Aerosol-Ice Nucleating Particle Closure

Aerosol-Ice Nucleating Particle Closure#

Contributed by Yijia Sun and Daniel A. Knopf

Overview#

Clouds observed during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) are typically mixed-phase clouds where supercooled liquid droplets and ice crystals coexist. Ice-nucleating particles (INPs) from the ambient aerosol particle size distribution (PSD) can initiate ice crystal formation. The ability to predict INPs from ambient aerosol can be evaluated by conducting a so-called closure study as outlined in Knopf et al. (2021). Different immersion freezing (IMF) parameterizations can be applied to derive the number concentration of predicted INPs for the COMBLE and Zeppelin observatory locations. Zeppelin is upwind and COMBLE downwind of the cold-air outbreak event.

The quickcalc module, based on the simplified 1D aerosol-cloud model (Knopf et al., 2023), allows inputting the PSD information measured at COMBLE and Zeppelin locations to derive the number concentration of INPs for different immersion freezing parameterizations. These predicted INP number concentrations are then compared to locally measured INP number concentrations, constituting a closure exercise.

For this initial closure exercise, we chose Zeppelin observations at 0 UTC March 13 and COMBLE observations at 18 UTC March 13. The ambient PSD measurements are applied to derive INP number concentrations under the assumption that all aerosol particles are sea spray aerosols (SSA) while halving the number concentration of particles in the accumulation mode and neglecting particles in the Aitken mode. Five different IMF parameterizations were evaluated:

M2018: A singular ice nucleation active site (INAS) density-based parameterization of SSA (McCluskey et al., 2018).
A2022 : A singular INAS density-based parameterization of SSA (Alpert et al., 2022).
ABIFM: The water-activity based immersion freezing model, a classical nucleation theory (CNT)-based parameterization where ice nucleation is treated stochastically and is time dependent (Knopf and Alpert, 2013; SSA coefficients from Alpert et al., 2022).
Fletcher: An empirical temperature-dependent parameterization (Fletcher, 1962).
Cooper: An empirical temperature-dependent parameterization (Cooper, 1986).

The singular parameterizations (M2018, A2022) are deterministic with no time dependence, where ice nucleates on special active sites. In contrast, ABIFM treats nucleation as stochastic and time dependent. Fletcher and Cooper are empirical parameterizations included for comparison. Typically, these IMF parameterizations are uncertain by ±1–3 orders of magnitude. The results of this closure exercise can be seen here.

Figure 1: COMBLE observed INP vs. predicted INP on March 13. Predicted INP number concentrations (y-axis) are compared to observed INP number concentrations (x-axis) at the COMBLE site (18 UTC, March 13). Each colored symbol represents a predicted INP value at a given temperature (colorbar, °C). The five IMF parameterizations are: ABIFM (circles; CNT-based, Knopf and Alpert, 2013; Alpert et al., 2022), A2022 (squares; singular INAS, Alpert et al., 2022), M2018 (triangles; singular INAS, McCluskey et al., 2018), Fletcher (diamonds; empirical, Fletcher, 1962), and Cooper (five-pointed stars; empirical, Cooper, 1986). Black symbols with error bars denote observed INP concentrations. The 1:1 line (solid black) indicates perfect closure.

https://github.com/yijias33/COMBLEZeppelinfigures/blob/main/COMBLE_FINAL.png?raw=true

Figure 2: Zeppelin observed INP vs. predicted INP on March 13. Same as Figure 1, but for the Zeppelin observatory (0 UTC, March 13), which is located upwind of the cold-air outbreak.

https://github.com/yijias33/COMBLEZeppelinfigures/blob/main/ZEPPELIN_FINAL.png?raw=true

Prerequisites#

System requirements: Implement numpy, xarray, pandas, pint and flake8 in the python environment and run this jupyter notebook in this folder.

Imports#

from quickcalc import quickcalc
import numpy as np
from time import time
import xarray as xr
import pandas as pd

The applied aerosol PSD parameters for Zeppelin and COMBLE locations can be accessed in this folder.

aer_info1= {"name": "COMBLE18UTCMarch13",             #COMBLE18UTCMarch13case
                     "n_init_max": [21.66e6, 7.14e6],
                      "psd": {"type": "multi_logn",
                              "diam_mean": [0.13e-6,0.33e-6],
                              "geom_sd":[1.67,1.69],
                              "n_bins":50,
                              "diam_min":(0.0078e-6,5.577e-6),
                              "m_ratio":1.5},
                              "nucleus_type": "SSA"}

aer_info2= {"name": "ZEPPELIN0UTCMarch13",             #ZEPPELIN0UTCMarch13case
                     "n_init_max": [100.605e6, 24.58e6],
                      "psd": {"type": "multi_logn",
                              "diam_mean": [0.13e-6,0.61e-6],
                              "geom_sd":[2.2,1.9],
                              "n_bins":50,
                              "diam_min":(0.0059e-6,11.59e-6),
                              "m_ratio":1.5},
                              "nucleus_type": "SSA"}

Calculate the predicted INPs#

For the time-dependent IMF parameterization (ABIFM) an ice nucleation activation time, based on the INP measurements has to be applied. The chosen activation time is 1 min following the recommendation given in Alpert et al., (2022). The implemented equations for selected SSA IMF parameterizations can be found in the file AER.py.

COMBLEAL2022INP=[]
COMBLEMC2018INP=[]
COMBLEABIFMINP=[]
ZeppelinAL2022INP=[]
ZeppelinMC2018INP=[]
ZeppelinABIFMINP=[]

for T in range(1, 48, 1):
        #COMBLE18UTCMarch13case
        COMBLEAL2022 = quickcalc(aer_info_dict={**aer_info1, **{"singular_fun": "AL2022"}}, T_in=243+(T-1)*0.5, use_ABIFM=False,RH_in=99.5)
        COMBLEAL2022INP.append(COMBLEAL2022.variables['inp_tot'].values)
        COMBLEMC2018 = quickcalc(aer_info_dict={**aer_info1, **{"singular_fun": "MC2018"}}, T_in=243+(T-1)*0.5, use_ABIFM=False,RH_in=99.5)
        COMBLEMC2018INP.append(COMBLEMC2018.variables['inp_tot'].values)
        COMBLEABIFM =quickcalc(aer_info_dict=aer_info1, T_in=243+(T-1)*0.5, use_ABIFM=True,RH_in=99.5, ABIFM_delta_t=60.)
        COMBLEABIFMINP.append(COMBLEABIFM.variables['inp_tot'].values)
        #ZEPPELIN0UTCMarch13case
        ZeppelinAL2022 = quickcalc(aer_info_dict={**aer_info2, **{"singular_fun": "AL2022"}}, T_in=243+(T-1)*0.5, use_ABIFM=False,RH_in=99.5)
        ZeppelinAL2022INP.append(ZeppelinAL2022.variables['inp_tot'].values)
        ZeppelinMC2018 = quickcalc(aer_info_dict={**aer_info2, **{"singular_fun": "MC2018"}}, T_in=243+(T-1)*0.5, use_ABIFM=False,RH_in=99.5)
        ZeppelinMC2018INP.append(ZeppelinMC2018.variables['inp_tot'].values)
        ZeppelinABIFM =quickcalc(aer_info_dict=aer_info2, T_in=243+(T-1)*0.5, use_ABIFM=True,RH_in=99.5, ABIFM_delta_t=60.)
        ZeppelinABIFMINP.append(ZeppelinABIFM.variables['inp_tot'].values)

Export the Datasets of Predicted INPs#

Derived Closure Datasets as netcdf Files#

COMBLEAL2022.to_netcdf('SSAAL2022COMBLE18UTCMarch13.nc')
COMBLEMC2018.to_netcdf('SSAMC2018COMBLE18UTCMarch13.nc')
COMBLEABIFM.to_netcdf('SSAABIFMCOMBLE18UTCMarch13.nc')
ZeppelinAL2022.to_netcdf('SSAAL2022ZEPPELIN0UTCMarch13.nc')
ZeppelinMC2018.to_netcdf('SSAMC2018ZEPPELIN0UTCMarch13.nc')
ZeppelinABIFM.to_netcdf('SSAABIFMZEPPELIN0UTCMarch13.nc')

Derived Closure Datasets as csv Files#

test=COMBLEAL2022.to_dataframe()
test.to_csv('SSAPT2022COMBLE18UTCMarch13.csv')
test=COMBLEMC2018.to_dataframe()
test.to_csv('SSAMC2018COMBLE18UTCMarch13.csv')
test=COMBLEABIFM.to_dataframe()
test.to_csv('SSAABIFMCOMBLE18UTCMarch13.csv')
test=ZeppelinAL2022.to_dataframe()
test.to_csv('SSAPT2022ZEPPELIN0UTCMarch13.csv')
test=ZeppelinMC2018.to_dataframe()
test.to_csv('SSAMC2018ZEPPELIN0UTCMarch13.csv')
test=ZeppelinABIFM.to_dataframe()
test.to_csv('SSAABIFMZEPPELIN0UTCMarch13.csv')

Comparison of the Predicted INPs with Observed INPs#

The observed INP number concentrations are given in a Google sheet (here). Provided matlab codes named “COMBLE” and “ZEPPELIN” allow for quickly plotting the results.

Summary#

The aerosol-INP closure exercise at Zeppelin and COMBLE observatories is based on the assumption that all aerosol particles constitute sea spray aerosol (SSA) particles and their particle number concentration in the accumulation mode is halved and particles in the Aitken mode are neglected. In general, within the typically given uncertainties of applied IMF parameterizations, all approaches can represent observed INP number concentrations. However, there are differently achieved degrees of closure among the INP representations:

At Zeppelin observatory, which is located in a mountainous region and is upwind compared to COMBLE, i.e., the boundary layer had less interaction with open ocean surfaces that potentially serve as a source of SSA and INPs, the singular M2018 parameterization represents observed INPs best.

At COMBLE, after the air masses travelled long distance over open ocean, the observed INP number concentrations are best represented by the SSA IMF parameterization ABIFM.

Acknowledgment and References#

We thank Israel Silber who wrote the original version of the quickcalc module.
The AC-1D model code is available at https://github.com/open-atmos/AC-1D.
Sun, Y., Fridlind, A. M., Silber, I., Riemer, N., and Knopf, D. A.: Prognostic simulations of mixed-phase clouds with model AC-1D v1.0: the impact of aerosol types and freezing parameterizations on ice crystal budgets, Geosci. Model Dev., 19, 1581–1617, https://doi.org/10.5194/gmd-19-1581-2026, 2026.
Knopf, D. A., Barry, K. R., Brubaker, T. A., Jahl, L. G., Jankowski, K. A. L., Li, J., Lu, Y., Monroe, L. W., Moore, K. A., Rivera-Adorno, F. A., Sauceda, K. A., Shi, Y., Tomlin, J. M., Vepuri, H. S. K., Wang, P., Lata, N. N., Levin, E. J. T., Creamean, J. M., Hill, T. C. J., China, S., Alpert, P. A., Moffet, R. C., Hiranuma, N., Sullivan, R. C., Fridlind, A. M., West, M., Riemer, N., Laskin, A., DeMott, P. J., Liu, X., Aerosol–Ice Formation Closure: A Southern Great Plains Field Campaign, B. Am. Meteorol. Soc., 102, 10, E1952–E1971, 2021, doi: 10.1175/BAMS-D-20-0151.1.
Knopf, D. A., Silber, I., Riemer, N., Fridlind, A. M., & Ackerman, A. S. (2023). A 1D model for nucleation of ice from aerosol particles: An application to a mixed-phase Arctic stratus cloud layer. Journal of Advances in Modeling Earth Systems, 15, e2023MS003663, doi:10.1029/2023MS003663.
Knopf, D. A., and P. A. Alpert (2013), A water activity based model of heterogeneous ice nucleation kinetics for freezing of water and aqueous solution droplets, Faraday Discuss., 165, 513–534, doi:10.1039/c3fd00035d.
McCluskey, C. S., et al. (2018), Marine and Terrestrial Organic Ice-Nucleating Particles in Pristine Marine to Continentally Influenced Northeast Atlantic Air Masses, J. Geophys. Res.-Atmos., 123(11), 6196-6212, doi:10.1029/2017jd028033.
Alpert, P. A., W. P. Kilthau, R. E. O’Brien, R. C. Moffet, M. K. Gilles, B. Wang, A. Laskin, J. Y. Aller, and D. A. Knopf (2022), Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multimodal freezing model, Sci Adv, 8(44), eabq6842, doi:10.1126/sciadv.abq6842.
Fletcher, N. H. (1962), The Physics of Rainclouds, Cambridge University Press, doi:10.1017/CBO9780511735639.
Cooper, W. A. (1986), Ice initiation in natural clouds, Precipitation Enhancement — A Scientific Challenge, Meteor. Monogr., No. 43, Amer. Meteor. Soc., 29–32, doi:10.1175/0065-9401-21.43.29.
Contact: yijia.sun@stonybrook.edu