Sub-seasonal aerosol characterization at the Middle East regions of AERONET site

Highlights

• Characterization were utilized using the Angstrom Exponent (AE)

• Characterization using standardized anomaly presents robust contributing factor

• Results shows biomass and dust aerosols predominantly driven by near surface wind

• AE values <0.6 display coarse mode dominance originating from dust storms

Abstract

The present study used Angstrom Exponent (AE) and relationship with Precipitable water vapor (PW) and cloud fraction (CF) obtained from space-based direct sky-radiometer of Moderate Resolution Imaging Spectroradiometer (MODIS) and direct Sun algorithm surface-based AERONET network (level 2.0 version 3) to evaluate aerosol optical depth (AOD) predominance and characterization at the Middle East sites: IASBS (36 N, 48E), EILAT (29 N, 34E), KAUST_CAMPUS (22 N, 39E) and MEZAIRA (23 N, 53E). The study was strictly limited to the monsoon period (June–September), where dust is exposing to be dominant AOD. To address AE robustness, standardize and standard deviation method of analysis was used to indicate seasonal connection of aerosols, precipitable water vapor and cloud fraction. In order to remove the large influence of annual cycle, the data were first deseasonalize. The results show that IASBS site indicated AE and AOD standard deviation of (1.41 ± 1.28) in June with corresponding values of (0.82 ± 1.17) in July, (0.20 ± 0.91) in August and (0.25 ± 0.57) in September. EILAT site indicated AE and AOD standard deviation of (1.11 ± 1.07) in June with corresponding values of (1.12 ± 0.87) in July, (0.49 ± 0.83) in August and (0.85 ± 1.24) in September. KAUST_CAMPUS site indicated AE and AOD standard deviation of (0.61 ± 0.63) in June with corresponding values of (0.30 ± 0.78) in July, (0.86 ± 1.27) in August and (0.36 ± 1.24) in September. MEZAIRA site located in United Arab Emirates presents AE and AOD standard deviation of (1.04 ± 0.96) in June with corresponding values of (0.99 ± 1.22) in July, (0.88 ± 1.03) in August and (0.80 ± 0.50) in September. The AE > 1 (fine-mode aerosol) dominate mostly in June but did not prevail over other monsoon month. Further integrated assessment is needed to confirm this study interpretation using other site.

Introduction

The knowledge of atmospheric dynamical processes of aerosol plays a crucial role in determining the Earth's radiative budget. However, aerosols scatter or absorb short wave radiation and modify the cloud microphysical properties (Ramanathan et al., 2001). The ability of aerosols to influence the radiative budget of the Earth and their effects on air quality and clouds significantly depends on size (Dusek et al., 2006). The size distribution linearly depends on production mechanism. In order to characterize contributions of aerosol optical depth (AOD) from smaller to bigger particles is a step forward to study and understand its abundance (Lee et al., 2010). This idea requires knowledge of size distributions. Some method detailed in (Nakajima et al., 1996; O'Neill et al., 2003) exits to derive the number concentration from spectral observation of AOD from ground-based radiometer. However, this number concentration of aerosols varies significantly with space and time. Progress in the past two decades (Kahn et al., 2010; Hsu et al., 2012) has been made using space-based remote sensing to estimate aerosol properties over the global and continental scale. However, only few space-based remote sensors were able to provide aerosol properties like size distribution, single scattering albedo and Angstrom exponent over land. The network of AERONET has been established to provide dense validation of space-based measurement using surface-based. In algorithm outlined by Banks and Brindley (2013), AERONET data is regarded as representative for grid cells within a 25 km radius of the AERONET site. The observations are regarded as dust when the Angstrom coefficient α ≤ 0.6 (Dubovik et al., 2002), where α is ranges between 440 and 675 nm. The AERONET measurement uncertainties are of the order 0.01 to 0.02 (Holben et al., 1998). It is apparent to know that high dominance loading of aerosol-type has clear implication of local climate. Therefore, is relatively accurate to develop current characterizations of aerosol sources over the Middle East stations. The Middle East is associated with mineral dust aerosols originating from the Saharan/Arabian region (Chudnovsky et al., 2017; Alam et al., 2014) and being propagated by wind storm. However, located in the extratropical region, wind transport blows from East and North during winter and reverse during monsoon with strong component of South-East. The climatic conditions of the regions are detailed in (Garibzadeh and Alam, 2019) mostly due to strong latitudinal effect to solar influences (Anoruo and Okeke, 2020). The AERONET data provide “ground-truth” of aerosol dominance over the monsoon period. As detailed in many previous studies, the monsoon period is the season of maximum aerosol distribution, reflecting a combination of easterly and westerly winds (Anoruo, 2020a). The chemistry and optical interactions of aerosol-type with climate vary. Therefore, there is call to continuously monitor this coupling. Pinker et al. (2010) and Eck et al. (2010) attributed that intense aerosol distribution over the Sahara region are mainly due to harmattan wind over the dry season. It is apparent that the monsoon climate has great impact on the temporal and spatial distribution of local aerosols. The atmospheric aerosols are among the major climatic forcing agents. Nevertheless, aerosol effect remains difficult to estimate mainly because of incomplete knowledge of the optical properties (Hansen et al., 2000). Ogunjobi et al. (2004) emphasized that extensive analyses of aerosol optical properties at many locations on earth are highly essential. Therefore, the Angstrom parameter considered at visible and near infrared wavelengths are valuable in atmospheric remote sensing study. Hsu et al. (2012) used space-based algorithm and determined long-term trends of aerosol variability over land. The results indicated seasonal cycle strengths of dust emission and transport with negative tendency over the desert region. Prospero et al. (2002) concluded that mineral dust emitted into the atmosphere primarily has topographic depressions origin. However, this action consistently has wind acceleration between plateaus (Evan et al., 2016). It is therefore seen that meteorology plays major roles in the seasonality of dust emissions and transport (Horowitz et al., 2017). Che et al. (2018) and Zheng et al. (2017) used Angstrom Exponent to classify aerosols particle size. Also, Mielonen et al. (2009) analyzed aerosol size using AE values of fine and coarse particles corresponding to AE > 1.2 and AE < 0.6 respectively. However, Myhre et al. (2007) and Storelvmo et al. (2006) estimated the indirect effects on cloud climatic factors using space-based and model comparisons. The distribution of aerosols leads to the variations in cloud microphysics. However, aerosols modify cloud albedo and lifetime. This has indirect effect and great contribution on formation and precipitation pattern (Alam et al., 2010). The present scientific idea that contributes to aerosol-cloud-precipitable water interactions using seasonal and hemispheric estimation on long-term trends is not dense. The aerosol-cloud-precipitatble water interactions can have very noticable climatic impact due to dense extent of spatial and temporal scale (Rosenfeld et al., 2001). Boucher et al. (2013) estimated aerosol forcing and interactions with clouds from pre-industrial period to present day and concluded that cloud micro and macrophysics give a clear understanding of aerosols dynamics. In the current study, for the first time, subseasonal characterization of aerosol types was carried out using standardized/standard deviation ratio anomaly over the Middle East regions for the last decade (2010–2019). The relationship between AOD and AE, with PW and CF were analyzed to investigate the dominant aerosol peak during the monsoon period. This section introduces the objectives of the study, where section 2 detailed the data and methodology with corresponding results and discussion in section 3.