Surface Shortwave Radiation Research Articles

An Artificial Neuron Network With Parameterization Scheme for Estimating Net Surface Shortwave Radiation From Satellite Data Under Clear Sky—Application to Simulated GF-5 Data Set

Net surface shortwave radiation (NSSR) is a key parameter that drives the surface material exchange and energy balance. Herein, we propose an improved artificial neuron network (ANN) with parameterized (ANN-P) method to first calculate the albedo at the top of atmosphere (TOA) by considering the surface non-Lambertian effect. Subsequently, the NSSR is estimated based on the relationship between TOA broadband albedo and the Earth’s surface-absorbed shortwave radiation using a parameterized method under clear sky. The modeling process is implemented with Chinese Gaofen-5 (GF-5) visible/near-infrared channels data simulated via MODTRAN. For comparison, a previously reported lookup table (LUT) with parameterized (LUT-P) method and an ANN method are also employed. The performances of all these methods are evaluated. In terms of model simulation part, the root-mean-square errors (RMSEs) are 15.01 (17.07), 10.04 (13.67), and 20.39 (29.99) W/m2 for land, water, and snow/ice surfaces, respectively, for the ANN-P (versus LUT-P) method. Their mean bias errors (MBEs) are within 0.9 W/m2. With respect to the direct ANN method, it shows the highest accuracy yet relatively large deviation for water surface. Additionally, the sensitivity analysis of water vapor content (WVC) confirms that the ANN-P method is more stable than the LUT-P and ANN methods and is, thereby, recommended for clear-sky NSSR estimation. Finally, the ground validations indicate that the mean RMSEs (MBEs) for the LUT-P, ANN-P, and ANN methods are 49.33 (−3.01), 47.55 (1.75), and 104.24 (−75.72) W/m2, respectively.

Role of the Atlantic multidecadal variability in modulating East Asian climate

We assess the effects of the North Atlantic Ocean Sea Surface Temperature (NASST) on North East Asian (NEA) surface temperature. We use a set of sensitivity experiments, performed with MetUM-GOML2, an atmospheric general circulation model coupled to a multi-level ocean mixed layer model, to mimic warming and cooling over the North Atlantic Ocean. Results show that a warming of the NASST is associated with a significant warming over NEA. Two mechanisms are pointed out to explain the NASST—North East Asia surface temperature relationship. First, the warming of the NASST is associated with a modulation of the northern hemisphere circulation, due to the propagation of a Rossby wave (i.e. the circumglobal teleconnection). The change in the atmosphere circulation is associated with advections of heat from the Pacific Ocean to NEA and with an increase in net surface shortwave radiation over NEA, both acting to increase NEA surface temperature. Second, the warming of the NASST is associated with a cooling (warming) over the eastern (western) Pacific Ocean, which modulates the circulation over the western Pacific Ocean and NEA. Additional simulations, in which Pacific Ocean sea surface temperatures are kept constant, show that the modulation of the circumglobal teleconnection is key to explaining impacts of the NASST on NEA surface temperature.

Transient Climate Response to Arctic Sea Ice Loss with Two Ice-Constraining Methods

AbstractThe impact of Arctic sea ice loss on the ocean and atmosphere is investigated focusing on a gradual reduction of Arctic sea ice by 20% of the annual mean, occurring within 30 years, starting from present-day conditions. Two ice-constraining methods are explored to melt Arctic sea ice in a coupled climate model, while keeping present-day conditions for external forcing. The first method uses a reduction of sea ice albedo, which modifies the incoming surface shortwave radiation. The second method uses a reduction of thermal conductivity, which changes the heat conduction flux inside ice. Reduced thermal conductivity inhibits oceanic cooling in winter and sea ice basal growth, reducing the seasonality of sea ice thickness. For similar Arctic sea ice area loss, decreasing the albedo induces larger Arctic warming than reducing the conductivity, especially in spring. Both ice-constraining methods produce similar climate impacts, but with smaller anomalies when reducing the conductivity. In the Arctic, the sea ice loss leads to an increase of the North Atlantic water inflow in the Barents Sea and eastern Arctic, while the salinity decreases and the gyre intensifies in the Beaufort Sea. In the North Atlantic, the subtropical gyre shifts southward and the Atlantic meridional overturning circulation weakens. A dipole of sea level pressure anomalies sets up in winter over northern Siberia and the North Atlantic, which resembles the negative phase of the North Atlantic Oscillation. In the tropics, the Atlantic intertropical convergence zone shifts southward as the South Atlantic Ocean warms. In addition, Walker circulation reorganizes and the southeastern Pacific Ocean cools.

The accuracy improvement of clear-sky surface shortwave radiation derived from CERES SSF dataset with a simulation analysis

Towards the Xiaotang region along the northern margin of the China's largest desert, a quantitative assessment of the precision of clear-sky satellite observations (the Single Scanner Footprint TOA/Surface Fluxes and Clouds downward surface shortwave radiation product of Clouds and the Earth's Radiant Energy System (CERES), DSSRCER) is conducted, the localized inversion mode of “absolutely clear-sky” downward surface shortwave radiation (DSSR) is established, and the “absolutely clear-sky” DSSR in Xiaotang during 2005–2018 is simulated by the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model. In general, under the “absolutely clear-sky” condition of Xiaotang region, there is a significant error in DSSRCER, and the simulated results of SBDART (DSSRSBD) with same input parameters as DSSRCER is better and more comparable. Single scattering albedo (SSA), asymmetry parameter (ASY) and aerosol optical depth (AOD) play crucial roles in deciding the accuracy of DSSR, and after parameter adjustment, the DSSRSBD is better than the initial, which is improved remarkably with all indexes of the fitting results greatly improved. The temporal variation of the DSSR during 2005–2018 indicates that the highest annual average value is found in 2008 (770.00 W·m−2), while the lowest appears in 2010 (600.97 W·m−2). Besides, the highest seasonal mean DSSR appears in summer, which between 860.6 and 935.07 W·m−2, while reaches the lowest in winter (403.79–587.53 W·m−2). Moreover, the monthly average DSSR changes as a curve with a single peak and is close to normal distribution, the highest appears in June (934.61 W·m−2), while the minimum with the value of 390.34 W·m−2 is found in December. All of the solar elevation angle, the characteristics of climate and aerosol particles in different seasons may contribute to the temporal variation.

Why Does a Colder (Warmer) Winter Tend to Be Followed by a Warmer (Cooler) Summer over Northeast Eurasia?

AbstractThis study reveals a pronounced out-of-phase relationship between surface air temperature (SAT) anomalies over northeast Eurasia in boreal winter and the following summer during 1980–2017. A colder (warmer) winter over northeast Eurasia tends to be followed by a warmer (cooler) summer of next year. The processes for the out-of-phase relation of winter and summer SAT involve the Arctic Oscillation (AO), the air–sea interaction in the North Atlantic Ocean, and a Eurasian anomalous atmospheric circulation pattern induced by the North Atlantic sea surface temperature (SST) anomalies. Winter negative AO/North Atlantic Oscillation (NAO)-like atmospheric circulation anomalies lead to continental cooling over Eurasia via anomalous advection and a tripolar SST anomaly pattern in the North Atlantic. The North Atlantic SST anomaly pattern switches to a dipolar pattern in the following summer via air–sea interaction processes and associated surface heat flux changes. The summer North Atlantic dipolar SST anomaly pattern induces a downstream atmospheric wave train, including large-scale positive geopotential height anomalies over northeast Eurasia, which contributes to positive SAT anomalies there via enhancement of downward surface shortwave radiation and anomalous advection. Barotropic model experiments verify the role of the summer North Atlantic SST anomalies in triggering the atmospheric wave train over Eurasia. Through the above processes, a colder winter is followed by a warmer summer over northeast Eurasia. The above processes apply to the years when warmer winters are followed by cooler summers except for opposite signs of SAT, atmospheric circulation, and SST anomalies.

Modulation of radiative aerosols effects by atmospheric circulation over the Euro-Mediterranean region

Abstract. The present work aims at better understanding regional climate–aerosol interactions by studying the relationships between aerosols and synoptic atmospheric circulation over the Euro-Mediterranean region. Two 40-year simulations (1979–2018) have been carried out with version 6.3 of the Centre National de Recherches Météorologiques (National Centre for Meteorological Research) – Aire Limitée Adaptation dynamique Développement InterNational (CNRM-ALADIN) regional climate model, one using interactive aerosols and the other one without any aerosol. The simulation with aerosols has been evaluated in terms of different climate and aerosol parameters. This evaluation shows a good agreement between the model and observations, significant improvements compared to the previous model version and consequently the relevance of using this model for the study of climate–aerosol interactions over this region. A first attempt to explain the climate variability of aerosols is based on the use of the North Atlantic Oscillation (NAO) index. The latter explains a significant part of the interannual variability, notably in winter for the export of dust aerosols over the Atlantic Ocean and the eastern Mediterranean, and in summer for the positive anomalies of anthropogenic aerosols over western Europe. This index is however not sufficient to fully understand the variations of aerosols in this region, notably at daily scale. The use of “weather regimes”, namely persisting meteorological patterns, stable at synoptic scale for a few days, provides a relevant description of atmospheric circulation, which drives the emission, transport and deposition of aerosols. The four weather regimes usually defined in this area in winter and in summer bring significant information to answer this question. The blocking and NAO+ regimes are largely favourable to strong aerosol effects on shortwave surface radiation and near-surface temperature, either because of higher aerosol loads or because of weaker cloud fraction, which reinforces the direct aerosol effect. Inversely, the NAO− and Atlantic Ridge regimes are unfavourable to aerosol radiative effects, because of weaker aerosol concentrations and increased cloud cover. This study thus puts forward the strong dependence of aerosol loads on the synoptic circulation from interannual to daily scales and, as a consequence, the important modulation of the aerosol effects on shortwave surface radiation and near-surface temperature by atmospheric circulation. The role of cloud cover is essential in this modulation as shown by the use of weather regimes.

The Southeastern Tropical Atlantic SST Bias Investigated with a Coupled Atmosphere–Ocean Single-Column Model at a PIRATA Mooring Site

AbstractWarm sea surface temperature (SST) biases in the tropical Atlantic Ocean form a longstanding problem in coupled general circulation models (CGCMs). Considerable efforts to understand the origins of these biases and alleviate them have been undertaken, but state-of-the-art CGCMs still suffer from biases that are very similar to those of the generation of models before. In this study, we use a powerful combination of in situ moored buoy observations and a new coupled ocean–atmosphere single-column model (SCM) with parameterization that is identical to that of a three-dimensional CGCM to investigate the SST bias. We place the SCM at the location of a Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) mooring in the southeastern tropical Atlantic, where large SST biases occur in CGCMs. The SCM version of the EC-Earth state-of-the-art coupled GCM performs well for the first five days of the simulation. Then, it develops an SST bias that is very similar to that of its three-dimensional counterpart. Through a series of sensitivity experiments we demonstrate that the SST bias can be reduced by 70%. We achieve this result by enhancing the turbulent vertical ocean mixing efficiency in the ocean parameterization scheme. The underrepresentation of vertical mixing in three-dimensional CGCMs is a candidate for causing the warm SST bias. We further show that surface shortwave radiation does not cause the SST bias at the location of the PIRATA mooring. Rather, a warm atmospheric near-surface temperature bias and a wet moisture bias contribute to it. Strongly nudging the atmosphere to profiles from reanalysis data reduces the SST bias by 40%.

Vegetation Trajectories and Shortwave Radiative Forcing Following Boreal Forest Disturbance in Eastern Siberia

AbstractMajor boreal forest disturbance and associated carbon emissions have been reported in the coldest region of the Northern Hemisphere. Related biophysical feedbacks to climate remain highly uncertain but might reduce warming effects expected from carbon emissions. This study quantifies albedo change after disturbance, primarily fires, in larch‐dominated forests around Yakutsk as compared to undisturbed areas with natural albedo variability, using satellite‐based time series. The related annual mean shortwave radiative forcing was −6.015 W/m2 for the 13 years following forest disturbance. It was highly negative during snow‐covered months (−3.738 to −13.638 W/m2), but positive (+5.441 W/m2) for the summer months in the first year after disturbance, decreasing afterward and also turning into a negative forcing after 5 years. Forcing by surface shortwave radiation must be considered to assess the impact of boreal forest disturbance on climate and additional feedbacks, such as increased permafrost thaw or transition to alternative ecosystem states.

A review of the estimation of downward surface shortwave radiation based on satellite data: Methods, progress and problems

The estimation of downward surface shortwave radiation (DSSR) is important for the Earth’s energy budget and climate change studies. This review was organised from the perspectives of satellite sensors, algorithms and future trends, retrospects and summaries of the satellite-based retrieval methods of DSSR that have been developed over the past 10 years. The shortwave radiation reaching the Earth’s surface is affected by both atmospheric and land surface parameters. In recent years, studies have given detailed considerations to the factors which affect DSSR. It is important to improve the retrieval accuracy of cloud microphysical parameters and aerosols and to reduce the uncertainties caused by complex topographies and high-albedo surfaces (such as snow-covered areas) on DSSR estimation. This review classified DSSR retrieval methods into four categories: empirical, parameterisation, look-up table and machine-learning methods, and evaluated their advantages, disadvantages and accuracy. Further efforts are needed to improve the calculation accuracy of atmospheric parameters such as cloud, haze, water vapor and other land surface parameters such as albedo of complex terrain and bright surface, organically combine machine learning and other methods, use the new-generation geostationary satellite and polar orbit satellite data to produce high-resolution DSSR products, and promote the application of radiation products in hydrological and climate models.

Estimation of Shortwave Solar Radiation on Clear-Sky Days for a Valley Glacier with Sentinel-2 Time Series

Downward surface shortwave radiation (DSSR) is the main energy source for most glacial melting, and Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) data have been used extensively in the inversion of input parameters for estimating DSSR. However, for valley glaciers under complex climatic conditions, the values of MODIS atmospheric products, especially aerosol products, are often invalid, and TM images are always saturated with snow. Furthermore, an estimation model based on optical satellite images must simultaneously consider terrain and atmospheric effects and the transient nature of ice/snow albedo. Based on a high-resolution (12 m) digital elevation model (DEM), the newly launched Sentinel-2 satellites, rather than MODIS and TM, were used to provide input data for our published mountain radiation scheme in a valley glacier. Considering Laohugou Glacier No. 12 as the study area, 62 typical Sentinel-2 scenes were selected and spatiotemporal DSSR variations on the glacier surface were obtained with a 10 m spatial resolution during a mass-balance year from September 2017 to August 2018. Ground-based measurements on 52 clear-sky days were used for validation and the mean bias error (MBE = −16.0 W/m2) and root-mean-square difference (RMSD = 73.6 W/m2) were relatively low. The results confirm that DSSR is affected mainly by the solar zenith angle and atmospheric attenuation in flat areas of valley glaciers, while in areas with complex terrain, the DSSR received by the glacier surface is affected primarily by the terrain and ice/snow albedo, which exhibits very high spatial heterogeneity.

Changes in atmospheric shortwave absorption as important driver of dimming and brightening

The amount of solar (shortwave) radiation that reaches the Earth’s surface underwent substantial variations over recent decades. Since the 1950s, surface shortwave radiation gradually decreased at widespread locations. In Europe, this so-called surface dimming continued until the late 1980s, when surface brightening set in and surface shortwave radiation increased again. In China, the dimming levelled off in the 1980s, but did not turn into brightening until 2005. Changes in clouds and aerosol are the prime potential causes for the phenomenon, but the scientific community has not yet reached a consensus about the relative role of the different potential forcing agents. Here we bring together co-located long-term observational data from surface and space to study decadal changes of the shortwave energy balance in Europe and China from 1985 to 2015. Within this observation-based framework, we show that an increasing net shortwave radiation at the top of the atmosphere and a decreasing atmospheric shortwave absorption each contribute roughly half of the observed brightening trends in Europe. For China, we find that the continued dimming until 2005 and the subsequent brightening occurred despite opposing trends in the top-of-the-atmosphere net shortwave radiation. This shows that changes in atmospheric shortwave absorption are a major driver of European brightening and the dominant cause for the Chinese surface trends. Although the observed variations cannot be attributed unambiguously, we discuss potential causes for the observed changes. Changes in the atmospheric absorption of shortwave radiation, probably through cloud and aerosol effects, is the main reason for the dimming and brightening over China and Europe in past decades, according to co-located surface and space observations.

Estimation of Surface Shortwave Radiation From Himawari-8 Satellite Data Based on a Combination of Radiative Transfer and Deep Neural Network

In this article, we developed a hybrid method to estimate surface shortwave radiation (SSR) for the new-generation Himawari-8 geostationary satellite. This hybrid method combines the advantages of a deep neural network (DNN) with high speed and radiative transfer model (RTM) to achieve high accuracy: the RTM provides training data for the DNN under various cloud and aerosol conditions (including heavy aerosol loadings). Moreover, our hybrid method can simultaneously output the byproducts of photosynthetically active radiation (PAR), ultraviolet A (UVA), and Ultraviolet B (UVB), the direct and diffuse components at the surface, and the upward solar radiation at the top-of-atmosphere (TOA). The trained DNN was applied to the Himawari-8 satellite atmospheric products for 2016 and comprehensively validated using a total of 118 stations from four networks located in the full-disk regions of Himawari-8. The results showed an RMSE of 125.9 Wm−2 for instantaneous SSR, 105.4 Wm−2 for hourly SSR, 31.9 Wm−2 for daily SSR, and respective mean bias error (MBE) scores of 8.1, 27.6, and 12.3 Wm−2. The hybrid method developed in this study performed well, achieving high accuracy and high speed, and it is capable of providing near-real-time SSR estimates for many applied energy fields.

Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation

Satellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an R 2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W / m 2 , and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W / m 2 , respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.

Retrieval of daily net surface shortwave radiation climatic resources based on machine learning

Retrieval of daily net surface shortwave radiation climatic resources based on machine learning

Uncertainty in Net Surface Heat Flux due to Differences in Commonly Used Albedo Products

AbstractThe ocean surface albedo is responsible for the distribution of solar (shortwave) radiant energy between the atmosphere and ocean and therefore is a key parameter in Earth’s surface energy budget. In situ ocean observations typically do not measure upward reflected solar radiation, which is necessary to compute net solar radiation into the ocean. Instead, the upward component is computed from the measured downward component using an albedo estimate. At two NOAA Ocean Climate Station buoy sites in the North Pacific, the International Satellite Cloud Climatology Project (ISCCP) monthly climatological albedo has been used, while for the NOAA Global Tropical Buoy Array a constant albedo is used. This constant albedo is also used in the Coupled Ocean–Atmosphere Response Experiment (COARE) bulk flux algorithm. This study considers the impacts of using the more recently available NASA Cloud and the Earth’s Radiant Energy System (CERES) albedo product for these ocean surface heat flux products. Differences between albedo estimates in global satellite products like these imply uncertainty in the net surface solar radiation heat flux estimates that locally exceed the target uncertainty of 1.0 W m−2 for the global mean, set by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO). Albedo has large spatiotemporal variability on hourly, monthly, and interannual time scales. Biases in high-resolution SWnet (the difference between surface downwelling and upwelling shortwave radiation) can arise if the albedo diurnal cycle is unresolved. As a result, for periods when satellite albedo data are not available it is recommended that an hourly climatology be used when computing high-resolution net surface shortwave radiation.

Trends in downward surface shortwave radiation from multi‐source data over China during 1984–2015

AbstractThe clear knowledge of decadal variability of surface solar radiation (SSR) is of vitally significant for understanding hydrological and biological processes and climate prediction. However, existing studies have shown observed SSR over China may have large discrepancies and inhomogeneity in decadal variability due to sensitivity drift, inaccurate calibrations and instrument replacement. Therefore, a new procedure of station selection was proposed to eliminate errors and to derive “true” SSR values in this study. Afterward, two satellite retrieves of SSR, including Clouds and the Earth's Radiant Energy System‐energy balanced and filled product (CERES‐EBAF) (edition 4) and Global Energy and Water Cycle Experiment‐Surface Radiation Budget (GEWEX‐SRB) (Version 3.0), and three reanalysis products, including National Centers for Environmental Prediction‐National Center for Atmospheric Research (NCEP‐NCAR), national centers for environmental prediction‐/department of energy (NCEP‐DOE) and Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA‐2) were evaluated using “true” SSR values at 39 homogeneous stations from the China Meteorological Administration and it was found that although all five products overestimated SSR, two satellite retrieves showed better accuracy with an overall R of 0.95, an root mean squared error (RMSE) of 20.4 W m−2 and mean absolute bias error (MAE) of 14.9 W m−2 for CERES‐EBAF and an overall R of 0.92, an RMSE of 27.7 W m−2 and MAE of 21.2 W m−2 for GEWEX‐SRB across China. Meanwhile, inter‐comparisons between trends of observations and trends of two satellite retrieves in this study showed that the new trends derived from two satellite retrieves (+0.78 W m−2 decade−1) were good agreement with trends of observation (+0.92 W m−2 decade−1) from 1994 to 2015. However, trends of SSR (+5.8 W m−2 decade−1) in situ measurements were still in disagreement with the trends of SSR (−3.7 W m−2 decade−1) derived from two satellite retrieves from 1984 to 1993 because of the sensitivity drift and instrument replacement in this period. The possible reasons for decadal variability of SSR in China were detected and it was found that variations in aerosol optical depth (AOD) and aerosol‐cloud interaction, rather than cloud, were suggested to be likely the main influencing factor of decadal variability of SSR across China from 1984 to 2015.

Observed monsoon precipitation suppression caused by anomalous interhemispheric aerosol transport

This study uses observations and atmospheric reanalysis products in order to understand the impacts of smoke aerosols advected from the Southern Hemisphere on the dynamics of the West African monsoon. Seasonal biomass burning and resulting aerosol emissions have been well documented to affect regional weather patterns, especially low-level convection. Out of all monsoon months, precipitation shows the most variability over land during August, in which anomalous smoke aerosol values can increase (decrease) by 33% (29%) in the Northern Gulf of Guinea and precipitation can decrease (increase) by up to ~ 2.5 mm day−1 (~ 3 mm day−1) along the West African monsoon region accounting for a 17% (18%) change in precipitation. Smoke aerosols produced by biomass burning occurring near Central Africa are advected towards the Gulf of Guinea at elevations around the 850 hPa level. Satellite observations show an increase (decrease) in cloud fraction and optical depth below (above) the 300-hPa level in the Gulf of Guinea and along the West African coastline along with concurrent decreases (increases) in cloud droplet radius during dirty (clean) aerosol episodes. Additional observations of shortwave radiation quantify changes in cloud coverage and monsoon dynamics. On average, reductions in surface shortwave radiation of ~ 10–15 W m−2 occur over the Gulf of Guinea during increased aerosol transport, with aerosols accounting for ~ 33–50% of that reduction. Reductions in shortwave radiation are associated with decreased convective available potential energy (CAPE). This demonstrates that increased transport of aerosols perturbs surface radiation, convection in the lower troposphere and eventually cloud coverage, potentially leading to the observed monsoon precipitation suppression. In a broader social context, this region houses 200 million people and thus understanding these climate patterns may carry great importance.

Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 2: Evaluation)

High frequency knowledge of the spatio-temporal distribution of the downwelling surface shortwave flux (DSSF) and its diffuse fraction (fd) at the surface is nowadays essential for understanding climate processes at the surface–atmosphere interface, plant photosynthesis and carbon cycle, and for the solar energy sector. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis operationally delivers estimation of the MDSSFTD (MSG Downwelling Surface Short-wave radiation Fluxes—Total and Diffuse fraction) product with an operational status since the year 2019. The method for retrieval was presented in a companion paper. Part 2 now focuses on the evaluation of the MDSSFTD algorithm and presents a comparison of the corresponding outputs, i.e., total DSSF and diffuse fraction (fd) components, against in situ measurements acquired at four Baseline Surface Radiation Network (BSRN) stations over a seven-month period. The validation is performed on an instantaneous basis. We show that the satellite estimates of DSSF and fd meet the target requirements defined by the user community for all-sky (clear and cloudy) conditions. For DSSF, the requirements are 20 Wm−2 for DSSF < 200 Wm−2, and 10% for DSSF ≥ 200 Wm−2. The mean bias error (MBE) and relative mean bias error (rMBE) compared to the ground measurements are 3.618 Wm−2 and 0.252%, respectively. For fd, the requirements are 0.1 for fd < 0.5, and 20% for fd ≥ 0.5. The MBE and rMBE compared to the ground measurements are −0.044% and −17.699%, respectively. The study also provides a separate analysis of the product performances for clear sky and cloudy sky conditions. The importance of representing the cloud–aerosol radiative coupling in the MDSSFTD method is discussed. Finally, it is concluded that the quality of the aerosol optical depth (AOD) forecasts currently available is accurate enough to obtain reliable diffuse solar flux estimates. This quality of AOD forecasts was still a limitation a few years ago.

Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data

Net surface shortwave radiation (NSSR) is one of the most important fundamental parameters in various land processes. Benefiting from its efficient nonlinear fitting ability, machine learning algorithms have a great potential in the retrieval of NSSR. However, few studies have explored the level of accuracy that machine learning algorithms can reach for different land covers on the worldwide scale and what the optimal independent variables are in the machine learning-based NSSR model. To guide the use of machine learning algorithms correctly in the retrieval of NSSR, it is necessary to give a comprehensive analysis from algorithm complexity, accuracy, and other aspects. In this study, three classic machine learning algorithms, including Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were built well to estimate instantaneous NSSR with optimal hyperparameters by elaborately selecting different independent variables, including top of atmosphere (TOA) channel spectral reflectance, geographic parameters, surface information, and atmosphere conditions. Global FLUXNET in situ measurements throughout 2014 were used to validate the accuracies of retrieved NSSR over various land cover types. The root mean square error (RMSE) is below 55 W/m2, and the distributions of error histogram are also similar. Approximately 50% of absolute error were within 25 W/m2. There was a performance difference of NSSR estimations in various surface types, and the performance of three machine learning methods in a specific surface type was also different. However, the RF method may be considered as the optimal methodology to retrieve NSSR from MODIS data, owing to its relatively better precision and concise hyperparameter-tuned process. The importance analysis of the proposed independent variables of NSSR retrieval shows that the introduction of geographic information can effectively reduce the error of NSSR retrieval, and surface information and atmosphere information are not necessary. It was also found that a combination of geographic information and blue band TOA reflectance already have a pretty good accuracy in NSSR retrieval, which implies there is a possibility to transfer our NSSR model to other satellite sensors, especially with insufficient channels. In a word, the NSSR model with machine learning algorithms would be an efficient, concise, and general method in the future.

Evaluation of Surface Radiative Fluxes over the Tropical Oceans in AMIP Simulations

The performance of 20 models from the Atmospheric Model Intercomparison Project (AMIP) was evaluated concerning surface radiation over the tropical oceans (30° S–30° N) from 1979 to 2000. The model ensemble mean of the net surface shortwave radiation (QSW) was underestimated compared to the International Satellite Cloud Climatology Project (ISCCP) data by 4 W m−2. On the other hand, net longwave radiation (QLW) was overestimated by 4 W m−2, leading to an underestimation of the net surface radiation (Qrad) by 8 W m−2. The most prominent bias in the Qrad appears to be over regions of low-level clouds in the off-equatorial eastern Pacific, eastern Atlantic, and the south-eastern Indian Ocean. The root means squared error of QLW was larger than that of QSW in 17 out of 20 AMIP models. Overestimation of the total cloud cover and atmospheric humidity contributed to the underestimation of Qrad. In general, models with higher horizontal resolutions performed slightly better than those with coarser horizontal resolutions, although some systematic bias persists in all models and in all seasons, in particular, in regions of low-level clouds for QLW, and high-level clouds for QSW. The ensemble mean performed better than most models, but two high-resolution models (GFDL-HIRAM-C180 and GFDL-HIRAM-C360) outperform the model ensemble.