Abstract

The relationship between sea surface temperature (SST) and albedo or cloud cover is examined for two tropical regions with high values of cloud radiative forcing and persistent marine stratocumulus (mSc)–one off the west coast of Peru, the other off the west cost of Angola. The data span five years, from December 1984 to November 1989. Albedos are from the Earth Radiation Budget Experiment, cloud covers are from the International Satellite Cloud Climatology Project, and SSTs are from the Climate Analysis Center.

Negative correlation coefficients between albedo and SST are found to be about −0.8 when the seasonal variation of the entire dataset is analyzed. The interannual variation and the spatial variation of individual months also yields correlation coefficients that are negative. The correlation between cloud cover and SST is found to be similar to but weaker than the correlation between albedo and SST, suggesting a decrease in cloud amount and a decrease in cloud albedo with increasing SST for these regions. The corresponding albedo sensitivity averages −0.018 K⁻¹ with local values reaching −0.04 K ⁻¹. These findings are valid from 19°C to 25°*C for the Peru mSc and 22°C to 27°C for the Angola mSc. These temperatures approximately bound the domains over which mSc is the prevalent cloud type within each region.

These results imply a potential positive feedback to global warming by marine stratocumulus that ranges from ∼0.14 W m⁻² K⁻¹ to ∼1 W m⁻² K⁻¹, depending on whether or not our results apply to all marine stratocumulus. While these values are uncertain to at least ±50%, the sensitivity of albedo to see surface temperature in the present climate may serve as a useful diagnostic tool in monitoring the performance of global climate models.

Abstract

The distribution of mesoscale precipitation exhibits diverse patterns: precipitation can be intense but sporadic, or it can be light but widespread. This range of behaviors is a reflection of the different weather systems in the global atmosphere. Using MODIS global cloud regimes as proxies for different atmospheric systems, this study investigates the subgrid precipitation properties within these systems. Taking advantage of the high resolution of Integrated Multisatellite Retrievals for GPM (IMERG; GPM is the Global Precipitation Measurement mission), precipitation values at 0.1° are composited with each cloud regime at 1° grid cells to characterize the regime’s spatial subgrid precipitation properties. The results reveal the diversity of the subgrid precipitation behavior of the atmospheric systems. Organized convection is associated with the highest grid-mean precipitation rates and precipitating fraction, although on average only half the grid is precipitating and there is substantial variability between different occurrences. Summer extratropical storms have the next highest precipitation, driven mainly by moderate precipitation rates over large areas. These systems produce more precipitation than isolated convective systems, for which the lower precipitating fractions balance out the high intensities. Most systems produce heavier precipitation in the afternoon than in the morning. The grid-mean precipitation rate is also found to scale with the fraction of precipitation within the grid in a faster-than-linear relationship for most systems. This study elucidates the precipitation properties within cloud regimes, thus advancing our understanding of the precipitation structures of these atmospheric systems.

Abstract

Due to cloud heterogeneity and the nonlinear dependence of albedo on cloud water content, the average albedo of a cloudy scene found by calculating the albedo of independent pixels within the scene tends to be different from the albedo calculated using the average cloud water in the scene. This difference, termed the plane parallel albedo bias (PPH bias), which has previously been estimated from limited case studies, is evaluated here for the first time using an extensive set of Advanced Very High Resolution Radiometer data over oceanic scenes. This dataset yields visible PPH biases that range from 0.02 to 0.30, depending in part on the size of the scene, the viewing–illumination directions, and the assumptions made retrieving cloud optical depths.

The PPH biases increase when atmospheric effects are accounted for but are relatively insensitive to assumptions about cloud microphysics. Due to the limitations of a one-dimensional retrieval, they tend to increase with solar zenith angle and to be larger in the backscattering than the forward scattering direction. Placed in the context of those general circulation models that do not provide subgrid-scale information on cloud amount, these biases are even larger. PPH biases in the broadband-reflected shortwave flux from general circulation models are estimated to exceed 30 W m⁻², typically requiring the introduction of a compensatory bias in the model’s treatment of cloud water content.

The resolution of the satellite sensor and the averaging/sampling of the satellite substantially influences the calculated PPH bias. The authors find a significant drop in albedo bias (∼0.02–0.05) when averaging/sampling original local area coverage (LAC) data to global area coverage (GAC) resolution or when Landsat data were averaged to LAC resolution. These results, along with stochastic simulations of internal LAC pixel variability indicate that the bias discrepancies among variable resolution satellite data are mostly due to the neglect of subpixel cloud fraction, which makes clouds appear thinner than they actually are.

Abstract

Using the same satellite observations as in Part I of this paper, the authors explore ways to remove the cloud albedo bias (or plane parallel albedo bias), the difference between the plane parallel homogeneous albedo and the average albedo of independent pixels, in regions similar in size to climate model grid boxes.

Scaling regional mean optical depths with the reduction factor of R. F. Cahalan et al. provides albedos close to the independent pixel values. Computed albedos approach the independent pixel values within 0.01 for ∼40% of the regions tested and give standard deviations ∼0.02–0.04. Fitting lognormal distributions to the observed optical depth distributions gives albedos within 0.01 of the independent pixel values more than 70% of the time, with standard deviations ∼0.02–0.06. Gamma distributions are less successful than lognormal distributions, giving acceptable results (average bias ∼0.01–0.02, standard deviation ∼0.05–0.08) only when their parameters are estimated from the maximum likelihood estimates method. The poor performance of the gamma distribution when the method of moments is used for parameter estimation (as H. W. Barker et al. did) is attributed to the presence of high optical depth values in our retrieved fields.

To apply any of the above corrections in GCMs, quantities that are not presently provided by these models are required. The reduction factor and “gamma IP” method require the mean logarithm of optical depth, whereas the lognormal method also requires the variance. The authors suggest a parameterization of these quantities in terms of mean optical depth and cloud fraction, variables available in most GCMs. The albedos resulting from the parameterized versions of the correction methods are still much closer to the independent pixel values than the albedos of the plane parallel homogeneous assumption. Although the “lognormal IP” gives the best overall performance, it requires knowledge of two logarithmic moments and numerical integration. It may therefore prove more appealing for observational than modeling applications.

Abstract

No Abstract available.

Abstract

Two full months (July 2003 and January 2004) of Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Level-3 data from the Terra and Aqua satellites are analyzed in order to characterize the horizontal variability of vertically integrated cloud optical thickness (“cloud inhomogeneity”) at global scales. The monthly climatology of cloud inhomogeneity is expressed in terms of standard parameters, initially calculated for each day of the month at spatial scales of 1° × 1° and subsequently averaged at monthly, zonal, and global scales. Geographical, diurnal, and seasonal changes of inhomogeneity parameters are examined separately for liquid and ice phases and separately over land and ocean. It is found that cloud inhomogeneity is overall weaker in summer than in winter. For liquid clouds, it is also consistently weaker for local morning than local afternoon and over land than ocean. Cloud inhomogeneity is comparable for liquid and ice clouds on a global scale, but with stronger spatial and temporal variations for the ice phase, and exhibits an average tendency to be weaker for near-overcast or overcast grid points of both phases. Depending on cloud phase, hemisphere, surface type, season, and time of day, hemispheric means of the inhomogeneity parameter ν (roughly the square of the ratio of optical thickness mean to standard deviation) have a wide range of ∼1.7 to 4, while for the inhomogeneity parameter χ (the ratio of the logarithmic to linear mean) the range is from ∼0.65 to 0.8. The results demonstrate that the MODIS Level-3 dataset is suitable for studying various aspects of cloud inhomogeneity and may prove invaluable for validating future cloud schemes in large-scale models capable of predicting subgrid variability.

Abstract

We present an updated cloud regime (CR) dataset based on Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 cloud products, specifically, joint histograms that partition cloud fraction within distinct combinations of cloud-top pressure and cloud optical thickness ranges. The paper focuses on an edition of the CR dataset derived from our own aggregation of MODIS pixel-level cloud retrievals on an equal-area grid and prespecified 3-h UTC intervals that spatiotemporally match International Satellite Cloud Climatology Project (ISCCP) gridded cloud data. The other edition comes from the 1° daily aggregation provided by standard MODIS Level-3 data, as in previous versions of the MODIS CRs, for easier use with datasets mapped on equal-angle grids. Both editions consist of 11 clusters whose centroids are nearly identical. We provide a physical interpretation of the new CRs and aspects of their climatology that have not been previously examined, such as seasonal and interannual variability of CR frequency of occurrence. We also examine the makeup and precipitation properties of the CRs assisted by independent datasets originating from active observations and provide a first glimpse of how MODIS CRs relate to clouds as seen by ISCCP.

Abstract

The authors present the global plane-parallel shortwave albedo bias of liquid clouds for two months, July 2003 and January 2004. The cloud optical properties necessary to perform the bias calculations come from the operational Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and MODIS Aqua level-3 datasets. These data, along with ancillary surface albedo and atmospheric information consistent with the MODIS retrievals, are inserted into a broadband shortwave radiative transfer model to calculate the fluxes at the atmospheric column boundaries. The plane-parallel homogeneous (PPH) calculations are based on the mean cloud properties, while independent column approximation (ICA) calculations are based either on 1D histograms of optical thickness or joint 2D histograms of optical thickness and effective radius. The (positive) PPH albedo bias is simply the difference between PPH and ICA albedo calculations. Two types of biases are therefore examined: 1) the bias due to the horizontal inhomogeneity of optical thickness alone (the effective radius is set to the grid mean value) and 2) the bias due to simultaneous variations of optical thickness and effective radius as derived from their joint histograms. The authors find that the global bias of albedo (liquid cloud portion of the grid boxes only) is ∼+0.03, which corresponds to roughly 8% of the global liquid cloud albedo and is only modestly sensitive to the inclusion of horizontal effective radius variability and time of day, but depends strongly on season and latitude. This albedo bias translates to ∼3–3.5 W m⁻² of bias (stronger negative values) in the diurnally averaged global shortwave cloud radiative forcing, assuming homogeneous conditions for the fraction of the grid box not covered by liquid clouds; zonal values can be as high as 8 W m⁻². Finally, the (positive) broadband atmospheric absorptance bias is about an order of magnitude smaller than the albedo bias. The substantial magnitude of the PPH bias underlines the importance of predicting subgrid variability in GCMs and accounting for its effects on cloud–radiation interactions.

Abstract

Precipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor’s channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems.

Abstract

The authors propose a new cloud property retrieval technique that accounts for cloud side illumination and shadowing effects present at high solar zenith angles. The technique uses the normalized difference of nadir reflectivities (NDNR) at a conservative and an absorbing (with respect to liquid water) wavelength. It can be further combined with the inverse nonlocal independent pixel approximation (NIPA) of that corrects for radiative smoothing, thus providing a retrieval framework where all 3D cloud effects can potentially be accounted for. The effectiveness of the new technique is demonstrated using Monte Carlo simulations. Real-world application is shown to be feasible using Thematic Mapper (TM) radiance observations from Landsat-5 over the Southern Great Plains (SGP) site of the Atmospheric Radiation Measurement (ARM) Program. For the moderately oblique (45°) solar zenith angle of the available Landsat scene, NDNR gives similar regional statistics and histograms when compared with standard independent pixel approximation (IPA), but significant differences at the pixel level. Inverse NIPA is also applied for the first time on observed high-resolution radiances of overcast Landsat subscenes. The dependence of the NIPA-retrieved cloud fields on the parameters of the method is illustrated and practical issues related to the optimal choice of these parameters are discussed.

It is natural to compare novel cloud retrieval techniques with standard IPA retrievals. IPA is useful in revealing the inadequacy of plane parallel theory in certain situations and in demonstrating sensitivities to parameter choices, parameterizations, and assumptions. For example, it is found that IPA has problems in matching modeled and observed band-7 (2.2 μm) reflectance values for ∼6% of the pixels, most of which are at cloud edges. For simultaneous cloud optical depth–droplet effective radius retrievals (where a conservative and an absorptive TM band are needed), it is found that the band-4 (0.83 μm)–band-7 pair was the most well behaved, having less saturation, smaller changes in nominal calibration, and better overall consistency with modeled values than other bands. Mean values of optical depth, effective radius, and liquid water path (LWP) for typical IPA retrievals using this pair are τ = 22, r _e = 11 μm, and LWP = 157 g m⁻², respectively. Inclusion of aerosol scattering above clouds results in ∼8% decrease in mean cloud optical depth for an aerosol optical depth of 0.2. Degradation of instrument resolution up to ∼2 km has small effects on the optical property means and histograms, suggesting small actual cloud variability at these scales and/or radiative smoothing. Comparisons with surface instruments (microwave radiometer, pyranometer, and pyrgeometer) verify the statisitical adequacy of the IPA retrievals. Last, cloud fractions derived with a simple threshold method are compared with those from an automated procedure called Automatic Cloud Cover Assessment now in operational use for Landsat-7. For the northernmost 2000 scanlines of the scene, the cloud fraction A _c is 0.585 from thresholding, as compared with A _c = 0.563 for the automated procedure, and the full scene values are A _c = 0.870 and A _c = 0.865, respectively. This suggests that the Landsat-7 automated procedure will likely give reliable scene-averaged cloud fractions for moderately thick clouds over continental U.S. scenes similar to SGP.