Atmospheric carbon monoxide (CO) has been decreasing globally for the last two decades. Recently, positive fire trends in Northern Hemisphere boreal regions may have impacted the decreasing CO. Additionally, time-varying air quality policies will have different impacts on atmospheric composition and related trends. Aerosols are co-emitted with CO from both fires and anthropogenic sources. Consequently, a combined trend analysis of CO and aerosol optical depth (AOD) measurements from space can help elucidate the drivers of regional differences in the CO trend. We use valuable long-term records from two instruments aboard the Terra satellite. Measurements of Pollution in the Troposphere (MOPITT) CO and AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument are examined hemispherically and in sub-regions to determine trends between 2002 and 2018. The records are further split into two sub-periods in order to examine temporal stability in the trend values. We also assess the CO trends in monthly percentile values to use seasonal information when interpreting trend contributions. Our focus is on four major population centers: Southeast USA, Europe, Northeast China and North India, as well as biomass burning regions in both hemispheres. Our results show that globally, CO declines faster in the first half of the record compared to the second half. Both atmospheric species are important when interpreting trends in the smaller regions. Northern Hemisphere boreal fire regions show a regime-shift in their seasonality for both CO and AOD, which may counteract the downward trend in CO. Anthropogenic regions with minimal air quality management such as North India become more globally relevant as the global CO trend weakens. We also find clear evidence of the atmospheric impact of policy choices. Overall, we observe that local changes in biomass burning and air quality can counteract the global downward trend in CO.

Passive satellite observations play an important role in monitoring global aerosol properties and helping quantify aerosol radiative forcing in the climate system. The quality of aerosol retrievals from the satellite platform relies on well-calibrated radiance measurements from multiple spectral bands, and the availability of appropriate particle optical models. Inaccurate scattering phase function assumptions can introduce large retrieval errors. High-spatial resolution, dual-view observations from the Advanced Baseline Imagers (ABI) on board the two most recent Geostationary Operational Environmental Satellites (GOES), East and West, provide a unique opportunity to better constrain the aerosol phase function. Using dual GOES reflectance measurements for a dust event in the Gulf of Mexico in 2019, we demonstrate how a first-guess phase function can be reconstructed by considering the variations in observed scattering angle throughout the day. Using the reconstructed phase function, aerosol optical depth retrievals from the two satellites are self-consistent and agree well with surface-based optical depth estimates. We evaluate our methodology and reconstructed phase function against independent retrievals made from low-Earth-orbit multi-angle observations for a different dust event in 2020. Our new aerosol optical depth retrievals have a root-mean-square-difference of 0.028 – 0.087. Furthermore, the retrievals between the two geostationary satellites for this case agree within about 0.06±0.073, as compared to larger discrepancies between the operational GOES products, which do not employ the dual-view technique.

The Dark-target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate-resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering-angle-dependent biases in its retrievals of AOD, Angstrom Exponent (AE) and Fine Mode Fraction (FMF) for dust aerosols. Following a two-step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in ‘Part I’), in this ‘Part II’, we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET-identified dusty pixels. The application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error (EE) from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering-angle-dependent AE bias is largely eliminated. In testing with wo full months of data (April and July), the new retrieval reduces the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced.

To prepare for implementation of a new aerosol retrieval specifically designed for dust aerosol over ocean in the operational Dark Target (DT) algorithms for the Moderate-resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors, we focus on the challenge of detecting dust. We first survey the literature on existing dust detection algorithms and then develop an innovative algorithm that combines near-UV (deep blue), visible, and thermal infrared (TIR) wavelength spectral tests. The new detection algorithm is applied to Terra and Aqua MODIS granules and compared with other dust detection possibilities from existing MODIS products. Quantitative evaluation of the new dust detection algorithm is conducted using both a collocated AERONET - MODIS dataset and collocated CALIPSO – MODIS dataset. From comparison with both AERONET and CALIOP measurements, we estimate the new dust detection algorithm detects about 30% of weakly dusty pixels and more than 80% of heavily dusty pixels, with false detections in the range of 1-2%. The very low false detection rate is particularly noteworthy in comparison with existing literature. Compared with the dust flag currently available as part of the MODIS cloud mask product (MOD35/MYD35), and dust classification based on commonly used thresholds with AOD and AE, the new dust detection algorithm finds more dusty pixels and fewer false detections.