Aerosol-Ice Formation Closure Pilot Study
7 October 2019 - 28 October 2019
Lead Scientist: Daniel Knopf
Atmospheric ice nucleation represents one of the least understood atmospheric processes, hampering prediction of cloud processes and climate. The goal of this project is to conduct an aerosol-ice formation closure measurement and to evaluate its suitability to better constrain the prediction of ice nucleating particles (INPs) by current climate models. A key difference from previous studies is to base the study on the total ambient aerosol population rather than a limited portion of it. It is well known that particle size and composition impact ice nucleation. Here we aim to simultaneously characterize ambient immersion-mode INPs and leading characteristics of the aerosol population relevant to ice crystal formation via immersion freezing. Aerosol composition, mixing state and INP number concentrations will be determined by online and offline instrumentation typically employed by the community. The aerosol particle data will serve as input to predict INP number concentrations using various state-of-the-art ice nucleation parameterizations whose agreement with the INP measurements can then be evaluated within propagated uncertainties.
Since the closure approach is motivated by and well-designed for improving observational constraints on ice nucleation prediction by climate models, we will additionally compare aerosol properties and INPs from two climate models in the same parameterization phase space. Lastly, the importance of aerosol population mixing state for the prediction of INP number concentrations will be evaluated. This pilot study serves to identify potential obstacles to achieving meaningful aerosol-ice formation closure and climate model evaluation, including those associated with sampling inlets, instrumentation, and modeling approaches.
We expect that results will also provide new insights into the following fundamental questions regarding prediction of INP numbers in the atmosphere:
- What are the crucial aerosol physicochemical property measurements to accurately guide ice nucleation representations in models?
- What level of parameter details need to be known to achieve aerosol-ice formation closure within current measurement uncertainties?
- What are the leading causes for climate model bias in INP predictions?
- What ancillary aerosol property observations are most useful to accompany long-term INP measurements for the combined purpose of constraining INP parameterizations and climate model skill?
|Jessie Creamean||Ann Fridlind||Nicole Riemer|
|Daniel Cziczo||Naruki (seonggi) Hiranuma||Ryan Sullivan|
|Paul DeMott||Xiaohong Liu|
Knopf D, K Barry, T Brubaker, L Jahl, K Jankowski, J Li, Y Lu, L Monroe, K Moore, F Rivera-Adorno, K Sauceda, Y Shi, J Tomlin, H Vepuri, P Wang, N Lata, E Levin, J Creamean, T Hill, S China, P Alpert, R Moffet, N Hiranuma, R Sullivan, A Fridlind, M West, N Riemer, A Laskin, P DeMott, and X Liu. 2021. "Aerosol–Ice Formation Closure: A Southern Great Plains Field Campaign." Bulletin of the American Meteorological Society, , 10.1175/BAMS-D-20-0151.1. ONLINE.
Knopf DA, P DeMott, J Creamean, T Hill, N Riemer, N Hiranuma, A Laskin, R Sullivan, A Fridlind, X Liu, and M West. 2020. Aerosol-Ice Formation Closure Pilot Study (AEROICESTUDY) Field Campaign Report. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-20-017.
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Campaign Data Sets
|IOP Participant||Data Source Name||Final Data|
|Jessie Creamean||DRUM aerosol sampler||Order Data|
|Paul DeMott||Continuous Flow Thermal Diffusion Chamber||Order Data|
|Paul DeMott||Waveband Integrated Bioaerosol Sensor mark 4||Order Data|
|Thomas Hill||Ice Spectrometer||Order Data|
|Naruki (seonggi) Hiranuma||Portable Ice Nucleation Experiment chamber||Order Data|
|Daniel Knopf||Microorifice Uniform Deposit Impactor||Order Data|
|Ryan Sullivan||Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS) Time-of-Flight Aerosol Mass Spectrometer (HR-TOF-AMS) Laser Ablation Aerosol Particle Time-of-Flight mass spectrometer (LAAPTOF)||Order Data|