Numerical Assessment of Downward Incoming Solar Irradiance in Smoke Influenced Regions—A Case Study in Brazilian Amazon and Cerrado

Smoke aerosol plumes generated during the biomass burning season in Brazil suffer long-range transport, resulting in large aerosol optical depths over an extensive domain. As a consequence, downward surface solar irradiance, and in particular the direct component, can be significantly reduced. Accurate solar energy assessments considering the radiative contribution of biomass burning aerosols are required to support Brazil’s solar power sector. This work presents the 2nd generation of the radiative transfer model BRASIL-SR, developed to improve the aerosol representation and reduce the uncertainties in surface solar irradiance estimates in cloudless hazy conditions and clean conditions. Two numerical experiments allowed to assess the model’s skill using observational or regional MERRA-2 reanalysis AOD data in a region frequently affected by smoke. Four ground measurement sites provided data for the model output validation. Results for DNI obtained using δ-Eddington scaling and without scaling are compared, with the latter presenting the best skill in all sites and for both experiments. An increase in the relative error of DNI results obtained with δ-Eddington optical depth scaling as AOD increases is evidenced. For DNI, MBD deviations ranged from −2.3 to −0.5%, RMSD between 2.3 and 4.7% and OVER between 0 and 5.3% when using in-situ AOD data. Overall, our results indicate a good skill of BRASIL-SR for the estimation of both GHI and DNI.

Spectral Derivatives of Optical Depth for Partitioning Aerosol Type and Loading

Quantifying aerosol compositions (e.g., type, loading) from remotely sensed measurements by spaceborne, suborbital and ground-based platforms is a challenging task. In this study, the first and second-order spectral derivatives of aerosol optical depth (AOD) with respect to wavelength are explored to determine the partitions of the major components of aerosols based on the spectral dependence of their particle optical size and complex refractive index. With theoretical simulations from the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) model, AOD spectral derivatives are characterized for collective models of aerosol types, such as mineral dust (DS) particles, biomass-burning (BB) aerosols and anthropogenic pollutants (AP), as well as stretching out to the mixtures among them. Based on the intrinsic values from normalized spectral derivatives, referenced as the Normalized Derivative Aerosol Index (NDAI), a unique pattern is clearly exhibited for bounding the major aerosol components; in turn, fractions of the total AOD (fAOD) for major aerosol components can be extracted. The subtlety of this NDAI method is examined by using measurements of typical aerosol cases identified carefully by the ground-based Aerosol Robotic Network (AERONET) sun–sky spectroradiometer. The results may be highly practicable for quantifying fAOD among mixed-type aerosols by means of the normalized AOD spectral derivatives.

Characterization of Stratospheric Smoke Particles over the Antarctica by Remote Sensing Instruments

Australian smoke from the extraordinary biomass burning in December 2019 was observed over Marambio, Antarctica from the 7th to the 10th January, 2020. The smoke plume was transported thousands of kilometers over the Pacific Ocean, and reached the Antarctic Peninsula at a hight of 13 km, as determined by satellite lidar observations. The proposed origin and trajectory of the aerosol are supported by back-trajectory model analyses. Ground-based Sun–Sky–Moon photometer belonging to the Aerosol Robotic Network (AERONET) measured aerosol optical depth (500 nm wavelength) above 0.3, which is unprecedented for the site. Inversion of sky radiances provide the optical and microphysical properties of the smoke over Marambio. The AERONET data near the fire origin in Tumbarumba, Australia, was used to investigate the changes in the measured aerosol properties after transport and ageing. The analysis shows an increase in the fine mode particle radius and a reduction in absorption (increase in the single scattering albedo). The available long-term AOD data series at Marambio suggests that smoke particles could have remained over Antarctica for several weeks after the analyzed event.

Gradient boosting machine learning to improve satellite-derived column water vapor measurement error

The atmospheric products of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm include column water vapor (CWV) at a 1 km resolution, derived from daily overpasses of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites. We have recently shown that machine learning using extreme gradient boosting (XGBoost) can improve the estimation of MAIAC aerosol optical depth (AOD). Although MAIAC CWV is generally well validated (Pearson's R >0.97 versus CWV from AERONET sun photometers), it has not yet been assessed whether machine-learning approaches can further improve CWV. Using a novel spatiotemporal cross-validation approach to avoid overfitting, our XGBoost model, with nine features derived from land use terms, date, and ancillary variables from the MAIAC retrieval, quantifies and can correct a substantial portion of measurement error relative to collocated measurements at AERONET sites (26.9% and 16.5% decrease in root mean square error (RMSE) for Terra and Aqua datasets, respectively) in the Northeastern USA, 2000-2015. We use machine-learning interpretation tools to illustrate complex patterns of measurement error and describe a positive bias in MAIAC Terra CWV worsening in recent summertime conditions. We validate our predictive model on MAIAC CWV estimates at independent stations from the SuomiNet GPS network where our corrections decrease the RMSE by 19.7% and 9.5% for Terra and Aqua MAIAC CWV. Empirically correcting for measurement error with machine-learning algorithms is a postprocessing opportunity to improve satellite-derived CWV data for Earth science and remote sensing applications.

Aerosol optical, microphysical, chemical and radiative properties of high aerosol load cases over the Arctic based on AERONET measurements

Columnar mass concentrations of aerosol components over the Arctic are estimated using microphysical parameters derived from direct sun extinction and sky radiance measurements of Aerosol Robotic Network. Aerosol optical, microphysical, chemical and radiative properties show that Arctic aerosols are dominated by fine mode particles, especially for high aerosol load cases. The average aerosol optical depth (AOD) of the selected Arctic sites in the sampling period is approximately 0.08, with 75% composed of fine mode particles. The fine mode fraction mostly exceeds 0.9 when AOD greater than 0.4. The ammonium sulfate-like component (AS) contributes about 68% of total dry aerosol mass for high-AOD events. The estimated compositions and back trajectories show that the transported aerosol particles from biomass burning events have large amounts of black carbon (BC) and brown carbon, while those from pollution events are characterised by large AS fractions. The instantaneous radiative forcing at the top-of-atmosphere is higher for the more absorbing components, and varies greatly with surface albedo and solar zenith angle. A regression model of columnar composition and radiative forcing within the atmosphere (RF_ATM) for Arctic aerosol is established, showing that BC dominates a positive RF_ATM with a high warming efficiency.

Modeling the radiative effects of biomass burning aerosols on carbon fluxes in the Amazon region

Every year, a dense smoke haze covers a large portion of South America originating from fires in the Amazon Basin and central parts of Brazil during the dry biomass burning season between August and October. Over a large portion of South America, the average aerosol optical depth at 550 nm exceeds 1.0 during the fire season, while the background value during the rainy season is below 0.2. Biomass burning aerosol particles increase scattering and absorption of the incident solar radiation. The regional-scale aerosol layer reduces the amount of solar energy reaching the surface, cools the near-surface air, and increases the diffuse radiation fraction over a large disturbed area of the Amazon rainforest. These factors affect the energy and CO2 fluxes at the surface. In this work, we applied a fully integrated atmospheric model to assess the impact of biomass burning aerosols in CO2 fluxes in the Amazon region during 2010. We address the effects of the attenuation of global solar radiation and the enhancement of the diffuse solar radiation flux inside the vegetation canopy. Our results indicate that biomass burning aerosols led to increases of about 27% in the gross primary productivity of Amazonia and 10% in plant respiration as well as a decline in soil respiration of 3%. Consequently, in our model Amazonia became a net carbon sink; net ecosystem exchange during September 2010 dropped from +101 to -104 TgC when the aerosol effects are considered, mainly due to the aerosol diffuse radiation effect. For the forest biome, our results point to a dominance of the diffuse radiation effect on CO2 fluxes, reaching a balance of 50-50% between the diffuse and direct aerosol effects for high aerosol loads. For C3 grasses and savanna (cerrado), as expected, the contribution of the diffuse radiation effect is much lower, tending to zero with the increase in aerosol load. Taking all biomes together, our model shows the Amazon during the dry season, in the presence of high biomass burning aerosol loads, changing from being a source to being a sink of CO2 to the atmosphere.