Abstract. Forty years ago, lichens were identified as extraordinary biological icenucleators (INs) that enable ice formation at temperatures close to0 ∘C. By employing INs, lichens thrive in freezing environmentsthat surpass the physiological limits of other vegetation, thus making themthe majority of vegetative biomass in northern ecosystems. Aerosolizedlichen INs might further impact cloud glaciation and have the potential toalter atmospheric processes in a warming Arctic. Despite the ecologicalimportance and formidable ice nucleation activities, the abundance,diversity, sources, and role of ice nucleation in lichens remain poorlyunderstood. Here, we investigate the ice nucleation capabilities of lichenscollected from various ecosystems across Alaska. We find ice nucleatingactivity in lichen to be widespread, particularly in the coastal rainforestof southeast Alaska. Across 29 investigated lichen, all species show icenucleation temperatures above −15 ∘C, and ∼30 %initiate freezing at temperatures above −6 ∘C. Concentrationseries of lichen ice nucleation assays in combination with statisticalanalysis reveal that the lichens contain two subpopulations of INs, similarto previous observations in bacteria. However, unlike the bacterial INs, thelichen INs appear as independent subpopulations resistant to freeze–thawcycles and against temperature treatment. The ubiquity and high stability ofthe lichen INs suggest that they can impact local atmospheric processes andthat ice nucleation activity is an essential trait for their survival incold environments. 
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                            HUB: a method to model and extract the distribution of ice nucleation temperatures from drop-freezing experiments
                        
                    
    
            The heterogeneous nucleation of ice is an importantatmospheric process facilitated by a wide range of aerosols. Drop-freezingexperiments are key for the determination of the ice nucleation activity ofbiotic and abiotic ice nucleators (INs). The results of these experimentsare reported as the fraction of frozen droplets fice(T) as a functionof decreasing temperature and the corresponding cumulative freezing spectraNm(T) computed using Gabor Vali's methodology. The differential freezingspectrum nm(T) is an approximant to the underlying distribution ofheterogeneous ice nucleation temperatures Pu(T) that represents thecharacteristic freezing temperatures of all INs in the sample. However,Nm(T) can be noisy, resulting in a differential form nm(T) that is challenging to interpret. Furthermore, there is no rigorousstatistical analysis of how many droplets and dilutions are needed to obtaina well-converged nm(T) that represents the underlying distributionPu(T). Here, we present the HUB (heterogeneousunderlying-based) method and associated Python codes thatmodel (HUB-forward code) and interpret (HUB-backward code) the results ofdrop-freezing experiments. HUB-forward predicts fice(T) and Nm(T)from a proposed distribution Pu(T) of IN temperatures, allowing itsusers to test hypotheses regarding the role of subpopulations of nuclei infreezing spectra and providing a guide for a more efficient collection offreezing data. HUB-backward uses a stochastic optimization method to computenm(T) from either Nm(T) or fice(T). The differential spectrumcomputed with HUB-backward is an analytical function that can be used toreveal and characterize the underlying number of IN subpopulations ofcomplex biological samples (e.g., ice-nucleating bacteria, fungi, pollen)and to quantify the dependence of these subpopulations on environmentalvariables. By delivering a way to compute the differential spectrum fromdrop-freezing data, and vice versa, the HUB-forward and HUB-backward codesprovide a hub to connect experiments and interpretative physical quantitiesthat can be analyzed with kinetic models and nucleation theory. 
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                            - Award ID(s):
- 2308172
- PAR ID:
- 10575697
- Publisher / Repository:
- Copernicus Publications
- Date Published:
- Journal Name:
- Atmospheric Chemistry and Physics
- Volume:
- 23
- Issue:
- 10
- ISSN:
- 1680-7324
- Page Range / eLocation ID:
- 5623 to 5639
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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