2020, Hodzic, Alma, Campuzano-Jost, Pedro, Bian, Huisheng, Chin, Mian, Colarco, Peter R., Day, Douglas A., Froyd, Karl D., Heinold, Bernd, Katich, Joseph M., Jo, Duseong S., Kodros, John K., Nault, Benjamin A., Pierce, Jeffrey R., Ray, Eric, Schacht, Jacob, Schill, Gregory P., Schroder, Jason C., Schwarz, Joshua P., Sueper, Donna T., Tegen, Ina, Tilmes, Simone, Tsigaridis, Kostas, Yu, Pengfei, Jimenez, Jose L.

The spatial distribution and properties of submicron organic aerosol (OA) are among the key sources of uncertainty in our understanding of aerosol effects on climate. Uncertainties are particularly large over remote regions of the free troposphere and Southern Ocean, where very few data have been available and where OA predictions from AeroCom Phase II global models span 2 to 3 orders of magnitude, greatly exceeding the model spread over source regions. The (nearly) pole-to-pole vertical distribution of nonrefractory aerosols was measured with an aerosol mass spectrometer onboard the NASA DC-8 aircraft as part of the Atmospheric Tomography (ATom) mission during the Northern Hemisphere summer (August 2016) and winter (February 2017). This study presents the first extensive characterization of OA mass concentrations and their level of oxidation in the remote atmosphere. OA and sulfate are the major contributors by mass to submicron aerosols in the remote troposphere, together with sea salt in the marine boundary layer. Sulfate was dominant in the lower stratosphere. OA concentrations have a strong seasonal and zonal variability, with the highest levels measured in the lower troposphere in the summer and over the regions influenced by biomass burning from Africa (up to 10 μgsm-3). Lower concentrations (~ 0:1 0.3 μgsm-3) are observed in the northern middle and high latitudes and very low concentrations (< 0:1 μgsm-3) in the southern middle and high latitudes. The ATom dataset is used to evaluate predictions of eight current global chemistry models that implement a variety of commonly used representations of OA sources and chemistry, as well as of the AeroCom-II ensemble. The current model ensemble captures the average vertical and spatial distribution of measured OA concentrations, and the spread of the individual models remains within a factor of 5. These results are significantly improved over the AeroCom-II model ensemble, which shows large overestimations over these regions. However, some of the improved agreement with observations occurs for the wrong reasons, as models have the tendency to greatly overestimate the primary OA fraction and underestimate the sec-ondary fraction. Measured OA in the remote free troposphere is highly oxygenated, with organic aerosol to organic carbon (OA= OC) ratios of ~ 2.2 2.8, and is 30 % 60% more oxygenated than in current models, which can lead to significant errors in OA concentrations. The model measurement comparisons presented here support the concept of a more dynamic OA system as proposed by Hodzic et al. (2016), with enhanced removal of primary OA and a stronger production of secondary OA in global models needed to provide better agreement with observations. © 2020 IEEE Computer Society. All rights reserved.

2018, Benedetti, Angela, Reid, Jeffrey S., Knippertz, Peter, Marsham, John H., Di Giuseppe, Francesca, Rémy, Samuel, Basart, Sara, Boucher, Olivier, Brooks, Ian M., Menut, Laurent, Mona, Lucia, Laj, Paolo, Pappalardo, Gelsomina, Wiedensohler, Alfred, Baklanov, Alexander, Brooks, Malcolm, Colarco, Peter R., Cuevas, Emilio, da Silva, Arlindo, Escribano, Jeronimo, Flemming, Johannes, Huneeus, Nicolas, Jorba, Oriol, Kazadzis, Stelios, Kinne, Stefan, Popp, Thomas, Quinn, Patricia K., Sekiyama, Thomas T., Tanaka, Taichu, Terradellas, Enric

Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol-climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.