viewing zenith angle ranges, and including fluxes derived at the most oblique angels unique to S-NPP (〉66 ∘ ) can lead to differences of 0.8 Wm − 2 and 0.3 Wm − 2 in global monthly mean instantaneous SW flux and LW flux. To ensure continuity, the viewing zenith angle ranges common to all CERES instruments (

Description: The physical and radiative properties of tropical deep convective systems for the period from January to August 1998 are examined with the use of Clouds and the Earth’s Radiant Energy System Single-Scanner Footprint (SSF) data from the Tropical Rainfall Measuring Mission satellite. Deep convective (DC) cloud objects are contiguous regions of satellite footprints that fulfill the DC criteria (i.e., overcast footprints with cloud optical depths 〉10 and cloud-top heights 〉10 km). Extended cloud objects (ECOs) start with the original cloud object but include all other cloudy footprints within a rectangular box that completely covers the original cloud object. Most of the non-DC footprints are overcast but have optical depths and/or cloud-top heights that are too low to fit the DC criteria. The histograms of cloud physical and radiative properties are analyzed according to the size of the ECO and the SST of the underlying ocean. Larger ECOs are associated with greater magnitudes of large-scale upward motion, which supports stronger convection for larger sizes of ECOs. This leads to shifts toward higher values in the DC distributions of cloud-top height, albedo, condensate water path, and cloud optical depth. However, non-DC footprints become less reflective with increasing ECO size, as the longer-lived large convective systems have more time to develop thin cirrus anvils. The proportion of DC footprints remains fairly constant with size. The proportion of DC footprints also remains nearly constant with SST within a given size class, although the number of footprints per object increases with SST for large objects. As SSTs increase, there is a decrease in the proportion of updraft water that goes into detrainment, causing the non-DC distributions of albedo, condensate water path, and cloud optical depth to shift toward lower values. The all-cloud distributions of cloud-top temperature and outgoing longwave radiation (OLR) shift toward lower values as SST increases owing to the increase in convective instability with SST. Both the DC and non-DC distributions of cloud-top temperature do not change much with satellite precession cycle, supporting the fixed anvil temperature hypothesis of Hartmann and Larson. When a joint histogram is formed from the cloud-top pressures and cloud optical depths of the ECOs, it is very similar to the corresponding histogram of the deep convective weather state obtained by cluster analysis of International Satellite Cloud Climatology Project data.

Description: This study presents an objective classification methodology that uses Earth Observing System (EOS) satellite data to classify distinct “cloud objects” defined by cloud-system types, sizes, geographic locations, and matched large-scale environments. This analysis method identifies a cloud object as a contiguous region of the earth with a single dominant cloud-system type. It determines the shape and size of the cloud object from the satellite data and the cloud-system selection criteria. The statistical properties of the identified cloud objects are analyzed in terms of probability density functions (PDFs) based upon the Clouds and the Earth’s Radiant Energy System (CERES) Single Satellite Footprint (SSF) data. Four distinct types of oceanic cloud objects—tropical deep convection, boundary layer cumulus, transition stratocumulus, and solid stratus—are initially identified from the CERES data collected from the Tropical Rainfall Measuring Mission (TRMM) satellite for this study. Preliminary results are presented from the analysis of the grand-mean PDFs of these four distinct types of cloud objects associated with the strong 1997/98 El Niño in March 1998 and the very weak 2000 La Niña in March 2000. A majority of the CERES footprint statistical characteristics of observed tropical deep convection are similar between the two periods in spite of the climatological contrast. There are, however, statistically significant differences in some cloud macrophysical properties such as the cloud-top height and cloud-top pressure and moderately significant differences in outgoing longwave radiation (OLR), cloud-top temperature, and ice diameter. The footprint statistical characteristics of the three observed boundary layer cloud-system types are distinctly different from one another in all cloud microphysical, macrophysical, optical properties, and radiative fluxes. The differences between the two periods are not significant for most cloud microphysical and optical properties and the top-of-the-atmosphere albedo, but are statistically significant for some cloud macrophysical properties and OLR. These characteristics of the grand-mean PDFs of cloud microphysical, macrophysical, and optical properties and radiative fluxes can be usefully compared with cloud model simulations. Furthermore, the proportion of different boundary layer cloud types is changed between the two periods in spite of small differences in their grand-mean statistical properties. An increase of the stratus population and a decrease of the cumulus population are evident in the El Niño period compared to the very weak La Niña period. The number of the largest tropical convective cloud objects is larger during the El Niño period, but the total number of tropical convective cloud objects is approximately the same in the two periods.

Description: Characteristics of tropical deep convective cloud objects observed over the tropical Pacific during January–August 1998 are examined using the Tropical Rainfall Measuring Mission/Clouds and the Earth’s Radiant Energy System Single Scanner Footprint (SSF) data. These characteristics include the frequencies of occurrence and statistical distributions of cloud physical properties. Their variations with cloud object size, sea surface temperature (SST), and satellite precession cycle are analyzed in detail. A cloud object is defined as a contiguous patch of the earth composed of satellite footprints within a single dominant cloud-system type. It is found that statistical distributions of cloud physical properties are significantly different among three size categories of cloud objects with equivalent diameters of 100–150 (small), 150–300 (medium), and 〉300 km (large), except for the distributions of ice particle size. The distributions for the larger-size category of cloud objects are more skewed toward high SSTs, high cloud tops, low cloud-top temperature, large ice water path, high cloud optical depth, low outgoing longwave (LW) radiation, and high albedo than the smaller-size category. As SST varied from one satellite precession cycle to another, the changes in macrophysical properties of cloud objects over the entire tropical Pacific were small for the large-size category of cloud objects, relative to those of the small- and medium-size categories. This evidence supports the fixed anvil temperature hypothesis of Hartmann and Larson for the large-size category. Combined with the result that a higher percentage of the large-size category of cloud objects occurs during higher SST subperiods, this implies that macrophysical properties of cloud objects would be less sensitive to further warming of the climate. On the other hand, when cloud objects are classified according to SST ranges, statistical characteristics of cloud microphysical properties, optical depth, and albedo are not sensitive to the SST, but those of cloud macrophysical properties are dependent upon the SST. This result is related to larger differences in large-scale dynamics among the SST ranges than among the satellite precession cycles. Frequency distributions of vertical velocity from the European Centre for Medium-Range Weather Forecasts model that is matched to each cloud object are used to further understand some of the findings in this study.

Description: Simulations of climate change have yet to reach a consensus on the sign and magnitude of the changes in physical properties of marine boundary layer clouds. In this study, the authors analyze how cloud and radiative properties vary with SST anomaly in low-cloud regions, based on five years (March 2000–February 2005) of Clouds and the Earth’s Radiant Energy System (CERES)–Terra monthly gridded data and matched European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological reanalaysis data. In particular, this study focuses on the changes in cloud radiative effect, cloud fraction, and cloud optical depth with SST anomaly. The major findings are as follows. First, the low-cloud amount (−1.9% to −3.4% K−1) and the logarithm of low-cloud optical depth (−0.085 to −0.100 K−1) tend to decrease while the net cloud radiative effect (3.86 W m−2 K−1) becomes less negative as SST anomalies increase. These results are broadly consistent with previous observational studies. Second, after the changes in cloud and radiative properties with SST anomaly are separated into dynamic, thermodynamic, and residual components, changes in the dynamic component (taken as the vertical velocity at 700 hPa) have relatively little effect on cloud and radiative properties. However, the estimated inversion strength decreases with increasing SST, accounting for a large portion of the measured decreases in cloud fraction and cloud optical depth. The residual positive change in net cloud radiative effect (1.48 W m−2 K−1) and small changes in low-cloud amount (−0.81% to 0.22% K−1) and decrease in the logarithm of optical depth (–0.035 to –0.046 K−1) with SST are interpreted as a positive cloud feedback, with cloud optical depth feedback being the dominant contributor. Last, the magnitudes of the residual changes differ greatly among the six low-cloud regions examined in this study, with the largest positive feedbacks (∼4 W m−2 K−1) in the southeast and northeast Atlantic regions and a slightly negative feedback (−0.2 W m−2 K−1) in the south-central Pacific region. Because the retrievals of cloud optical depth and/or cloud fraction are difficult in the presence of aerosols, the transport of heavy African continental aerosols may contribute to the large magnitudes of estimated cloud feedback in the two Atlantic regions.

Description: Relationships between physical properties are studied for three types of marine boundary layer cloud objects identified with the Clouds and the Earth’s Radiant Energy System (CERES) footprint data from the Tropical Rainfall Measuring Mission satellite between 30°S and 30°N. Each cloud object is a contiguous region of CERES footprints that have cloud-top heights below 3 km, and cloud fractions of 99%–100% (overcast type), 40%–99% (stratocumulus type), or 10%–40% (shallow cumulus type). These cloud fractions represent the fraction of ∼2 km × 2 km Visible/Infrared Scanner pixels that are cloudy within each ∼10 km × 10 km footprint. The cloud objects have effective diameters that are greater than 300 km for the overcast and stratocumulus types, and greater than 150 km for the shallow cumulus type. The Spearman rank correlation coefficient is calculated between many microphysical/optical [effective radius (re), cloud optical depth (τ), albedo, liquid water path, and shortwave cloud radiative forcing (SW CRF)] and macrophysical [outgoing longwave radiation (OLR), cloud fraction, cloud-top temperature, longwave cloud radiative forcing (LW CRF), and sea surface temperature (SST)] properties for each of the three cloud object types. When both physical properties are of the same category (microphysical/optical or macrophysical), the magnitude of the correlation tends to be higher than when they are from different categories. The magnitudes of the correlations also change with cloud object type, with the correlations for overcast and stratocumulus cloud objects tending to be higher than those for shallow cumulus cloud objects. Three pairs of physical properties are studied in detail, using a k-means cluster analysis: re and τ, OLR and SST, and LW CRF and SW CRF. The cluster analysis of re and τ reveals that for each of the cloud types, there is a cluster of cloud objects with negative slopes, a cluster with slopes near zero, and two clusters with positive slopes. The joint OLR and SST probability plots show that the OLR tends to decrease with SST in regions with boundary layer clouds for SSTs above approximately 298 K. When the cloud objects are split into “dry” and “moist” clusters based on the amount of precipitable water above 700 hPa, the associated OLRs increase with SST throughout the SST range for the dry clusters, but the OLRs are roughly constant with SST for the moist cluster. An analysis of the joint PDFs of LW CRF and SW CRF reveals that while the magnitudes of both LW and SW CRFs generally increase with cloud fraction, there is a cluster of overcast cloud objects that has low values of LW and SW CRF. These objects are generally located near the Sahara Desert, and may be contaminated with dust. Many of these overcast objects also appear in the re and τ cluster with negative slopes.

Description: The present study evaluates the ability of a cloud-resolving model (CRM) to simulate the physical properties of tropical deep convective cloud objects identified from a Clouds and the Earth’s Radiant Energy System (CERES) data product. The emphasis of this study is the comparisons among the small-, medium-, and large-size categories of cloud objects observed during March 1998 and between the large-size categories of cloud objects observed during March 1998 (strong El Niño) and March 2000 (weak La Niña). Results from the CRM simulations are analyzed in a way that is consistent with the CERES retrieval algorithm and they are averaged to match the scale of the CERES satellite footprints. Cloud physical properties are analyzed in terms of their summary histograms for each category. It is found that there is a general agreement in the overall shapes of all cloud physical properties between the simulated and observed distributions. Each cloud physical property produced by the CRM also exhibits different degrees of disagreement with observations over different ranges of the property. The simulated cloud tops are generally too high and cloud-top temperatures are too low except for the large-size category of March 1998. The probability densities of the simulated top-of-the-atmosphere (TOA) albedos for all four categories are underestimated for high albedos, while those of cloud optical depth are overestimated at its lowest bin. These disagreements are mainly related to uncertainties in the cloud microphysics parameterization and inputs such as cloud ice effective size to the radiation calculation. Summary histograms of cloud optical depth and TOA albedo from the CRM simulations of the large-size category of cloud objects do not differ significantly between the March 1998 and 2000 periods, consistent with the CERES observations. However, the CRM is unable to reproduce the significant differences in the observed cloud-top height while it overestimates the differences in the observed outgoing longwave radiation and cloud-top temperature between the two periods. Comparisons between the CRM results and the observations for most parameters in March 1998 consistently show that both the simulations and observations have larger differences between the large- and small-size categories than between the large- and medium-size, or between the medium- and small-size categories. However, the simulated cloud properties do not change as much with size as observed. These disagreements are likely related to the spatial averaging of the forcing data and the mismatch in time and space between the numerical weather prediction model from which the forcing data are produced and the CERES observed cloud systems.

Description: A two-dimensional cloud-resolving model (CRM) is used to perform five sets of simulations of 68 deep convective cloud objects identified with Clouds and the Earth’s Radiant Energy System (CERES) data to examine their sensitivity to changes in thermodynamic and dynamic forcings. The control set of simulations uses observed sea surface temperatures (SSTs) and is forced by advective cooling and moistening tendencies derived from a large-scale model analysis matched to the time and location of each cloud object. Cloud properties, such as albedo, effective cloud height, cloud ice and snow path, and cloud radiative forcing (CRF), are analyzed in terms of their frequency distributions rather than their mean values. Two sets of simulations, F+50% and F−50%, use advective tendencies that are 50% greater and 50% smaller than the control tendencies, respectively. The increased cooling and moistening tendencies cause more widespread convection in the F+50% set of simulations, resulting in clouds that are optically thicker and higher than those produced by the control and F−50% sets of simulations. The magnitudes of both longwave and shortwave CRF are skewed toward higher values with the increase in advective forcing. These significant changes in overall cloud properties are associated with a substantial increase in deep convective cloud fraction (from 0.13 for the F−50% simulations to 0.34 for the F+50% simulations) and changes in the properties of non–deep convective clouds, rather than with changes in the properties of deep convective clouds. Two other sets of simulations, SST+2K and SST−2K, use SSTs that are 2 K higher and 2 K lower than those observed, respectively. The updrafts in the SST+2K simulations tend to be slightly stronger than those of the control and SST−2K simulations, which may cause the SST+2K cloud tops to be higher. The changes in cloud properties, though smaller than those due to changes in the dynamic forcings, occur in both deep convective and non–deep convective cloud categories. The overall changes in some cloud properties are moderately significant when the SST is changed by 4 K. The changes in the domain-averaged shortwave and longwave CRFs are larger in the dynamic forcing sensitivity sets than in the SST sensitivity sets. The cloud feedback effects estimated from the SST−2K and SST+2K sets are comparable to prior studies.

Description: This study uses a numerical model to simulate deep convection both in the Tropics over the ocean and the midlatitudes over land. The vertical grid that was used extends into the stratosphere, allowing for the simultaneous examination of the convection and the vertically propagating gravity waves that it generates. A large number of trajectories are used to evaluate the behavior of tracers in the troposphere, and it is found that the tracers can be segregated into different types based upon their position in a diagram of normalized vertical velocity versus displacement. Conditional sampling is also used to identify updrafts in the troposphere and calculate their contribution to the kinetic energy budget of the troposphere. In addition, Fourier analysis is used to characterize the waves in the stratosphere; it was found that the waves simulated in this study have similarities to those observed and simulated by other researchers. Finally, this study examines the wave energy flux as a means to provide a link between the tropospheric behavior of the convection and the strength of the waves in the stratosphere.