We examined the role of arbuscular mycorrhizal fungi (AMF) in ecosystems using soil aggregate stability and C and N storage as representative ecosystem processes. We utilized a wide gradient in AMF abundance, obtained through long-term (17 and 6 years) large-scale field manipulations. Burning and N-fertilization increased soil AMF hyphae, glomalin-related soil protein (GRSP) pools and water-stable macroaggregates while fungicide applications reduced AMF hyphae, GRSP and water-stable macroaggregates. We found that AMF abundance was a surprisingly dominant factor explaining the vast majority of variability in soil aggregation. This experimental field study, involving long-term diverse management practices of native multispecies prairie communities, invariably showed a close positive correlation between AMF hyphal abundance and soil aggregation, and C and N sequestration. This highly significant linear correlation suggests there are serious consequences to the loss of AMF from ecosystems.

The Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) project aims to study the impacts of cloud seeding on winter orographic clouds. The field campaign took place in Idaho between 7 January and 17 March 2017 and employed a comprehensive suite of instrumentation, including ground-based radars and airborne sensors, to collect in situ and remotely sensed data in and around clouds containing supercooled liquid water before and after seeding with silver iodide aerosol particles. The seeding material was released primarily by an aircraft. It was hypothesized that the dispersal of the seeding material from aircraft would produce zigzag lines of silver iodide as it dispersed downwind. In several cases, unambiguous zigzag lines of reflectivity were detected by radar, and in situ measurements within these lines have been examined to determine the microphysical response of the cloud to seeding. The measurements from SNOWIE aim to address long-standing questions about the efficacy of cloud seeding, starting with documenting the physical chain of events following seeding. The data will also be used to evaluate and improve computer modeling parameterizations, including a new cloud-seeding parameterization designed to further evaluate and quantify the impacts of cloud seeding.

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