ALBERT

Description: The 2015 fire season and related smoke pollution in Indonesia was more severe than the major 2006 episode, making it the most severe season observed by the NASA Earth Observing System satellites that go back to the early 2000s, namely active fire detections from the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS), MODIS aerosol optical depth, Terra Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO), Aqua Atmospheric Infrared Sounder (AIRS) CO, Aura Ozone Monitoring Instrument (OMI) aerosol index, and Aura Microwave Limb Sounder (MLS) CO. The MLS CO in the upper troposphere showed a plume of pollution stretching from East Africa to the western Pacific Ocean that persisted for 2 mo. Longer-term records of airport visibility in Sumatra and Kalimantan show that 2015 ranked after 1997 and alongside 1991 and 1994 as among the worst episodes on record. Analysis of yearly dry season rainfall from the Tropical Rainfall Measurement Mission (TRMM) and rain gauges shows that, due to the continued use of fire to clear and prepare land on degraded peat, the Indonesian fire environment continues to have nonlinear sensitivity to dry conditions during prolonged periods with less than 4 mm/d of precipitation, and this sensitivity appears to have increased over Kalimantan. Without significant reforms in land use and the adoption of early warning triggers tied to precipitation forecasts, these intense fire episodes will reoccur during future droughts, usually associated with El Niño events.

Description: To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1∘×1∘ daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1∘×1∘ grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

Description: The aerosol single-scattering albedo (SSA) retrieved by the near-UV algorithm applied to the Aura Ozone Monitoring Instrument (OMI) measurements (OMAERUV) is compared with an independent inversion product derived from the sky radiometer network SKYNET – a ground-based radiation observation network with sites in Asia and Europe. The present work continues previous efforts to evaluate the consistency between the retrieved SSA from satellite and ground sensors. The automated spectral measurements of direct downwelling solar flux and sky radiances made by the SKYNET Sun-sky radiometer are used as input to an inversion algorithm that derives spectral aerosol optical depth (AOD) and single-scattering albedo (SSA) in the near-UV to near-IR spectral range. The availability of SKYNET SSA measurements in the ultraviolet region of the spectrum allows, for the first time, a direct comparison with OMI SSA retrievals eliminating the need of extrapolating the satellite retrievals to the visible wavelengths as is the case in the evaluation against the Aerosol Robotic Network (AERONET). An analysis of the collocated retrievals from over 25 SKYNET sites reveals that about 61 % (84 %) of OMI–SKYNET matchups agree within the absolute difference of ±0.03 (±0.05) for carbonaceous aerosols, 50 % (72 %) for dust aerosols, and 45 % (75 %) for urban–industrial aerosol types. Regionally, the agreement between the two inversion products is robust over several sites in Japan influenced by carbonaceous and urban–industrial aerosols; at the biomass burning site Phimai in Thailand; and the polluted urban site in New Delhi, India. The collocated dataset yields fewer matchups identified as dust aerosols mostly over the site Dunhuang with more than half of the matchup points confined to within ±0.03 limits. Altogether, the OMI–SKYNET retrievals agree within ±0.03 when SKYNET AOD (388 or 400 nm) is larger than 0.5 and the OMI UV Aerosol Index is larger than 0.2. The remaining uncertainties in both inversion products can be attributed to specific assumptions made in the retrieval algorithms, i.e., the uncertain calibration constant, assumption of spectral surface albedo and particle shape, and subpixel cloud contamination. The assumption of fixed and spectrally neutral surface albedo (0.1) in the SKYNET inversion appears to be unrealistic, leading to underestimated SSA, especially under lower aerosol load conditions. At higher AOD values for carbonaceous and dust aerosols, however, retrieved SSA values by the two independent inversion methods are generally consistent in spite of the differences in retrieval approaches.

Description: Black carbon (BC) emissions from open biomass burning (BB) are known to have a considerable impact on the radiative budget of the atmosphere at both global and regional scales; however, these emissions are poorly constrained in models by atmospheric observations, especially in remote regions. Here, we investigate the feasibility of constraining BC emissions from BB using satellite observations of the aerosol absorption optical depth (AAOD) and the aerosol extinction optical depth (AOD) retrieved from OMI (Ozone Monitoring Instrument) and MODIS (Moderate Resolution Imaging Spectroradiometer) measurements, respectively. We consider the case of Siberian BB BC emissions, which have the strong potential to impact the Arctic climate system. Using aerosol remote sensing data collected at Siberian sites of the AErosol RObotic NETwork (AERONET) along with the results of the fourth Fire Lab at Missoula Experiment (FLAME-4), we establish an empirical parameterization relating the ratio of the elemental carbon (EC) and organic carbon (OC) contents in BB aerosol to the ratio of AAOD and AOD at the wavelengths of the satellite observations. Applying this parameterization to the BC and OC column amounts simulated using the CHIMERE chemistry transport model, we optimize the parameters of the BB emission model based on MODIS measurements of the fire radiative power (FRP); we then obtain top-down optimized estimates of the total monthly BB BC amounts emitted from intense Siberian fires that occurred from May to September 2012. The top-down estimates are compared to the corresponding values obtained using the Global Fire Emissions Database (GFED4) and the Fire Emission Inventory–northern Eurasia (FEI-NE). Our simulations using the optimized BB aerosol emissions are verified against AAOD and AOD data that were withheld from the estimation procedure. The simulations are further evaluated against in situ EC and OC measurements at the Zotino Tall Tower Observatory (ZOTTO) and also against aircraft aerosol measurement data collected in the framework of the Airborne Extensive Regional Observations in SIBeria (YAK-AEROSIB) experiments. We conclude that our BC and OC emission estimates, considered with their confidence intervals, are consistent with the ensemble of the measurement data analyzed in this study. Siberian fires are found to emit 0.41±0.14 Tg of BC over the whole 5-month period considered; this estimate is a factor of 2 larger and a factor of 1.5 smaller than the corresponding estimates based on the GFED4 (0.20 Tg) and FEI-NE (0.61 Tg) data, respectively. Our estimates of monthly BC emissions are also found to be larger than the BC amounts calculated using the GFED4 data and smaller than those calculated using the FEI-NE data for any of the 5 months. Particularly large positive differences of our monthly BC emission estimates with respect to the GFED4 data are found in May and September. This finding indicates that the GFED4 database is likely to strongly underestimate BC emissions from agricultural burns and grass fires in Siberia. All of these differences have important implications for climate change in the Arctic, as it is found that about a quarter of the huge BB BC mass emitted in Siberia during the fire season of 2012 was transported across the polar circle into the Arctic. Overall, the results of our analysis indicate that a combination of the available satellite observations of AAOD and AOD can provide the necessary constraints on BB BC emissions.

Description: Estimates of the direct radiative effect (DRE) from absorbing smoke aerosols over the southeast Atlantic Ocean (SAO) require simulation of the microphysical and optical properties of stratocumulus clouds as well as of the altitude and shortwave (SW) optical properties of biomass burning aerosols (BBAs). In this study, we take advantage of the large number of observations acquired during the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES-2016) and Layered Atlantic Smoke Interactions with Clouds (LASIC) projects during September 2016 and compare them with datasets from the ALADIN-Climate (Aire Limitée Adaptation dynamique Développement InterNational) regional model. The model provides a good representation of the liquid water path but the low cloud fraction is underestimated compared to satellite data. The modeled total-column smoke aerosol optical depth (AOD) and above-cloud AOD are consistent (∼0.7 over continental sources and ∼0.3 over the SAO at 550 nm) with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Ozone Monitoring Instrument (OMI) or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulations indicate smoke transport over the SAO occurs mainly between 2 and 4 km, consistent with surface and aircraft lidar observations. The BBA single scattering albedo is slightly overestimated compared to the Aerosol Robotic Network (AERONET) and more significantly when compared to Ascension Island surface observations. The difference could be due to the absence of internal mixing treatment in the ALADIN-Climate model. The SSA overestimate leads to an underestimation of the simulated SW radiative heating compared to ORACLES data. ALADIN-Climate simulates a positive (monthly mean) SW DRE of about +6 W m−2 over the SAO (20∘ S–10∘ N and 10∘ W–20∘ E) at the top of the atmosphere and in all-sky conditions. Over the continent, the presence of BBA is shown to significantly decrease the net surface SW flux, through direct and semi-direct effects, which is compensated by a decrease (monthly mean) in sensible heat fluxes (−25 W m−2) and surface land temperature (−1.5 ∘C) over Angola, Zambia and the Democratic Republic of the Congo, notably. The surface cooling and the lower tropospheric heating decrease the continental planetary boundary layer height by about ∼200 m.

Description: Quantifying aerosol absorption at ultraviolet (UV) wavelengths is important for monitoring air pollution and aerosol amounts using current (e.g., Aura/OMI) and future (e.g., TROPOMI, TEMPO, GEMS, and Sentinel-4) satellite measurements. Measurements of column average atmospheric aerosol single scattering albedo (SSA) are performed on the ground by the NASA AERONET in the visible (VIS) and near-infrared (NIR) wavelengths and in the UV-VIS-NIR by the SKYNET networks. Previous comparison studies have focused on VIS and NIR wavelengths due to the lack of co-incident measurements of aerosol and gaseous absorption properties in the UV. This study compares the SKYNET-retrieved SSA in the UV with the SSA derived from a combination of AERONET, MFRSR, and Pandora (AMP) retrievals in Seoul, South Korea, in spring and summer 2016. The results show that the spectrally invariant surface albedo assumed in the SKYNET SSA retrievals leads to underestimated SSA compared to AMP values at near UV wavelengths. Re-processed SKYNET inversions using spectrally varying surface albedo, consistent with the AERONET retrieval improve agreement with AMP SSA. The combined AMP inversions allow for separating aerosol and gaseous (NO2 and O3) absorption and provide aerosol retrievals from the shortest UVB (305 nm) through VIS to NIR wavelengths (870 nm).

Description: Since about three years after the launch the Ozone Monitoring Instrument (OMI) on the EOS-Aura satellite, the sensor's viewing capability has been affected by what is believed to be an internal obstruction that has reduced OMI's spatial coverage. It currently affects about half of the instrument's 60 viewing positions. In this work we carry out an analysis to assess the effect of the reduced spatial coverage on the monthly average values of retrieved aerosol optical depth (AOD), single scattering albedo (SSA) and the UV Aerosol Index (UVAI) using the 2005–2007 three-year period prior to the onset of the row anomaly. Regional monthly average values calculated using viewing positions 1 through 30 were compared to similarly obtained values using positions 31 through 60, with the expectation of finding close agreement between the two calculations. As expected, mean monthly values of AOD and SSA obtained with these two scattering-angle dependent subsets of OMI observations agreed over regions where carbonaceous or sulphate aerosol particles are the predominant aerosol type. However, over arid regions, where desert dust is the main aerosol type, significant differences between the two sets of calculated regional mean values of AOD were observed. As it turned out, the difference in retrieved desert dust AOD between the scattering-angle dependent observation subsets was due to the incorrect representation of desert dust scattering phase function. A sensitivity analysis using radiative transfer calculations demonstrated that the source of the observed AOD bias was the spherical shape assumption of desert dust particles. A similar analysis in terms of UVAI yielded large differences in the monthly mean values for the two sets of calculations over cloudy regions. On the contrary, in arid regions with minimum cloud presence, the resulting UVAI monthly average values for the two sets of observations were in very close agreement. The discrepancy under cloudy conditions was found to be caused by the parameterization of clouds as opaque Lambertian reflectors. When properly accounting for cloud scattering effects using Mie theory, the observed UVAI angular bias was significantly reduced. The analysis discussed here has uncovered important algorithmic deficiencies associated with the model representation of the angular dependence of scattering effects of desert dust aerosols and cloud droplets. The resulting improvements in the handling of desert dust and cloud scattering have been incorporated in an improved version of the OMAERUV algorithm.

Description: Organic aerosol (OA) is one of the main components of the global particulate burden and intimately links natural and anthropogenic emissions with air quality and climate. It is challenging to accurately represent OA in global models. Direct quantification of global OA abundance is not possible with current remote sensing technology; however, it may be possible to exploit correlations of OA with remotely observable quantities to infer OA spatiotemporal distributions. In particular, formaldehyde (HCHO) and OA share common sources via both primary emissions and secondary production from oxidation of volatile organic compounds (VOCs). Here, we examine OA–HCHO correlations using data from summertime airborne campaigns investigating biogenic (NASA SEAC4RS and DC3), biomass burning (NASA SEAC4RS), and anthropogenic conditions (NOAA CalNex and NASA KORUS-AQ). In situ OA correlates well with HCHO (r=0.59–0.97), and the slope and intercept of this relationship depend on the chemical regime. For biogenic and anthropogenic regions, the OA–HCHO slopes are higher in low NOx conditions, because HCHO yields are lower and aerosol yields are likely higher. The OA–HCHO slope of wildfires is over 9 times higher than that for biogenic and anthropogenic sources. The OA–HCHO slope is higher for highly polluted anthropogenic sources (e.g., KORUS-AQ) than less polluted (e.g., CalNex) anthropogenic sources. Near-surface OAs over the continental US are estimated by combining the observed in situ relationships with HCHO column retrievals from NASA's Ozone Monitoring Instrument (OMI). HCHO vertical profiles used in OA estimates are from climatology a priori profiles in the OMI HCHO retrieval or output of specific period from a newer version of GEOS-Chem. Our OA estimates compare well with US EPA IMPROVE data obtained over summer months (e.g., slope =0.60–0.62, r=0.56 for August 2013), with correlation performance comparable to intensively validated GEOS-Chem (e.g., slope =0.57, r=0.56) with IMPROVE OA and superior to the satellite-derived total aerosol extinction (r=0.41) with IMPROVE OA. This indicates that OA estimates are not very sensitive to these HCHO vertical profiles and that a priori profiles from OMI HCHO retrieval have a similar performance to that of the newer model version in estimating OA. Improving the detection limit of satellite HCHO and expanding in situ airborne HCHO and OA coverage in future missions will improve the quality and spatiotemporal coverage of our OA estimates, potentially enabling constraints on global OA distribution.