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Norman G. Loeb
and
Natividad Manalo-Smith

Abstract

The direct radiative effect of aerosols (DREA) is defined as the difference between radiative fluxes in the absence and presence of aerosols. In this study, the direct radiative effect of aerosols is estimated for 46 months (March 2000–December 2003) of merged Clouds and the Earth’s Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra global measurements over ocean. This analysis includes the contribution from clear regions in both clear and partly cloudy CERES footprints. MODIS–CERES narrow-to-broadband regressions are developed to convert clear-sky MODIS narrowband radiances to broadband shortwave (SW) radiances, and CERES clear-sky angular distribution models (ADMs) are used to estimate the corresponding top-of-atmosphere (TOA) radiative fluxes that are needed to determine the DREA. Clear-sky MODIS pixels are identified using two independent cloud masks: (i) the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) algorithm that is used for inferring aerosol properties from MODIS on the CERES Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product (NOAA SSF); and (ii) the standard algorithm that is used by the MODIS aerosol group to produce the MODIS aerosol product (MOD04). Over global oceans, direct radiative cooling by aerosols for clear scenes that are identified from MOD04 is estimated to be 40% larger than for clear scenes from NOAA SSF (5.5 compared to 3.8 W m−2). Regionally, differences are largest in areas that are affected by dust aerosol, such as oceanic regions that are adjacent to the Sahara and Saudi Arabian deserts, and in northern Pacific Ocean regions that are influenced by dust transported from Asia. The net total-sky (clear and cloudy) DREA is negative (cooling) and is estimated to be −2.0 W m−2 from MOD04, and −1.6 W m−2 from NOAA SSF. The DREA is shown to have pronounced seasonal cycles in the Northern Hemisphere and large year-to-year fluctuations near deserts. However, no systematic trend in deseasonalized anomalies of the DREA is observed over the 46-month time series that is considered.

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Patrick C. Taylor
and
Norman G. Loeb

Abstract

Satellite observations of the earth’s radiation budget (ERB) are a critical component of the climate observing system. Recent observations have been made from sun-synchronous orbits, which provide excellent spatial coverage with global measurements twice daily but do not resolve the full diurnal cycle. Previous investigations show that significant errors can occur in time-averaged energy budgets from sun-synchronous orbits if diurnal variations are ignored. However, the impact of incomplete diurnal sampling on top-of-atmosphere (TOA) flux variability and trends has received less attention. A total of 68 months of 3-hourly tropical outgoing longwave radiation (OLR) and reflected shortwave radiation (RSW) fluxes from the Clouds and the Earth’s Radiant Energy System (CERES) synoptic (SYN) data product is used to examine the impact of incomplete diurnal sampling on TOA flux variability. Tropical OLR and RSW interannual variability and trends derived from sun-synchronous time sampling consistent with the Terra satellite from 2000 to 2005 show no statistically significant differences at the 95% confidence level with those obtained at 3-hourly time sampling at both 1° × 1° and 10° × 10° regional scales, as well as for tropical means. Monthly, 3-hourly OLR composite anomalies are decomposed into diurnally uniform and diurnal cycle shape change contributions to explain the impact of sampling on observed TOA flux variability. Diurnally uniform contributions to OLR variability account for more than 80% of interannual OLR variability at 1° × 1° spatial scales. Diurnal cycle shape variations are most important in equatorial land regions, contributing up to 50% to OLR variability over Africa. At spatial scales of 10° × 10° or larger, OLR variance contributions from diurnal cycle shape changes remain smaller than 20%.

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Norman G. Loeb
and
Wenying Su

Abstract

To provide a lower bound for the uncertainty in measurement-based clear- and all-sky direct aerosol radiative forcing (DARF), a radiative perturbation analysis is performed for the ideal case in which the perturbations in global mean aerosol properties are given by published values of systematic uncertainty in Aerosol Robotic Network (AERONET) aerosol measurements. DARF calculations for base-state climatological cloud and aerosol properties over ocean and land are performed, and then repeated after perturbing individual aerosol optical properties (aerosol optical depth, single-scattering albedo, asymmetry parameter, scale height, and anthropogenic fraction) from their base values, keeping all other parameters fixed. The total DARF uncertainty from all aerosol parameters combined is 0.5–1.0 W m−2, a factor of 2–4 greater than the value cited in the Intergovernmental Panel on Climate Change’s (IPCC’s) Fourth Assessment Report. Most of the total DARF uncertainty in this analysis is associated with single-scattering albedo uncertainty. Owing to the greater sensitivity to single-scattering albedo in cloudy columns, DARF uncertainty in all-sky conditions is greater than in clear-sky conditions, even though the global mean clear-sky DARF is more than twice as large as the all-sky DARF.

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Konstantin Loukachine
and
Norman G. Loeb

Abstract

The Clouds and the Earth's Radiant Energy System (CERES) provides top-of-atmosphere (TOA) radiative flux estimates from shortwave (SW) and longwave (LW) radiance measurements by applying empirical angular distribution models (ADMs) for scene types defined by coincident high-resolution imager-based cloud retrievals. In this study, CERES ADMs are simulated using a feed-forward error back-propagation (FFEB) artificial neural network (ANN) simulation to provide a means of estimating TOA SW and LW radiative fluxes for different scene types in the absence of imager radiance measurements. In all cases, the ANN-derived TOA fluxes deviate from CERES TOA fluxes by less than 0.3 W m−2, on average, and show a smaller dependence on viewing geometry than TOA fluxes derived using ADMs from the Earth Radiation Budget Experiment (ERBE). The ANN-derived TOA SW and LW fluxes show a significant improvement in accuracy over the CERES ERBE-like fluxes when compared regionally.

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Norman G. Loeb
and
Seiji Kato

Abstract

Nine months of the Clouds and the Earth's Radiant Energy System (CERES)/Tropical Rainfall Measuring Mission (TRMM) broadband fluxes combined with the TRMM visible infrared scanner (VIRS) high-resolution imager measurements are used to estimate the daily average direct radiative effect of aerosols for clear-sky conditions over the tropical oceans. On average, aerosols have a cooling effect over the Tropics of 4.6 ± 1 W m–2. The magnitude is ≈2 W m–2 smaller over the southern tropical oceans than it is over northern tropical oceans. The direct effect derived from CERES is highly correlated with coincident aerosol optical depth (τ) retrievals inferred from 0.63-μm VIRS radiances (correlation coefficient of 0.96). The slope of the regression line is ≈−32 W m–2 τ –1 over the equatorial Pacific Ocean, but changes both regionally and seasonally, depending on the aerosol characteristics. Near sources of biomass burning and desert dust, the aerosol direct effect reaches −25 to −30 W m–2. The direct effect from CERES also shows a dependence on wind speed. The reason for this dependence is unclear—it may be due to increased aerosol (e.g., sea-salt or aerosol transport) or increased surface reflection (e.g., due to whitecaps). The uncertainty in the tropical average direct effect from CERES is ≈1 W m–2 (≈20%) due mainly to cloud contamination, the radiance-to-flux conversion, and instrument calibration. By comparison, uncertainties in the direct effect from the Earth Radiation Budget Experiment (ERBE) and CERES “ERBE-like” products are a factor of 3–5 times larger.

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Wuyin Lin
,
Minghua Zhang
, and
Norman G. Loeb

Abstract

Marine boundary layer (MBL) clouds can significantly regulate the sensitivity of climate models, yet they are currently poorly simulated. This study aims to characterize the seasonal variations of physical properties of these clouds and their associated processes by using multisatellite data. Measurements from several independent satellite datasets [International Satellite Cloud Climatology Project (ISCCP), Clouds and the Earth’s Radiant Energy System–Moderate Resolution Imaging Spectroradiometer (CERES–MODIS), Geoscience Laser Altimeter System (GLAS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)], in conjunction with balloon soundings from the mobile facility of the Atmospheric Radiation Measurement (ARM) program at Point Reyes and reanalysis products, are used to characterize the seasonal variations of MBL cloud-top and cloud-base heights, cloud thickness, the degree of decoupling between clouds and MBL, and inversion strength off the California coast.

The main results from this study are as follows: (i) MBL clouds over the northeast subtropical Pacific in the summer are more prevalent and associated with a larger in-cloud water path than in winter. The cloud-top and cloud-base heights are lower in the summer than in the winter. (ii) Although the lower-tropospheric stability of the atmosphere is higher in the summer, the MBL inversion strength is only weakly stronger in the summer because of a negative feedback from the cloud-top altitude. Summertime MBL clouds are more homogeneous and are associated with lower surface latent heat flux than those in the winter. (iii) Seasonal variations of low-cloud properties from summer to winter resemble the downstream stratocumulus-to-cumulus transition of MBL clouds in terms of MBL depth, cloud-top and cloud-base heights, inversion strength, and spatial homogeneity. The “deepening–warming” mechanism of Bretherton and Wyant for the stratocumulus-to-trade-cumulus transition downstream of the cold eastern ocean can also explain the seasonal variation of low clouds from the summer to the winter, except that warming of the sea surface temperature needs to be taken as relative to the free-tropospheric air temperature, which occurs in the winter. The observed variation of low clouds from summer to winter is attributed to the much larger seasonal cooling of the free-tropospheric air temperature than that of the sea surface temperature.

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Xianglei Huang
,
Norman G. Loeb
, and
Huiwen Chuang

Abstract

Clouds and the Earth’s Radiant Energy System (CERES) daytime longwave (LW) radiances are determined from the difference between a total (TOT) channel (0.3–200 μm) measurement and a shortwave (SW) channel (0.3–5 μm) measurement, while nighttime LW radiances are obtained directly from the TOT channel. This means that a drift in the SW channel or the SW portion of the TOT channel could impact the daytime longwave radiances, but not the nighttime ones. This study evaluates daytime and nighttime CERES LW radiances for a possible secular drift in CERES LW observations using spectral radiances observed by Atmospheric Infrared Sounder (AIRS). By examining the coincidental AIRS and CERES Flight Model 3 (FM3) measurements over the tropical clear-sky oceans for all of January and July months since 2005, a secular drift of about −0.11% yr−1 in the daytime CERES-FM3 longwave unfiltered radiance can be identified in the CERES Single Scanner Footprint (SSF) Edition 2 product. This provides an upper-bound estimation for the drift in daytime outgoing longwave radiation, which is approximately −0.323 W m−2 yr−1. This estimation is consistent with the independent assessment concluded by the CERES calibration team. Such secular drift has been greatly reduced in the latest CERES SSF Edition 3 product. Comparisons are conducted for the CERES window channel as well, and it shows essentially no drift. This study serves as a practical example illustrating how the measurements of spectrally resolved radiances can be used to help evaluate data products from other narrowband or broadband measurements.

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Norman G. Loeb
and
J. A. Coakley Jr.

Abstract

The validity of plane-parallel (1D) radiative transfer theory for cloudy atmospheres is examined by directly comparing calculated and observed visible reflectances for one month of Global Area Coverage Advanced Very High Resolution Radiometer satellite observations of marine stratus cloud layers off the coasts of California, Peru, and Angola. Marine stratus are an excellent testbed, as they arguably are the closest to plane-parallel found in nature. Optical depths in a 1D radiative transfer model are adjusted so that 1D model reflectances match those observed at nadir on a pixel-by-pixel basis. The 1D cloud optical depth distributions are then used in the plane-parallel model to generate reflectance distributions for different sun–earth–satellite viewing geometries. These reflectance distributions are directly compared with the observations. Separate analyses are performed for overcast and broken cloud layers as identified by the spatial coherence method.

When 1D reflectances are directly compared with observations at different view angles, relative differences are generally small (≲10%) in the backscattering direction for solar zenith angles ≲60° and show no systematic view angle dependence. In contrast, 1D reflectances increase much more rapidly with view angle than the observed reflectances in the forward-scattering direction. Relative differences in the forward-scattering direction are ≈2–3 times larger than in the backscattering direction. At solar zenith angles ≳60°, the 1D model underestimates observed reflectances at nadir by 20%–30% and overestimates reflectances at the most oblique view angles in the forward scattering direction by 15%–20%. Consequently, when inferred on a pixel-by-pixel basis, nadir-derived cloud optical depths show a systematic increase with solar zenith angle, both for overcast and broken cloud layers, and cloud optical depths decrease with view angle in the forward scattering direction. Interestingly, in the case of broken marine stratocumulus, the common practice of assuming that pixels are overcast when they are not mitigates this bias to some extent, thereby confounding its detection. But even for broken clouds, the bias remains.

Because of the nonlinear dependence of cloud albedo on cloud optical depth, errors in cloud optical depth lead to large errors in cloud albedo—and therefore energy budget calculations—regardless of whether cloud layers are overcast or broken. These findings suggest that as a minimum requirement, direct application of the plane-parallel model approximation should be restricted to moderate–high sun elevations and to view angles in the backscattering direction. Based on Monte Carlo simulations, the likely reason for the discrepancies between observed radiances and radiances calculated on the basis of 1D theory is because real clouds have inhomogeneous (i.e., bumpy) tops.

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Bijoy Vengasseril Thampi
,
Takmeng Wong
,
Constantin Lukashin
, and
Norman G. Loeb

Abstract

Continuous monitoring of the earth radiation budget (ERB) is critical to the understanding of Earth’s climate and its variability with time. The Clouds and the Earth’s Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is the first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of the modern artificial intelligence (AI) paradigm called machine learning (ML) algorithms. This paper describes the development and application of the ML algorithm known as random forests (RF), which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice, and bright desert surface types, while lower values (<10%) for the forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (<4%) for most surface types after thin cloud data are excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has a substantial influence on determining the classification error.

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Norman G. Loeb
,
Tamás Várnai
, and
David M. Winker

Abstract

Recent observational studies have shown that satellite retrievals of cloud optical depth based on plane-parallel model theory suffer from systematic biases that depend on viewing geometry, even when observations are restricted to overcast marine stratus layers, arguably the closest to plane parallel in nature. At moderate to low sun elevations, the plane-parallel model significantly overestimates the reflectance dependence on view angle in the forward-scattering direction but shows a similar dependence in the backscattering direction. Theoretical simulations are performed that show that the likely cause for this discrepancy is because the plane-parallel model assumption does not account for subpixel-scale variations in cloud-top height (i.e., “cloud bumps”). Monte Carlo simulations comparing 1D model radiances to radiances from overcast cloud fields with 1) cloud-top height variations but constant cloud volume extinction, 2) flat tops but horizontal variations in cloud volume extinction, and 3) variations in both cloud-top height and cloud extinction are performed over a ≈4 km × 4 km domain (roughly the size of an individual GAC AVHRR pixel). The comparisons show that when cloud-top height variations are included, departures from 1D theory are remarkably similar (qualitatively) to those obtained observationally. In contrast, when clouds are assumed flat and only cloud extinction is variable, reflectance differences are much smaller and do not show any view-angle dependence. When both cloud-top height and cloud extinction variations are included, however, large increases in cloud extinction variability can enhance reflectance differences. The reason 3D–1D reflectance differences are more sensitive to cloud-top height variations in the forward-scattering direction (at moderate to low sun elevations) is because photons leaving the cloud field in that direction experience fewer scattering events (low-order scattering) and are restricted to the topmost portions of the cloud. While reflectance deviations from 1D theory are much larger for bumpy clouds than for flat clouds with variable cloud extinction, differences in cloud albedo are comparable for these two cases.

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