Abstract. A number of new measurement methods for ice nucleating particles (INPs) have been introduced in recent years, and it is important to address how these methods compare. Laboratory comparisons of instruments sampling major INP types are common, but few comparisons have occurred for ambient aerosol measurements exploring the utility, consistency and complementarity of different methods to cover the large dynamic range of INP concentrations that exists in the atmosphere. In this study, we assess the comparability of four offline immersion freezing measurement methods (Colorado State University ice spectrometer, IS; North Carolina State University cold stage, CS; National Institute for Polar Research Cryogenic Refrigerator Applied to Freezing Test, CRAFT; University of British Columbia microorifice uniform deposit impactor-droplet freezing technique, MOUDI-DFT) and an online method (continuous flow diffusion chamber, CFDC) used in a manner deemed to promote/maximize immersion freezing, for the detection of INPs in ambient aerosols at different locations and in different sampling scenarios. We also investigated the comparability of different aerosol collection methods used with offline immersion freezing instruments. Excellent agreement between all methods could be obtained for several cases of co-sampling with perfect temporal overlap. Even for sampling periods that were not fully equivalent, the deviations between atmospheric INP number concentrations measured with different methods were mostly less than 1 order of magnitude. In some cases, however, the deviations were larger and not explicable without sampling and measurement artifacts. Overall, the immersion freezing methods seem toPublished by Copernicus Publications on behalf of the European Geosciences Union. , although more comparisons are needed in this temperature regime that is difficult to access with online methods. Relative to the CFDC method, three immersion freezing methods that disperse particles into a bulk liquid (IS, CS, CRAFT) exhibit a positive bias in measured INP number concentrations below −20 • C, increasing with decreasing temperature. This bias was present but much less pronounced for a method that condenses separate water droplets onto limited numbers of particles prior to cooling and freezing (MOUDI-DFT). Potential reasons for the observed differences are discussed, and further investigations proposed to elucidate the role of all factors involved.

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A major component of California's yearly precipitation comes from wintertime atmospheric river events which bring large amounts of moisture from the tropics up to the midlatitudes. Understanding these systems, specifically the effects of aerosol particles on precipitation associated with these storms, was a major focus of the 2015 Atmospheric Radiation Measurement Cloud Aerosol Precipitation Experiment, which was part of the wintertime California Water 2015 campaign. The measurement campaign provided sampling platforms on four aircraft, including the Atmospheric Radiation Measurement Aerial Facility G‐1, as well as the National Oceanic and Atmospheric Administration Ronald H. Brown research vessel and at a ground station in Bodega Bay, CA. Measurements of ice nucleating particles (INPs) were made with the Colorado State University Continuous Flow Diffusion Chamber aboard the G‐1, and aerosol filters were collected on the G‐1, at the Bodega Bay site and on the Ronald H. Brown for postprocessing via immersion freezing in the Colorado State University Ice Spectrometer. Aerosol composition was measured aboard the G‐1 with the Aerosol Time‐of‐Flight Mass Spectrometer. Here we present INP concentrations and aerosol chemical compositions during the course of the aircraft campaign. During the atmospheric river event, we found that marine aerosol was the main aerosol type and that marine INPs were dominant at cloud activation temperatures, which is in stark contrast to the dominance of dust INPs during the atmospheric river events in the California Water 2011 campaign.

Nitrogen (N) availability has been considered as a critical factor for the cycling and storage of soil organic carbon (SOC), but effects of N enrichment on the SOC pool appear highly variable. Given the complex nature of the SOC pool, recent frameworks suggest that separating this pool into different functional components, for example, particulate organic carbon (POC) and mineral‐associated organic carbon (MAOC), is of great importance for understanding and predicting SOC dynamics. Importantly, little is known about how these N‐induced changes in SOC components (e.g., changes in the ratios among these fractions) would affect the functionality of the SOC pool, given the differences in nutrient density, resistance to disturbance, and turnover time between POC and MAOC pool. Here, we conducted a global meta‐analysis of 803 paired observations from 98 published studies to assess the effect of N addition on these SOC components, and the ratios among these fractions. We found that N addition, on average, significantly increased POC and MAOC pools by 16.4% and 3.7%, respectively. In contrast, both the ratios of MAOC to SOC and MAOC to POC were remarkably decreased by N enrichment (4.1% and 10.1%, respectively). Increases in the POC pool were positively correlated with changes in aboveground plant biomass and with hydrolytic enzymes. However, the positive responses of MAOC to N enrichment were correlated with increases in microbial biomass. Our results suggest that although reactive N deposition could facilitate soil C sequestration to some extent, it might decrease the nutrient density, turnover time, and resistance to disturbance of the SOC pool. Our study provides mechanistic insights into the effects of N enrichment on the SOC pool and its functionality at global scale, which is pivotal for understanding soil C dynamics especially in future scenarios with more frequent and severe perturbations.

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.