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As anthropogenic eutrophication and the associated increase of cyanobacteria continue to plague inland waterbodies, local officials are seeking novel methods to proactively manage water resources. Cyanobacteria are of particular concern to health officials due to their ability to produce dangerous hepatotoxins and neurotoxins, which can threaten waterbodies for recreational and drinking-water purposes. Presently, however, there is no cyanobacteria outlook that can provide advance warning of a potential threat at the seasonal time scale. In this study, a statistical model is developed utilizing local and global scale season-ahead hydroclimatic predictors to evaluate the potential for informative cyanobacteria biomass and associated beach closure forecasts across the June–August season for a eutrophic lake in Wisconsin (United States). This model is developed as part of a subseasonal to seasonal cyanobacteria forecasting system to optimize lake management across the peak cyanobacteria season. Model skill is significant in comparison to June–August cyanobacteria observations (Pearson correlation coefficient = 0.62, Heidke skill score = 0.38). The modeling framework proposed here demonstrates encouraging prediction skill and offers the possibility of advanced beach management applications. 

This Research Work in Progress paper presents a case study that demonstrates how a secondary school teacher with a non-STEM background identifies parallels between the engineering design process and music creation to embrace teaching an engineering course for the first time. Multiple interviews and classroom observations were open coded using a two-cycle coding approach to reveal four themes: overcoming imposter syndrome, connections between engineering and music, challenges encountered, and changes in practice. These themes highlight the processes involved in transferring a pedagogical philosophy that can inform future efforts to explore the necessary preconditions for bridging seemingly disparate and unconnected content areas. Further exploration building on these findings will inform efforts to broaden the pool of teachers capable of teaching pre-college engineering classes. 

Abstract Summer temperatures across eastern North America (hereafter East) will soon reach a level consistently above any observation experienced during the instrumental period. Increasing temperatures will have negative impacts on natural (e.g., water, plant and animal communities) and human (e.g., health, infrastructure, economies) systems upon which the large and growing centres of human population across the region depend. Within the network of Northern Hemisphere tree‐ring temperature proxy records, one of the most obvious geographic holes is the East, where few temperature‐sensitive proxies exist. Here we present the first steps towards building a network of temperature‐sensitive proxy records across the East using blue light intensity (BI) methods applied to the tree rings of multiple temperature sensitive tree species situated from North Carolina to Maine, USA. Our overall objective is to report on the most viable species for BI analysis across different regions of the East (e.g., Southeast US, Midwest US, Northeast US/Canadian Maritimes) by exploring temporal (e.g., since ca. 1900) and spatial relationships between instrumental temperatures and BI metrics. We found BI to be a strong predictor of March–October mean air temperature (R²= 0.61) across the Northeast US/eastern Canada, and Sep‐Oct maximum air temperature (R²= 0.42) across the Southeast US. Of all species tested,Tsuga canadensisandPicea rubenscontained the strongest BI temperature signal. Adding more BI sites from these and potentially other species, as well as inclusion of other temperature proxies (e.g., ring widths) will allow for the development of a skilful broad‐scale and long‐term temperature field reconstruction across the East.