Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity

Abstract

Abstract. Many of the next generation of global climate models will include aerosol schemes which explicitly simulate the microphysical processes that determine the particle size distribution. These models enable aerosol optical properties and cloud condensation nuclei (CCN) concentrations to be determined by fundamental aerosol processes, which should lead to a more physically based simulation of aerosol direct and indirect radiative forcings. This study examines the global variation in particle size distribution simulated by 12 global aerosol microphysics models to quantify model diversity and to identify any common biases against observations. Evaluation against size distribution measurements from a new European network of aerosol supersites shows that the mean model agrees quite well with the observations at many sites on the annual mean, but there are some seasonal biases common to many sites. In particular, at many of these European sites, the accumulation mode number concentration is biased low during winter and Aitken mode concentrations tend to be overestimated in winter and underestimated in summer. At high northern latitudes, the models strongly underpredict Aitken and accumulation particle concentrations compared to the measurements, consistent with previous studies that have highlighted the poor performance of global aerosol models in the Arctic. In the marine boundary layer, the models capture the observed meridional variation in the size distribution, which is dominated by the Aitken mode at high latitudes, with an increasing concentration of accumulation particles with decreasing latitude. Considering vertical profiles, the models reproduce the observed peak in total particle concentrations in the upper troposphere due to new particle formation, although modelled peak concentrations tend to be biased high over Europe. Overall, the multimodel-mean data set simulates the global variation of the particle size distribution with a good degree of skill, suggesting that most of the individual global aerosol microphysics models are performing well, although the large model diversity indicates that some models are in poor agreement with the observations. Further work is required to better constrain size-resolved primary and secondary particle number sources, and an improved understanding of nucleation and growth (e.g. the role of nitrate and secondary organics) will improve the fidelity of simulated particle size distributions.

Modelled black carbon radiative forcing and atmospheric lifetime in AeroCom Phase II constrained by aircraft observations

“…Only three models (IMPACT, GMI, GISS-MATRIX) exhibit both a low bias or RMS error and a high correlation, with no single obvious factor linking their aerosol treatments. AeroCom Phase II models use a wide variety of microphysics schemes (Mann et al, 2014). Meteorology and treatment of BC aging and wet scavenging also vary, and will impact the vertical profiles (Kipling et al, 2013;Bauer et al, 2013).…”

The IAGOS-CORE aerosol package: instrument design, operation and performance for continuous measurement aboard in-service aircraft

“…On the contrary, vertically resolved in-situ data of aerosol properties are urgently needed for model validation and improvement (Aquila et al, 2011;Mann et al, 2014).…”

Potential of polarization/Raman lidar to separate fine dust, coarse dust, maritime, and anthropogenic aerosol profiles

“…Furthermore, we demonstrated that polarization lidars have the potential to contribute to vertical profiling of cloud condensation nucleus (CCN) and ice-nucleating particle (INP) concentrations Ansmann, 2015, 2016). The retrieval of these higher level lidar products is driven by the fact that state-of-the-art environmental (regional) and global atmospheric models include aerosol schemes which simulate microphysical aerosol processes and life cycles of a variety of aerosol components and can be used to predict dust events and enable the simulation of CCN and INP profiles (Huneeus et al, 2011;Koffi et al, 2012Koffi et al, , 2016Mann et al, 2014;Kim et. al., 2014;Nickovic et al, 2016;Hande et al, 2016).…”