Harri Kokkola, Bjørn Hallvard Samset, Duncan Watson-Parris, Philippe Le Sager, Dirk Jan Leo Oliviè, Twan van Noije, Jonas Gliß, Kostas Tsigaridis, Alf Kirkevåg, Paul Ginoux, Mian Chin, Maria Sand, Philip Stier, Susanne E. Bauer, Marianne Tronstad Lund, Michael Schulz, Gunnar Myhre, Camilla Weum Stjern, Hitoshi Matsui, Huisheng Bian, Zak Kipling, Svetlana Tsyro, Samuel Remy, Ramiro Checa-Garcia, Toshihiko Takemura, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Maria Sand, Bjørn H. Samset, Gunnar Myhre Camilla W. Stjern, and Marianne T. Lund have been supported by the Research Council of Norway (grantnos. 244141 (NetBC), 315195 (ACCEPT), 250573 (SUPER), and 248834 (QUISARC)). Ramiro Checa-Garcia, Alf Kirkevåg, and Michael Schulz were supported by the European Union Horizon 2020 grant (grant no. 641816, CRESCENDO). Hitoshi Matsui was supported by the Ministry of Education, Culture, Sports, Science and Technology of Japan and the Japan Society for the Promotion of Science (MEXT/JSPS), KAKENHI (grantnos. JP17H04709, JP19H05699, and JP20H00638), the MEXT Arctic Challenge for Sustainability II (ArCS-II) project (grant no. JPMXD1420318865), and the Environment Research and Technology Development Fund 2–2003 (grant no. JPMEERF20202003) of the Environmental Restoration and Conservation Agency. Toshihiko Takemura was supported by the NEC SX supercomputer system of the National Institute for Environmental Studies, Japan, the Environment Research and Technology Development Fund (grant no. JPMEERF20202F01) of the Environmental Restoration and Conservation Agency, Japan, and the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant no. JP19H05669), and Philip Stier acknowledges support from the European Research Council (ERC) project RECAP under the European Union’s Horizon 2020 research and innovation programme (grant no. 724602). Duncan Watson-Parris and Philippe Le Sager acknowledge support from the UK Natural Environment Research Council (grant nos. NE/P013406/1 (A-CURE) and NE/S005390/1 (ACRUISE)), and from the European Union’s Horizon 2020 research and innovation programme iMIRACLI under the Marie Skłodowska-Curie (grant no. 860100). Michael Schulz, Alf Kirkevåg, and Dirk J. L. Olivié acknowledge funding from the European Union’s Horizon 2020 Research and Innovation programme, project FORCeS (grant no. 821205), by the Research Council of Norway INES (grant no. 270061), and KeyClim (grant no. 295046). The AeroCom database is maintained by the computing infrastructure efforts provided by the Norwegian Meteorological Institute.
Aerosol-induced absorption of shortwave radiation can modify the climate through local atmospheric heating, which affects lapse rates, precipitation, and cloud formation. Presently, the total amount of aerosol absorption is poorly constrained, and the main absorbing aerosol species (black carbon (BC), organic aerosols (OA), and mineral dust) are diversely quantified in global climate models. As part of the third phase of the Aerosol Comparisons between Observations and Models (AeroCom) intercomparison initiative (AeroCom phase III), we here document the distribution and magnitude of aerosol absorption in current global aerosol models and quantify the sources of intermodel spread, highlighting the difficulties of attributing absorption to different species. In total, 15 models have provided total present-day absorption at 550 nm (using year 2010 emissions), 11 of which have provided absorption per absorbing species. The multi-model global annual mean total absorption aerosol optical depth (AAOD) is 0.0054 (0.0020 to 0.0098; 550 nm), with the range given as the minimum and maximum model values. This is 28 % higher compared to the 0.0042 (0.0021 to 0.0076) multi-model mean in AeroCom phase II (using year 2000 emissions), but the difference is within 1 standard deviation, which, in this study, is 0.0023 (0.0019 in Phase II). Of the summed component AAOD, 60 % (range 36 %–84 %) is estimated to be due to BC, 31 % (12 %–49 %) is due to dust, and 11 % (0 %–24 %) is due to OA; however, the components are not independent in terms of their absorbing efficiency. In models with internal mixtures of absorbing aerosols, a major challenge is the lack of a common and simple method to attribute absorption to the different absorbing species. Therefore, when possible, the models with internally mixed aerosols in the present study have performed simulations using the same method for estimating absorption due to BC, OA, and dust, namely by removing it and comparing runs with and without the absorbing species. We discuss the challenges of attributing absorption to different species; we compare burden, refractive indices, and density; and we contrast models with internal mixing to models with external mixing. The model mean BC mass absorption coefficient (MAC) value is 10.1 (3.1 to 17.7) m2 g−1 (550 nm), and the model mean BC AAOD is 0.0030 (0.0007 to 0.0077). The difference in lifetime (and burden) in the models explains as much of the BC AAOD spread as the difference in BC MAC values. The difference in the spectral dependency between the models is striking. Several models have an absorption Ångstrøm exponent (AAE) close to 1, which likely is too low given current knowledge of spectral aerosol optical properties. Most models do not account for brown carbon and underestimate the spectral dependency for OA.