The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) updates NASA's previous satellite era (1980 - onward) reanalysis system to include additional observations and improvements to the Goddard Earth Observing System, Version 5 (GEOS-5) Earth system model. As a major step towards a full Integrated Earth Systems Analysis (IESA), in addition to meteorological observations, MERRA-2 now includes assimilation of aerosol optical depth (AOD) from various ground- and space-based remote sensing platforms. Here, in the first of a pair of studies, we document the MERRA-2 aerosol assimilation, including a description of the prognostic model (GEOS-5 coupled to the GOCART aerosol module), aerosol emissions, and the quality control of ingested observations. We provide initial validation and evaluation of the analyzed AOD fields using independent observations from ground, aircraft, and shipborne instruments. We demonstrate the positive impact of the AOD assimilation on simulated aerosols by comparing MERRA-2 aerosol fields to an identical control simulation that does not include AOD assimilation. Having shown the AOD evaluation, we take a first look at aerosol-climate interactions by examining the shortwave, clear-sky aerosol direct radiative effect. In our companion paper, we evaluate and validate available MERRA-2 aerosol properties not directly impacted by the AOD assimilation (e.g. aerosol vertical distribution and absorption). Importantly, while highlighting the skill of the MERRA-2 aerosol assimilation products, both studies point out caveats that must be considered when using this new reanalysis product for future studies of aerosols and their interactions with weather and climate.

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA’s latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA-1), including aerosol assimilation for the entire period. MERRA-2 assimilates bias-corrected aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer and the Advanced Very High Resolution Radiometer instruments. Additionally, MERRA-2 assimilates (non bias corrected) AOD from the Multiangle Imaging SpectroRadiometer over bright surfaces and AOD from Aerosol Robotic Network sunphotometer stations. This paper, the second of a pair, summarizes the efforts to assess the quality of the MERRA-2 aerosol products. First, MERRA-2 aerosols are evaluated using independent observations. It is shown that the MERRA-2 absorption aerosol optical depth (AAOD) and ultraviolet aerosol index (AI) compare well with Ozone Monitoring Instrument observations. Next, aerosol vertical structure and surface fine particulate matter (PM2.5) are evaluated using available satellite, aircraft, and ground-based observations. While MERRA-2 generally compares well to these observations, the assimilation cannot correct for all deficiencies in the model (e.g., missing emissions). Such deficiencies can explain many of the biases with observations. Finally, a focus is placed on several major aerosol events to illustrate successes and weaknesses of the AOD assimilation: the Mount Pinatubo eruption, a Saharan dust transport episode, the California Rim Fire, and an extreme pollution event over China. The article concludes with a summary that points to best practices for using the MERRA-2 aerosol reanalysis in future studies.

Abstract. A radiative transfer interface has been developed to simulate the UV aerosol index (AI) from the NASA Goddard Earth Observing System version 5 (GEOS-5) aerosol assimilated fields. The purpose of this work is to use the AI and aerosol absorption optical depth (AAOD) derived from the Ozone Monitoring Instrument (OMI) measurements as independent validation for the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Since AI is dependent on aerosol concentration, optical properties and altitude of the aerosol layer, we make use of complementary observations to fully diagnose the model, including AOD from the Multi-angle Imaging SpectroRadiometer (MISR), aerosol retrievals from the AErosol RObotic NETwork (AERONET) and attenuated backscatter coefficients from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to ascertain potential misplacement of plume height by the model. By sampling dust, biomass burning and pollution events in 2007 we have compared model-produced AI and AAOD with the corresponding OMI products, identifying regions where the model representation of absorbing aerosols was deficient. As a result of this study over the Saharan dust region, we have obtained a new set of dust aerosol optical properties that retains consistency with the MODIS AOD data that were assimilated, while resulting in better agreement with aerosol absorption measurements from OMI. The analysis conducted over the southern African and South American biomass burning regions indicates that revising the spectrally dependent aerosol absorption properties in the near-UV region improves the modeled-observed AI comparisons. Finally, during a period where the Asian region was mainly dominated by anthropogenic aerosols, we have performed a qualitative analysis in which the specification of anthropogenic emissions in GEOS-5 is adjusted to provide insight into discrepancies observed in AI comparisons.

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Abstract. An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system for which WRF-Chem forecasts are performed with a detailed sectional model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module, providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA, where improvements on analyses and forecasts is demonstrated. A first set of simulations was performed, comparing the assimilation impacts of using the operational MODIS (Moderate Resolution Imaging Spectroradiometer) dark target retrievals to those using observationally constrained ones, i.e., calibrated with AERONET (Aerosol RObotic NETwork) data. It was found that using the observationally constrained retrievals produced the best results when evaluated against ground based monitors, with the error in PM 2.5 predictions reduced at over 90 % of the stations and AOD errors reduced at 100 % of the monitors, along with larger overall error reductions when grouping all sites. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.