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  • Author or Editor: R. G. Barry x
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D. A. Knopf, K. R. Barry, T. A. Brubaker, L. G. Jahl, K. A. Jankowski, J. Li, Y. Lu, L. W. Monroe, K. A. Moore, F. A. Rivera-Adorno, K. A. Sauceda, Y. Shi, J. M. Tomlin, H. S. K. Vepuri, P. Wang, N. N. Lata, E. J. T. Levin, J. M. Creamean, T. C. J. Hill, S. China, P. A. Alpert, R. C. Moffet, N. Hiranuma, R. C. Sullivan, A. M. Fridlind, M. West, N. Riemer, A. Laskin, P. J. DeMott, and X. Liu

Abstract

Prediction of ice formation in clouds presents one of the grand challenges in the atmospheric sciences. Immersion freezing initiated by ice-nucleating particles (INPs) is the dominant pathway of primary ice crystal formation in mixed-phase clouds, where supercooled water droplets and ice crystals coexist, with important implications for the hydrological cycle and climate. However, derivation of INP number concentrations from an ambient aerosol population in cloud-resolving and climate models remains highly uncertain. We conducted an aerosol–ice formation closure pilot study using a field-observational approach to evaluate the predictive capability of immersion freezing INPs. The closure study relies on collocated measurements of the ambient size-resolved and single-particle composition and INP number concentrations. The acquired particle data serve as input in several immersion freezing parameterizations, which are employed in cloud-resolving and climate models, for prediction of INP number concentrations. We discuss in detail one closure case study in which a front passed through the measurement site, resulting in a change of ambient particle and INP populations. We achieved closure in some circumstances within uncertainties, but we emphasize the need for freezing parameterization of potentially missing INP types and evaluation of the choice of parameterization to be employed. Overall, this closure pilot study aims to assess the level of parameter details and measurement strategies needed to achieve aerosol–ice formation closure. The closure approach is designed to accurately guide immersion freezing schemes in models, and ultimately identify the leading causes for climate model bias in INP predictions.

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Taneil Uttal, Sandra Starkweather, James R. Drummond, Timo Vihma, Alexander P. Makshtas, Lisa S. Darby, John F. Burkhart, Christopher J. Cox, Lauren N. Schmeisser, Thomas Haiden, Marion Maturilli, Matthew D. Shupe, Gijs De Boer, Auromeet Saha, Andrey A. Grachev, Sara M. Crepinsek, Lori Bruhwiler, Barry Goodison, Bruce McArthur, Von P. Walden, Edward J. Dlugokencky, P. Ola G. Persson, Glen Lesins, Tuomas Laurila, John A. Ogren, Robert Stone, Charles N. Long, Sangeeta Sharma, Andreas Massling, David D. Turner, Diane M. Stanitski, Eija Asmi, Mika Aurela, Henrik Skov, Konstantinos Eleftheriadis, Aki Virkkula, Andrew Platt, Eirik J. Førland, Yoshihiro Iijima, Ingeborg E. Nielsen, Michael H. Bergin, Lauren Candlish, Nikita S. Zimov, Sergey A. Zimov, Norman T. O’Neill, Pierre F. Fogal, Rigel Kivi, Elena A. Konopleva-Akish, Johannes Verlinde, Vasily Y. Kustov, Brian Vasel, Viktor M. Ivakhov, Yrjö Viisanen, and Janet M. Intrieri

Abstract

International Arctic Systems for Observing the Atmosphere (IASOA) activities and partnerships were initiated as a part of the 2007–09 International Polar Year (IPY) and are expected to continue for many decades as a legacy program. The IASOA focus is on coordinating intensive measurements of the Arctic atmosphere collected in the United States, Canada, Russia, Norway, Finland, and Greenland to create synthesis science that leads to an understanding of why and not just how the Arctic atmosphere is evolving. The IASOA premise is that there are limitations with Arctic modeling and satellite observations that can only be addressed with boots-on-the-ground, in situ observations and that the potential of combining individual station and network measurements into an integrated observing system is tremendous. The IASOA vision is that by further integrating with other network observing programs focusing on hydrology, glaciology, oceanography, terrestrial, and biological systems it will be possible to understand the mechanisms of the entire Arctic system, perhaps well enough for humans to mitigate undesirable variations and adapt to inevitable change.

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