Top-of-atmosphere Albedo Research Articles

Long-Term Variations and Its Driving Factors of the TOA Albedo over the Loess Plateau

Abstract Solar radiation balances significantly affect Earth’s surface energy balance and climate change. Studying top-of-the-atmosphere (TOA) albedo changes is of great significance for understanding Earth’s energy budget and atmospheric circulation. The Loess Plateau (LP), located in the middle reaches of the Yellow River in China, is one of the most severely eroded areas in the world. In this paper, long-term remote sensing data were used to analyze the changes in the TOA albedo in the LP from 1982 to 2016. The results showed that the TOA albedo, its atmospheric contribution (AC), and surface contribution (SC) exhibited decreasing trends: −0.0012, −0.0010, and −0.0003 a−1. The spatial pattern of the TOA albedo was similar to AC, which indicates that AC dominates the change in the TOA albedo. We detected driving factors for AC and SC and found that the cloud fraction (CF) was the main driving factor of the AC, whereas the soil moisture (SM) dominated the SC. The driving factors of two typical regions with a significantly decreasing trend in the TOA albedo were also detected. The Mu Us Desert, where vegetation improved significantly, showed a decreasing trend in the TOA albedo, and we found that NDVI was the main driving factor for the change in the SC of the TOA albedo. However, the Eastern Qilian Mountains, where snow cover decreased in recent years, also showed a significant decreasing trend in the TOA albedo; the SC here was mainly driven by the changes in snow cover days (SCD). These results indicate that changes in the surface environment alter the radiation balance. Significance Statement The Loess Plateau in China is one of the most severe cases of soil erosion in the world, and ecological restoration projects have been carried out to recover the fragile ecological environment. Our study was designed to explore changes in the top-of-the-atmosphere (TOA) albedo of the Loess Plateau between 1982 and 2016 using a long time series of multisource satellite products, and driving factors in the atmosphere and at the surface were analyzed. We concluded that the TOA albedo of the Loess Plateau decreased over 35 years, and its atmospheric contribution dominated the change in the TOA albedo. However, the significant ecological improvement in the Loess Plateau, especially in the central vegetation recovery region, such as the Mu Us Desert, was also strongly related to the regional changes in the surface contribution of the TOA albedo. The climate changes had a considerable impact on the eastern branch of the Qilian Mountains in the Qinghai region, where the decline in snow cover days affected the local Alpine meadow ecosystems; therefore, snow cover days also played a decisive role in the local variation of the surface contribution of the TOA albedo.

All-sky surface and top-of-atmosphere shortwave radiation components estimation: Surface shortwave radiation, PAR, UV radiation, and TOA albedo

Surface and top-of-atmosphere (TOA) shortwave radiation components are key parameters in the energy budget of the Earth-atmosphere system, which influence the global climate, ecology, hydrology, etc. However, except for total surface shortwave downward radiation (SWDR), only a few satellite missions consistently release other radiation components such as solar direct/diffuse radiation, photosynthetically active radiation (PAR), ultraviolet radiation-A/B (UVA/UVB), as well as TOA albedo, although they are indispensable for many land and ocean applications. In this study, a unified framework by combining multi-band LUT inversion and classification of the atmospheric condition is proposed, which enables the simultaneous derivation of nine surface and TOA radiation variables and emphasizes the separation of direct and diffuse components. The estimated radiation variables include total and direct components of SWDR, PAR, UVA, UVB, as well as TOA albedo. The proposed method is easy-to-use with only a few inputs and works well with reasonable accuracy. Using Moderate Resolution Imaging Spectroradiometer (MODIS) raw resolution images as test data, the surface radiation variables are validated by 80 global sites from BSRN, SURFRAD, and FLUXNET, and the instantaneous RMSEs (biases) of SWDR, SWDRdir, PAR, PARdiff, and UVB are 103.6 (0.1), 114 (−1.7), 47.8 (6.8), 32.9 (5.7), and 0.14 (0.003) W/m2, respectively, demonstrating comparable or even better accuracy compared with existing products. In particular, the estimated SWDR behaves more accurate than CERES in polar regions. Due to the lack of in-situ measurements, TOA albedo is compared with CERES TOA products and shows good agreement with R2 of 0.9 and RMSE (bias) of 0.055 (−0.003). The unified framework reveals obvious advantages over existing studies in generating almost all physically consistent shortwave components in the same manner with simple inputs, implying the great potentials in globally mapping spatio-temporally continuous multiple components of shortwave radiation with a unified scheme.

Long-term trends in albedo as seen from a lunar observatory

The Earth’s albedo is the fraction of short-wave solar radiation that is reflected back to space, and is key to understand the observed trends in climate change. Although there have been many observational approaches to estimate the Earth’s albedo, different methods have their own drawbacks, for example, artificial satellites have a finite life time, and are operating in a hard environment that could quickly degrade the detectors. The Moon, the only natural satellite of Earth, offers an additional platform for monitoring the Earth’s albedo. In this paper, we calculate the global TOA (top-of-atmosphere) albedo that would be observed by a theoretical observatory on the Moon, and compare it with long-term trends derived from CERES (Clouds and the Earth’s Radiant Energy System, 2000–2020) and earthshine data (1999–2017). We find that the global hourly mean albedo observed in the direction of the Moon is more variable because of the orbital movement of the Moon. However, the regional and long-term global mean albedo anomalies that would be observed by CERES and has a good general agreement by a lunar observer over 20 years the data spans. Similarly, earthshine data show a steady decline during two decades of about 0.7 W/m2 which is in line with the decline of 0.5 W/m2 that would be observed from the Moon. Besides, to compare the regional changes of albedo with CERES, the spatially-resolved decadal anomalies in the albedo are also calculated for analysis. We find that a lunar-based observatory would be capable of detecting similar changes seen from CERES. Thus, it is a a practical option to monitor the long-term trends in albedo from the point of view of capturing the variability in the TOA fluxes of the Earth’s atmosphere.

Satellite pose estimation using Earth radiation modeled by artificial neural networks

The thermal energy received by each surface of an Earth-orbiting satellite strongly depends on its position and orientation. In this sense, simultaneous orbit and attitude estimation (SOAE) using the surface temperature data has been focused in the present study. The Earth infrared (IR) radiation and the Earth’s top-of-atmosphere (TOA) albedo are two key sources of radiation affecting the satellite surface temperature rate. The Earth’s radiation information has been monitored for the past two decades by the Clouds and the Earth’s Radiant Energy System (CERES) project, producing a comprehensive set of Earth radiation budget (ERB) data for climate, weather and applied science research. The current study utilizes the ERB data to forecast the IR radiation flux (IRF) and the TOA albedo factor (AF) via an artificial neural network (ANN). In this respect, the satellite longitude, latitude and time constitute the input vector of the learning set, while the AF and IRF are considered as the output. The ANN is then employed to model the satellite surface temperature rate in the radiation-based SOAE process. The feasibility of the proposed pose estimation approach has been addressed and verified via Monte Carlo simulation. Obtained results demonstrate suitability of the algorithm in sunlight intervals with good accuracy.

A machine learning method trained by radiative transfer model inversion for generating seven global land and atmospheric estimates from VIIRS top-of-atmosphere observations

The Visible Infrared Imaging Radiometer Suite (VIIRS) has observed the Earth since 2011 and will continue for several decades. Unfortunately, few high-level land surface products have been produced and publicly released. The numerical inversion of radiative transfer model (RTM) has long been used for the retrieval of land surface and atmospheric variables from satellite data; however, it is computationally intensive. Some methods (e.g., using look-up tables) are efficient but are most suitable for estimating individual variable. Various machine learning (ML) models have been developed, mostly trained by either ground measurements that may not be adequate to represent different conditions, or RTM simulations that may not be realistic due to model uncertainty and unnecessary combinations of illumination-viewing geometries and variable values. In this study, we replaced the numerical inversion scheme by the ML algorithm that is trained using RTM inversion for estimating multiple variables simultaneously. Our training dataset for the ML models was generated by inverting the land and atmospheric variables from VIIRS top-of-atmosphere (TOA) reflectance data using a coupled land-surface–atmosphere RT model. Two multi-output ML algorithms were explored: backpropagation neural networks and random forest (RF) regression. The best-performing RF model was then applied to estimate seven land and atmospheric variables globally from VIIRS TOA data: the leaf area index (LAI), incident photosynthetically active radiation (PAR), fraction of absorbed photosynthetically active radiation (FAPAR), incident shortwave radiation (ISR), land surface albedo, land surface reflectance, and TOA albedo. The inversion results were validated using ground measurements at 54 sites and showed comparable accuracy to the numerical inversion scheme that is suitable for only small regions. An experiment was also conducted to test this method on a much larger region for detailed evaluation. Finally, seven global estimates in 2013 were experimentally produced. Inter-comparison with the existing satellite products showed that the VIIRS estimates had high consistency with the LAI and FAPAR products of the Global LAnd Surface Satellite (GLASS) products suite. Moreover, the VIIRS officially released the VNP43MA3 albedo and VNP09A1 surface reflectance products, the retrieved estimates also matched them well. It was also shown that simultaneous estimation of all seven variables was more accurate and efficient than estimating individual variable sequentially.

EOS: Atmospheric Radiative Transfer in Habitable Worlds with HELIOS

Abstract We present EOS, a procedure for determining the outgoing longwave radiation (OLR) and top-of-atmosphere (TOA) albedo for a wide range of conditions expected to be present in the atmospheres of rocky planets with temperate conditions. EOS is based on HELIOS and HELIOS-K, which are novel and publicly available atmospheric radiative transfer (RT) codes optimized for fast calculations with GPU processors. These codes were originally developed for the study of giant planets. In this paper we present an adaptation for applications to terrestrial-type, habitable planets, adding specific physical recipes for the gas opacity and vertical structure of the atmosphere. To test the reliability of the procedure, we assessed the impact of changing line opacity profile, continuum opacity model, atmospheric lapse rate, and tropopause position prescriptions on the OLR and the TOA albedo. The results obtained with EOS are in line with those of other RT codes running on traditional CPU processors, while being at least one order of magnitude faster. The adoption of OLR and TOA albedo data generated with EOS in a zonal and seasonal climate model correctly reproduces the fluxes of the present-day Earth measured by the CERES spacecraft. The results of this study disclose the possibility to incorporate fast RT calculations in climate models aimed at characterizing the atmospheres of habitable exoplanets.

Top-of-Atmosphere Clear-Sky Albedo Estimation Over Ocean: Preliminary Framework for MODIS

Top-of-atmosphere (TOA) albedo is a significant factor of earth energy budget, climate change, and environmental change. As tremendous regional and global changes are happening over ocean, more details are needed to monitor the ocean environment. However, there were still no high-spatial resolution TOA albedo products over ocean. In this study, a new algorithm for clear-sky TOA albedo estimation over ocean was proposed, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Instead of building angular distribution models, direct retrieval models between TOA reflectance and TOA albedo were developed based on extensive radiative transfer (RT) simulations, covering thousands of ocean and atmosphere types. Three-component ocean water albedo model was involved to take account for the ocean surface anisotropy at different wind speed, wind direction, and chlorophyll concentration, while Modtran 5 was utilized to simulate different atmospheric conditions. Our results showed good agreement with the Clouds and the Earth’s Radiant Energy System (CERES) based on a global comparison on August 4, 2011, with RMSE = 0.015 and bias = 0.002. And our MODIS-based products provide more spatial details due to higher spatial resolution (1 km), which will be a good data source for regional environmental and climatic research and will also enhance the understanding of Earth’s radiation budget.

Improved estimation of the global top-of-atmosphere albedo from AVHRR data

The top-of-atmosphere (TOA) albedo, a key component of the earth's energy balance, can be monitored regularly by satellite observations. Compared to the previous study Song et al. (2018), this paper estimates TOA albedo by directly linking Advanced Very High Resolution Radiometer (AVHRR) narrowband reflectance with TOA broadband albedo determined by NASA's Clouds and the Earth's Radiant Energy System (CERES) instead of Moderate Resolution Imaging Spectroradiometer (MODIS). The TOA albedo product developed in this study has an increased spatial resolution, from 1° to 0.05°, and its starting year has been extended from 2000 to 1981, compared to the CERES TOA albedo product. Models of lands and oceans are established separately under different atmospheric and surface conditions using gradient boosting regression tree (GBRT) method instead of the linear regression models in the previous study. The root mean square errors (RMSEs) of the cloudy-sky, clear-sky and snow-cover models over land are 11.2%, 9.2% and 2.3%, respectively; over oceans they are 14.6%, 10.6% and 5.6%, respectively. Compared to Song et al. (2018), the improvements of the three models over land are 28.8%, 29.2% and 68.6%, respectively. Compared to the CERES product, the new product is much more accurate than that from our previous study. The global monthly mean differences of the TOA albedo obtained with the GBRT product and CERES from 2001 to 2014 are mostly less than 5%.

Net Cloud Thinning, Low-Level Cloud Diminishment, and Hadley Circulation Weakening of Precipitating Clouds with Tropical West Pacific SST Using MISR and Other Satellite and Reanalysis Data

Daily gridded Multi-Angle Imaging Spectroradiometer (MISR) satellite data are used in conjunction with CERES, TRMM, and ERA-Interim reanalysis data to investigate horizontal and vertical high cloud structure, top-of-atmosphere (TOA) net cloud forcing and albedo, and dynamics relationships against local SST and precipitation as a function of the mean Tropical West Pacific (TWP; 120°E to 155°W; 30°S–30°N) SST. As the TWP warms, the SST mode (~29.5 °C) is constant, but the area of the mode grows, indicating increased kurtosis of SSTs and decreased SST gradients overall. This is associated with weaker low-level convergence and mid-tropospheric ascent (ω500) over the highest SSTs as the TWP warms, but also a broader area of weak ascent away from the deepest convection, albeit stronger when compared to when the mean TWP is cooler. These associated dynamics changes are collocated with less anvil and thick cloud cover over the highest SSTs and similar thin cold cloud fraction when the TWP is warmer, but broadly more anvil and cirrus clouds over lower local SSTs (SST < 27 °C). For all TWP SST quintiles, anvil cloud fraction, defined as clouds with tops > 9 km and TOA albedos between 0.3–0.6, is closely associated with rain rate, making it an excellent proxy for precipitation; but for a given heavier rain rate, cirrus clouds are more abundant with increasing domain-mean TWP SST. Clouds locally over SSTs between 29–30 °C have a much less negative net cloud forcing, up to 25 W m−2 greater, when the TWP is warm versus cool. When the local rain rate increases, while the net cloud fraction with tops < 9 km decreases, mid-level clouds (4 km < Ztop < 9 km) modestly increase. In contrast, combined low-level and mid-level clouds decrease as the domain-wide SST increases (−10% deg−1). More cirrus clouds for heavily precipitating systems exert a stronger positive TOA effect when the TWP is warmer, and anvil clouds over a higher TWP SST are less reflective and have a weaker cooling effect. For all precipitating systems, total high cloud cover increases modestly with higher TWP SST quintiles, and anvil + cirrus clouds are more expansive, suggesting more detrainment when TWP SSTs are higher. Total-domain anvil cloud fraction scales mostly with domain-mean ω500, but cirrus clouds mostly increase with domain-mean SST, invoking an explanation other than circulation. The overall thinning and greater top-heaviness of clouds over the TWP with warming are possible TWP positive feedbacks not previously identified.

How Much Do Clouds Mask the Impacts of Arctic Sea Ice and Snow Cover Variations? Different Perspectives from Observations and Reanalyses

Decreasing sea ice and snow cover are reducing the surface albedo and changing the Arctic surface energy balance. How these surface albedo changes influence the planetary albedo is a more complex question, though, that depends critically on the modulating effects of the intervening atmosphere. To answer this question, we partition the observed top of atmosphere (TOA) albedo into contributions from the surface and atmosphere, the latter being heavily dependent on clouds. While the surface albedo predictably declines with lower sea ice and snow cover, the TOA albedo decreases approximately half as much. This weaker response can be directly attributed to the fact that the atmosphere contributes more than 70% of the TOA albedo in the annual mean and is less dependent on surface cover. The surface accounts for a maximum of 30% of the TOA albedo in spring and less than 10% by the end of summer. Reanalyses (ASR versions 1 and 2, ERA-Interim, MERRA-2, and NCEP R2) represent the annual means of surface albedo fairly well, but biases are found in magnitudes of the TOA albedo and its contributions, likely due to their representations of clouds. Reanalyses show a wide range of TOA albedo sensitivity to changing sea ice concentration, 0.04–0.18 in September, compared to 0.11 in observations.

Long-term record of top-of-atmosphere albedo over land generated from AVHRR data

Top-of-atmosphere (TOA) albedo is a fundamental component of Earth's energy budget. To date, long-term global land TOA albedo products with spatial resolution higher than 20-km are not available. In this study, we propose a novel algorithm to retrieve TOA albedo from multispectral imager observations acquired by Advanced Very High Resolution Radiometer (AVHRR), which provides the longest continuous record of global satellite observations since 1981. Direct estimation models were established first to derive instantaneous TOA broadband albedo under various atmospheric and surface conditions, including cloudy-sky, clear-sky (snow-free) and snow-cover conditions. To perform long-term series analysis, the instantaneous TOA albedo were then converted to daily/monthly mean values based on the diurnal curves from multi-year Clouds and the Earth's Radiant Energy System (CERES) 3-hourly flux dataset. Calibration differences between sequential AVHRR sensors were further mitigated by invariant targets normalization. The retrieved TOA albedo at 0.05° × 0.05° was validated against two TOA albedo datasets, CM SAF (Climate Monitoring Satellite Application Facility) flux data and CERES flux data, at spatial resolutions of 0.05° × 0.05°, 20 km × 20 km and 1° × 1°. The instantaneous TOA albedo had an overall Root-Mean-Square-Error (RMSE) of 0.047 when compared with 20-km CERES fluxes, whereas the 1° by 1° monthly mean TOA albedo showed closer agreements with both CM SAF and CERES, with RMSE ranging from 0.029 to 0.040 and from 0.022 to 0.031, respectively. Moreover, our product was found to be highly consistent with both CERES and CM SAF at long-term trend detection. The extensive validation indicated the robustness of our algorithm and subsequently, comparable data quality with existing datasets. With global coverage and long time series (1981–2017), our product is expected to provide valuable information for climate change studies.

Estimating high-resolution top of atmosphere albedo from Moderate Resolution Imaging Spectroradiometer data

Abstract High spatial resolution top-of-atmosphere (TOA) albedo data is needed to study the radiative forcing of natural or anthropogenic events at regional scales. However, existing products are typically estimated using broadband sensors with coarse spatial resolutions. This paper presents a hybrid method to retrieve TOA albedo over land from multispectral data collected by Moderate Resolution Imaging Spectroradiometer (MODIS) at its native spatial resolution. The approach is based on extensive atmospheric radiative transfer (RT) simulations using representative surface and atmospheric conditions as inputs. The clear-sky algorithm explicitly takes surface reflectance anisotropy into account using the POLDER3/PARASOL bidirectional reflectance distribution function database as the boundary condition of RT simulations to first generate TOA spectral albedos and then convert them to broadband albedo. In the cloudy-sky method, surfaces are assumed to be Lambertian and surface spectra over the shortwave spectrum are used to directly obtain TOA broadband albedo. The TOA albedo retrieved from MODIS was compared with the Clouds and the Earth's Radiant Energy System (CERES) TOA flux products, using twelve days of global data (one day each month) in 2007. The two data sets are in good agreement, with a root mean square difference (RMSD) of 0.036 (8.6%) for all Terra instantaneous data and 0.039 (9.1%) for all Aqua instantaneous data. Further analysis revealed that larger discrepancies mainly occurred at pixels of large solar or view zenith angles. RMSD between the two data sets was reduced to ~ 0.02 when the solar zenith angles were limited to 60° and the view zenith angles were limited to 30°.

Sensitivity of a coupled climate model to canopy interception capacity

The canopy interception capacity is a small but key part of the surface hydrology, which affects the amount of water intercepted by vegetation and therefore the partitioning of evaporation and transpiration. However, little research with climate models has been done to understand the effects of a range of possible canopy interception capacity parameter values. This is in part due to the assumption that it does not significantly affect climate. Near global evapotranspiration products now make evaluation of canopy interception capacity parameterisations possible. We use a range of canopy water interception capacity values from the literature to investigate the effect on climate within the climate model HadCM3. We find that the global mean temperature is affected by up to −0.64 K globally and −1.9 K regionally. These temperature impacts are predominantly due to changes in the evaporative fraction and top of atmosphere albedo. In the tropics, the variations in evapotranspiration affect precipitation, significantly enhancing rainfall. Comparing the model output to measurements, we find that the default canopy interception capacity parameterisation overestimates canopy interception loss (i.e. canopy evaporation) and underestimates transpiration. Overall, decreasing canopy interception capacity improves the evapotranspiration partitioning in HadCM3, though the measurement literature more strongly supports an increase. The high sensitivity of climate to the parameterisation of canopy interception capacity is partially due to the high number of light rain-days in the climate model that means that interception is overestimated. This work highlights the hitherto underestimated importance of canopy interception capacity in climate model hydroclimatology and the need to acknowledge the role of precipitation representation limitations in determining parameterisations.

Observational constraints on Arctic Ocean clouds and radiative fluxes during the early 21st century

Arctic Ocean observations are combined to create a cloud and radiation climatology for the early 21st century (March 2000 to February 2011). Data sources include: active (CloudSat, CALIPSO) and passive (MODIS) satellite cloud observations, observed top‐of‐atmosphere (TOA) radiative fluxes (CERES‐EBAF), observationally constrained radiative flux calculations (2B‐FLXHR‐LIDAR), and observationally constrained cloud forcing calculations (CERES‐EBAF, 2B‐FLXHR‐LIDAR). Uncertainty in flux calculations is dominated by cloud uncertainty, not surface albedo uncertainty. The climatology exposes large geographic, seasonal, and interannual variability cloud forcing, but on average, Arctic Ocean clouds warm the surface (+10 W m−2, in 2B‐FLXHR‐LIDAR) and cool the TOA (−12 W m−2, in CERES‐EBAF and 2B‐FLXHR‐LIDAR). Shortwave TOA cloud cooling and longwave TOA cloud warming are stronger in 2B‐FLXHR‐LIDAR than in CERES‐EBAF, but these two differences compensate each other, yielding similar net TOA values. During the early 21st century, summer TOA albedo decreases are consistent with sea ice loss but are unrelated to summer cloud trends that are statistically insignificant. In contrast, both sea ice variability and cloud variability contribute to interannual variability in summer shortwave radiative fluxes. Summer 2007 had the largest persistent cloud, radiation, and sea ice anomalies in the climatology. During that summer, positive net shortwave radiation anomalies exceeded 20 W m−2 over much of the Arctic Ocean. This enhanced shortwave absorption resulted primarily from cloud reductions during early summer and sea ice loss during late summer. In summary, the observations show that while cloud variability influences absorbed shortwave radiation variability, there is no summer cloud trend affecting summer absorbed shortwave radiation.

Observed effects of sastrugi on CERES top‐of‐atmosphere clear‐sky reflected shortwave flux over Antarctica

Determining the clear‐sky top‐of‐atmosphere (TOA) albedo over snow from space requires knowledge of the bi‐directional reflectance distribution function (BRDF), which itself is strongly influenced by the surface roughness of the snow. Sastrugi, a common element of surface roughness on Antarctica, tend to have a preferred azimuth direction, meaning the BRDF depends on the location and time of sampling. In this study we demonstrate that a sastrugi signal is present in the Clouds and the Earth's Radiant Energy System (CERES) reflectance measurements and TOA albedo estimates, leading to a spurious variation in instantaneous albedo as a function of solar azimuth of up to 0.08. By using the difference in flux between oblique and nadir views, we estimate the biases in monthly‐ and annual‐mean 24‐hour energy weighted clear‐sky reflected TOA fluxes caused by sastrugi over Antarctica. At the grid box level, statistically significant monthly‐mean biases of between ±15 Wm−2are found. For the entire Antarctic continent, monthly‐mean biases are between 0.2 ± 0.9 Wm−2 to −1.7 ± 1.1 Wm−2 where a negative bias indicates the reflected flux is being underestimated. On an annual basis, the Antarctic bias is between −0.9 ± 1.1 Wm−2 and −1.0 ± 1.1 Wm−2. For the global annual mean clear‐sky TOA flux, the bias caused by the presence of sastrugi is insignificant, −0.01 ± 0.02 Wm−2. By examining the anisotropy and the wind direction we infer that the negative TOA flux biases are likely to caused by sastrugi perpendicular to the solar azimuth whereas the positive TOA flux biases are likely to be caused by sastrugi parallel to the solar azimuth.

Shortwave absorptance in a tropical cloudy atmosphere: Reconciling calculations and observations

[1] The absorption of shortwave (SW) radiation by clouds is a topic surrounded by contradictory reports and controversy. Some studies have shown large discrepancies between observed SW absorption and absorption predicted by models, while others have found no significant difference. In this study, values of column SW absorptance obtained by combining collocated top-of-atmosphere (TOA) and surface observations at an island site in the tropical western Pacific are compared to radiative transfer model (RTM) output. To compensate for the field of view difference between satellite and surface instruments, the surface data are averaged over time. Scatterplots and statistical measures show that there is a significant discrepancy between models and observations with the RTMs apparently underestimating SW absorptance. The large variability of the absorptance computed from the observations, including negative values, suggests that the field of view mismatch between satellite and surface observations remains even after averaging of the surface data. This mismatch may contribute to the observation-model bias. In previous observational studies showing highly enhanced absorption compared to models, the slope of a linear fit to dαTOA/dT (the derivative of TOA albedo with respect to transmittance) was used to quantify cloud SW absorption, while nonlinearity of dα TOA/dT was interpreted as a sign of sampling issues. Here the models produce a steeper slope (about −0.9) than observations (−0.6 to −0.8), indicating that models predict too little cloud SW absorption. However, when the surface observations are averaged over a longer period, their slope grows steeper, and the root-mean-square difference between linear and quadratic fits to dα TOA/dT is reduced. This implies that insufficient averaging of surface data contributes to the observed SW absorption discrepancy. Reexamination of the observational data using the difference between cloud fraction estimated from satellite and surface measurements as an estimate of field of view mismatch supports this hypothesis. High measured absorptance values are shown to correspond to occasions of large field of view mismatch. When such data are excluded, the difference between the linear and quadratic fits is reduced, and the slope of the best fit line becomes steeper. We conclude that averaging surface data over 3 h or less is not always sufficient to eliminate sampling issues. However, the possibility that shortcomings of the RTMs contribute to the discrepancy in SW absorption values cannot be excluded.

Evaluation of Model-Predicted Top-of-Atmosphere Radiation and Cloud Parameters over Africa with Observations from GERB and SEVIRI

Abstract This study compared the Regional Atmospheric Climate Model version 2 (RACMO) with satellite data by simultaneously looking at cloud properties and top-of-atmosphere (TOA) fluxes. This study used cloud properties retrieved from Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and TOA shortwave and longwave outgoing radiative fluxes measured by one of the Geostationary Earth Radiation Budget (GERB) sensors. Both SEVIRI and GERB resolve the diurnal cycle extremely well with 96 images per day. To test the physical parameterizations of the model, RACMO was run for a domain-enclosing Africa and part of the surrounding oceans. Simulations for July 2006, forced at the lateral boundaries by ERA-Interim reanalyses, show generally accurate positioning of the various cloud regimes but also some important model–observation differences, which the authors tried to reduce by altering model parameterizations. These differences are as follows: 1) TOA albedo differences in clear-sky regions like the Sahara and southern Africa. These differences were considerably reduced by prescribing the surface albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. 2) A considerable overestimation of outgoing longwave radiation within the continental ITCZ caused by the fact that modeled cirrus clouds are far too thin. 3) Underestimation by the model of cloud cover, condensed water path and albedo of the stratocumulus fields off the coast of Angola. The authors reduced these underestimations by suppressing the amount of turbulent mixing above the boundary layer, by prescribing droplet radii derived from SEVIRI data, and by assuming in-cloud horizontal homogeneity for the radiation calculations. 4) Overestimation by the model of the albedo of the trade wind cumulus fields over the Atlantic Ocean. This study argues that this overestimation is likely caused by a model overestimation of condensed water path. In general, the analyses demonstrate the power of the simultaneous evaluation of the TOA fluxes and cloud properties.

An estimate of aerosol indirect effect from satellite measurements with concurrent meteorological analysis

Many studies have used satellite retrievals to investigate the effect of aerosols on cloud properties, but these retrievals are subject to artifacts that can confound interpretation. Additionally, large‐scale meteorological differences over a study region dominate cloud dynamics and must be accounted for when studying aerosol and cloud interactions. We have developed an analysis method which minimizes the effect of retrieval artifacts and large‐scale meteorology on the assessment of the aerosol indirect effect. The method divides an oceanic study region into 1° × 1° grid boxes and separates the grid boxes into two populations according to back trajectory analysis: one population contains aerosols of oceanic origin, and the other population contains aerosols of continental origin. We account for variability in the large‐scale dynamical and thermodynamical conditions by stratifying these two populations according to vertical velocity (at 700 hPa) and estimated inversion strength and analyze differences in the aerosol optical depths, cloud properties, and top of atmosphere (TOA) albedos. We also stratify the differences by cloud liquid water path (LWP) in order to quantify the first aerosol indirect effect. We apply our method to a study region off the west coast of Africa and only consider single‐layer low‐level clouds. We find that grid boxes associated with aerosols of continental origin have higher cloud fraction than those associated with oceanic origin. Additionally, we limit our analysis to those grid boxes with cloud fractions larger than 80% to ensure that the two populations have similar retrieval biases. This is important for eliminating the retrieval biases in our difference analysis. We find a significant reduction in cloud droplet effective radius associated with continental aerosols relative to that associated with oceanic aerosols under all LWP ranges; the overall reduction is about 1.0 μm, when cloud fraction is not constrained, and is about 0.5 μm, when cloud fraction is constrained to be larger than 80%. We also find significant increases in cloud optical depth and TOA albedo associated with continental aerosols relative to those associated with oceanic aerosols under all LWP ranges. The overall increase in cloud optical depth is about 0.6, and the overall increase in TOA albedo is about 0.021, when we do not constrained cloud fraction. The overall increases in cloud optical depth and TOA albedo are 0.4 and 0.008, when we only use grid boxes with cloud fraction larger than 80%.

The CM-SAF operational scheme for the satellite based retrieval of solar surface irradiance — A LUT based eigenvector hybrid approach

The radiation budget at the earth surface is an essential climate variable for climate monitoring and analysis as well as for verification of climate model output and reanalysis data. Accurate solar surface irradiance data is a prerequisite for an accurate estimation of the radiation budget and for an efficient planning and operation of solar energy systems. This paper describes a new approach for the retrieval of the solar surface irradiance from satellite data. The method is based on radiative transfer modelling and enables the use of extended information about the atmospheric state. Accurate analysis of the interaction between the atmosphere, surface albedo, transmission and the top of atmosphere albedo has been the basis for the new method, characterised by a combination of parameterisations and “eigenvector” look-up tables. The method is characterised by a high computing performance combined with a high accuracy. The performed validation shows that the mean absolute deviation is of the same magnitude as the confidence level of the BSRN (Baseline Surface Radiation Measurement) ground based measurements and significant lower as the CM-SAF (Climate Monitoring Satellite Application Facility) target accuracy of 10 W/m 2. The mean absolute difference between monthly means of ground measurements and satellite based solar surface irradiance is 5 W/m 2 with a mean bias deviation of − 1 W/m 2 and a RMSD (Root Mean Square Deviation) of 5.4 W/m 2 for the investigated European sites. The results for the investigated African sites obtained by comparing instantaneous values are also encouraging. The mean absolute difference is with 2.8% even lower as for the European sites being 3.9%, but the mean bias deviation is with − 1.1% slightly higher as for the European sites, being 0.8%. Validation results over the ocean in the Mediterranean Sea using shipboard data complete the validation. The mean bias is − 3.6 W/m 2 and 2.3% respectively. The slightly higher mean bias deviation over ocean is at least partly resulting from inherent differences due to the movement of the ship (shadowing, allocation of satellite pixel). The validation results demonstrate that the high accuracy of the surface solar irradiance is given in different climate regions. The discussed method has also the potential to improve the treatment of radiation processes in climate and Numerical Weather Prediction (NWP) models, because of the high accuracy combined with a high computing speed.

Quantifying aerosol direct radiative effect with Multiangle Imaging Spectroradiometer observations: Top‐of‐atmosphere albedo change by aerosols based on land surface types

Using internally consistent albedo, aerosol, cloud, and surface data from the Multiangle Imaging Spectroradiometer (MISR) instrument onboard the Terra satellite, top‐of‐atmosphere (TOA) spectral albedo change (dα) in the presence of aerosols over land is estimated and its dependence on aerosol and surface properties is analyzed. Linear regressions between spectral TOA albedo and aerosol optical depth (AOD) for different surface types are examined to derive the aerosol‐free TOA albedo. MISR surface BiHemispherical Reflectance (BHR) values are used to differentiate surface types. We find relatively high correlations between spectral TOA albedo and AOD for BHR‐stratified data in 2° × 2° grid cells. The global mean values of cloud‐free dα over land for June–September 2007 are estimated to be 0.018 ± 0.003 (blue), 0.010 ± 0.003 (green), 0.007 ± 0.003 (red), and 0.008 ± 0.006 (near‐infrared). Individual regions show large variations from these values. Global patterns of dα are determined mainly by AOD and aerosol radiative efficiency. Large positive values of dα are observed over regions with high aerosol loading and large single‐scattering albedo, where the aerosol scattering effect is dominant. The presence of light‐absorbing aerosols reduces aerosol radiative efficiency and dα. Surface reflectance influences both aerosol scattering and absorbing effects. Generally, the aerosol radiative efficiency decreases with increasing BHR. We also examined dα‐AOD correlations over different vegetation types. We find the smallest dα values are over needleleaf forests and shrublands, whereas the largest values are over cropland and barren regions. The aerosol radiative efficiencies are lowest over needleleaf forests and barren regions and highest over grasslands and croplands.