Merging regional and global aerosol optical depth records from major available satellite products

Larisa Sogacheva,Pavel Litvinov,Antti Arola,N Christina Hsu,Alexei Lyapustin,Pekka Kolmonen,Omar Torres,Peter North,Michael J Garay,Thomas Popp,Miriam Kosmale,Ralph A Kahn,Oleg Dubovik,Oleg Dubovik,Hiren Jethva,Hiren Jethva,Andreas Heckel,Gerrit De Leeuw,Robert C Levy,Andrew M Sayer

doi:10.5194/acp-20-2031-2020

Abstract

Abstract. Satellite instruments provide a vantage point for studying aerosol loading consistently over different regions of the world. However, the typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies, the use of multiple satellite sensors should be considered. Discrepancies exist between aerosol optical depth (AOD) products due to differences in their information content, spatial and temporal sampling, calibration, cloud masking, and algorithmic assumptions. Users of satellite-based AOD time-series are confronted with the challenge of choosing an appropriate dataset for the intended application. In this study, 16 monthly AOD products obtained from different satellite sensors and with different algorithms were inter-compared and evaluated against Aerosol Robotic Network (AERONET) monthly AOD. Global and regional analyses indicate that products tend to agree qualitatively on the annual, seasonal and monthly timescales but may be offset in magnitude. Several approaches were then investigated to merge the AOD records from different satellites and create an optimised AOD dataset. With few exceptions, all merging approaches lead to similar results, indicating the robustness and stability of the merged AOD products. We introduce a gridded monthly AOD merged product for the period 1995–2017. We show that the quality of the merged product is as least as good as that of individual products. Optimal agreement of the AOD merged product with AERONET further demonstrates the advantage of merging multiple products. This merged dataset provides a long-term perspective on AOD changes over different regions of the world, and users are encouraged to use this dataset.

Highlights

Interactions of atmospheric aerosols with clouds and radiation are the largest source of uncertainty in modelling efforts to quantify current climate and predict climate change (IPCC, 2018)
Aerosol Robotic Network (AERONET) provides data over certain locations within a grid cell, whereas satellites cover a larger fraction of the area of a grid cell
For example, if AERONET is likely to miss extreme high values, that will result in AERONET showing lower aerosol optical depth (AOD) than from a satellite

Summary

Introduction

Interactions of atmospheric aerosols with clouds and radiation are the largest source of uncertainty in modelling efforts to quantify current climate and predict climate change (IPCC, 2018). To reduce such uncertainties, we need observations to constrain climate models. L. Sogacheva et al.: Merging regional and global satellite AOD records sistent or at least well-characterised uncertainties, and provide adequate temporal and spatial sampling over a long period of time. Sogacheva et al.: Merging regional and global satellite AOD records sistent or at least well-characterised uncertainties, and provide adequate temporal and spatial sampling over a long period of time With their ability to cover the globe systematically, satellites provide this global and temporal perspective. The application of satellite observations for climate change studies requires using products from multiple sources to derive consistent regional conclusions

Methods

Results

Conclusion