A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

Abstract

Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known a AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOMY dark target aerosol retrievals and the International Cooperative for Aerosol Prediction core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total AOD550. Differences were adjudicated bythe global shipboard Maritime Aerosol Network (M) and selected island AERON sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the 0C4C and its members, while having good correlations overall, have a persistent +04 to +0.02 biases relative to MODIS and AERON for typical AOD550 values (84th% < 0.0028), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and.C4C members are systematically overestimating AOD550and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved m Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s 1, the C4C and MODIS AOD550 also overestimate CAOD550 and FAOD550 respectively, for wind speeds above12.m/s. Finally, sampling bias inherent in MA, as well as other circumstantial evidence, suggests biases in MODIS are likely even larger than what was diagnosed here. We conclude with a discussion on how MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed reaserch.