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1. Emerging Anthropogenic Influences on the Southcentral Alaska Temperature and Precipitation Extremes and Related Fires in 2019

   https://doi.org/10.3390/land10010082  

   Bhatt, Uma S.; Lader, Rick T.; Walsh, John E.; Bieniek, Peter A.; Thoman, Richard; Berman, Matthew; Borries-Strigle, Cecilia; Bulock, Kristi; Chriest, Jonathan; Hahn, Micah; et al (January 2021, Land)

   null (Ed.)

   The late-season extreme fire activity in Southcentral Alaska during 2019 was highly unusual and consequential. Firefighting operations had to be extended by a month in 2019 due to the extreme conditions of hot summer temperature and prolonged drought. The ongoing fires created poor air quality in the region containing most of Alaska’s population, leading to substantial impacts to public health. Suppression costs totaled over $70 million for Southcentral Alaska. This study’s main goals are to place the 2019 season into historical context, provide an attribution analysis, and assess future changes in wildfire risk in the region. The primary tools are meteorological observations and climate model simulations from the NCAR CESM Large Ensemble (LENS). The 2019 fire season in Southcentral Alaska included the hottest and driest June–August season over the 1979–2019 period. The LENS simulation analysis suggests that the anthropogenic signal of increased fire risk had not yet emerged in 2019 because of the CESM’s internal variability, but that the anthropogenic signal will emerge by the 2040–2080 period. The effect of warming temperatures dominates the effect of enhanced precipitation in the trend towards increased fire risk. 

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2. Predicting Cloud‐To‐Ground Lightning in the Western United States From the Large‐Scale Environment Using Explainable Neural Networks

   https://doi.org/10.1029/2024JD042147  

   Kalashnikov, Dmitri A; Davenport, Frances V; Labe, Zachary M; Loikith, Paul C; Abatzoglou, John T; Singh, Deepti (November 2024, Journal of Geophysical Research: Atmospheres)

   Abstract Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud‐to‐ground (CG) lightning across the WUS during June–September from the spatial patterns of seven large‐scale meteorological variables from reanalysis (1995–2022). Individually trained CNN models at each 1° × 1° grid cell (n = 285 CNNs) show high skill at predicting CG lightning days across the WUS (median AUC = 0.8) and perform best in parts of the interior Southwest where summertime CG lightning is most common. Further, interannual correlation between observed and predicted CG lightning days is high (medianr = 0.87), demonstrating that locally trained CNNs realistically capture year‐to‐year variation in CG lightning activity across the WUS. We then use layer‐wise relevance propagation (LRP) to investigate the relevance of predictor variables to successful CG lightning prediction in each grid cell. Using maximum LRP values, our results show that two thermodynamic variables—ratio of surface moist static energy to free‐tropospheric saturation moist static energy, and the 700–500 hPa lapse rate—are the most relevant CG lightning predictors for 93%–96% of CNNs depending on the LRP variant used. As lightning is not directly simulated by global climate models, these CNNs could be used to parameterize CG lightning in climate models to assess changes in future CG lightning occurrence with projected climate change. Understanding changes in CG lightning risk and consequently lightning‐caused wildfire risk across the WUS could inform fire management, planning, and disaster preparedness. 

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3. Meteorological and geographical factors associated with dry lightning in central and northern California

   https://doi.org/10.1088/2752-5295/ac84a0  

   Kalashnikov, Dmitri A; Abatzoglou, John T; Nauslar, Nicholas J; Swain, Daniel L; Touma, Danielle; Singh, Deepti (August 2022, Environmental Research: Climate)

   Abstract Lightning occurring with less than 2.5 mm of rainfall—typically referred to as ‘dry lightning’—is a major source of wildfire ignition in central and northern California. Despite being rare, dry lightning outbreaks have resulted in destructive fires in this region due to the intersection of dense, dry vegetation and a large population living adjacent to fire-prone lands. Since thunderstorms are much less common in this region relative to the interior West, the climatology and drivers of dry lightning have not been widely investigated in central and northern California. Using daily gridded lightning and precipitation observations (1987–2020) in combination with atmospheric reanalyses, we characterize the climatology of dry lightning and the associated meteorological conditions during the warm season (May–October) when wildfire risk is highest. Across the domain, nearly half (∼46%) of all cloud-to-ground lightning flashes occurred as dry lightning during the study period. We find that higher elevations (>2000 m) receive more dry lightning compared to lower elevations (<1000 m) with activity concentrated in July-August. Although local meteorological conditions show substantial spatial variation, we find regionwide enhancements in mid-tropospheric moisture and instability on dry lightning days relative to background climatology. Additionally, surface temperatures, lower-tropospheric dryness, and mid-tropospheric instability are increased across the region on dry versus wet lightning days. We also identify widespread dry lightning outbreaks in the historical record, quantify their seasonality and spatial extent, and analyze associated large-scale atmospheric patterns. Three of these four atmospheric patterns are characterized by different configurations of ridging over the continental interior and offshore troughing. Understanding the meteorology of dry lightning across this region can inform forecasting of possible wildfire ignitions and is relevant for assessing changes in dry lightning and wildfire risk in climate projections. 

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4. Impact of Smoke Aerosol Loading on Lightning Characteristics of Pyrocumulonimbus Compared With Other High‐Based Thunderstorms

   https://doi.org/10.1029/2024JD042285  

   Dworak, Elena; Peterson, David A; Saide, Pablo E; Thapa, Laura; Bortnik, Jacob (June 2025, Journal of Geophysical Research: Atmospheres)

   Abstract Pyrocumulonimbus (pyroCb) is a form of deep convection that is generated by the heating from large wildfires and specific meteorology known for producing lightning. We study the lightning characteristics of five pyroCb events in British Columbia, Canada, from June 29 to July 1 of 2021, and compare them to other clean and smoke‐filled high‐based thunderstorms in the same region and season using ground‐based lightning detection data, satellite retrievals, meteorological and atmospheric composition reanalysis, and observed thermodynamic profiles. One large pyroCb event over the Sparks Lake fire that generated persistent overshooting tops had a remarkable amount of lightning activity, with 5,600 total lightning strikes, while the rest of the pyroCb events corresponded with lower injection altitudes and minimal to no observed lightning activity. The cloud‐to‐cloud (CC) to cloud‐to‐ground (CG) lightning ratio (CC:CG) in this Sparks Lake pyroCb was significantly higher than in other high‐based storms but displayed similar lightning density and slightly lower peak current distributions. All clean and smoke‐filled thunderstorms produced significant levels of lightning activity, regardless of their cloud‐top altitudes. However, ingestion of smoke significantly reduced the percentage of positive polarity CG strikes when compared to clean cases. These results set a reference for identifying the characteristics of pyrogenic lightning and improved predictions of lightning‐caused fire ignitions, which will aid in understanding pyroCb activity and related impacts. 

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5. Assessing the Influence of a Bias Correction Method on Future Climate Scenarios Using SWAT as an Impact Model Indicator

   https://doi.org/10.3390/w15040750  

   Brighenti, Tássia Mattos; Gassman, Philip W.; Gutowski, William J.; Thompson, Janette R. (February 2023, Water)

   In this study, we evaluate the implications of a bias correction method on a combination of Global/Regional Climate Models (GCM and RCM) for simulating precipitation and, subsequently, streamflow, surface runoff, and water yield in the Soil and Water Assessment Tool (SWAT). The study area is the Des Moines River Basin, U.S.A. The climate projections are two RCMs driven by two GCMs for historical simulations (1981–2005) and future projections (2030–2050). Bias correction improves historical precipitation for annual volumes, seasonality, spatial distribution, and mean error. Simulated monthly historical streamflow was compared across 26 monitoring stations with mostly satisfactory results for percent bias (Pbias). There were no changes in annual trends for future scenarios except for raw WRF models. Seasonal variability remained the same; however, most models predicted an increase in monthly precipitation from January to March and a reduction for June and July. Meanwhile, the bias-corrected models showed changes in prediction signals. In some cases, raw models projected an increase in surface runoff and water yield, but the bias-corrected models projected a reduction in these variables. This suggests the bias correction may be larger than the climate-change signal and indicates the procedure is not a small correction but a major factor. 

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