Sensitivity of aerosol optical depth trends using long term measurements of different sun-photometers

• Main Authors: Karanikolas, Angelos, Kouremeti, Natalia, Gröbner, Julian, Egli, Luca, Kazadzis, Stelios
• Format: Text
• Language: English
• Published: 2022
• Subjects: Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/amt-2022-181; https://amt.copernicus.org/preprints/amt-2022-181/

This work aims to assess differences in the aerosol optical depth (AOD) trend estimations when using high quality AOD measurements from two different instruments with different technical characteristics, operational (e.g. measurement frequency), calibration and processing protocols. The different types of Sun photometers are the CIMEL that is part of AERONET (AErosol RObotic NETwork) and a precision Filter Radiometer (PFR), part of the Global Atmosphere Watch Precision Filter Radiometer network. The analysis operated for two wavelengths (500/501 nm and 870/862 nm for CIMEL/PFR) in Davos, Switzerland, for the period 2007–2019. For the synchronous AOD measurements, more than 95 % of the CIMEL-PFR AOD differences are within the WMO accepted limits, showing very good measurement agreement and homogeneity in calibration and post correction procedures. AOD trends per decade in AOD for Davos for the 13-year period of analysis were approximately -0.017 and -0.007 per decade for 501 nm and 862 nm (PFR), while the CIMEL-PFR trend differences have been found 0.0005 and 0.0003 respectively. The linear trend difference for 870/862 nm is larger than the linear fit standard error. When calculating monthly AODs using all PFR data (higher instrument frequency) and comparing them with the PFR measurements that are synchronous with CIMEL, the trend differences are smaller than the standard error. The trend differences are also larger than the trend uncertainty attributed to the instrument measurement uncertainty, with the exception of the comparison between the 2 PFR datasets (high and low frequency) at 862 nm. Finally, when calculating time-varying trends, they differ within their uncertainties.