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  • Author or Editor: Janel Hanrahan x
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Janel Hanrahan
and
Jason Shafer

Abstract

Mitigation of human-caused climate change is essential to lessen the extent of future negative impacts, but many people are not aware of the urgency of the situation. For meaningful climate change action to be realized, accurate information must be conveyed by experts to nonexperts. Improved climate change literacy may thus be achieved in two ways. First, we must increase the number of scientists who are knowledgeable about climate change, and second, we must encourage these experts to engage with nonexperts and provide them with adequate resources to do so. Such efforts are currently being implemented in the undergraduate Atmospheric Sciences/Meteorology program (ATM) at Northern Vermont University–Lyndon. To increase knowledge, all ATM students regardless of career pathway are required to take courses that cover the science of human-caused climate change. They are then encouraged to communicate this knowledge to the public. Students are creating informational content for a department-run website and are regularly given opportunities to engage with the public at local schools and events. The results of these curricular and extracurricular changes are promising. Student interest in the topic of climate change has increased and they have demonstrated a heighted sense of responsibility to engage with the public about this challenging topic.

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Janel Hanrahan
,
Alexandria Maynard
,
Sarah Y. Murphy
,
Colton Zercher
, and
Allison Fitzpatrick

Abstract

As demand for renewable energy grows, so does the need for an improved understanding of renewable energy sources. Paradoxically, the climate change mitigation strategy of fossil fuel divestment is in itself subject to shifts in weather patterns resulting from climate change. This is particularly true with solar power, which depends on local cloud cover. However, because observed shortwave radiation data usually span a decade or less, persistent long-term trends may not be identified. A simple linear regression model is created here using diurnal temperature range (DTR) during 2002–15 as a predictor variable to estimate long-term shortwave radiation (SR) values in the northeastern United States. Using an extended DTR dataset, SR values are computed for 1956–2015. Statistically significant decreases in shortwave radiation are identified that are dominated by changes during the summer months. Because this coincides with the season of greatest insolation and the highest potential for energy production, financial implications may be large for the solar energy industry if such trends persist into the future.

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Janel Hanrahan
,
Jessica Langlois
,
Lauren Cornell
,
Huanping Huang
,
Jonathan M. Winter
,
Patrick J. Clemins
,
Brian Beckage
, and
Cindy Bruyère

Abstract

Most inland water bodies are not resolved by general circulation models, requiring that lake surface temperatures be estimated. Given the large spatial and temporal variability of the surface temperatures of the North American Great Lakes, such estimations can introduce errors when used as lower boundary conditions for dynamical downscaling. Lake surface temperatures (LSTs) influence moisture and heat fluxes, thus impacting precipitation within the immediate region and potentially in regions downwind of the lakes. For this study, the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) was used to simulate precipitation over the six New England states during a 5-yr historical period. The model simulation was repeated with perturbed LSTs, ranging from 10°C below to 10°C above baseline values obtained from reanalysis data, to determine whether the inclusion of erroneous LST values has an impact on simulated precipitation and synoptic-scale features. Results show that simulated precipitation in New England is statistically correlated with LST perturbations, but this region falls on a wet–dry line of a larger bimodal distribution. Wetter conditions occur to the north and drier conditions occur to the south with increasing LSTs, particularly during the warm season. The precipitation differences coincide with large-scale anomalous temperature, pressure, and moisture patterns. Care must therefore be taken to ensure reasonably accurate Great Lakes surface temperatures when simulating precipitation, especially in southeastern Canada, Maine, and the mid-Atlantic region.

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