A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition.

Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system's energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition.

Database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions.

Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the 'New Arctic', is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic.

The influence of recent and future climate change on spring Arctic cyclones.

In recent decades, the Arctic has experienced rapid atmospheric warming and sea ice loss, with an ice-free Arctic projected by the end of this century. Cyclones are synoptic weather events that transport heat and moisture into the Arctic, and have complex impacts on sea ice, and the local and global climate. However, the effect of a changing climate on Arctic cyclone behavior remains poorly understood. This study uses high resolution (4 km), regional modeling techniques and downscaled global climate reconstructions and projections to examine how recent and future climatic changes alter cyclone behavior. Results suggest that recent climate change has not yet had an appreciable effect on Arctic cyclone characteristics. However, future sea ice loss and increasing surface temperatures drive large increases in the near-surface temperature gradient, sensible and latent heat fluxes, and convection during cyclones. The future climate can alter cyclone trajectories and increase and prolong intensity with greatly augmented wind speeds, temperatures, and precipitation. Such changes in cyclone characteristics could exacerbate sea ice loss and Arctic warming through positive feedbacks. The increasing extreme nature of these weather events has implications for local ecosystems, communities, and socio-economic activities.

Using Geographic Information Systems (GIS) for Targeted National Recruitment of Community-Dwelling Caregivers Managing Dementia-Related Behavioral and Psychological Symptoms: A Recruitment Approach for a Randomized Clinical Trial.

Over 16 million caregivers of people living with dementia require support in a range of issues, including self-care, disease education, and guidance for how to manage behavioral and psychological symptoms of dementia (BPSD). Non-pharmacological interventions are needed to address these areas, and online applications have been shown to be safe and effective. To ensure the efficacy of such interventions, racially, ethnically, geographically, and socioeconomically diverse participants must be recruited to increase the generalizability of study outcomes. This protocol paper describes a recruitment plan using Geographic Information Systems (GIS) to reach a representative sample of caregivers across the United States for a national Phase III clinical study. Using publicly available census data from the American Community Survey (ACS), combined with location data for local aging resources such as Area Agencies on Aging (AAA), recruitment will be derived from data analysis conducted in ESRI ArcGIS v10.7.1. Datasets including age, gender, income, and education will be assessed nationally at the county and census tract spatial scale in a nine-step process to develop recruitment priority areas containing high concentrations of eligible participants living in the community. Overall, the current protocol will demonstrate the value of GIS in tailoring targeted outreach strategies to recruit community-dwelling populations through local resource institutions. This novel approach may have far-reaching implications in future recruitment initiatives and help to secure racially/ethnically diverse samples.