IFFEXO

The broad aim of IFFExO is to better understand and quantify microphysical processes underlying ice-fog (IF) life cycle to help evaluate their representation in numerical weather prediction (NWP) algorithms and regional climate models. Ice-fog occurrence is defined as visibility (Vis) <1 km, caused mainly by ice crystals. Changing climate over arctic regions is occurring much faster than that of lower latitudes, which has overarching repercussions on IF formation and hence atmospheric extinction. This not only affects visibility as a direct impact, but also modifies atmospheric heat/moisture budget. Ice nuclei (IN) concentration parameterizations based on aerosol number concentration and temperature show at least one-order of uncertainty in dealing with cold clouds, and even larger uncertainty for arctic regions, yet data available on relevant crucial microphysical parameters are meager to advance scientific knowledge of IF life cycle. Broad scientific goals of IFFExO are:

• Understand ice-fog formation in relation to nucleation/aerosol processes and aerosol dynamics
• Study ice-fog life cycle and its dissipation mechanisms (e.g., radiation and turbulence)
• Characterize ice-fog particle spectra and their relation to microphysical conditions
• Evaluate impact of ice fog on surface heat and moisture budgets
• Evaluate NWP models for ice-fog microphysics and visibility (extinction) prediction
• Improve microphysical parameterizations for ice-fog conditions in the boundary layer.

The project capitalizes on the tethered balloon system (TBS) platform at Oliktok Point, and will allow us to gather high-quality, boundary-layer data to characterize IF microphysics in relation to radiative properties, aerosols, and flow/turbulence dynamics. IF may also provide conditions for precipitation of very light snow (LSN), for which conditions the heat and moisture budgets can be evaluated. Such information is invaluable in evaluating the effects of IF and LSN on climate change, which can be negative because of the dehydration of atmosphere, thus leading to cooling over the northern regions and underestimation of precipitation in the Arctic. Instrumentation Systems: Both ARM Mobile Facility (AMF3) and investigator-provided instruments will be used. The former include both surface-based and TBS-suspended instruments. The major investigator-provided platform is the so called Gondola (from University of Ontario Institute of Technology) with ice microphysical sensors, which will be suspended on the TBS for vertical profiling of microphysical and environmental parameters. In all, observations include aerosol spectra, condensation nuclei concentration, particle composition, particle phase, and ice crystal images. Snow crystals measured will be used to discriminate IF conditions. The Pacific Northwest National Laboratory will provide chemical filtering analysis based on TBS and on the ground for determining aerosol composition. Data Analysis: Ice-fog events will be identified based on large-scale synoptic conditions. TBS-based level sampling at100-m intervals up to 1 km taken at different times will be used to study vertical structure of IF properties in the boundary layer, an aspect that has not been studied hitherto. Profiling and ground-based measurements will be classified into various IF types (e.g., deposition, freezing, and supercooled) and investigated over their life cycles. The remote-sensing platforms will be used for describing cloud macro- and microphysical properties: for example, optical thickness and cloud-top height, liquid water path (LWP), reflectivity, and the thickness of the IF layer. WRF-CHEM and Polar WRF simulations are planned to hindcast IF periods, and evaluate efficacy of these models. A new visibility (extinction) parameterization will be attempted based on aerosol type and number concentration as well as environmental conditions. An auto-conversion algorithm will be developed to improve IF water content (IWC) prediction. All algorithms and data products resulting from IFFExO will be made available to the scientific community via the ARM Data Center after the usual QA/QC process.

Timeline

Campaign Data Sets

OLI Data Sources