A document by Raf Antwerpen. Click on the document to view its contents.

Microplastics have become ubiquitous in all reaches of the world. Due to their small size, low density, and environmental persistence, they are transported throughout the Earth's system. Despite its importance, little is known about microplastic transport and deposition, especially by snow particles, and most people are not aware of the extent of the problem. The PlastiX-Snow Citizen Science Project aims to fill these research and informational gaps using crowd-sourcing to achieve scientific research outputs, educational programming, and active outreach and engagement. We will initially measure the spatial distribution of snow deposited microplastics throughout a region in New York State and expand nationally using community partners. As trained partners, the Snow Ambassadors will inform the local community about microplastics, recruit participants, and assist in leading trainings. As nodes of the project, they will expand the reach to a large demographic of people across the country, including both life-long learners and school groups. Citizen scientists will collect, examine, and report the snow-deposited microplastics in their own backyard. The PlastiX-Snow team also will collect snowmelt samples from participants to robustly analyze the microplastics at Lamont-Doherty Earth Observatory. According to a National Academy of Sciences, a primary concern regarding citizen science projects is the lack of engagement and feedback to the participants of the program's findings. We specifically address these challenges by actively and continually engaging our participants and partners through direct and virtual public programming, classroom visits, media, newsletters, and an interactive website. PlastiX-Snow goals are to 1. Collect data for a deeper understanding of microplastics disseminated by snow, 2. Teach the public about the dangers of microplastics and potential solutions, 3. Engage communities, students, educators, and the public to participate in groundbreaking, relevant scientific research. We aim to shed light on the severity of microplastic pollution, build a bridge between the public and the scientific community, connect citizen scientists to their natural environment through field work, and encourage them to serve as environmental stewards and leaders in their own communities.

Over the past several decades, the Greenland Ice Sheet has been losing mass through a combination of increased surface runoff and accelerating ice flux to the ocean. Our understanding of the surface component is drawn heavily from satellite observations and climate models. The MAR (Modèle Atmosphère Régional) model is a 3D regional climate model used extensively over Greenland. Our study focuses on the surface snow and the ice down to 15-meter in depth. A light-weighted surface model for us to integrate the local observation data and force many simulations is needed. Our goal is to implement a surface-only model, derived from MAR, as a tool for understanding the glacial surface components, correlations, and MAR biases to improve projections of surface runoff. This model includes the ability to integrate observations from surface weather stations, translate the data into a model forcing format, force different simulations with various configurations or datasets, visualize model outputs, find key correlations between atmospheric drivers and modeled firn densification. In the model development, we extract the surface code from the original MAR for the simulations initialized and forced with the following snow and atmospheric fields: snow depth, temperature, density, water volume, and grain size. We then verify that the surface model generates the same outputs as the full MAR does if fetched with the identical data. The bias is checked with snowpack time-depth plots for multiple sites around Greenland, including Summit and Swiss Camp. We have found a very small bias when compared to the fully-coupled MAR. We perform quality control for the data inputs, such as replacing missing data from the station measurements, defining the max and min for each dataset, filtering out the data outliers by statistics standard deviations. As the result, our model software can provide multiple simulations in sequential and concurrent mode with user-friendly interfaces, and run robustly. The model’s first release is currently being deployed over different sites across Greenland to understand the importance of atmospheric forcing versus snow model biases in projections of future mass loss due to surface melt.

Accurate estimates of surface mass balance over the Greenland ice sheet (GrIS) would contribute to understanding the cause of recent changes and would help to better estimate the future contribution of the GrIS to sea-level rise. Given the limitations of in-situ measurement, modeling, and remote sensing, it is critical to explore the opportunity to merge the available data to better characterize the spatial and temporal variation of the GrIS SMB. This work utilizes a particle batch smoother data assimilation technique that yields SMB estimates that benefit from the snow/ice model Crocus and a 16-day albedo product derived from satellite remote sensing data. Comparison of the results against in-situ SMB measurements from a transect located nearby the town of Kangerlussuaq shows that the assimilation of the albedo product reduces the root mean square error (RMSE) of the posterior estimates of SMB by 51% and reduces bias by 95%.