the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Aug 2025
Long-term measurements of ice nucleating particles at Atmospheric Radiation Measurement (ARM) sites worldwide
Abstract. Ice nucleating particles (INPs) play a critical role in cloud microphysics and precipitation formation, yet long-term, spatially extensive observational datasets remain limited. Here, we present one of the most comprehensive publicly available datasets of immersion-mode INP concentrations using a single analytical method, generated through the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility. INP filter samples have been collected across a broad range of environments—including agricultural plains, Arctic coastlines, high-elevation mountain sites, marine regions, and urban areas—via fixed observatories, mobile facility deployments, and vertically-resolved tethered balloon system operations. We describe the standardized processing and quality assurance pipeline, from filter collection and processing using the Ice Nucleation Spectrometer to final data products archived on the ARM Data Discovery portal. The dataset includes both total INP concentrations and selectively treated samples, allowing for classification of biological, organic, and inorganic INP types. It features a continuous 5-year record of INP measurements from a central U.S. site, with data collection still ongoing. Seasonal and site-specific differences in INP concentrations are illustrated through intercomparisons at −10 °C and −20 °C, revealing distinct regional sources and atmospheric drivers. We also outline mechanisms for researchers to access existing data, request additional sample analyses, and propose future field campaigns involving ARM INP measurements. This dataset supports a wide range of scientific applications, from observational and mechanistic studies to model development, and provides critical constraints on aerosol-cloud interactions across diverse atmospheric regimes.
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Status: final response (author comments only)
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RC1: 'Comment on essd-2025-352', Anonymous Referee #1, 06 Sep 2025
- AC1: 'Author response to reviewers on essd-2025-352', Jessie Creamean, 23 Oct 2025
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RC2: 'Comment on essd-2025-352', Anonymous Referee #2, 15 Sep 2025
Review of ESSD discussion paper essd-2025-352
Date: 2025-09-15
Summary
This manuscript describes the U.S. DOE ARM program’s immersion-mode ice-nucleating particle (INP) dataset, spanning fixed observatories and ARM Mobile Facility (AMF) deployments, as well as vertically resolved sampling with tethered balloon systems (TBS). The paper documents sampling protocols, INS processing (including heat and H₂O₂ treatments), and automated analysis with the OLAF DaQ INS software, and points readers to data access via ARM Data Discovery. The work represents a highly valuable, standardized, multi-site record with strong potential for model evaluation, parameterization development, and process studies.
Specific comments A. Major issues
- Sampling duration vs. frequency (Table 1; Section 2.2.1, lines 177–181): Table 1 reports ~6-day collection frequency at several sites, whereas Section 2.2.1 states that sampling is “typically after 24 hours”. Please reconcile this apparent contradiction explicitly in the text and encode, per site and period, the actual filter exposure duration in the metadata. If some sites use multi-day exposures, discuss the potential for on-filter chemical aging (photo-/heterogeneous oxidation) to bias INP spectra—particularly depressing labile organics at warm T (−5 to −15 °C) and enriching refractory INPs. Consider a sensitivity check comparing daily versus 6-day exposures during IOPs.
- Coordinate error (Section 2.1.1, lines 82–84; Table 1): The SGP site is given as 97.488° E, but it should be 97.488° W. Please correct this typo and ensure it propagates to Table 1, Figure 1, and metadata exports.
- Automated monotonicity correction (Section 3.3, lines 297–302; Fig. 3): Section 3.3 describes adjusting bins to enforce monotonicity when blank subtraction yields decreasing K(T). Later it says bins exceeding a threshold are “flagged with an error signal”. Please quantify how often this correction is applied per sample/site, expose a counter and QC flag in the NetCDF, and—if possible—retain pre-correction values.
- Uncertainty budget (Equation 1, Section 3.1, lines 232–239): Beyond Agresti–Coull intervals, please include systematic and volumetric terms: flow meter accuracy (±2%), temperature measurement (±0.2 °C, including block gradients), droplet volume tolerance, and suspension volume uncertainties. Discuss edge cases (0/32 and 32/32) and how LOD/LOQ are reported. Providing per-bin combined uncertainties will improve usability.
- Blank strategy and Oliktok (Sections 2.1.2–2.1.3, Table 1; Section 3.2.1, lines 254–255): Clarify, in a table and in the data files, which sites have field versus lab blanks. For Oliktok (OLI), state explicitly that only lab blanks were available and flag affected samples in the data (e.g., “blank_type = lab-only; use with caution”).
- TBS sampling details (Section 2.1.3, lines 144–152; Table 2): For TBS deployments, please document the standard conditions implied by the logged volumetric flow (Slpm) and any conversions applied to STP volumes at altitude. Include face velocity across the filter, residence time per filter/altitude, flow stability during ascents/descents, and the barometric/temperature corrections used. If these are detailed in Dexheimer et al. (2024), cross-reference explicitly.
B. Moderate suggestions
- Positive control (Section 3.2.3, lines 272–283): Report periodic measurements of a standard INP material (e.g., Snomax, illite NX) to track sensitivity drift.
- Cross-site comparability (Figures 6–7; Section 4.3, lines 358–364): Daily sampling resolves episodic events better than 6-day routine sampling. Note this caveat and consider a sensitivity test subsampling daily periods to pseudo-6-day to quantify biases in seasonal boxplots.
- Treatment fractions (Section 2.3.1, line 224–225; Fig. 5, lines 342–346): State, per site and season, the fraction and number of samples undergoing heat and H₂O₂ treatments. Add treatment identifiers to records.
- Software versioning (Section 3.3, line 286): Cite a DOI/Zenodo release or commit hash for OLAF DaQ INS and include version in dataset metadata.
C. Minor and editorial corrections
- DOIs (lines 432 and 450): Several DOIs are written as https//doi.org/... (missing colon). Please correct to https://doi.org/....
- Typo (Table 3): Remove duplicated word 'the the ARM G-1'.
- Purge gas (line 213): Replace 'pre-cooled slightly above block temperature' with 'pre-cooled near block temperature'.
- Figures: 4 (lines 318–330): Add number of samples per line. Fig. 5 (lines 342–346): Clarify number of treated samples. Figs. 6–7 (lines 358–364): Add sample counts per boxplot and note filter durations.
- Tables: Table 1: add typical duration per filter and blank type. Table 2 (line 153): add residence-time assumptions.
I recommend “major revisions.” The manuscript’s core contribution is strong, but resolving sampling-duration ambiguity, fully documenting automated corrections/QC, and expanding the uncertainty budget are essential for a durable ESSD dataset paper.
Citation: https://doi.org/10.5194/essd-2025-352-RC2 - AC1: 'Author response to reviewers on essd-2025-352', Jessie Creamean, 23 Oct 2025
- AC1: 'Author response to reviewers on essd-2025-352', Jessie Creamean, 23 Oct 2025
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This data description paper by Creamean et al. offers a bodacious ice-nucleating particle dataset. The manuscript is well-written and fulfills the journal's scope. This reviewer recommends publishing this paper for ESSD after the authors address the following comments.
The authors did a good job showing applications of their INP data. How about limitations? What do the data users need to be aware of when they apply the offered data in observation-driven Earth system models etc. on different spatial-temporal scales? Perhaps, the authors might address it according to the challenges discussed in Burrows et al. (2022; DOI - https://doi.org/10.1029/2021RG000745)? This reviewer believes that clearly stating limitations is as important as demonstrating the applicability of any data.
L66-67: It seems controversial that the authors raise the concern of “not routine” INP measurements here, while they offer several single IOP data in this manuscript. The authors might want to rephrase this sentence; otherwise, clarify the concern rigorously.
L180-181: Has the time span between collection and analysis been consistent for all samples? If not, this reviewer would like to see if the authors can discuss the impact of various sample storage intervals.
L220-221: What is the rational procedure of H2O2 application for organic removal? This reviewer is aware that the H2O2 concentration ranges (e.g., Perkins et al., 2020: DOI - 10.1021/acsearthspacechem.9b00304). It seems the peroxide digestion method is operational without verification. The authors might consider including a brief yet clear statement of what needs to be investigated in terms of H2O2 application down the road in the manuscript. Doing this will benefit the community and mitigate the concerns of readers.
L232: Missing a negative sign on RHS in Eqn (1)?
L233: How do the authors determine V_suspension values? Please clarify in the manuscript.