Preprints
https://doi.org/10.5194/essd-2026-41
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2026-41
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 Jan 2026
| 30 Jan 2026
Status: this preprint is currently under review for the journal ESSD.
GloPINE dataset: model-ready measurements of INP concentrations using PINE instruments
Abstract. Ice-nucleating particles (INPs) are a subset of aerosol particles that facilitate the freezing of supercooled cloud droplets heterogeneously and influence the radiative properties of supercooled clouds. The role of INPs in the Earth system remains unquantified in part due to poorly constrained representations of their spatial distributions and properties in global and regional models. In this study, we present a quality-controlled dataset, called GloPINE, comprising 70,000 hours of INP concentrations measured using Portable Ice Nucleation Experiment (PINE) instruments that use an expansion chamber to make automated long-term (months to years) and high temporal resolution (< 10 mins). We collate measurements from 20 recent ground-based PINE field campaigns in the Northern Hemisphere conducted between January 2018 and December 2023, totalling more than 400,000 expansions performed under conditions relevant for mixed-phase clouds. In the GloPINE dataset, we subset and average the PINE measurements across synoptically relevant time intervals of 6 h and 2 K temperature bins, providing 36,000 INP measurements. Combining PINE expansions over these intervals enhances counting statistics at higher freezing temperatures, decreases the lower limit of measurable INP concentrations, and provides an INP dataset readily applicable to model simulation data and meteorological reanalysis products. The frequency and duration of measurements combined with the lack of instrument or methodological variability provides a means to robustly evaluate and constrain global models on a scale that has not previously been possible.
How to cite. Herbert, R. J., Lacher, L., Böhmländer, A., Tarn, M. D., Canzi, A., Pantoya, A., Freney, E., Höhler, K., Bogert, P., Planche, C., Tian, P., Adams, M., Barr, S., Brus, D., Büttner, N., Daily, M., Doulgeris, K., Eleftheridadis, K., Forster, G., Fösig, R., Georgakopoulos, D., Gini, M., Hallar, A. G., Krejci, R., Ludewig, E., Mazzola, M., McCubbin, I. B., Petäjä, T., Robinson, J., Vogel, F., Zieger, P., Arnold, S. R., Carslaw, K. S., Hiranuma, N., Möhler, O., and Murray, B. J.: GloPINE dataset: model-ready measurements of INP concentrations using PINE instruments, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2026-41, in review, 2026.
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Data sets
GloPINE dataset: model-ready measurements of INP concentrations using PINE instruments Ross James Herbert https://doi.org/10.5281/zenodo.16745514
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Ross James Herbert
CORRESPONDING AUTHOR
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
Larissa Lacher
Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Alexander Böhmländer
Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Mark D. Tarn
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
Antione Canzi
Université Clermont Auvergne, CNRS INSU, Laboratoire de Météorologie Physique (LaMP), UMR 6016, F-63000, Clermont-Ferrand, France
Aidan Pantoya
College of Engineering, WTAMU, Texas, United States
Evelyn Freney
Université Clermont Auvergne, CNRS INSU, Laboratoire de Météorologie Physique (LaMP), UMR 6016, F-63000, Clermont-Ferrand, France
Kristina Höhler
Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Pia Bogert
Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Celine Planche
Université Clermont Auvergne, CNRS INSU, Laboratoire de Météorologie Physique (LaMP), UMR 6016, F-63000, Clermont-Ferrand, France
Institut Universitaire de France (IUF), France
Ping Tian
Beijing Weather Modification Center, Beijing Meteorological Bureau, Beijing, China
Michael Adams
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
Sarah Barr
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
National Centre for Atmospheric Science, Natural Environment Research Council, Leeds, United Kingdom
David Brus
Finnish Meteorological Institute, Erik Palménin aukio 1, FI-00506, Helsinki, Finland
Nicole Büttner
Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Martin Daily
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
Konstantinos Doulgeris
Finnish Meteorological Institute, Erik Palménin aukio 1, FI-00506, Helsinki, Finland
Konstantinos Eleftheridadis
Environmental Radioactivity and Aerosol Technology for Atmospheric and Climate Impact Lab, National Centre For Scientific Research “Demokritos”, 15341 Agia Paraskevi, Athens, Greece
Grant Forster
National Centre for Atmospheric Science, Natural Environment Research Council, Leeds, United Kingdom
Weybourne Atmospheric Laboratory, University of East Anglia, Norwich, United Kingdom
Romy Fösig
Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Dimitrios Georgakopoulos
Agricultural University of Athens, Department of Crop Science, Laboratory of General and Agricultural Microbiology, 118 55 Athens, Greece
Maria Gini
Environmental Radioactivity and Aerosol Technology for Atmospheric and Climate Impact Lab, National Centre For Scientific Research “Demokritos”, 15341 Agia Paraskevi, Athens, Greece
A. Gannett Hallar
Storm Peak Laboratory, Department of Atmospheric Sciences, University of Utah, Salt Lake City, United States
Radovan Krejci
Department of Environmental Science, Stockholm University, 11419 Stockholm, Sweden
Elke Ludewig
Sonnblick Observatory, Geosphere Austria, Salzburg, Austria
Mauro Mazzola
Institute of Polar Sciences, National Research Council, Bologna, Italy
Ian B. McCubbin
Storm Peak Laboratory, Department of Atmospheric Sciences, University of Utah, Salt Lake City, United States
Tuukka Petäjä
Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland
Joseph Robinson
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
Franziska Vogel
Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy
Paul Zieger
Bolin Centre for Climate Research, Stockholm University, 11419 Stockholm, Sweden
Stephen R. Arnold
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
Kenneth S. Carslaw
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
Naruki Hiranuma
Department of Life, Earth, and Environmental Sciences, West Texas A&M University (WTAMU), Texas, United States
Department of Physics, University of Texas El Paso, El Paso, Texas, United States
Ottmar Möhler
Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Benjamin J. Murray
Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
Short summary
Ice formation in sub-zero clouds is influenced by airborne particles called ice-nucleating particles (INPs), whose concentrations vary substantially over short time and spatial scales. To assess the role of INPs in our climate, a comprehensive and consistent global dataset is essential. Our GloPINE model-ready dataset is a major step in this direction, comprising 36,000 measurements made using a single instrument design (PINE) over 70,000 hours of operation at 20 northern hemisphere sites.
Ice formation in sub-zero clouds is influenced by airborne particles called ice-nucleating...
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General Comments
This study documents an extremely valuable resource for the global observational and modeling-based ice nucleating particle communities. The data set is indeed a special one in containing data from multiple sites in the Northern Hemisphere that were collected in some cases for very long periods of time and over diurnal cycles in many cases. I did stumble with accepting the position that a special advantage of the sole use of PINE data removes the need for multiple instruments. While I understand that it could be possible to operate a network of such instruments to emphasize a procedure (not unified in this study) to focus on collecting higher temperature data at INP concentrations higher than about 0.01 per standard liter, the study also makes a compelling statement to me that confirms the need for integrating measurements such as those made offline using filter samples to adequately cover the full span of cloud conditions expected in the atmosphere. The PINE data represent a great advancement on the acquisition of substantial data that has special application to modeling needs but cannot easily access the range of temperatures and lower INP concentrations that may be most critical to representing and simulating mixed phase clouds. Am I wrong to conclude that? I also emphasize below the need to be a little more explicit about the methodological analysis of INP concentrations using raw PINE data. It may be obvious to the PINE community, but it bears attention for detailing to those not versed in using this instrument. Further, discussion of inferred sample volumes will help the casual reader understand better the extent to which the global atmosphere can be interrogated using real-time sampling instruments. My recommendation is that this paper could be modestly revised and be fully acceptable for publication.
Specific Comments
Abstract
Lines 4,10: The statements regarding hours of sampling or numbers of INP measurements are important and relevant, but also important and relevant are the volumes collected. Certainly, that information is more revealing about the sampling possible with real-time measurements. Perhaps give it in standard liters or standard cubic meters. Is that possible? I think, based on what I learned later in the discussion around Figure 2 it means that the data set herein represents an assessment based around 800 m3 of air at defined conditions. It is a lot by the standards of historical INP measurements but not necessarily by the standards of trying to represent what is present in the global atmosphere. This is not a negative point I am asking to be documented, just a practical one.
Introduction
Line 51: I find the statement that INP concentrations are “known to vary by several orders of magnitude during a single diurnal cycle” to be a bit misleading regarding the more typical scenarios at one set of conditions. I suggest "can sometimes vary" describes the situation more accurately. Certainly, with air mass changes there can be such a variation in a diurnal cycle and I believe this has been captured in a few earlier papers regarding changes following atmospheric aerosol perturbations (e.g., ones documenting changes following rain events, or passing through smoke plumes when operating continuous flow chambers) besides the one referenced. But such data, including the reference cited, also shows sometimes days at a time without a major diurnal cycle. This can vary by site and the season. Your point though is understood and valid, nonetheless, so only recommending this small change.
Line 62: Is the “less than 0.01 L-1” lower bound before or after consolidating measurements into the 6 h time intervals?
Line 91: Before the next paragraph on uncertainties, I expected to learn how the concentrations are defined. Are they continuously measured during the expansion and somehow integrated over narrow temperature ranges or is some time used around the lowest pressure and temperature of the expansion for which concentrations are defined by the volume sampled? This information is in older papers I believe, but it is not in section 2.3. The size distribution is mentioned, but over what time interval and acquired volume is that defined in each expansion?
Campaigns
Considering the previous comment, could information on single INP measurement sample volume also be included in the information for each project. Otherwise, there are details relevant to PINE users about times spent flushing and expanding and refilling, but not on effective sample volumes per sample. Is it always the same in each study?
PINE analysis and software
Lines 227-228: Is the OPC particle size distribution mentioned as critical information that is measured at the minimum temperature? Or over what time to that point? Again, trying to confirm understanding of how concentrations are determined, exactly, and what volume is represented.
Data subsetting
Line 244: This is the first mention of the 2 L volume. Please integrate this into the above discussions to make it clear how this is determined.
Figure 2: This is a very nice figure!
The GLoPINE INP data set
I understand that this is a database paper emphasizing the utility of a unique and comprehensive data set for constraining model representations of INPs. Still, is there a need to make any statement regarding the suitability of the method for providing more measurements at above 255 K, or if this method can be stand alone to characterize that regime where there are still many time periods below detection limits? I have also not raised the issue that concentration measurements are but one way to characterize INPs, though surely even the modeling community is going to ultimately want to know more information about validating their representation of sources and if ways are imagined for integrating PINE technology with other techniques to address that question.
Conclusions
The last comment above should perhaps be addressed here instead. At least one statement on the deficiencies of single instrument use could be helpful. The limitations at higher temperatures seem clear and a continued need for the community. One imagines that PINE data is no less valuable if combined with another, especially for “robust” constraint of model representation of INPs. Otherwise, it seems that model parameterizations are wholly trusted for comparison to total INP concentrations.