The DAO-K field campaign with comprehensive observations of physical, chemical, and optical properties of aerosol particles, solar radiation, vertical structures of the atmosphere, and land surface albedo in the Taklimakan Desert region was designed to provide high-quality data for aerosol radiative forcing estimates. Kashi is located at the edge of the Taklimakan Desert; it is surrounded by the Tianshan Mountains in the north, the Pamir plateau in the west, and the Kunlun Mountains in the south (Fig. 1). The DAO-K field campaign was conducted at the Kashi campus of the Aerospace Information Research Institute, Chinese Academy of Sciences (39.50∘ N, 75.93∘ E; 1320 m above mean sea level). The campus hosts a long-term observation station within the Sun–sky radiometer Observation NETwork (SONET; http://www.sonet.ac.cn/index.php, last access: July 2019) (Li et al., 2018). In addition to the Kashi station near the Taklimakan Desert, SONET also maintains two dust aerosol observation stations (i.e., Zhangye and Minqin stations) in the Gobi Desert, which is another important source of Asian dust. Although some studies reported that the dust generated in Taklimakan Desert exerts less influence on long-range downstream regions due to the unique terrain and low-level background wind climatology compared to those in Gobi Desert (Chen et al., 2017; Liu et al., 2019), the Taklimakan Desert is a better representative for studying the effects of dust aerosol solar radiative forcing on local regions rather than the Gobi Desert because of its huge dust emission capability (Chen et al., 2017).

Kashi represents a place heavily affected by dust aerosol particles. It is influenced by local anthropogenic pollution and pollution transported from surrounding arid and desert areas. According to the SONET long-term measurements from 2013, the Kashi site is frequently affected by dust, where the multi-year average AOD is up to 0.56±0.18 at 500 nm. Moreover, the Ångström exponent (AE; 440–870 nm) and fine-mode fraction (FMF; 500 nm) at Kashi are the lowest (with the multi-year average values of 0.54±0.27 and 0.40±0.14, respectively; low values of AE indicate the presence of large dust particles) among all 16 sites within SONET around China (Li et al., 2018). In contrast, the multi-year average AODs (500 nm) at Zhangye (0.28±0.11) and Minqin (0.26±0.11) are only half of that at Kashi or less; meanwhile, their average values of AE and FMF are also greater than those at Kashi (Li et al., 2018). These data imply that coarse particles are more dominant in the Taklimakan Desert in comparison to the Gobi Desert. Every year, FMF reaches the lowest value, and the volume particle size distribution presents a predominant coarse mode from March to May at Kashi (Li et al., 2018), due to the frequent dust invasions in spring. Chen et al. (2014) also reported that the dust radiative forcing had relatively small interannual variation but a distinct seasonal course with maximum values in late spring and early summer during the period of 2007 to 2011 in the Taklimakan Desert. Sun et al. (2012) found that the solar radiative heating peaks appear in April in southern Xinjiang and in May for northern Xinjiang. Thus, the DAO-K intensive field campaign was carried out in April 2019 and lasted for nearly a month. During the campaign, several dust events were observed on the base of a coordinated deployment of multiple in situ and remote sensing platforms and state-of-the-art instruments based on passive and active detection technologies.

Columnar aerosol properties are essential parameters for quantifying radiative forcing of atmospheric aerosol particles. However, high loading and complex light scattering processes corresponding to diverse particle shapes bring challenges to remote sensing of mineral dust in the atmosphere (Dubovik et al., 2006; Bi et al., 2010; Li et al., 2019). Ground-based detection by Sun–sky radiometer works out a solution by modeling dust particles as randomly oriented spheroids in the retrieval framework (Dubovik et al., 2006). From these activities, quality-assured databases of dust aerosol properties became available based on both the AERONET and SONET Sun–sky radiometer retrievals (Holben et al. 1998; Li et al., 2018). During the DAO-K campaign, four Cimel Sun–sky radiometers, including a polarized Sun–sky–Moon radiometer CE318-TP (no. 1150), two unpolarized Sun–sky–Moon radiometers CE318-T (nos. 1098 and 1141), and a polarized Sun–sky radiometer CE318-DP (no. 0971), were deployed at Kashi (Fig. 2a). CE318 nos. 1150 and 1141 were calibrated rigorously at the AERONET Izaña Observatory with the accuracy of AOD about 0.25 %–0.5 %, while AOD-related measurements and sky radiance measurements of CE318 nos. 1098 and 0971 were calibrated via the master instrument (no. 1150) by a vicarious/transfer calibration method before the field campaign (Holben et al., 1998; Li et al., 2008, 2018). The volume aerosol parameters of AOD, SSA, AE, and asymmetry factor (i.e., g) in four channels with center wavelengths of 440, 675, 870, and 1020 nm were retrieved following the SONET level-1.5 data criteria (Li et al., 2018). Observations from the CE318 no. 1141 also joined in the AERONET dataset. The consistency of the products following the AERONET and SONET retrieval frameworks has been validated by Li et al. (2018). The multi-wavelength properties of AOD, SSA, AE, and g were applied in RT simulations. In addition to Sun–sky radiometers, a METONE BAM-1020 continuous particulate monitor was also deployed to measure PM2.5 mass concentration (mg m-3) (Fig. 2a). The hourly PM10 mass concentrations (mg m-3) were collected from the routine measurements of the ambient air quality continuous automated monitoring system in Kashi operated by the China National Environmental Monitoring Center. The aerosol parameters including AOD, PM2.5, and PM10 mass concentrations were assimilated in the WRF-Chem model simulation in this study.

Aerosol radiative effects also depend on the surface albedo and the vertical structure of atmosphere (Wendisch et al., 2004). During the DAO-K campaign, atmospheric profiles, including the vertical distributions of the atmospheric pressure, temperature, and relative humidity, were collected from sounding balloon measurements. The sounding balloons were operationally launched twice a day around 00:00 and 12:00 UTC at the Kashi weather station (39.46∘ N, 75.98∘ E; 1291 m above mean sea level). Normally, about 3000 individual measurements are recorded during one balloon flight, which corresponds to a sampling frequency of 1 s (Guo et al., 2016; Chen et al., 2019). The data quality was controlled following the operational specifications for conventional upper-air meteorological observations (China Meteorological Administration, 2010). The accuracy of the temperature profile in the troposphere is within ±0.1 K (Zhang et al., 2018; Guo et al, 2019). In addition to pressure, temperature, and relative humidity profiles, ozone profiles obtained by the Ozone Monitoring Instrument (OMI)/Aura satellite (Bhartia et al., 1996) were used as input for the RT model. The satellite observations of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua were employed to collect the surface reflection during the DAO-K campaign. The MODIS products of shortwave bidirectional reflectance distribution function (BRDF) parameters, black-sky albedo (BSA), and white-sky albedo (WSA) were adopted to derive the surface albedo during daytime (Schaaf and Wang, 2015). A solar radiation monitoring station, equipped with an EKO MS-57 pyrheliometer and two MS-80 pyranometers, was used for measuring the direct, diffuse, and total solar irradiances (W m-2) in the range of 0.28–3.0 µm (Fig. 2a). The pyrheliometer and pyranometers have been calibrated before the campaign with uncertainties of 0.55 % and 0.66 %, respectively. They satisfy the requirements of class A under ISO 9060:2018 with response time of less than 0.2 and 0.4 s, separately. The fraction of diffuse skylight radiation deduced from the diffuse and total irradiances also gave a key weighting index to modulate the diurnal changes of the surface albedo.

Further instruments provided independent evidence of the existence of dust and cloud layers during the observations. Multi-wavelength Mie–Raman polarization lidar (LILAS) developed by the Laboratoire d'Optique Atmosphérique, Université de Lille 1 (Fig. 2b), was equipped with three elastic wavelengths (all linearly polarized) at 355, 532, and 1064 nm, and three Raman wavelengths at 387, 530, and 408 nm, from which the vertical distribution of multiple optical and physical properties of dust aerosol particles can be obtained (Veselovskii et al., 2016, 2018; Hu et al., 2019). The backscattering coefficient profile at 355 nm wavelength was applied in this study to distinguish the two-layer structure of dust. The YNT all-sky view camera ASC200 equipped with two wide-dynamic full-sky visible and infrared imagers, recorded dynamic states of clouds during day and night with 10 min (or less than 10 min) resolution. An overview of the instruments and corresponding parameters employed in the study is listed in Table 1. Considering different durations of various measurements, we calculated and discussed the ASRF from 2 to 25 April 2019, when simultaneous measurements are available.

The direct solar radiative forcing of atmospheric aerosol particles is calculated using the following equations (Babu et al., 2002; Adesina et al., 2014; Esteve et al., 2014): 1ASRFTOA=Fnet,TOAa-Fnet,TOA0,2ASRFBOA=Fnet,BOAa-Fnet,BOA0,3ASRFATM=ASRFTOA-ASRFBOA,4Fnet=F↓-F↑, where ASRFTOA, ASRFBOA, and ASRFATM denote the direct aerosol solar radiative forcing at the TOA, BOA, and in the atmosphere (ATM), respectively. Fneta and Fnet0 indicate the net irradiances with and without aerosols, respectively. F↓ and F↑ separately represent the downward and upward irradiances. All the above quantities are measured in physical units of W m-2. The radiative forcing efficiency is defined as the rate at which the atmosphere is forced per unit of aerosol optical depth at 550 nm (García et al., 2008, 2012): 5ASRFE=ASRF/τ550, where ASRFE (in W m-2 τ550-1) is the aerosol solar radiative forcing efficiency at the TOA, BOA, or in ATM. Since the effects of aerosol loading on radiative forcing have been eliminated, radiative forcing efficiency has unique advantage on evaluation of the direct radiative effects of different types of aerosols (García et al., 2008).

The focus of this study is to quantify the direct ASRF and ASRFE at the TOA, BOA, and in ATM under cloud-free conditions using the SBDART model fed with comprehensive ground-based and satellite observations collected during the DAO-K campaign. SBDART is a radiative transfer software tool that has been widely applied in atmospheric radiative energy balance studies (Ricchiazzi et al., 1998; Li et al., 2018). The discrete ordinate method is adopted in the code, which provides a numerically stable algorithm to solve the equations of plane-parallel radiative transfer in a vertically inhomogeneous atmosphere (Ricchiazzi et al.,1998). The simulations cover the same wavelength range (i.e., 0.28–3.0 µm) as the pyranometer for convenience of comparison. Simulations of the ASRF by the SBDART model are susceptible to the input conditions including the aerosol properties, atmosphere profiles, and land surface albedo. These input data were specified based on the high-quality dataset obtained in the DAO-K campaign.

The time series of the measured values of AOD, AE, PM2.5, and PM10 mass concentrations collected during the DAO-K campaign are shown in Fig. 4. The average value of AOD at 550 nm wavelength is 0.65 during the campaign. According to AOD, five high aerosol loading episodes are identified: 09:26–12:15 UTC on 2 April 2019, 09:13 on 3 April 2019 until 05:11 UTC on 5 April 2019, 01:52 on 8 April 2019 until 04:20 UTC on 10 April 2019, 01:47 on 13 April 2019 until 12:32 UTC on 16 April 2019, and 01:30 on 24 April 2019 until 04:11 UTC on 25 April 2019. The highest values of AOD at 550 nm (2.3) were observed from 24 to 25 April 2019 during a severe dust storm event. From Fig. 4, a negative correlation between AOD and AE becomes obvious. For the five high aerosol loading episodes, the AEs show very low values, suggesting that the heavy aerosol outbreaks at Kashi were dominated by dust particles. As a qualitative indicator of aerosol particle size, the values of AE are always less than 1.0 during the DAO-K campaign, illustrating the fact that aerosol particles around the Taklimakan Desert are mainly dominated by coarse particles (even for clear situations). This is consistent with the results obtained in a previous study (Fig. 4 in Li et al., 2018). Comparatively high values of AE (> 0.4) are observed on 7, 12, 19, and 23 April 2019, implying relatively small particle enrichments for these days. The time series of PM2.5 and PM10 mass concentrations generally concur with that of AOD. However, for some days, such as 19 and 23 April 2019, relatively high PM2.5 corresponding to low AOD has been observed, indicating an enhanced influence of anthropogenic pollution. For the measurements on 7 and 12 April 2019, high AE values corresponding to low PM2.5 concentrations could be down to the very low turbidity conditions. It should be noted that the errors in computations of AE significantly increase under low aerosol loading conditions (Kaskaoutis et al., 2007).

Results of instantaneous ASRF and ASRFE during the DAO-K campaign are given in Fig. 5. Both positive and negative values of ASRF, corresponding to warming and cooling effects, respectively, can be found at top of the atmosphere (Fig. 5a). However, aerosols have only warming effects in the atmosphere (Fig. 5c) and cooling effects at the surface (Fig. 5e) during the DAO-K campaign. ASRF values at the TOA and BOA exhibit obvious negative correlations with AOD. Positive correlations are observed between ASRF within the atmospheric column and AOD. From Fig. 5, it is evident that the dust aerosol has strong influence on the solar radiation budget. For the five high aerosol loading episodes (Fig. 4), the dust-dominant aerosol population exerts stronger cooling effects at the TOA and BOA, and more significant warming effects in the atmosphere than other low aerosol loading situations. Moreover, the cooling effects at the BOA are more noticeable than at the TOA, with minimum values around -217 and -119 W m-2, respectively.

When ASRF is normalized by aerosol optical depth at 550 nm wavelength, the ASRFE is obtained. This quantity is mostly insensitive to the aerosol loading, at least if a linear relation between ASRF and AOD is assumed. Nevertheless, a weak negative correlation between ASRFE and AE can be observed at the BOA (Fig. 5f). That means the ASRFE at the surface can roughly indicate the radiative forcing effects of different types of aerosols (García et al., 2008). A relatively large fraction of small particles associated with high AE has stronger ASRFE for cooling the surface than other low AE situations. But for TOA and ATM (Fig. 5b, d), there is no obvious correlation between ASRFE and AE. Generally, the cooling effect of aerosols at Kashi is more efficient at the BOA than that at the TOA. It is in accordance with the results of ASRF. In comparison to ASRF, the variation of ASRFE is relatively moderate during the campaign. The strongest cooling effects on the TOA and BOA all appear in the episode of dust storm outbreak (i.e., 24 and 25 April 2019) (see Fig. 5a, e). But large dust particles in this case do not show extreme radiative forcing efficiency (Fig. 5b, f). Strong cooling efficiencies at the surface during the DAO-K campaign occur in the very clear cases with high AE on 7 April 2019 (Fig. 5f).

During the DAO-K campaign, the average values of daily mean ASRF at Kashi are -19±13 W m-2 at the TOA and 36±23 W m-2 at the BOA, which are slightly stronger than the multi-year average values at this site (i.e., -16 W m-2 at the TOA and -18 W m-2 at the BOA) obtained by the previous study (Li et al., 2018). These results are reasonable, since the campaign was performed in the dust-prone season and higher aerosol loading situations have stronger ASRF effects as discussed above. Likewise, the average values of daily mean ASRFE at the TOA and BOA during the DAO-K campaign are -27±9 W m-2 τ550-1 and -55±10 W m-2 τ550-1, respectively, which are more efficient than the corresponding multi-year average values (i.e., -21 W m-2 τ550-1 at the TOA and -24 W m-2 τ550-1 at the BOA) reported in the previous study (Li et al., 2018).

Figure 10 describes the influence of atmospheric profile and land surface albedo on the simulations of total irradiances and ASRF. The differences in the results of total downward irradiance (TDI), total upward irradiance (TUI), and ASRF at the TOA and BOA simulated with the pre-defined midlatitude winter profile and user-specified profiles, and simulated with local noon surface albedo and instantaneous surface albedo are given, respectively. According to Fig. 10a, different settings of profiles have no influence on the TDI at the TOA. For the TUI, the absolute differences are less than 9 W m-2. However, the atmospheric profile has significant impacts on both the TDI and TUI at the surface. The influence on TDI is generally stronger than which on TUI. The maximum absolute difference is up to 138 W m-2 (Fig. 10c). For ASRF at the TOA, the effects of atmospheric profiles are less than 5 W m-2. However, the serious influence of atmospheric profiles on ASRF can be up to 103 W m-2 at the BOA (Fig. 10e). The average effect of different profiles on ASRF is 0.8 W m-2 at the TOA, which is quite small in comparison with the average values of daily ASRF (-19 W m-2). However, the average difference of 13 W m-2 for ASRF affected by atmospheric profiles cannot be ignored relative to the average ASRF (-36 W m-2) at the BOA. As a result, the cooling effects of aerosol radiative forcing will be significantly underestimated at the BOA simulated with the pre-defined midlatitude winter profile instead of the user-specified Kashi atmospheric profiles.

Like atmospheric profile, different settings of LSA have also no influence on TDI at the TOA (Fig. 10b). They have small effects on TDI at the BOA (absolute difference less than 3 W m-2) but obvious impacts on TUI at the TOA and BOA (absolute difference up to 22 W m-2) (Fig. 10b, d). From Fig. 3, the local noon albedo is often less than the daily mean albedo. Especially for a clear day, the minimum of LSA occurs around the local noon. Then, the TUI at the TOA and BOA will generally be underestimated by using the local noon albedo instead of instantaneous surface albedo in the simulations. But for ASRF (Fig. 10f), two LSA settings lead to moderate impacts at the TOA and BOA with average absolute differences of 1.8 and 1.7 W m-2, respectively. Therefore, simulations using the local noon albedo tend to overestimate the cooling effects of the aerosol radiative forcing both at the TOA and BOA.

Aerosol radiative forcing at the TOA and BOA involves operational products provided routinely by AERONET. Measurements of CE318 no. 1141 during the DAO-K campaign have been processed by AERONET. Therefore, we can compare the ASRF product from AERONET with our simulations. For AERONET, broadband upward and downward irradiances in the SW range from 0.2 to 4.0 µm were calculated by radiative transfer model with retrieved aerosol properties as model inputs (http://aeronet.gsfc.nasa.gov, last access: July 2019). However, AERONET adopts a different definition of ASRF, only taking the downward irradiance at the BOA and the upward irradiance at the TOA into consideration (García et al., 2012). The upward irradiances with and without aerosols in Eq. (2), along with the downward irradiances with and without aerosols in Eq. (1), are not taken into account. Omitting the downward irradiances will not make much difference in ASRF at the TOA. But for ASRF at the BOA, it is predictable that neglecting the upward irradiance will lead to an obvious difference. Some existing studies have executed this kind of comparison (García et al., 2008, 2012; Bi et al., 2014) and reported that AERONET tends to overestimate aerosol ASRF at the BOA (García et al., 2012).

Figure 11 presents the correlations of instantaneous aerosol ASRF between the RT model simulations and the AERONET products. It is obvious that there are linear relationships between our RT simulations and the AERONET results with R2 up to 0.98 and 0.99 at the TOA and BOA, respectively. Two ASRF results at the TOA show good consistency with a slope of 1.01, even though the calculated SW ranges are not an exact match (i.e., 0.28–3.0 µm for this study and 0.2–4.0 µm for AERONET). But, for BOA, the AERONET products are obviously stronger than the corresponding RT model simulations (with a slope of 1.24), which agrees with the conclusion of the previous study (García et al., 2012).

Figure 12 compares the assimilated aerosols to the observations. Evidently, the assimilation greatly improves the particulate matter concentrations and show reasonable variations in accordance with the dust episodes. However, two disadvantages are noticeable. One is that the assimilation fails to reproduce the extremely high PM2.5 and PM10 on 24–25 April 2019, because the background error covariance is not specific for the model error in the strong dust storm. A better model result for the specific dust storm requires improving the model capability of simulating dust emission and the transport of dust particulates besides data assimilation. Another is the assimilated AOD indeed increases but not well approaches the observations. The reason is that we only assimilated AOD by assuming the invariable extinction coefficients. Hence, this low bias in AOD cannot be eliminated by choosing a scaling factor smaller than 50 % in the observation error for that it will damage the surface-layer particulate results.

Figure 13 illustrates the results of daily mean ASRFs during DAO-K campaign simulated by the SBDART and WRF-Chem models. The two results show similar variation patterns. However, there are obvious differences between the WRF-Chem results and the RT simulations in some dust-polluted cases (e.g., 9, 24, and 25 April 2019). According to the RT simulations, the strongest radiative forcing occurred on 25 April 2019. However, the most significant ASRF of WRF-Chem simulation is found on 24 April 2019, followed by 25 April 2019. As mentioned above, heavy dust storms broke out on these 2 d during the DAO-K campaign. The percent differences are sometimes greater than 50 % between the RT and WRF-Chem simulations. The significant differences between the two kinds of simulated results should be further evaluated.

Figure 14 directly compares the RT and WRF-Chem simulated downward irradiances at surface with the ground-based measurements under three different sky conditions (i.e., clear case, heavy dust case, and two-layer dust case). The RT simulations of total, direct, and diffuse downward irradiances in the three situations agree well with high-precision measurements of pyrheliometer and pyranometers. The percent differences of RT-simulated total irradiance with respect to the measurements are only 0.03 % for the clear case, -2.67 % for the heavy dust case, and -0.43 % for the two-layer dust case. Except for the heavy dust case, they are within the pyranometer measurement uncertainties (0.66 %). As for the WRF-Chem simulations, the total irradiances in the clear-sky case are consistent with RT simulations and measurements (Fig. 14a). But for the direct irradiances, there are obvious differences between the WRF-Chem simulations and the corresponding measurements (Fig. 14b). Moreover, the WRF-Chem simulated diffuse irradiances in the clear case (Fig. 14c), the total, direct, and diffuse irradiances in the heavy dust and two-layer dust cases (Fig. 14d–i) are significantly distinct from the measurements and RT simulations.

One of the most noticeable features in the curves of WRF-Chem results is the sudden jump around 06:00 UTC, which can be attributed to data assimilation restarting at 06:00 UTC and running to the next analysis time (12:00 UTC). The WRF-Chem results are greatly improved after 06:00 UTC in the dust-polluted cases. It is evident that data assimilation can ameliorate the WRF-Chem simulations in dust cases, but the correction effects are still limited. So, the problems of the WRF-Chem simulation have not yet been fully resolved by the assimilation of aerosol optical depth and particulate matter concentrations. This conclusion is in accordance with Figs. 12 and 13. Our measurements have proved that the simulations of RT model are reliable in both of clear and high aerosol loading situations. The WRF-Chem model performs better in clear-sky than in the dust-polluted conditions. There is still room for improving the WRF-Chem simulation of dust aerosol radiative forcing.