A global analysis of climate-relevant aerosol properties retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories

Laj, Paolo; Bigi, Alessandro; Rose, Clémence; Andrews, Elisabeth; Myhre, Cathrine Lund; Coen, Martine Collaud; Lin, Yong; Wiedensohler, Alfred; Schulz, Mıchael; Ogren, J. A.; Fiebig, Markus; Gliß, Jonas; Mortier, Augustin; Pandolfi, M.; Petäj̈ä, Tuukka; Kim, Sang Woo; Aas, Wenche; Putaud, Jean‐Philippe; Mayol-Bracero, O. L.; Keywood, Melita; Labrador, Lorenzo; Aalto, Pasi; Ahlberg, Erik; Arboledas, Lucas Alados; Alastuey, Andrés; Andrade, Marcos; Artı́ñano, Begoña; Ausmeel, Stina; Arsov, Todor; Asmi, Eija; Backman, John; Baltensperger, Urs; Bastian, Susanne; Bath, Olaf; Beukes, Johan P.; Brem, Benjamin T.; Bukowiecki, Nicolas; Conil, Sébastien; Couret, C; Day, Derek E.; Dayantolis, Wan; Degórska, Anna; Eleftheriadis, Konstantinos; Fetfatzis, P.; Favez, Olivier; Flentje, H.; Gini, Maria I.; Gregorič, Asta; Gysel‐Beer, Martin; Hallar, A. Gannet; Hand, Jenny L.; Hoffer, András; Hueglin, Christoph; Hooda, Rakesh K.; Hyvärinen, Antti; Kalapov, Ivo; Kalivitis, Nikos; Kasper-Giebl, Anne; Kim, Jeong Eun; Kouvarakis, Giorgos; Kranjc, Irena; Krejčí, Radovan; Kulmala, Markku; Labuschagne, Casper; Lee, Haejung; Lihavainen, Heikki; Lin, Neng-Huei; Löschau, G.; Luoma, Krista; Marinoni, Angela; Santos, Sebastiao Martins Dos; Meinhardt, F.; Merkel, Maik; Metzger, Jean‐Marc; Mihalopoulos, Nikolaos; Nguyen, Nhat Anh; Ondráček, Jakub; Pérez, Noemí; Perrone, Maria Rita; Petit, Jean-Eudes; Picard, David; Pichon, Maxime; Pont, Véronique; Prats, Natalia; Prenni, A. J.; Reisen, Fabienne; Romano, Salvatore; Sellegri, Karine; Sharma, Sangeeta; Schauer, Gerhard; Sheridan, Patrick J.; Sherman, James P.; Schütze, Maik; Schwerin, Andreas; Sohmer, Ralf; Sorribas, M.; Steinbacher, Martin; Sun, Junying; Titos, Gloria; Toczko, Barbara; Tuch, Thomas; Tulet, Pierre; Tunved, Peter; Vakkari, Ville; Velarde, Fernando; Velásquez, Patricio; Villani, Paolo; Vratolis, Stergios; Wang, Sheng Hsiang; Weinhold, Kay; Weller, Rolf; Yela, Margarita; Yus-Díez, Jesús; Ždı́mal, Vladimı́r; Zieger, Paul; Zíková, Naděžda

doi:10.5194/amt-13-4353-2020

Cited by 68 publications

(21 citation statements)

References 86 publications

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“…Zieger et al, 2013). Recent work showed that some models tend to overestimate the scattering enhancement factor at low RH (and high RH) and, hence, overestimate the light scattering coefficients at relatively dry conditions (Latimer and Martin, 2019;Burgos et al, 2020). Kinne et al (2006) provided a first analysis of modelled column aerosol optical properties of 20 aerosol models participating in the initial AeroCom phase 1 (AP1) experiments.…”

Section: Introductionmentioning

confidence: 99%

AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using ground- and space-based remote sensing as well as surface in situ observations

et al. 2021

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Abstract. Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

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Section: Introductionmentioning

confidence: 99%

AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using ground- and space-based remote sensing as well as surface in situ observations

et al. 2021

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“…Black carbon (BC) and brown carbon (BrC) are major light-absorbing aerosol species. They differ both in the extent of light absorption per mass and its wavelength dependence (Bond et al, 2013;Laskin et al, 2015). Furthermore, BC is a primary component and it is emitted in every incomplete combustion process.…”

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confidence: 99%

Determination of Aethalometer multiple-scattering enhancement parameters and impact on source apportionment during the winter 2017/18 EMEP/ACTRIS/COLOSSAL campaign in Milan

Bernardoni¹,

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Bolzacchini

et al. 2021

Atmos. Meas. Tech.

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Abstract. In the frame of the EMEP/ACTRIS/COLOSSAL campaign in Milan during winter 2018, equivalent black carbon measurements using the Aethalometer 31 (AE31), the Aethalometer 33 (AE33), and a Multi-Angle Absorption Photometer (MAAP) were carried out together with levoglucosan analyses on 12 h resolved PM2.5 samples collected in parallel. From AE31 and AE33 data, the loading-corrected aerosol attenuation coefficients (bATN) were calculated at seven wavelengths (λ, where λ values are 370, 470, 520, 590, 660, 880, and 950 nm). The aerosol absorption coefficient at 637 nm (babs_MAAP) was determined by MAAP measurements. Furthermore, babs was also measured at four wavelengths (405, 532, 635, 780 nm) on the 12 h resolved PM2.5 samples by a polar photometer (PP_UniMI). After comparing PP_UniMI and MAAP results, we exploited PP_UniMI data to evaluate the filter multiple-scattering enhancement parameter at different wavelengths for AE31 and AE33. We obtained instrument- and wavelength-dependent multiple-scattering enhancement parameters by linear regression of the Aethalometer bATN against the babs measured by PP_UniMI. We found significant dependence of the multiple-scattering enhancement parameter on filter material, hence on the instrument, with a difference of up to 30 % between the AE31 and the AE33 tapes. The wavelength dependence and day–night variations were small – the difference between the smallest and largest value was up to 6 %. Data from the different instruments were used as input to the so-called “Aethalometer model” for optical source apportionment, and instrument dependence of the results was investigated. Inconsistencies among the source apportionment were found fixing the AE31 and AE33 multiple-scattering enhancement parameters to their usual values. In contrast, optimised multiple-scattering enhancement parameters led to a 5 % agreement among the approaches. Also, the component apportionment “MWAA model” (Multi-Wavelength Absorption Analyzer model) was applied to the dataset. It was less sensitive to the instrument and the number of wavelengths, whereas significant differences in the determination of the absorption Ångström exponent for brown carbon were found (up to 22 %).

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“…11). Matalas and Sankarasubramanian (2003) provided a simple way to compute the limiting values of VIF for a sufficiently large sample size and a first-order autocorrelation:…”

Section: The Prewhitening Methodsmentioning

confidence: 99%

Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann–Kendall trend detection and the associated Sen's slope

et al. 2020

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Abstract. The Mann–Kendall test associated with the Sen's slope is a very widely used non-parametric method for trend analysis. It requires serially uncorrelated time series, yet most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have therefore been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction of the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman lidar water vapor mixing ratio, as well as tropopause and zero-degree temperature levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study evaluates the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the number of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series.

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A global analysis of climate-relevant aerosol properties retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories

Cited by 68 publications

References 86 publications

AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using ground- and space-based remote sensing as well as surface in situ observations

AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using ground- and space-based remote sensing as well as surface in situ observations

Determination of Aethalometer multiple-scattering enhancement parameters and impact on source apportionment during the winter 2017/18 EMEP/ACTRIS/COLOSSAL campaign in Milan

Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann–Kendall trend detection and the associated Sen's slope

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