Sensitivity of aerosol optical depth trends using long-term measurements of different sun photometers
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Published:2022-10-11
Issue:19
Volume:15
Page:5667-5680
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Karanikolas AngelosORCID, Kouremeti Natalia, Gröbner Julian, Egli Luca, Kazadzis Stelios
Abstract
Abstract. 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 and 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) that is part of the Global Atmosphere Watch Precision Filter
Radiometer network. The analysis operated for two wavelengths (500 and 501
and 870 and 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 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 and 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. Linear trend differences of the CIMEL and
PFR time series presented here are not within the calculated trend
uncertainties (based on measurement uncertainty) for 870 and 862 nm. On the
contrary, PFR trends, when comparing high- and low-measurement-frequency
datasets are within such an uncertainty estimation for both wavelengths.
Finally, for time-varying trends all trend differences are well within the
calculated trend uncertainties.
Funder
European Metrology Programme for Innovation and Research
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference66 articles.
1. Andrew, C.: Forecasting, structural time series models and the Kalman filter, First paperback edition, Cambridge University Press, ISBN 0521405734, 1990. 2. Bais, A. F., Lucas, R. M., Bornman, J. F., Williamson, C. E., Sulzberger, B., Austin, A. T., Wilson, S. R., Andrady, A. L., Bernhard, G., McKenzie, R. L., Aucamp, P. J., Madronich, S., Neale, R. E., Yazar, S., Young, A. R., de Gruijl, F. R., Norval, M., Takizawa, Y., Barnes, P. W., Robson, T. M., Robinson, S. A., Ballaré, C. L., Flint, S. D., Neale, P. J., Hylander, S., Rose, K. C., Wängberg, S.-Å., Häder, D.-P., Worrest, R. C., Zepp, R. G., Paul, N. D., Cory, R. M., Solomon, K. R., Longstreth, J., Pandey, K. K., Redhwi, H. H., Torikai, A., and Heikkilä, A. M.: Environmental effects of ozone depletion, UV radiation and interactions with climate change: UNEP Environmental Effects Assessment Panel, update 2017, Photochem. Photobio. S., 17, 127–179, https://doi.org/10.1039/c7pp90043k, 2018. 3. Bashiri, M. and Moslemi, A.: The analysis of residuals variation and outliers to obtain robust response surface, Journal of Industrial Engineering International, 9, 1695–1702, https://doi.org/10.1186/2251-712x-9-2, 2013. 4. Benedetti, A., Reid, J. S., Knippertz, P., Marsham, J. H., Di Giuseppe, F., Rémy, S., Basart, S., Boucher, O., Brooks, I. M., Menut, L., Mona, L., Laj, P., Pappalardo, G., Wiedensohler, A., Baklanov, A., Brooks, M., Colarco, P. R., Cuevas, E., da Silva, A., Escribano, J., Flemming, J., Huneeus, N., Jorba, O., Kazadzis, S., Kinne, S., Popp, T., Quinn, P. K., Sekiyama, T. T., Tanaka, T., and Terradellas, E.: Status and future of numerical atmospheric aerosol prediction with a focus on data requirements, Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, 2018. 5. Cherian, R. and Quaas, J.: Trends in AOD, Clouds, and Cloud Radiative Effects in Satellite Data and CMIP5 and CMIP6 Model Simulations Over Aerosol Source Regions, Geophys. Res. Lett., 47, e2020GL087132, https://doi.org/10.1029/2020gl087132, 2020.
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