ARL Weekly News – August 4, 2023

Recent Events

Short term forecasting assistance to Morristown WFO

On 7 August, Temple Lee, Tom Wood, Kurt Daniels, Dominick Christensen, and Randy White performed a weather balloon launch from ATDD to support short-term weather forecasting needs for the National Weather Service Weather Forecast Office in Morristown, Tennessee prior to the onset of severe storms. The storms moved through the area that afternoon and produced significant damage within Morristown’s County Warning Area.

Publications Published

Impacts of the COVID-19 economic slowdown on soybean crop yields in the United States.

Christopoulos, J., Tong, D., Campbell, P.C. et al. Impacts of the COVID-19 economic slowdown on soybean crop yields in the United States. Sci Rep 13, 12574 (2023). https://doi.org/10.1038/s41598-023-39531-6

It is without question that the COVID-19 pandemic has taken its toll on the U.S. economy. Stay-at-home orders led to reduced vehicular traffic and widespread declines in anthropogenic emissions (e.g., nitrogen oxides (NO_x)). This study is the first to explore the potential consequences of O₃ changes resulting from the economic shutdown in the United States on soybean crop yields for 2020. The pandemic’s impact on surface O₃ is quantified using the NOAA’s National Air Quality Forecasting Capability (NAQFC), which is based on the Community Multi-Scale Air Quality (CMAQ) model for May–July 2020. The “would-be”, 2020 level business-as-usual (BAU) emissions are compared to a simulation that uses representative COVID-19 (C19) emissions. For each emissions scenario, crop exposures are calculated using the AOT40 cumulative exposure index and then combined with county-level soybean production totals to determine regional yield losses. Exposure changes ranged between – 2 and 2 ppmVhr⁻¹. It was further shown that increased exposures (0.5 to 1.10 ppmVhr⁻¹) in the Southeast U.S. counteracted decreased exposures (0.8 to 0.5 ppmVhr⁻¹) in the other soybean-producing regions. As a result, corresponding yield improvements counteracted yield losses around the Mississippi River Valley and allowed for minimal improvements in soybean production loss totaling $6.5 million over CONUS.

Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires

Dong Chen, Michael Billmire, Christopher P. Loughner, Allison Bredder, Nancy H.F. French, Hyun Cheol Kim, Tatiana V. Loboda, 2023: Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires. Science of The Total Environment, 898, 165594, https://doi.org/10.1016/j.scitotenv.2023.165594.

Abstract: Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM2.5. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM2.5 concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM2.5 concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires affect human health which creates the basis for the development of effective and efficient mitigation efforts.