remote sensing

Article Clear-Sky Surface Solar Radiation and the Radiative Effect of Aerosol and Water Vapor Based on Simulations and Satellite Observations over Northern China

Guang Zhang 1,* and Yingying Ma 2,3

1 School of Electronic Science and Engineering (National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu 611731, China 2 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; [email protected] 3 Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China * Correspondence: [email protected]



Received: 3 May 2020; Accepted: 11 June 2020; Published: 15 June 2020


Abstract: The distribution and trend of clear-sky surface solar radiation (SSR) and the quantitative effects of aerosol and water vapor are investigated in northern China during 2001–2015 using radiation simulations and satellite observations. Clear-sky SSR in northern China is high in summer and low in winter, which is dominated by astronomical factors and strongly modulated by the seasonal variations of radiative effects of aerosol (ARE) and water vapor (WVRE). The larger variation of WVRE than ARE indicates that water vapor plays a more important role in moderating the seasonal variation of clear-sky SSR. Clear-sky SSR shows an overall decreasing trend of –0.12 W/m2 per year, with decrease more strongly than –0.60 W/m2 per year in west-central Shandong and increase (about 0.40 W/m2) in south-central Inner Mongolia. The consistency of spatial distribution and high correlation between clear-sky SSR and ARE trend indicate that the clear-sky SSR trend is mainly determined by aerosol variation. Dust mass concentration decreases about 16% in south-central Inner Mongolia from 2001 to 2015, resulting in the increase in clear-sky SSR. In contrast, sulfate aerosol increases about 92% in west-central Shandong, leading to the decreasing trend of clear-sky SSR.

Keywords: surface solar radiation; aerosol radiative effect; water vapor radiative effect

1. Introduction Solar radiation reaching the Earth’s surface, also known as the surface solar radiation (SSR), is the primary energy source for life on the planet [1,2]. It drives the majority of the terrestrial processes (e.g., the carbon cycle and hydrological cycle) and plays a crucial role in a large number of sectors (e.g., agriculture and solar energy production) [3–5]. In the past decade, the role of solar energy in the global energy transformation has increased rapidly. The percentage of solar power in global electricity generation has increased from 0.1% in 2008 to 2.2% in 2018 [6]. SSR is controlled by many factors such as solar geometries and atmospheric constituents [7,8]. Solar geometries including the Earth–Sun distance and the solar zenith angle determine solar radiation reaching the top of the atmosphere and drive the seasonal and diurnal variations of SSR [9,10]. Solar radiation is modulated by atmospheric constituents as it passes through the atmosphere. Cloud has been regarded as a key modulator of solar radiation in the atmosphere. Previous studies have found that the reduction in solar radiation from the 1960s to the 1980s at many sites coincides with the increase of cloud cover [11,12]. However, cloud change cannot always explain the variation

Remote Sens. 2020, 12, 1931; doi:10.3390/rs12121931 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1931 2 of 19 of SSR. Most regions in China have experienced simultaneous decrease of cloud cover and SSR from 1960s to 1990s [13,14], which indicates the important role of other atmospheric constituents except for clouds. Some important efforts have been made to separate SSR variation into cloudy-sky and clear-sky conditions to exclude effects of cloud cover and cloud properties on solar radiation and highlight the effects of other atmospheric constituents [15,16]. Under clear-sky conditions, solar radiation is modulated by aerosol, water vapor, and radiative active gases, in which aerosol and water vapor exert the most important effects on solar radiation [7,17]. Depending on its composition, aerosols can absorb or scatter solar radiation, thus reducing solar radiation reaching the surface [18,19]. In addition, a great number of aerosol particles act as cloud condensation nuclei and affect cloud properties and lifetime [20,21]. In the past few decades, many studies have been conducted to analyze the effect of aerosols on SSR in regional and global scales [22–26]. Water vapor strongly absorbs solar shortwave radiation in some bands with wavelengths larger than 700 nm, which has an important influence on SSR, especially in regions and seasons with abundant water vapor. Some previous studies have made quantitative analyses on the radiative effect of water vapor [27–30]. The roles that aerosol and water vapor play in the seasonal and inter-annual variations of SSR are still uncertain, considering the huge disparities of spatial and temporal distributions of aerosol and water vapor among regions. Northern China is a major economic zone in China and East Asia. Over the past few decades, northern China has experienced rapid industrialization and urbanization, which resulted in tremendous increase in the consumption of fossil fuel and in the emission of pollutants and anthropogenic aerosol in the atmosphere [31,32]. It also suffers from long-distance transported dust aerosol from northwest China, especially in spring [33]. Influenced by the complex topography and Asian monsoon, the seasonal variation and spatial distribution of water vapor differ widely in different areas of northern China [34]. Some studies have analyzed the aerosol radiative effect based on models and site observations in northern China [35,36], which have provided information on the spatial and temporal variation of ARE. Song et al. (2018) investigated the daytime variation of aerosol optical on aerosol direct radiative effects in northern China [36]. Che et al. (2014) studied the column aerosol optical properties and aerosol radiative forcing during a serious haze-fog month over the North China Plain in 2013 [37]. However, knowledge about the role of water vapor in clear-sky SSR variation is still lacking, and a comprehensive and quantitative analysis on the roles of water vapor and aerosol in the seasonal and inter-annual variation of SSR is imperative. This study simulates clear-sky SSR in northern China from 2001 to 2015 based on the Mesoscale Atmospheric Global Irradiance Code (MAGIC), and data from satellite observations and atmospheric reanalysis products. The aerosol direct radiative effect (ARE) and water vapor radiative effect (WVRE) are also calculated based on simulations assuming no aerosol and no water vapor, respectively. The temporal and spatial distribution of clear-sky SSR in northern China is then investigated and the roles of aerosol and water vapor on the seasonal variation and long-term trend of clear-sky SSR are analyzed quantitatively base on the variation of ARE and WVRE. Data and methods used in this study are shown in Section2, and Section3 introduces main results of clear-sky SSR variation and discussion on the driving factors. In this study, the months of year have been divided into four seasons, namely spring (March, April, and May), summer (June, July, and August), autumn (September, October, and November), and winter (December, January, and February).

2. Materials and Methods

2.1. Data The satellite measurements of aerosol properties are taken from the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which monitors the Earth’s surface in 36 channels from 0.41 µm to 14.4 µm with near-daily global coverage and moderate resolution [38]. The MODIS C6 product includes aerosol optical depth (AOD) using refined retrieval algorithm [39] and provides Remote Sens. 2020, 12, 1931 3 of 19 a combinedRemote Sens. dataset, 2020, 12, combining x FOR PEER REVIEW the DT and DB data. The resultant merged AOD field is more3 of spatially 21 complete than either the DB or DT AOD fields [40], which provides extended spatial coverage and combined dataset, combining the DT and DB data. The resultant merged AOD field is more spatially is importantcomplete for than investigating either the DB aerosolor DT AOD loading fields over[40], which areas withprovides complex extended topography spatial coverage and underlying and is surfaceimportant in North for China. investigating We use aerosol the combined loading over Dark areas Target with and complex Deep Blue topography product and from underlying Terra MODIS Level-3surface monthly in North data China. in 1◦ We1 ◦useresolution. the combined Ruiz-Arias Dark Target (2013) and assessed Deep Blue the product Level-3 from MODIS Terra AOD MODIS against × moreLevel-3 than 500 monthly AERONET data in ground 1°×1° resolution. stations aroundRuiz-Arias the (2013) globe assessed and the the root Level-3 mean MODIS square AOD error against is 0.14 [41]. Singlemore than scattering 500 AERONET albedo ground (SSA) usedstations for around solar radiationthe globe and modeling the root come mean from square the error Kinne is 0.14 aerosol climatology,[41]. which is developed based on merging of data from Aerosol Comparisons between ObservationsSingle and scattering Models albedo (AEROCOM) (SSA) used global for solar modeling radiation and modeling ground measurementscome from the Kinne from aerosol the Aerosol climatology, which is developed based on merging of data from Aerosol Comparisons between Robotic Network (AERONET) [42–44]. The Kinne aerosol climatology contains monthly SSA data Observations and Models (AEROCOM) global modeling and ground measurements from the in 1 1 resolution with RMSE of 0.06 in comparison with SSA from AERONET [42]. ◦ Aerosol× ◦ Robotic Network (AERONET) [42–44]. The Kinne aerosol climatology contains monthly SSA Thedata waterin 1°×1° vapor resolution data with is obtained RMSE of 0.06 from in thecomparison ERA-Interim with SSA data, from which AERONET is a global [42]. atmospheric reanalysisThe data water provided vapor bydata the is Europeanobtained from Centre the forERA-Interim Medium-Range data, which Weather is a global Forecasting atmospheric (ECMWF). The datareanalysis provides data provided meteorological by the European parameters Centre such for Medium-Range as surface temperature, Weather Forecasting soil temperature, (ECMWF). and relativeThe humiditydata provides [45]. meteorological The ERA-Interim parameters data usedsuch hereas surface is the temperature, monthly water soil temperature, vapor with aand spatial resolutionrelative of humidity 0.25 0.25[45]. The. The ERA-Interim RMSE of waterdata used vapor here is is 2.8 the kg monthly/m2 in comparisonwater vapor with with a radiosondespatial ◦ × ◦ observationsresolution [46 of]. 0.25°×0.25°. The RMSE of water vapor is 2.8 kg/m2 in comparison with radiosonde Theobservations solar radiation [46]. data in ground observation stations is from the daily data set of basic elements The solar radiation data in ground observation stations is from the daily data set of basic of meteorological radiation in China provided by the National Meteorological Information Center. elements of meteorological radiation in China provided by the National Meteorological Information SSR from 10 stations during 2001–2015 is used to validate the modeled SSR in this study, and the Center. SSR from 10 stations during 2001–2015 is used to validate the modeled SSR in this study, and locationsthe locations of these of stations these statio arens shown are shown in Figure in Figure1. 1.

FigureFigure 1. Elevation 1. Elevation map map and and stations stations distribution in inthe the study study area. area. DT, TY, DT, HM, TY, BJ, HM, TJ, LT, BJ, FS, TJ, JN, LT, JX, FS, JN, JX, andand ZZ ZZ represent represent Datong,Datong, Taiyuan, Houma, Houma, Beijing, Beijing, Tianjin, Tianjin, Laoting, Laoting, Fushan, Fushan, Jinan, Jinan, JUxian, JUxian, and and Zhengzhou,Zhengzhou, respectively. respectively.

AerosolAerosol data data from from the the second second Modern-Era Modern-Era RetrospectiveRetrospective analysis analysis for for Research Research and andApplications Applications (MERRA-2)(MERRA-2) is also is also used. used. MERRA-2 MERRA-2 productproduct is an atmospheric atmospheric reanalysis reanalysis provided provided by the by NASA the NASA Global Modeling and Assimilation Office (GMAO) [47,48]. In this study, the surface aerosol mass Global Modeling and Assimilation Office (GMAO) [47,48]. In this study, the surface aerosol mass concentration data with a spatial resolution of 0.625°× 0.5° from the “tavgM_2d_aer_Nx” dataset is concentration data with a spatial resolution of 0.625 0.5 from the “tavgM_2d_aer_Nx” dataset is used to analyze the influence of aerosol compositions◦ ×on clear-sky◦ SSR variation. used to analyze the influence of aerosol compositions on clear-sky SSR variation.

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2.2. Methodology

2.2.1. Simulation of Clear-Sky SSR and Calculation of ARE and WVRE In this study, the clear-sky SSR is simulated using MAGIC [49], which is based on radiation simulated by a Radiative Transfer Model (RTM) and stored in look-up tables to improve the computing efficiency. It assumes plane atmosphere and uses the modified Lambert function for each wavelengths under cloud-free conditions [49].The RTM model libRadtran [50] has been used for the extensive analysis of the interaction between radiation and the atmosphere and the computation of the LUTs. All RTM calculations for LUTs establishment are performed for the US standard atmosphere, which provides vertical information on the total number density of atmospheric constituents. The clear-sky SSR is simulated with input of aerosol properties from MODIS product and Kinne aerosol climatology, water vapor from ERA-Interim water vapor and surface albedo from the Surface and Radiation Budget/Clouds and the Earth’s Radiant Energy System (SARB/CERES) [51]. The clear-sky SSR assuming conditions without aerosol and without water vapor is then simulated by adjusting the extinction of aerosol and water vapor to zero, respectively, remaining other inputs unchanged. All the simulations are conducted in time interval of 15 min and resolution of 0.5 0.5 . The instantaneous ARE (ARE ) ◦ × ◦ int and WVRE (WVREint) is calculated as follows:

AREint = Fclear Fno aerosol (1) − −

WVREint = Fclear Fno wv (2) − − where Fclear is the clear-sky solar flux including aerosol and water vapor, Fno aerosol is the clear-sky solar − flux without aerosol and with water vapor, and Fno wv is the clear-sky solar flux with aerosol and − without water vapor. Then, the daily ARE (AREdaily ) and WVRE (WVREdaily ) are calculated by the integration of the instantaneous data over 24 h:

P24 AREintdt ARE = 0 (3) daily 24h

P24 WVREintdt WVRE = 0 (4) daily 24h The trends of clear-sky SSR, ARE, and WVRE are calculated based on their monthly mean values. In order to eliminate the great influence of seasonal cycle, the data is first deseasonalized by subtracting the seasonal cycle obtained from the harmonic regression [52,53]. The trends (ω) of clear-sky SSR, ARE, and WVRE are then calculated by applying the least-square fit to the deseasonalized time series. The significance of trend is evaluated using the ratio of |ω/σω|, where σω is the standard deviation of the trend [54]. The trend is regarded as significant at the 95% confidential level when |ω/σω| is larger than 2.

2.2.2. Validation of Simulated Clear-Sky SSR The reliability of simulated clear-sky SSR is validated by comparison with the ground observations. As SSR in stations are observed under all-sky conditions, SSR observations should be distinguished between cloudy and clear-sky conditions. An empirical threshold optimization method is used to select clear-sky conditions from the SSR observations. If the observed daily SSR value exceeds 89% of the clear-sky solar radiation simulated by the MAGIC, a given day can be identified as a clear-sky condition [55]. The threshold for clear-sky situations refers to Bartok (2017) [55], who selected the empirical optimum threshold by testing different thresholds against the clear-sky detection and interpolation algorithm of Long and Ackerman (2000) [56] using the Baseline Surface Radiation Network (BSRN) data. As the consequence, the optimal threshold showing the higher average between clear-sky and all-sky matches is detected to be 89%. Remote Sens. 2020, 12, 1931 5 of 19

Table1 and Figure2 shows the statistical analysis between observed and simulated clear-sky SSR at 10 stations. The overall slope of fitted straight line between observations and simulations at 10 stations is 1.03, the determination coefficient (R2) is 0.98, the mean absolute error (MAE) is 8.13 W/m2 (3.62%), and the root mean square error (RMSE) is 10.60 W/m2 (4.17%). The accuracy of simulation from this study is better than results from Otunla (2019) [57] (RMSE: 15–26%) and Zhang and Wen (2014) [58] (RMSE: 9.6%). R2 in all the stations are higher than 0.96, and MAE range from 6.17–12.98 W/m2. RMSE at nine stations are less than 7% in the Tianjin station. From above, the comparison shows a very good agreement, which confirms the reliability of the simulation in this study.

Table 1. The statistical analysis between monthly observed and simulated clear-sky SSR.

Stations Correlation Equation R2 MAE (W/m2) MAE(%) RMSE (W/m2) RMSE(%) Ratio of Clear Sky Datong y = 1.01x + 5.39 0.99 10.98 4.44% 12.92 5.22% 25.16% TaiyuanRemote Sens. y2020= 1.00x, 12, x+ FOR4.67 PEER REVIEW 0.96 12.22 4.99% 15.33 6.27%5 18.09% of 21 Houma y = 1.09x 10.90 0.98 12.98 4.87% 14.56 5.48% 10.88% − Beijing y = 1.02x 3.43 0.99 6.25 2.89% 7.72 3.57% 29.12% (3.62%), and the root− mean square error (RMSE) is 10.60 W/m2 (4.17%). The accuracy of simulation Tianjin y = 1.00x 3.24 0.97 8.68 4.09% 13.44 6.33% 35.16% − Laotingfrom this study y = 1.01x is better0.78 than results 0.99 from 6.17Otunla (2019) 2.87% [57] (RMSE: 8.05 15-26%) and 3.74% Zhang and 34.79%Wen − Fushan(2014) [58] y (RMSE:= 1.03x 9.6%).1.31 R2 in 0.99all the stations 7.00 are higher 3.15% than 0.96, and 8.57 MAE range 3.86% from 6.17–12.98 30.87% − JinanW/m2. RMSE y = 1.00xat nine+ 0.89 stations are 0.98 less than 8.22 7% in the Tianjin 3.70% station. 10.07 From above, 4.53% the comparison 27.24% JUxian y = 1.04x 1.02 0.99 7.93 3.58% 9.33 4.22% 26.34% shows a very good− agreement, which confirms the reliability of the simulation in this study. Zhengzhou y = 1.04x 4.82 0.99 6.63 2.96% 8.36 3.73% 25.68% − all y = 1.03x 2.71 0.98 8.14 3.62% 10.60 4.17% 26.33% −

Figure 2. ComparisonFigure 2. Comparison between between monthly monthly observed observed and and simulated simulated clear- clear-skysky surface surfacesolar radiation solar radiation (SSR) at (a) Beijing; (b) Datong; (c) Fushan; (d) Houma; (e) Jinan; (f) JUxian; (g) Laoting; (h) Taiyuan; (SSR) at (a)(i) Beijing; Tianjin; (j) (b Zhengzhou;) Datong; and (c) (k) Fushan; all the sites. (d) Houma; (e) Jinan; (f) JUxian; (g) Laoting; (h) Taiyuan; (i) Tianjin; (j) Zhengzhou; and (k) all the sites. Table 1. The statistical analysis between monthly observed and simulated clear-sky SSR.

Correlation MAE RMSE Ratio of Stations R2 MAE(%) RMSE(%) Equation (W/m2) (W/m2) Clear Sky Datong y = 1.01x + 5.39 0.99 10.98 4.44% 12.92 5.22% 25.16% Taiyuan y = 1.00x + 4.67 0.96 12.22 4.99% 15.33 6.27% 18.09%

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y = 1.09x – Houma 0.98 12.98 4.87% 14.56 5.48% 10.88% 10.90 Beijing y = 1.02x – 3.43 0.99 6.25 2.89% 7.72 3.57% 29.12% Tianjin y = 1.00x – 3.24 0.97 8.68 4.09% 13.44 6.33% 35.16% Laoting y = 1.01x – 0.78 0.99 6.17 2.87% 8.05 3.74% 34.79% Remote Sens. 2020Fushan, 12, 1931 y = 1.03x – 1.31 0.99 7.00 3.15% 8.57 3.86% 30.87% 6 of 19 Jinan y = 1.00x + 0.89 0.98 8.22 3.70% 10.07 4.53% 27.24% JUxian y = 1.04x – 1.02 0.99 7.93 3.58% 9.33 4.22% 26.34% 3. Results Zhengzhou y = 1.04x – 4.82 0.99 6.63 2.96% 8.36 3.73% 25.68% all y = 1.03x – 2.71 0.98 8.14 3.62% 10.60 4.17% 26.33% 3.1. Spatial Pattern of Clear-Sky SSR and Associated Factors in North China 3. Results As shown in Figure3, the 15-year average of clear-sky SSR in North China presents a clear distribution3.1. of Spatial high Pattern values of Clear-Sky in the northwest SSR and Associated and low Factors values in North in theChina southeast. A low-value center with clear-sky SSRAs less shown than in 215 Figure W/m 3,2 theis found15-year inaverage the neighboring of clear-sky SSR region in North of Shandong, China presents Hebei, a clear and Tianjin, which correspondsdistribution toof high core values of the in Norththe northwest China and Plain low values (NCP). in the To southeast. the southeast A low-value of the center low-value with center, clear-sky SSR less than 215 W/m2 is found in the neighboring region of Shandong, Hebei, and Tianjin, the clear-sky SSR increase slightly to about 225 W/m2 in south and east of Shandong. And to the which corresponds to core of the North China Plain (NCP). To the southeast of the low-value center, 2 northwestthe of clear-sky the low-value SSR increase center, slightly the to clear-sky about 225 SSRW/m2 increasein south and dramatically east of Shandong. to more And thanto the 240 W/m in Shanxi,northwest northwest of the Hebei low-value and center, south-central the clear-sky Inner SSR increase Mongolia. dramatically Yu et al.to more (2020) than studied 240 W/m2 the in clear-sky solar radiationShanxi, over northwest arid Hebei and semi-aridand south-central areas Inner in china, Mongol andia. Yu their et al. (2020) results studied shows the thatclear-sky the solar clear-sky SSR radiation over arid and semi-arid areas in china, and their results shows that the clear-sky SSR over over the south-central Inner Mongolia is about 250 W/m2, which is consistent to our results. the south-central Inner Mongolia is about 250 W/m2, which is consistent to our results.

Figure 3. SpatialFigure 3. distributionSpatial distribution of annual of annual averaged averaged clea clear-skyr-sky SSR SSR in northern in northern China Chinafrom 2001 from to 2015. 2001 to 2015. Comparing spatial distribution of clear-sky SSR in Figure 3 and altitudes in Figure 1, the Comparinglocations spatial with low distribution clear-sky SSR of correspond clear-sky SSRto those in Figurewith low3 and altitudes. altitudes The low-altitude in Figure1 ,plain the locations with lowregions clear-sky are usually SSR correspondcharacterized with to thosedense population with low and altitudes. developed industry, The low-altitude and a large amount plain regions are usuallyof anthropogenic characterized aerosol with is denseemitted population by industrial and domestic developed activities industry, [59]. Comparing and a large spatial amount of anthropogenicpattern aerosol of clear-sky is emitted SSR in Figure by industrial 3 and ARE in and Figure domestic 4a, regions activities with low [ 59clear-sky]. Comparing SSR correspond spatial pattern to regions with strong attenuation effect of aerosol on solar radiation. High ARE (larger than -40 of clear-sky SSR in Figure3 and ARE in Figure4a, regions with low clear-sky SSR correspond to regions with strong attenuation effect of aerosol on solar radiation. High ARE (larger than 40 W/m2) − are found in the neighboring region of Shandong, Hebei and Tianjin, which are much higher than the national mean ARE in China ( 15.70 W/m2)[60]. However, low ARE (smaller than 15 W/m2) − − are found in north Hebei and south-central Inner Mongolia. High terrain altitude induces low total atmospheric column and large clear-sky SSR, also contributing to the spatial distribution of SSR [61]. The WVRE (Figure4b) shows an obvious distribution of high values in the southeast and low values in the northwest as the distribution of water vapor is influenced by the distance to the sea, underlying surface, latitude, and topography [34,62]. The strongest attenuation effect of water vapor on solar radiation (larger than 40 W/m2) occur in north Henan and south Shandong. While the − weakest (around 30 W/m2) occur in south-central Inner Mongolia due to high altitude and latitude, − dry climate and long distance to the sea [63]. The spatial difference of extinction of water vapor on solar radiation contributes to the spatial distribution of clear-sky SSR. However, the spatial difference of WVRE is much smaller than ARE, and the spatial distribution of clear-sky SSR is more strongly influenced by aerosol. Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 21

W/m2) are found in the neighboring region of Shandong, Hebei and Tianjin, which are much higher than the national mean ARE in China (-15.70 W/m2) [60]. However, low ARE (smaller than -15 W/m2) Remoteare Sens. found2020, in12 ,north 1931 Hebei and south-central Inner Mongolia. High terrain altitude induces low total7 of 19 atmospheric column and large clear-sky SSR, also contributing to the spatial distribution of SSR [61].

FigureFigure 4. Spatial 4. Spatial distribution distribution of of annual annual averaged averaged ( a)) aerosol aerosol direct direct radiative radiative effect eff ect(ARE) (ARE) and and(b) water (b) water vaporvapor radiative radiative effect effect (WVRE) (WVRE) in in northern northern China China fromfrom 2001 to to 2015. 2015.

3.2. SeasonalThe VariationWVRE (Figure of Clear-Sky 4b) shows SSR an and obvious Driving distributi Factorson in of North high Chinavalues in the southeast and low values in the northwest as the distribution of water vapor is influenced by the distance to the sea, Figureunderlying5 shows surface, the latitude, spatial and distribution topography of [34,62]. seasonal The averagedstrongest attenuation clear-sky SSReffect in of northernwater vapor China duringon 2001–2015.solar radiation The (larger clear-sky than SSR –40 shows W/m2) a occur significant in north seasonal Henan variationand south with Shandong. high values Whilein the spring and summerweakest (around (281.06 –30 W/ W/mm2 and2) occur 304.90 in south-central W/m2) and Inner low valuesMongolia in due autumn to high and altitude winter and (191.73 latitude, W /m2 and 137.62dry climate W/m and2). Thelong seasonaldistance to variation the sea [63]. of solar The spatial radiation difference at a given of extinction location of iswater mainly vapor driven on by the astronomicalsolar radiation factors contributes such to as the the sp tiltatial of distribution the Earth’s of rotationclear-sky axisSSR. withHowever, respect the tospatial the Earth’sdifference orbital planeof around WVRE theis much Sun andsmaller the than resulting ARE, seasonalityand the spatial of insolationdistribution and of clear-sky the Earth–Sun SSR is more distance. strongly Figure 6 influenced by aerosol. shows the monthly variation of regional average of clear-sky solar radiation at the surface and the top of3.2. the Seasonal atmosphere Variation (TOA) of Clear-Sky in northern SSR and Driving China. Factors The regional in North China average of clear-sky SSR increases from 127.58 W/m2 in January to the peak of 323.24 W/m2 in May and then decreases to a low level Figure 5 shows the spatial distribution of seasonal averaged clear-sky SSR in northern China in December, showing a single-peak change. The seasonal pattern of clear-sky SSR is similar to that at during 2001–2015. The clear-sky SSR shows a significant seasonal variation with high values in spring the TOA, indicating that the seasonal variation of clear-sky SSR is primarily determined by the solar and summer (281.06 W/m2 and 304.90 W/m2) and low values in autumn and winter (191.73 W/m2 and radiation reaching2 the TOA, which is modulated by the astronomical factors. 137.62Remote W/m Sens.). The2020, 12seasonal, x FOR PEER variation REVIEW of solar radiation at a given location is mainly driven8 of 21by the astronomical factors such as the tilt of the Earth’s rotation axis with respect to the Earth’s orbital plane around the Sun and the resulting seasonality of insolation and the Earth–Sun distance. Figure 6 shows the monthly variation of regional average of clear-sky solar radiation at the surface and the top of the atmosphere (TOA) in northern China. The regional average of clear-sky SSR increases from 127.58 W/m2 in January to the peak of 323.24 W/m2 in May and then decreases to a low level in December, showing a single-peak change. The seasonal pattern of clear-sky SSR is similar to that at the TOA, indicating that the seasonal variation of clear-sky SSR is primarily determined by the solar radiation reaching the TOA, which is modulated by the astronomical factors.

Figure 5. SpatialFigure 5. distributionSpatial distribution ofseasonal of seasonal meanmean clear- clear-skysky SSR SSRin northern in northern China from China 2001 fromto 2015: 2001 (a) to 2015: Spring; (b) Summer; (c) Autumn; and (d) Winter. (a) Spring; (b) Summer; (c) Autumn; and (d) Winter.

Figure 6. Monthly variations of solar radiation at the top of atmosphere and clear-sky SSR in northern China during 2001–2015.

As shown in Figure 5, Shanxi, northwest Hebei, and south-central Inner Mongolia are characterized with high clear-sky SSR in all seasons, but the values changes with seasons. In spring, high clear-sky SSR (larger than 270 W/m2) occur in Shanxi, northwest Hebei, and south-central Inner Mongolia, while low ones are found in west Shandong, southeast Hebei, and parts of north Henan.

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Remote Sens. 2020, 12, 1931 8 of 19 Figure 5. Spatial distribution of seasonal mean clear-sky SSR in northern China from 2001 to 2015: (a) Spring; (b) Summer; (c) Autumn; and (d) Winter.

Figure 6. MonthlyFigure 6. variations Monthly variations of solar of solar radiation radiation at at the top top of ofatmosphere atmosphere and clear-sky andclear-sky SSR in northern SSR in northern China during 2001–2015. China during 2001–2015. As shown in Figure 5, Shanxi, northwest Hebei, and south-central Inner Mongolia are As showncharacterized in Figure with5, Shanxi, high clear-sky northwest SSR in all Hebei, seasons, and but south-central the values changes Inner with Mongoliaseasons. In spring, are characterized 2 with high clear-skyhigh clear-sky SSR SSR in all(larger seasons, than 270 butW/m the) occur values in Shanxi, changes northwest with Hebei, seasons. and south-central In spring, Inner high clear-sky Mongolia, while low ones are found in west Shandong, southeast Hebei, and parts of north Henan. SSR (larger than 270 W/m2) occur in Shanxi, northwest Hebei, and south-central Inner Mongolia, while low ones are found in west Shandong, southeast Hebei, and parts of north Henan. Figures7 and8 show the spatial distribution of seasonal mean ARE and WVRE in northern China. ARE and WVRE in spring are lower than 20 W/m2 and 40 W/m2 in the northwest regions of northern China, respectively, − − and about 45 W/m2 and 50 W/m2 in west Shandong, southeast Hebei, and parts of north Henan. − − In summer, dramatic increases of clear-sky SSR (about 50 W/m2) is found in the northwest part of northern China, which is due to the increased solar radiation reaching the TOA as a result of short Earth–Sun distance in summer [10]. However, clear-sky SSR show little increase in the boundary of Shandong, Hebei, and Henan. In these regions, ARE show a significant increase to more than 60 W/m2, − indicating a much stronger attenuation effect of aerosol on solar radiation in summer. WVRE also shows a large enhancement from spring to summer. In autumn, a large decrease in clear-sky SSR is found and the differences among regions are reduced in northern China. The decrease is mainly dominated by the astronomical factors as the radiative effects of aerosol and water vapor on solar radiation are weakened simultaneously. In winter, clear-sky SSR decrease to lower than 170 W/m2 in most regions of northern China and the lowest values (about 130 W/m2) are found in the boundary − of Tianjin, Shandong, and Hebei. The attenuation effect of aerosol and water vapor on solar radiation also reach the valley in winter. ARE values are even lower than 7 W/m2 in parts of Hebei and Inner − Mongolia, and WVRE values are lower than 20 W/m2 in most of northern China. − The seasonal variation of clear-sky SSR is dominated by the solar radiation reaching the TOA, as discussed above. However, it should be noted that there is some difference between the monthly pattern of solar radiation at the surface and that at the TOA. The peak at the surface shows in May, and solar radiation are strongly attenuated in June, July, and August. Figure9 shows the monthly variation of ARE and WVRE. ARE increases from January as temperature increases, which is mainly due to the increase of gas-particle transformation and hygroscopic growth of aerosol particles with temperature and humidity [64,65]. In spring, sandstorms occur frequently in northwest China, and dust aerosol is transported to northern China, which increases aerosol loading and its extinction on solar radiation [33]. Dust aerosol is characterized with high absorption in low wavelengths and results in low SSA in this season, which also contributes the increase in ARE. Pani et al. (2018) studied the ARE over an urban atmosphere in northern peninsular southeast Asia (PSEA) in 2014, and showed ARE ranged between 45.3 to 103.4 W/m2 from March to April [66], which is larger than that (lower − − than 30 W/m2) in our study during the same period. The difference mainly come from different − aerosol loading and properties. Influenced by increased aerosol emission from biomass burning from Remote Sens. 2020, 12, x FOR PEER REVIEW 9 of 21

Figure 7 and Figure 8 show the spatial distribution of seasonal mean ARE and WVRE in northern China. ARE and WVRE in spring are lower than –20 W/m2 and –40 W/m2 in the northwest regions of northern China, respectively, and about -45 W/m2 and -50 W/m2 in west Shandong, southeast Hebei, and parts of north Henan. In summer, dramatic increases of clear-sky SSR (about 50 W/m2) is found Remote Sens. 2020, 12, 1931 9 of 19 in the northwest part of northern China, which is due to the increased solar radiation reaching the TOA as a result of short Earth–Sun distance in summer [10]. However, clear-sky SSR show little increase in the boundary of Shandong, Hebei, and Henan. In these regions, ARE show a significant Februaryincrease to April, to more AOD than was –60 largerW/m2, indicating than 1 and a much SSA stronger is lower attenuation than 0.9effect in of some aerosol regions on solar of PSEA, which contributedradiation to the in highersummer. ARE WVRE [66 also,67 shows]. The a large maximum enhancement ARE from ( 38.90spring Wto summer./m2) occurs In autumn, in July, a and then ARE large decrease in clear-sky SSR is found and the differences among− regions are reduced in northern decreasesChina. sharply The decrease in August is mainly and dominated September. by the The astronomical decrease factors of AODas the radiative due to effects the wet of aerosol deposition by heavy rain causesand a water decrease vapor on in solar ARE radiation in August are weakened and September simultaneously. [68 In]. winter, Moreover, clear-sky large SSR decrease SSA values (0.94) are found in theseto lower months, than 170 W/m and2 in the most decrease regions of of northern aerosol China absorption and the lowest also values contributes (about –130 to W/m the2) decrease in ARE. are found in the boundary of Tianjin, Shandong, and Hebei. The attenuation effect of aerosol and The minimum ARE ( 11.40 W/m2) occurs in December, when the Asian winter monsoon brings clean water vapor on− solar radiation also reach the valley in winter. ARE values are even lower than –7 and dry airW/m to2 in northern parts of Hebei China. and Inner Mongolia, and WVRE values are lower than -20 W/m2 in most of northern China.

Figure 7. Spatial distribution of seasonal mean ARE in northern China from 2001 to 2015: (a) Spring, Figure 7.(bSpatial) Summer, distribution (c) Autumn, and of (d seasonal) Winter. mean ARE in northern China from 2001 to 2015: (a) Spring,

(b) Summer,Remote ( cSens.) Autumn, 2020, 12, x FOR and PEER (d REVIEW) Winter. 10 of 21

Figure 8. SpatialFigure distribution 8. Spatial distribution of seasonal of seasonal mean mean WVRE WVRE in in northern northern China China from 2001 from to 2015: 2001 (a) toSpring, 2015: (a) Spring, (b) Summer, (c) Autumn, and (d) Winter. (b) Summer, (c) Autumn, and (d) Winter. The seasonal variation of clear-sky SSR is dominated by the solar radiation reaching the TOA, as discussed above. However, it should be noted that there is some difference between the monthly pattern of solar radiation at the surface and that at the TOA. The peak at the surface shows in May, and solar radiation are strongly attenuated in June, July, and August. Figure 9 shows the monthly variation of ARE and WVRE. ARE increases from January as temperature increases, which is mainly due to the increase of gas-particle transformation and hygroscopic growth of aerosol particles with temperature and humidity [64,65]. In spring, sandstorms occur frequently in northwest China, and dust aerosol is transported to northern China, which increases aerosol loading and its extinction on solar radiation [33]. Dust aerosol is characterized with high absorption in low wavelengths and results in low SSA in this season, which also contributes the increase in ARE. Pani et al. (2018) studied the ARE over an urban atmosphere in northern peninsular southeast Asia (PSEA) in 2014, and showed ARE ranged between –45.3 to –103.4 W/m2 from March to April [66], which is larger than that (lower than –30 W/m2) in our study during the same period. The difference mainly come from different aerosol loading and properties. Influenced by increased aerosol emission from biomass burning from February to April, AOD was larger than 1 and SSA is lower than 0.9 in some regions of PSEA, which contributed to the higher ARE [66,67]. The maximum ARE (–38.90 W/m2) occurs in July, and then ARE decreases sharply in August and September. The decrease of AOD due to the wet deposition by heavy rain causes a decrease in ARE in August and September [68]. Moreover, large SSA values (0.94) are found in these months, and the decrease of aerosol absorption also contributes to the decrease in ARE. The minimum ARE (–11.40 W/m2) occurs in December, when the Asian winter monsoon brings clean and dry air to northern China.

Remote Sens. 2020, 12, 1931 10 of 19 Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 21 Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 21

Figure 9.FigureMonthly 9. Monthly variations variations of AREof ARE and and WVREWVRE in in northern northern China China during during 2001–2015. 2001–2015.

WVRE are low in winter and even lower than ARE in January and February. However, the Figure 9. Monthly variations of ARE and WVRE in northern China during 2001–2015. WVREextinction are low of solar in winter radiation and by water even vapor lower increa thanses AREdramatically in January from March and to February. July as the Asian However, the extinctionsummer of solar monsoon radiation brings by warm water and vapor humid increasesair [69,70]. As dramatically shown in Figure from 10, water March vapor to Julyincreases as the Asian WVRE are low in winter and even lower than ARE in January and February. However, the summer monsoonsimultaneously. brings WVRE warm reaches and the humid maximum air [ of69 –70.88,70]. AsW/m shown2 in July, in which Figure is about 10, water1.82 times vapor that increases extinction of solar radiation by water vapor increases dramatically from March to July as the Asian simultaneously.of ARE. WVRE In August reaches and September, the maximum though a of large –70.88 decrease W/m is2 foundin July, in WVRE, which the is decrease about 1.82 in ARE times that of summeris more monsoon obvious, brings and thewarm ratios and of hu WVREmid airto ARE [69,70]. increase As shown to 2.14 inand Fi gure2.24, respectively.10, water vapor The highincreases ARE.simultaneously. In Augustvalues of and WVRE WVRE September, and reaches ARE in though the June, maximum July, a large and ofAugust decrease –70.88 are W/m responsible is found2 in July, infor which WVRE,the inconsistent is about the decrease 1.82 seasonal times in that ARE is moreof obvious, ARE.variation In andAugust of the solar and ratios radiation September, of WVRE at the though tosurface ARE a andlarge increase TOA. decrease In to the 2.14 is following found and 2.24, in months, WVRE, respectively. WVREthe decrease decreases The highin ARE values of WVREis more anddramatically obvious, ARE in and June, reachesthe July, ratios to and theof AugustWVREminimum to are ofARE -13.84 responsible increase W/m2 intofor December 2.14 the and inconsistent as2.24, a result respectively. of seasonal the dry The air variation high of solarvalues radiationcondition of WVRE atthe caused and surface byARE the and inwinter June, TOA. monsoon July, In theand [69,70]. followingAugust As shownare months, responsible in Figure WVRE 9, for the the decreasesmonthly inconsistent variation dramatically seasonal of and variationWVRE of issolar much radiation more drastic at thanthe ARE,surface2 and and water TOA. vapor In plays the a followingmore important months, role inWVRE moderating decreases reaches tothe the seasonal minimum variation of of13.84 clear-sky W/ SSR.m in December as a result of the dry air condition caused by − 2 the winterdramatically monsoon and [reaches69,70]. to As the shown minimum in Figure of -13.849, the W/m monthly in December variation as a of result WVRE of the is much dry air more condition caused by the winter monsoon [69,70]. As shown in Figure 9, the monthly variation of drastic than ARE, and water vapor plays a more important role in moderating the seasonal variation of WVRE is much more drastic than ARE, and water vapor plays a more important role in moderating clear-sky SSR. the seasonal variation of clear-sky SSR.

Figure 10. Monthly variations of aerosol optical depth (AOD) and water vapor in northern China during 2001–2015. Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 21 Remote Sens. 2020, 12, 1931 11 of 19 Figure 10. Monthly variations of aerosol optical depth (AOD) and water vapor in northern China during 2001–2015. 3.3. Long-Term Trend of Clear-Sky SSR and the Roles of Aerosol and Water Vapor in Northern China 3.3. Long-Term Trend of Clear-Sky SSR and the Roles of Aerosol and Water Vapor in Northern China Figure 11 shows the monthly clear-sky SSR time series and the deseasonlized clear-sky SSR trend Figure 11 shows the monthly clear-sky SSR time series and the deseasonlized clear-sky SSR trend in northernin Chinanorthern from China 2001 from to2001 2015. to 2015. The The clear-sky clear-sky SSR SSR in in North North China China shows shows an overall an decreasing overall decreasing 2 trend of –0.12trend W of/ –0.12m per W/m year2 per butyear isbut not is not significant significant at the the 95% 95% confidential confidential level. level.Figure 12 Figure shows 12 the shows the spatial distributionspatial distribution of clear-sky of clear-sky SSR SSR trends trends in in northern northern China China from from 2001 2001 to 2015. to The 2015. trends The among trends among different regionsdifferent vary regions greatly. vary greatly. There isThere on significantis on significant trend trend over over Beijing, Beijing, northnorth Hebei, and and south south Shanxi. Shanxi. While positive trend with annual growth rate larger than 0.30 W/m2 are found in south- 2 While positivecentral trend Inner Mongolia. with annual On the growth other hand, rate a largersignificant than decreasing 0.30 W trend/m are (larger found than -0.60 in south-central W/m2 per Inner 2 Mongolia.year) On theoccurs other in west-central hand, a significant Shandong, in decreasing some places trendof which (larger trend thanup to –0.800.60 W/m W/2m perper year year) is occurs − in west-centraleven found. Shandong, Clear-sky in SSR some in these places regions of decrease which about trend 4% up in to15 years. –0.80 W/m2 per year is even found. Clear-sky SSR in these regions decrease about 4% in 15 years.

Figure 11. Figure(a) Monthly 11. (a) Monthly clear-sky clear-sky SSR time SSR seriestime series and (andb)deseasonalized (b) deseasonalized clear-sky clear-sky SSRSSR trendtrend in in northern northern China from 2001 to 2015. ChinaRemote from Sens. 2001 2020 to, 12 2015., x FOR PEER REVIEW 13 of 21

Figure 12.FigureSpatial 12. distributionSpatial distribution of deseasonalized of deseasonalized trends ofof clear-sky clear-sky SSR in SSR northern in northern China during China during 2001–2015.2001–2015. Regions Regions with significant with significant trends trends (p (pvalue value < 0.05)0.05) are are indicated indicated by dots. by dots. Figure 13a shows the spatial distribution of ARE trends in northern China from 2001 to 2015. FigureThe 13 15-yeara shows trend the of ARE spatial in northern distribution China is of–0.15 ARE W/m trends2, which inindicates northern that the China extinction from effect 2001 to 2015. 2 The 15-yearof aerosol trend ofon AREsolar radiation in northern is enhanced China during is –0.15 2001–2015. W/m ,The which weakening indicates of ARE that is found the extinction in the effect of aerosolnorthwest on solar of radiation northern isChina enhanced and is significant during 2001–2015.in south-central The Inner weakening Mongolia ofwith ARE an annual is found in the change rate of about 0.30 W/m2 per year. While significant enhancement of ARE occur in most of Shandong, southeast Hebei, northeast Henan, and annual change rates of larger than –0.60 W/m2 per year are mainly found in west-central Shandong.

Figure 13. Spatial distribution of deseasonalized trends of (a) ARE and (b) WVRE in northern China during 2001–2015. Regions with significant trends (p value < 0.05) are indicated by dots.

Comparing clear-sky SSR trend in Figure 12 and ARE trend in Figure 13a, the areas where clear- sky SSR decrease/increase have a good match with the areas where ARE is enhanced/weakened. The areas with the largest decrease of clear-sky SSR and the largest enhancement of ARE are located in west-central Shandong, while the areas with the largest increase of clear-sky SSR, and the largest weakening of ARE is found in south-central Inner Mongolia. The good consistency in the spatial distribution of clear-sky SSR and ARE trend suggests that the variation of ARE may be the dominated factor for the long-term variation of clear-sky SSR.

Remote Sens. 2020, 12, x FOR PEER REVIEW 13 of 21

Figure 12. Spatial distribution of deseasonalized trends of clear-sky SSR in northern China during Remote Sens. 2020,2001–2015.12, 1931 Regions with significant trends (p value < 0.05) are indicated by dots. 12 of 19

Figure 13a shows the spatial distribution of ARE trends in northern China from 2001 to 2015. The 15-year trend of ARE in northern China is –0.15 W/m2, which indicates that the extinction effect northwestof of aerosol northern on solar China radiation and isis significantenhanced during in south-central 2001–2015. The Innerweakening Mongolia of ARE withis found an in annual the change 2 rate of aboutnorthwest 0.30 W of/ mnorthernperyear. China While and is significantsignificant in enhancement south-central Inner ofARE Mongolia occur with in mostan annual of Shandong, southeastchange Hebei, rate northeast of about 0.30 Henan, W/m2 andper year. annual While change significant rates enhanc of largerement of than ARE –0.60 occur Win /mostm2 perof year are 2 mainly foundShandong, in west-central southeast Hebei, Shandong. northeast Henan, and annual change rates of larger than –0.60 W/m per year are mainly found in west-central Shandong.

Figure 13.FigureSpatial 13. distributionSpatial distribution of deseasonalized of deseasonalized trends of of (a) ( aARE) ARE and and(b) WVRE (b) WVRE in northern in northernChina China during 2001–2015. Regions with significant trends (p value < 0.05) are indicated by dots. during 2001–2015. Regions with significant trends (p value < 0.05) are indicated by dots. Comparing clear-sky SSR trend in Figure 12 and ARE trend in Figure 13a, the areas where clear- Comparingsky SSR decrease/increase clear-sky SSR have trend a good in Figurematch with 12 the and areas ARE where trend ARE is in enhanced/weakened. Figure 13a, the The areas where clear-sky SSRareas decrease with the largest/increase decrease have of aclear-sky good match SSR and with the largest the areas enhancement where AREof ARE is are enhanced located in/weakened. The areaswest-central with the largest Shandong, decrease while the of areas clear-sky with the SSR largest and increase the largest of clear-sky enhancement SSR, and ofthe ARElargest are located weakening of ARE is found in south-central Inner Mongolia. The good consistency in the spatial in west-centraldistribution Shandong, of clear-sky while SSR and the ARE areas trend with suggest thes largest that the variation increase of ofARE clear-sky may be the SSR, dominated and the largest weakeningfactor of AREfor the is long-term found variation in south-central of clear-sky InnerSSR. Mongolia. The good consistency in the spatial distribution of clear-sky SSR and ARE trend suggests that the variation of ARE may be the dominated factor for the long-term variation of clear-sky SSR. Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 21 Figure 14a shows the comparison of clear-sky SSR trend and co-located ARE trend. The slope of linear regressionFigure is about14a shows 0.981 the andcomparison R2 between of clear-sky clear-sky SSR trend SSR and and co-located ARE trendARE trend. is 0.995, The slope which of indicates that the clear-skylinear regression SSR trendis about is0.981 mainly and R2 determinedbetween clear-sky by SSR the and variation ARE trend of is 0.995, attenuation which indicates effect on solar that the clear-sky SSR trend is mainly determined by the variation of attenuation effect on solar radiation byradiation aerosol. by aerosol.

Figure 14.FigureLinear 14. regressionsLinear regressions between between clear-sky clear-sky SSR SSR trend trend and and(a) ARE (a) trend ARE and trend (b) WVRE and trend (b) WVRE in trend northern China during 2001–2015. in northern China during 2001–2015. Figure 13b shows the spatial distribution of WVRE trend in northern China during 2001–2015. FigureThe 13 weakeningb shows of the WVRE spatial is found distribution in most regions of WVRE of northern trend China. in northernVariations of China WVRE duringare slight 2001–2015. The weakeningover Beijing, of WVRE Tianjin, is and found most areas in most of Shandong regions and of Hebei. northern Significant China. weakening Variations of WVRE of WVRE occurs are slight 2 over Beijing,in north Tianjin, Henan and and most south-central areas of Inner Shandong Mongolia, and with Hebei. annual Significantchange rate being weakening about 0.07 of W/m WVRE occurs per year. The weakening WVRE contributes to the increase in clear-sky SSR in south-central Inner 2 in north HenanMongolia. and However, south-central the trend of Inner WVRE Mongolia, accounts for with only about annual 17% change of the clear-sky rate being SSR trend, about so it 0.07 W/m per year. Theis not weakeningthe governing WVREfactor forcontributes clear-sky SSR trend. to the Figure increase 14b shows in clear-sky the comparison SSR inbetween south-central clear- Inner Mongolia.sky However, SSR trend theand trendWVRE oftrend. WVRE The trend accounts of WVRE for va onlyries slightly about 17%as trend of of the clear-sky clear-sky SSR SSRchanges trend, so it is 2 not the governinggreatly, and factor R between for clear-sky them is lower SSR than trend. 0.07, Figure indicating 14 bthat shows the variation the comparison of extinction betweenby water clear-sky vapor has little impact on the trend of clear-sky SSR. SSR trend andFigure WVRE 15 shows trend. the variation The trend of annual of WVRE mean cl variesear-sky slightlySSR, ARE, asand trend WVRE of in clear-skynorthern China SSR changes from 2001 to 2015. The annual mean clear-sky SSR over the whole region changes drastically with the highest value in 2004 (232.88 W/m2) and the lowest value in 2006 (225.25 W/m2), and an overall decreasing trend is found. The annual mean ARE varies in the range of –19.53 W/m2 to –27.28 W/m2. The variation of annual mean ARE shows a similar pattern to clear-sky SSR, confirming the dominating effect of ARE on the variation of annual clear-sky SSR. The annual mean of WVRE varies around –39.00 W/m2, which is about 1.65 times that of ARE. However, the change of annual WVRE is so little that its influence on the variation of clear-sky SSR is quite limited.

Remote Sens. 2020, 12, 1931 13 of 19 greatly, and R2 between them is lower than 0.07, indicating that the variation of extinction by water vapor has little impact on the trend of clear-sky SSR. Figure 15 shows the variation of annual mean clear-sky SSR, ARE, and WVRE in northern China from 2001 to 2015. The annual mean clear-sky SSR over the whole region changes drastically with the highest value in 2004 (232.88 W/m2) and the lowest value in 2006 (225.25 W/m2), and an overall decreasing trend is found. The annual mean ARE varies in the range of 19.53 W/m2 to 27.28 W/m2. − − The variation of annual mean ARE shows a similar pattern to clear-sky SSR, confirming the dominating effect of ARE on the variation of annual clear-sky SSR. The annual mean of WVRE varies around 39.00 W/m2, which is about 1.65 times that of ARE. However, the change of annual WVRE is so little − that its influence on the variation of clear-sky SSR is quite limited. Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 21 Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 21

Figure 15. VariationsFigure 15. of Variations annual of (annuala) clear-sky (a) clear-sky SSR SSR and and (b (b) ARE) ARE and WVRE and WVREin northern in China northern from 2001 China from 2001 to 2015. to 2015. Figure 16a shows the variations of annual mean water vapor over two regions with significant Figure 16Figureatrend shows 15.(south-central Variations the variations ofInner annual Mongolia (a of) clear-sky annual and west-central SSR mean and (b Shandong).) waterARE and vapor WVREThere is in overno northern obvious two China trend regions fromof water 2001 with significant tovapor 2015. in west-central Shandong. Water vapor in south-central Inner Mongolia shows a small decrease trend (south-centralin 15 years, Inner which Mongolia slightly contributes and west-central to the increase in Shandong). clear-sky SSR. Figure There 16b is shows no obviousthe annual trend of water vapor in west-centralFigurevariation 16a Shandong.of shows AOD, whichthe variations Wateris an important vapor of annual indicato in south-centralmeanr of wateraerosol vapor loading. Inner over AOD Mongoliatwo in regionssouth-central showswith Innersignificant a small decrease trend (south-centralMongolia decreases Inner greatly Mongolia from 0.36 and in west-central 2001 to 0.19 inShandong). 2015. The large There decrease is no obviousof aerosol trend loading of water in 15 years, whichwould slightly be responsible contributes for the weakening to the of increase ARE and inincrease clear-sky of clear-sky SSR. SSR Figure in this region.16b shows In the annual vapor in west-central Shandong. Water vapor in south-central Inner Mongolia shows a small decrease variation of AOD,contrast, which AOD in is west-central an important Shandong indicator shows an incr ofease aerosol from 0.71 loading. in 2001 to AOD0.87 in 2015 in south-centraland is Inner in 15 years,responsible which for slightly the enhancement contributes of ARE to the and incr decreaseease inof clear-skyclear-sky SSR SSR. in thisFigure area. 16b shows the annual Mongoliavariation decreases of AOD, greatly which from is an 0.36 important in 2001 indicato to 0.19r of inaerosol 2015. loading. The large AOD decreasein south-central of aerosol Inner loading would be responsibleMongolia decreases for the greatly weakening from 0.36 of in ARE 2001 andto 0.19 increase in 2015. ofThe clear-sky large decrease SSR inof aerosol this region. loading In contrast, AOD in west-centralwould be responsible Shandong for the shows weakening an increase of ARE from and increase 0.71 in of 2001 clear-sky to 0.87 SSR in in 2015 this region. and is In responsible contrast, AOD in west-central Shandong shows an increase from 0.71 in 2001 to 0.87 in 2015 and is for the enhancementresponsible for of the ARE enhancement and decrease of ARE ofand clear-sky decrease of SSR clear-sky in this SSR area. in this area.

Figure 16. Variations of annual (a) water vapor and (b) AOD in south-central Inner Mongolia, west- central Shandong, and the whole study area from 2001 to 2015.

To figure out the roles of natural and anthropogenic aerosol emissions in clear-sky SSR variation, the annual variation of typical aerosol compositions such as dust and sulfate from MERRA2 is investigated. As shown in Figure 17, dust mass concentration at the surface decreases dramatically in North China, indicating a decreasing influence of dust aerosol. Dust mass concentration in south- central Inner Mongolia decreases about 16% from 2001 to 2015. As a result, the extinction of dust Figure 16.FigureVariations 16. Variations of annual of annual ( a(a)) waterwater vapor vapor and and(b) AOD (b) in AOD south-central in south-central Inner Mongolia, Inner west- Mongolia, central Shandong, and the whole study area from 2001 to 2015. west-central Shandong, and the whole study area from 2001 to 2015. To figure out the roles of natural and anthropogenic aerosol emissions in clear-sky SSR variation, the annual variation of typical aerosol compositions such as dust and sulfate from MERRA2 is investigated. As shown in Figure 17, dust mass concentration at the surface decreases dramatically in North China, indicating a decreasing influence of dust aerosol. Dust mass concentration in south- central Inner Mongolia decreases about 16% from 2001 to 2015. As a result, the extinction of dust

Remote Sens. 2020, 12, 1931 14 of 19

To figure out the roles of natural and anthropogenic aerosol emissions in clear-sky SSR variation, the annual variation of typical aerosol compositions such as dust and sulfate from MERRA2 is investigated. As shown in Figure 17, dust mass concentration at the surface decreases dramatically in North China, indicating a decreasing influence of dust aerosol. Dust mass concentration in south-central Inner

MongoliaRemote decreasesSens. 2020, 12,about x FOR PEER 16% REVIEW from2001 to 2015. As a result, the extinction of dust particles16 of on 21 solar radiation decreases and more solar radiation reaches the surface, causing the increase of clear-sky SSR inparticles this region. on solar In radiation contrast, decreases sulfate mass and more concentration solar radiation in this reaches region the is insurface, low levelcausing and the shows little variationincrease of due clear-sky to the SSR undeveloped in this region. industry In contrast, and sulfate sparse mass population concentration here. in Dust this massregion concentration is in low level and shows little variation due to the undeveloped industry and sparse population here. Dust in west and central Shandong also shows a great decrease. However, sulfate mass concentration mass concentration in west and central Shandong also shows a great decrease. However, sulfate mass increases about 92% from 2001 to 2015. The dramatic increase in sulfate aerosol leads to the decreasing concentration increases about 92% from 2001 to 2015. The dramatic increase in sulfate aerosol leads trendto of the clear-sky decreasing SSR trend in this of clear-sky area. SSR in this area.

FigureFigure 17. Variations17. Variations of annualof annual surface surface ( a(a)) dust dust andand ( b)) sulfate sulfate mass mass concentration concentration in south-central in south-central InnerInner Mongolia, Mongolia, west-central west-central Shandong, Shandong, and and thethe wholewhole study study area area from from 2001 2001 to 2015. to 2015.

4. Discussion4. Discussion To assessTo assess the the influence influence of of input input parameters parameters onon SSR simulation, simulation, we we conducted conducted sensitivity sensitivity tests tests for SSR simulation by adding a perturbed value for each parameter inputted in the MAGIC model. for SSR simulation by adding a perturbed value for each parameter inputted in the MAGIC model. The sensitivity test is based on two steps—first, SSR was simulated using the measured values as The sensitivity test is based on two steps—first, SSR was simulated using the measured values as input input parameters, obtaining the non-perturbed results. Second, we simulated SSR by adding a parameters,perturbed obtaining value for thean input non-perturbed parameter and results. keepin Second,g other parameters we simulated unchanged, SSR by thereby adding obtaining a perturbed valuea forperturbed an input result. parameter This andstep keepingis repeated other to parametersobtain perturbed unchanged, results therebyfor all obtainingthe input aaerosol perturbed result.parameters, This step such is repeated as AOD, toSSA, obtain and water perturbed vapor. resultsA similar for strategy all the has input been aerosol adopted parameters, for uncertainty such as AOD,estimation SSA, and in water previous vapor. studies A similar [71]. The strategy perturbed has beenvalues adopted of AOD, for SSA, uncertainty and water estimationvapor values in were previous studiesselected [71]. based The perturbedon the uncertainties values of of AOD,these para SSA,meters and as water shown vapor in Section values 2. wereAOD was selected perturbed based on the uncertaintiesby ±0.14, SSA ofwas these perturbed parameters by ±0.06, as an shownd water in vapor Section was2 .perturbed AOD was by perturbed ±2.80 kg/m2 by. Table0.14, 2 shows SSA was ± perturbedthe absolute by 0.06, and andpercentage water changes vapor was between perturbed the non-perturbed by 2.80 kg and/m2 .perturbed Table2 shows results. the An absolute increase and of 0.14 in ±AOD from the measurements causes an SSR decrease± of 3.71%. By contrast, a decrease of percentage changes between the non-perturbed and perturbed results. An increase of 0.14 in AOD 0.14 in AOD results in a decrease with similar magnitudes. The largest SSR change in this analysis is fromassociated the measurements with SSA causesuncertainty. an SSR An decrease increase ofof 3.71%.0.06 in SSA By contrast, provides aan decrease increase of of 0.14 4.54% in for AOD SSR. results in a decreaseFor water withvapor, similar an increase magnitudes. of 2.80 kg/m The2 in water largest vapor SSR causes change a 0.64% in this decrease analysis in SSR. is associated with SSA uncertainty. An increase of 0.06 in SSA provides an increase of 4.54% for SSR. For water vapor, an increaseTable of 2.802. Parameter kg/m2 in perturbations water vapor used causes in radiative a 0.64% perturbation decrease an inalysis SSR. and corresponding SSR changes.

Table 2.ParameterParameter perturbations Perturbation used in radiative Change of perturbation SSR (W/m2) analysis Change and correspondingof SSR (%) SSR changes.AOD 0.14|–0.14 –9.94 |10.37 –3.71|3.87 SSA 0.06|–0.06 12.18 |–11.28 4.54 |–4.21 Parameter Perturbation Change of SSR (W/m2) Change of SSR (%) Water Vapor (kg/m2) 2.80|–2.80 –1.72 |1.995 –0.64 |0.74 AOD 0.14| 0.14 9.94|10.37 3.71|3.87 − − − SSA 0.06| 0.06 12.18| 11.28 4.54| 4.21 5. Conclusions − − − Water Vapor (kg/m2) 2.80| 2.80 1.72|1.995 0.64|0.74 In this study, we analyzed the distribution− and trend− of clear-sky SSR and the quantitative− effects of aerosol and water vapor over northern China based on clear-sky SSR, ARE, and WVRE simulated by the MAGIC. The comparison between simulated and observed clear-sky SSR shows a R2 of 0.98,

Remote Sens. 2020, 12, 1931 15 of 19

5. Conclusions In this study, we analyzed the distribution and trend of clear-sky SSR and the quantitative effects of aerosol and water vapor over northern China based on clear-sky SSR, ARE, and WVRE simulated by the MAGIC. The comparison between simulated and observed clear-sky SSR shows a R2 of 0.98, MAE of 8.14 W/m2 (3.62%), and RMSE of 10.60 W/m2 (4.17%), confirming the reliability of the simulation in this study. During 2001–2015, clear-sky SSR shows a clear distribution of high values in the northwest and low values in the southeast. A low-value center with clear-sky SSR less than 215 W/m2 is found in the boundaries of Shandong, Hebei, and Tianjin, which is characterized with low altitude plains, developed industry, and high anthropogenic emissions. Regions with low clear-sky SSR correspond to areas with strong extinction effect of aerosol on solar radiation, indicating that aerosol loading plays an important role in the spatial distribution of clear-sky SSR. Clear-sky SSR in northern China shows a similar seasonal pattern with solar radiation at the TOA with high value (304.90 W/m2) in summer and low value (137.62 W/m2) in winter, which is mainly modulated by astronomical factors. The seasonal variation of WVRE is much more drastic than ARE, and water vapor plays a more important role in moderating the seasonal variation of clear-sky SSR. The clear-sky SSR in North China shows an overall decreasing trend of 0.12 W/m2 per year. − A decrease more strongly than 0.60 W/m2 per year occurs in west-central Shandong, and a positive − trend with annual growth rate larger than 0.30 W/m2 is found in south-central Inner Mongolia. The clear-sky SSR trend is mainly determined by the variation of aerosol’s effect on solar radiation. AOD decreases greatly from 0.36 in 2001 to 0.19 in 2015 over south-central Inner Mongolia and increases from 0.71 in 2001 to 0.87 in 2015 over west-central Shandong. Dust mass concentration over south-central Inner Mongolia decreases about 16% from 2001 to 2015, resulting in the decrease of the extinction of dust particles on solar radiation and the increase of clear-sky SSR. In contrast, sulfate aerosol over west-central Shandong increases about 92% in 15 years, leading to the decreasing trend of clear-sky SSR. Using fixed atmospheric vertical distribution is one source of error in this study, and we will carry out studies adopting observations of atmospheric vertical profiles in SSR simulations to reduce error in the future work.

Author Contributions: G.Z. and Y.M. conceived and designed the experiments. G.Z. collected and analyzed the data and wrote the paper. G.Z. and Y.M. revised the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Natural Science Foundation of China, grant number 41875038 and 41801261. Acknowledgments: We would like to thank the MODIS scientific teams for providing MODIS data, the National Meteorological Information Center for providing surface solar radiation data, and the NASA Global Modeling and Assimilation Office and the European Centre for Medium-Range Weather Forecasting for providing the atmospheric reanalysis data. Conflicts of Interest: The authors declare no conflict of interest.

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