| Summary: | This project will quantify the relationship between the ice-free area of the Arctic Ocean and surrounding seas, and evaporation and precipitation in the Arctic region. The methodology uses isotopic compositions of storm-by-storm precipitation samples, pan-Arctic meteorological data, and numerical modeling. Precipitation samples will be collected at nine Arctic observatories, and by three high schools in Greenland. Samples will be analyzed for D/H and 18O/16O ratios. The track of a given storm will be reconstructed using reanalysis data and a Lagrangian trajectory algorithm. The moisture sources are then localized, and the δ18O, δD and d-excess of the source vapor are calculated using the sea surface temperature, relative humidity and wind speed. The sea ice area transected by each storm will be obtained using high-resolution satellite products. A two-dimensional (2D) model of the atmospheric moisture cycle will be used to compute the isotopic composition of precipitation for each storm at each site using reanalysis values of meteorological variables at the times and regions identified by the Lagrangian back trajectory, and assuming a range of sea ice concentrations. The sea ice concentration that best fits our isotopic measurements is then compared with the satellite sea ice data. The isotope data will thus yield, via the 2D model, the sensitivity of Arctic evaporation and precipitation to ice area. The project will yield a quantitative understanding of the links among sea ice, and moisture sources and amounts of Arctic precipitation, which comprise an important part of climate dynamics on a wide range of time scales. The results will be pertinent to the interpretation of ice core data, ice age dynamics, sudden climate change, and global warming. This project also intends to integrate research with education and outreach, which will directly benefit participating Arctic communities. The project aims to improve community understanding of how climate change research is done, by providing Arctic students with new knowledge and skills, and by inspiring them to pursue advanced studies. At the same time, the PI's will benefit from learning traditional methods of observing, categorizing, and forecasting weather and climate from the communities participating in the study.
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