Albedo Bias Research Articles

A dissection of the surface temperature biases in the Community Earth System Model

Based upon the climate feedback-responses analysis method, a quantitative attribution analysis is conducted for the annual-mean surface temperature biases in the Community Earth System Model version 1 (CESM1). Surface temperature biases are decomposed into partial temperature biases associated with model biases in albedo, water vapor, cloud, sensible/latent heat flux, surface dynamics, and atmospheric dynamics. A globally-averaged cold bias of −1.22 K in CESM1 is largely attributable to albedo bias that accounts for approximately −0.80 K. Over land, albedo bias contributes −1.20 K to the averaged cold bias of −1.45 K. The cold bias over ocean, on the other hand, results from multiple factors including albedo, cloud, oceanic dynamics, and atmospheric dynamics. Bias in the model representation of oceanic dynamics is the primary cause of cold (warm) biases in the Northern (Southern) Hemisphere oceans while surface latent heat flux over oceans always acts to compensate for the overall temperature biases. Albedo bias resulted from the model’s simulation of snow cover and sea ice is the main contributor to temperature biases over high-latitude lands and the Arctic and Antarctic region. Longwave effect of water vapor is responsible for an overall warm (cold) bias in the subtropics (tropics) due to an overestimate (underestimate) of specific humidity in the region. Cloud forcing of temperature biases exhibits large regional variations and the model bias in the simulated ocean mixed layer depth is a key contributor to the partial sea surface temperature biases associated with oceanic dynamics. On a global scale, biases in the model representation of radiative processes account more for surface temperature biases compared to non-radiative, dynamical processes.

NEOWISE STUDIES OF SPECTROPHOTOMETRICALLY CLASSIFIED ASTEROIDS: PRELIMINARY RESULTS

The NEOWISE dataset offers the opportunity to study the variations in albedo for asteroid classification schemes based on visible and near-infrared observations for a large sample of minor planets. We have determined the albedos for nearly 1900 asteroids classified by the Tholen, Bus and Bus-DeMeo taxonomic classification schemes. We find that the S-complex spans a broad range of bright albedos, partially overlapping the low albedo C-complex at small sizes. As expected, the X-complex covers a wide range of albedos. The multi-wavelength infrared coverage provided by NEOWISE allows determination of the reflectivity at 3.4 and 4.6 $\mu$m relative to the visible albedo. The direct computation of the reflectivity at 3.4 and 4.6 $\mu$m enables a new means of comparing the various taxonomic classes. Although C, B, D and T asteroids all have similarly low visible albedos, the D and T types can be distinguished from the C and B types by examining their relative reflectance at 3.4 and 4.6 $\mu$m. All of the albedo distributions are strongly affected by selection biases against small, low albedo objects, as all objects selected for taxonomic classification were chosen according to their visible light brightness. Due to these strong selection biases, we are unable to determine whether or not there are correlations between size, albedo and space weathering. We argue that the current set of classified asteroids makes any such correlations difficult to verify. A sample of taxonomically classified asteroids drawn without significant albedo bias is needed in order to perform such an analysis.

Improved low-cloud simulation from a multiscale modeling framework with a third-order turbulence closure in its cloud-resolving model component

[1] In the original multiscale modeling framework (MMF), the Community Atmosphere Model (CAM3.5) is used as the host general circulation model (GCM), and the System for Atmospheric Modeling model with a first-order turbulence closure is used as the cloud resolving model (CRM) for representing cloud physical processes in each grid column of the GCM. This study introduces an upgrade of the MMF in which the first-order turbulence closure scheme is replaced by an advanced third-order turbulence closure in its CRM component. The results are compared between the upgraded and original MMFs, CAM3.5, and observations. The global distributions of low-level cloud amounts in the subtropics in the upgraded MMF show substantial improvement relative to the original MMF when both are compared with observations. The improved simulation of low-level clouds is attributed not only to the representation of subgrid-scale condensation in the embedded CRM but also is closely related to the increased surface sensible and latent heat fluxes, the increased lower tropospheric stability (LTS), and stronger longwave radiative cooling. Both MMF simulations show close agreement in the vertical structures of cloud amount and liquid water content of midlatitude storm-track clouds and subtropical low-level clouds, compared with observations, with the upgraded MMF being better at simulating the low-level cumulus regime. Since the upgraded MMF produces more subtropical low-level clouds and does not produce an excessive amount of optically thick high-level clouds in either the tropics or midlatitudes as the original MMF does, the global mean albedo decreases. The positive bias in albedo and longwave cloud radiative forcing (CRF) and negative bias in shortwave CRF are reduced in the tropical convective regions.

An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals

Abstract. MODIS Collection 5 retrieved aerosol optical depth (AOD) over land (MOD04/MYD04) was evaluated using 4 years of matching AERONET observations, to assess its suitability for aerosol data assimilation in numerical weather prediction models. Examination of errors revealed important sources of variation in random errors (e.g., atmospheric path length, scattering angle "hot spot"), and systematic biases (e.g., snow and cloud contamination, surface albedo bias). A set of quality assurance (QA) filters was developed to avoid conditions with potential for significant AOD error. An empirical correction for surface boundary condition using the MODIS 16-day albedo product captured 25% of the variability in the site mean bias at low AOD. A correction for regional microphysical bias using the AERONET fine/coarse partitioning information increased the global correlation between MODIS and AERONET from r2 = 0.62–0.65 to r2 = 0.71–0.73. Application of these filters and corrections improved the global fraction of MODIS AOD within (0.05 ± 20%) of AERONET to 77%, up from 67% using only built-in MODIS QA. The compliant fraction in individual regions was improved by as much as 20% (South America). An aggregated Level 3 product for use in a data assimilation system is described, along with a prognostic error model to estimate uncertainties on a per-observation basis. The new filtered and corrected Level 3 product has improved performance over built-in MODIS QA with less than a 15% reduction in overall data available for data assimilation.

Commentary on Wang and Zender—MODIS snow albedo bias at high solar zenith angles relative to theory and to in situ observations in Greenland

The recent paper by Wang and Zender [Wang, X., & Zender, C. S. (2010). MODIS snow albedo bias at high solar zenith angles relative to theory and to in situ observations in Greenland. Remote Sensing of Environment.] draws erroneous conclusions about solar zenith angle biases at high latitudes by not making appropriate use of the extensive quality flags available with the MODIS BRDF/Albedo. Coarse resolution MODIS white-sky albedo data are compared with actual blue-sky field albedometer measurements from the Greenland GC-Net. By utilizing large area averages of the MODIS data product that combine both high quality and poor quality data indiscriminately, the authors erroneously conclude that the accuracy deteriorates for solar zenith angle (SZA) > 55° and often becomes physically unrealistic for SZA > 65°. Once the quality flags are considered, however, the comparisons demonstrate that the MODIS product performs quite well out to the recommended limit for product use of 70° SZA. This verifies the conclusions of an earlier more rigorous evaluation performed by Stroeve et al. [Stroeve, J., Box, J. E., Gao, F., Liang, S., Nolin, A., & Schaaf, C. B. (2005). Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements. Remote Sensing of Environment.]. With over a decade of observations and products now available from the MODIS instrument, these data are increasingly being used to evaluate and tune climate and biogeochemical models. However, such use should take into account the documented quality and limitations of the satellite-derived product.

An Improved Snow Scheme for the ECMWF Land Surface Model: Description and Offline Validation

Abstract A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and temporal scales) includes simulations for several observation sites from the Snow Models Intercomparison Project-2 (SnowMIP2) and global simulations driven by the meteorological forcing from the Global Soil Wetness Project-2 (GSWP2) and by ECMWF Re-Analysis ERA-Interim. The new scheme reduces the end of season ablation biases from 10 to 2 days in open areas and from 21 to 13 days in forest areas. Global GSWP2 results are compared against basin-scale runoff and terrestrial water storage. The new snow density parameterization increases the snow thermal insulation, reducing soil freezing and leading to an improved hydrological cycle. Simulated snow cover fraction is compared against NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) with a reduction of the negative bias of snow-covered area of the original snow scheme. The original snow scheme had a systematic negative bias in surface albedo when compared against Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The new scheme reduces the albedo bias, consequently reducing the spatial- and time-averaged surface net shortwave radiation bias by 5.2 W m−2 in 14% of the Northern Hemisphere land. The new snow scheme described in this paper was introduced in the ECMWF operational forecast system in September 2009 (cycle 35R3).

MODIS snow albedo bias at high solar zenith angles relative to theory and to in situ observations in Greenland

In situ measurements of snow albedo at five stations along a north–south transect in the dry-snow facies of the interior of Greenland follow the theoretically expected dependence of snow albedo with solar zenith angle (SZA). Greenland Climate Network (GC-Net) measurements from 1997 through 2007 exhibit the trend of modest surface brightening with increasing SZA on both diurnal and seasonal timescales. SZA explains up to 50% of seasonal albedo variability. The two other environmental factors considered, temperature and cloudiness, play much less significant roles in seasonal albedo variability at the five stations studied. Compared to the 10-year record of these GC-Net measurements, the five-year record of MODIS satellite-retrieved snow albedo shows a systematic negative bias for SZA larger than about 55°. Larger bias of MODIS snow albedo exists at more northerly stations. MODIS albedos successfully capture the snow albedo dependence on SZA and have a relatively good agreement with GC-Net measurements for SZA < 55°. The discrepancy of MODIS albedo with in situ albedo and with theory is determined mainly by two related factors, SZA and retrieval quality. While the spatiotemporal structure, especially zonal features, of the MODIS-retrieved albedo may be correct for large SZA, the accuracy deteriorates for SZA > 55° and often becomes physically unrealistic for SZA > 65°. This unphysical behavior biases parameterizations of surface albedo and restricts the range of usefulness of the MODIS albedo products. Seasonal-to-interannual trends in surface brightness in Greenland, and in polar (i.e., large SZA) regions in general, and model simulations of these trends, should be evaluated in light of these limitations.

Tripleclouds: An Efficient Method for Representing Horizontal Cloud Inhomogeneity in 1D Radiation Schemes by Using Three Regions at Each Height

Abstract Radiation schemes in general circulation models currently make a number of simplifications when accounting for clouds, one of the most important being the removal of horizontal inhomogeneity. A new scheme is presented that attempts to account for the neglected inhomogeneity by using two regions of cloud in each vertical level of the model as opposed to one. One of these regions is used to represent the optically thinner cloud in the level, and the other represents the optically thicker cloud. So, along with the clear-sky region, the scheme has three regions in each model level and is referred to as “Tripleclouds.” In addition, the scheme has the capability to represent arbitrary vertical overlap between the three regions in pairs of adjacent levels. This scheme is implemented in the Edwards–Slingo radiation code and tested on 250 h of data from 12 different days. The data are derived from cloud retrievals using radar, lidar, and a microwave radiometer at Chilbolton, southern United Kingdom. When the data are grouped into periods equivalent in size to general circulation model grid boxes, the shortwave plane-parallel albedo bias is found to be 8%, while the corresponding bias is found to be less than 1% using Tripleclouds. Similar results are found for the longwave biases. Tripleclouds is then compared to a more conventional method of accounting for inhomogeneity that multiplies optical depths by a constant scaling factor, and Tripleclouds is seen to improve on this method both in terms of top-of-atmosphere radiative flux biases and internal heating rates.

The Plane-Parallel Albedo Bias of Liquid Clouds from MODIS Observations

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.

Effects of spatial and temporal variations in aerosol properties on mean cloud albedo

The mean cloud albedo over a spatial or temporal domain depends not only on the mean cloud condensation nucleus (CCN) spectrum but also on the CCN spectrum variation. When the variation of CCN spectrum is neglected, cloud albedo calculated using the mean CCN spectrum is positively biased. The CCN spectrum variation due to either variation in size distribution or chemical composition, as well as its corresponding effect on mean cloud albedo, was investigated using data collected at Pt. Reyes, California, during the July 2005 Marine Stratus Experiment (MASE). Even when the variation in chemical composition during the entire 28‐day project is neglected, the error in mean cloud albedo is small, and the corresponding error in mean upwelling irradiance is less than 0.5 W/m2. This small and nonsystematic error over such an extended period suggests that for study of mean cloud albedo and upwelling irradiance, the CCN spectrum can be parameterized using the average particle chemical composition or particle activation diameter based on location or air mass type. In contrast, neglecting the variation in aerosol size distribution or CCN spectrum results in positive bias in mean cloud albedo. The bias increases superlinearly with the relative standard deviation of CCN concentration over the domain of interest. On the basis of the MASE data, the average bias in mean upwelling irradiance within grid cells of typical global models ranges 0.4–0.5 W/m2 when only the variation of aerosol size distribution is neglected and increases to 0.5–0.7 W/m2 when the variation of CCN spectrum is neglected. The bias in mean upwelling irradiance can potentially, albeit infrequently, reach 12 W/m2 within grid cells of typical global models if the mean cloud albedo is derived using the mean size distribution or mean CCN spectrum. This suggests accurate evaluation of mean cloud albedo requires the variation of CCN spectrum or aerosol size distribution be taken into consideration, at least for high variability of CCN concentration.

Entrainment-Mixing and Radiative Transfer Simulation in Boundary Layer Clouds

Abstract In general circulation models, clouds are parameterized and radiative transfer calculations are performed using the plane-parallel approximation over the cloudy fraction of each model grid. The albedo bias resulting from the plane-parallel representation of spatially heterogeneous clouds has been extensively studied, but the impact of entrainment-mixing processes on cloud microphysics has been neglected up to now. In this paper, this issue is examined by using large-eddy simulations of stratocumulus clouds and tridimensional calculations of radiative transfer in the visible and near-infrared ranges. Two extreme scenarios of mixing are tested: the homogeneous mixing scheme with constant concentration and reduced droplet sizes, against the inhomogeneous mixing scheme, with reduced concentration and constant droplet sizes. The tests reveal that entrainment-mixing effects at cloud top may substantially bias the simulated albedo. In the worse case, which corresponds to a fragmented and thin stratocumulus cloud, the albedo bias changes from −3% to −31% when using both mixing schemes alternatively.

Coupling of water vapor convergence, clouds, precipitation, and land‐surface processes

On daily timescales, the climate over land is a complex balance of many coupled processes. ERA40 reanalysis data for subbasins of the Mississippi in summer are used to explore the links between these processes in a fully coupled model system, and observed surface precipitation and surface short‐wave fluxes derived by the International Satellite Cloud Climatology Project are used for evaluation. This paper proposes that the effective cloud albedo viewed from the surface is a useful link which connects the cloud fields to both surface and large‐scale processes. The reanalysis has a low bias in cloud albedo in all seasons except summer. In the coupled system in the warm season, on daily timescales, the lifting condensation level falls as soil moisture and precipitation increase. The ratio of the cloud short‐wave radiative forcing at the surface to the diabatic precipitation heating of the atmosphere is less in the reanalysis than in the observations. The surface energy budget is split into the surface net radiation and the evaporative fraction. The surface cloud radiative forcing largely determines the surface net radiation, while evaporative fraction, with fixed vegetation, is largely determined by temperature and near‐surface soil moisture.

Analysis of MODIS–MISR calibration differences using surface albedo around AERONET sites and cloud reflectance

MODIS and MISR are two Earth Observing System instruments flown onboard the Terra satellite. Their synergistic use could greatly benefit the broad user community by ensuring a global view of the Earth with high-quality products. A necessary condition for data fusion is radiometric calibration agreement between the two instruments. Earlier studies showed about 3% absolute radiometric difference between MISR and respective MODIS land bands in the visible and near-IR spectrum, which are also used in aerosol and cloud research. This study compared two surface albedo products derived from MODIS and MISR L1B data using the AERONET-based Surface Reflectance Validation Network (ASRVN). The ASRVN shows a positive MISR–MODIS albedo bias of + (0.01–0.03). Cross-sensor calibration inconsistencies were identified as a primary cause of the albedo biases. To establish an independent MODIS–MISR calibration link, top-of-atmosphere MODIS and MISR reflectances were regressed against each other over liquid water clouds. The empirical regression results have been adjusted for the differences in the respective MISR and MODIS spectral responses using radiative transfer simulations. The MISR–MODIS band gain differences for the top-of-atmosphere reflectance estimated with this technique are + 6.0% in the Blue, + 3.3% in the Green, + 2.7% in the Red, and + 0.8% in the NIR band. Applying the derived values to rescale the MODIS or MISR L1B data is shown to significantly reduce the cross-sensor ASRVN surface albedo biases. An absolute calibration scale for both sensors could be established based on independent ground-based measurements of the surface albedo at selected AERONET sites.

Evaluation of surface albedo and snow cover in AR4 coupled climate models

Surface albedo (ALB), snow cover fraction (SCF) and snow water equivalent (SWE) of state‐of‐the‐art coupled climate models are compared and validated against ground‐based and remote‐sensed climatologies. Most IPCC AR4 climate models predict excessive snow mass in spring and suffer from a delayed spring snow melt while the onset of the snow accumulation is generally well captured. This positive SWE bias is mainly caused by too heavy snowfall during the winter and spring season. Seasonal cycles of snow cover area (SCA) at continental scales are captured reasonably well by most participating models. Two models clearly overestimate SCA over both Eurasia and North America. Year‐to‐year variations are reasonably well captured over both Eurasia and North America in winter and spring. The most pronounced underestimation in the interannual SCA variability is generally simulated during snow melt. The pronounced negative SCA trend that has been observed from 1979 to 2000 is only partly reproduced in the AR4 model simulations. Furthermore, the computed trends show a large spread among the models. Results from time slice simulations with the ECHAM5 climate model suggest that accurate sea surface temperatures are vital for correctly predicting SCA trends. Simulated global mean annual surface albedos are slightly above the remote‐sensed surface albedo estimates. The participating AR4 models generally reproduce the seasonal cycle of the surface albedo with sufficient accuracy while systematic albedo biases are predicted over both snow‐free and snow‐covered areas, with the latter being distinctly more pronounced. The study shows that the surface albedo over snow‐covered forests is probably too high in various state‐of‐the‐art global climate models. The analysis demonstrates that positive biases in SCA are not necessarily related to positive albedo biases. Furthermore, an overestimation of area‐averaged SWEs is not necessarily related to positive SCA anomalies since the relationship between SWE and SCF is highly nonlinear.

Assessment of Snow Cover and Surface Albedo in the ECHAM5 General Circulation Model

AbstractLand surface albedo, snow cover fraction (SCF), and snow depth (SD) from two versions of the ECHAM climate model are compared to available ground-based and remote-sensed climatologies.ECHAM5 accurately reproduces the annual cycle of SD and correctly captures the timing of the snowmelt. ECHAM4, in contrast, simulates an excessive Eurasian snow mass in spring due to a delayed snowmelt. Annual cycles of continental snow cover area (SCA) are captured fairly well in both ECHAM4 and ECHAM5. The negative SCA trend observed during the last two decades of the twentieth century is evident also in the ECHAM5 simulation but less pronounced. ECHAM5 captures the interannual variability of SCA reasonably well, which is in contrast with results that were reported earlier for second-phase Atmospheric Model Intercomparison Project (AMIP II) models. An error analysis revealed that, for studies on SCA, it is essential to test the data records for their homogeneity and trends.The second part of the paper compares simulated surface albedos with remote-sensed climatologies derived from PINKER and the Moderate Resolution Imaging Spectroradiometer (MODIS). ECHAM5 is in better agreement with observations in the Himalayan–Tibetan area than ECHAM4. In contrast, the positive surface albedo bias over boreal forests under snow conditions in ECHAM4 is even more pronounced in ECHAM5. This deficiency is mainly due to the neglect of the snow-masking effect of stems and branches after trees have lost their foliage.The analysis demonstrates that positive biases in the SCA are not necessarily related to positive albedo biases. Furthermore, an overestimation of the area-averaged SD is not always related to positive SCF anomalies since the relationship between SD and SCF is highly nonlinear.

Impact of Cloud Cover on Solar Radiative Biases in Deep Convective Regimes

Abstract Conflicting claims have been made concerning the magnitude of the bias in solar radiative transfer calculations when horizontal photon transport is neglected for deep convective scenarios. The difficulty of obtaining a realistic set of cloud scenes for situations of complex cloud geometry, while certain characteristics such as total cloud cover are systematically controlled, has hindered the attempt to reach a consensus. Here, a simple alternative approach is adopted. An ensemble of cloud scenes generated by a cloud resolving model are modified by an idealized function that progressively alters the cirrus anvil coverage without affecting the realism of the scene produced. Comparing three-dimensional radiative calculations with the independent column approximation for all cloud scenes, it is found that the bias in scene albedo can reach as much as 22% when the sun is overhead and 46% at low sun angles. The bias is an asymmetrical function of cloud cover with a maximum attained at cirrus anvil cloud cover of approximately 30%–40%. With a cloud cover of 15%, the bias is half its maximum value, while it is limited for coverage exceeding 80%. The position of the peak occurs at the cloud cover coinciding with the maximum number of independent clouds present in the scene. Increasing the cloud cover past this point produces a decrease in the number of isolated clouds because of cloud merging, with a consequential bias reduction. With this systematic documentation of the biases as a function of total cloud cover, it is possible to identify two contributions to the total error: the geometrical consequences of the effective cloud cover increase at low sun angles and the true 3D scattering effect of photons deviating from the original path direction. An attempt to account for the former geometrical contribution to the 1D bias is made by performing a simple correction technique, whereby the field is sheared by the tangent of the solar zenith angle. It is found that this greatly reduces the 1D biases at low sun angles. Because of the small aspect ratio of the cirrus cloud deck, the remaining bias contribution is small in magnitude and almost independent of solar zenith angle.

Top‐of‐atmosphere shortwave broadband observed radiance and estimated irradiance over polar regions from Clouds and the Earth's Radiant Energy System (CERES) instruments on Terra

Empirical angular distribution models for estimating top‐of‐atmosphere shortwave irradiances from radiance measurements over permanent snow, fresh snow, and sea ice are developed using CERES measurements on Terra. Permanent snow angular distribution models depend on cloud fraction, cloud optical thickness, and snow brightness. Fresh snow and sea ice angular distribution models depend on snow and sea ice fraction, cloud fraction, cloud optical thickness, and snow and ice brightness. These classifications lead to 10 scene types for permanent snow and 25 scene types for fresh snow and sea ice. The average radiance over clear‐sky permanent snow is more isotropic with satellite viewing geometry than that over overcast permanent snow. On average, the albedo of clear‐sky permanent snow varies from 0.65 to 0.68 for solar zenith angles between 60° and 80°, while the corresponding albedo of overcast scenes varies from 0.70 to 0.73. Clear‐sky permanent snow albedos over Antarctica estimated from two independent angular distribution models are consistent to within 0.6%, on average. Despite significant variability in sea ice optical properties with season, the estimated mean relative albedo error is −1.0% for very dark sea ice and 0.1% for very bright sea ice when albedos derived from different viewing angles are averaged. The estimated regional root‐mean‐square (RMS) relative albedo error is 5.6% and 2.6% when the sea ice angular distribution models are applied to a region that contains very dark and very bright sea ice, respectively. Similarly, the estimated relative albedo bias error for fresh snow is −0.1% for very dark snow scenes and 0.1% for very bright snow scenes. The estimated regional RMS relative albedo error is 3.5% and 5.0% when angular distribution models are applied to a region that contains very dark and very bright fresh snow, respectively. These error estimates are only due to angular distribution model error and do not include the error caused by scene identification.

Computation of Domain-Averaged Irradiance Using Satellite-Derived Cloud Properties

Abstract The respective errors caused by the gamma-weighted two-stream approximation and the effective thickness approximation for computing the domain-averaged broadband shortwave irradiance are evaluated using cloud optical thicknesses derived from 1 h of radiance measurements by the Moderate Resolution Imaging Spectrometer (MODIS) over footprints of Clouds and the Earth’s Radiant Energy System (CERES) instruments. Domains are CERES footprints of which dimension varies approximately from 20 to 70 km, depending on the viewing zenith angle of the instruments. The average error in the top-of-atmosphere irradiance at a 30° solar zenith angle caused by the gamma-weighted two-stream approximation is 6.1 W m−2 (0.005 albedo bias) with a one-layer overcast cloud where a positive value indicates an overestimate by the approximation compared with the irradiance computed using the independent column approximation. Approximately one-half of the error is due to deviations of optical thickness distributions from a gamma distribution and the other half of the error is due to other approximations in the model. The error increases to 14.7 W m−2 (0.012 albedo bias) when the computational layer dividing the cloud layer is increased to four. The increase is because of difficulties in treating the correlation of cloud properties in the vertical direction. Because the optical thickness under partly cloudy conditions, which contribute two-thirds of cloudy footprints, is smaller, the error is smaller than under overcast conditions; the average error for partly cloudy condition is −2.4 W m−2 (−0.002 albedo bias) at a 30° solar zenith angle. The corresponding average error caused by the effective thickness approximation is 0.5 W m−2 for overcast conditions and −21.5 W m−2 (−0.018 albedo bias) for partly cloudy conditions. Although the error caused by the effective thickness approximation depends strongly on the optical thickness, its average error under overcast conditions is smaller than the error caused by the gamma-weighted two-stream approximation because the errors at small and large optical thicknesses cancel each other. Based on these error analyses, the daily average error caused by the gamma-weighted two-stream and effective thickness approximations is less than 2 W m−2.

Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements

The accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day albedo product (MOD43) is assessed using ground-based albedo observations from automatic weather stations (AWS) over spatially homogeneous snow and semihomogeneous ice-covered surfaces on the Greenland ice sheet. Data from 16 AWS locations, spanning the years 2000–2003, were used for this assessment. In situ reflected shortwave data were corrected for a systematic positive spectral sensitivity bias of between 0.01 and 0.09 on a site-by-site basis using precise optical black radiometer data. Results indicate that the MOD43 albedo product retrieves snow albedo with an average root mean square error (RMSE) of ±0.07 as compared to the station measurements, which have ±0.035 RMSE uncertainty. If we eliminate all satellite retrievals that rely on the backup algorithm and consider only the highest quality results from the primary bidirectional reflectance distribution function (BRDF) algorithm, the MODIS albedo RMSE is ±0.04, slightly larger than the in situ measurement uncertainty. There is general agreement between MODIS and in situ observations for albedo <0.7, while near the upper limit, a −0.05 MODIS albedo bias is evident from the scatter of the 16-site composite.

Use of Moderate‐Resolution Imaging Spectroradiometer bidirectional reflectance distribution function products to enhance simulated surface albedos

Moderate‐Resolution Imaging Spectroradiometer (MODIS) surface albedo at high spatial and spectral resolution is compared with other remotely sensed climatologies, ground‐based data, and albedos simulated with the European Center/Hamburg 4 (ECHAM4) global climate model at T42 resolution. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterizations in weather forecast and climate models. The remotely sensed PINKER surface albedo climatology follows the MODIS estimates fairly well in both the visible and near‐infrared spectra, whereas ECHAM4 simulates high positive albedo biases over snow‐covered boreal forests and the Himalayas. In contrast, the ECHAM4 albedo is probably too low over the Sahara sand desert and adjacent steppes. The study clearly indicates that neglecting albedo variations within T42 grid boxes leads to significant errors in the simulated regional climate and horizontal fluxes, mainly in mountainous and/or snow‐covered regions. MODIS surface albedo at 0.05 resolution agrees quite well with in situ field measurements collected at Baseline Surface Radiation Network (BSRN) sites during snow‐free periods, while significant positive biases are found under snow‐covered conditions, mainly due to differences in the vegetation cover at the BSRN site (short grass) and the vegetation within the larger MODIS grid box. Black sky (direct beam) albedo from the MODIS bidirectional reflectance distribution function model captures the diurnal albedo cycle at BSRN sites with sufficient accuracy. The greatest negative biases are generally found when the Sun is low. A realistic approach for relating albedo and zenith angle has been proposed. Detailed evaluations have demonstrated that ignoring the zenith angle dependence may lead to significant errors in the surface energy balance.