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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)

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   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. 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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5. Arctic and boreal paleofire records reveal drivers of fire activity and departures from Holocene variability

   https://doi.org/10.1002/ecy.3096  

   Hoecker, Tyler J.; Higuera, Philip E.; Kelly, Ryan; Hu, Feng Sheng (June 2020, Ecology)

   Abstract Boreal forest and tundra biomes are key components of the Earth system because the mobilization of large carbon stocks and changes in energy balance could act as positive feedbacks to ongoing climate change. In Alaska, wildfire is a primary driver of ecosystem structure and function, and a key mechanism coupling high‐latitude ecosystems to global climate. Paleoecological records reveal sensitivity of fire regimes to climatic and vegetation change over centennial–millennial time scales, highlighting increased burning concurrent with warming or elevated landscape flammability. To quantify spatiotemporal patterns in fire‐regime variability, we synthesized 27 published sediment‐charcoal records from four Alaskan ecoregions, and compared patterns to paleoclimate and paleovegetation records. Biomass burning and fire frequency increased significantly in boreal forest ecoregions with the expansion of black spruce, ca. 6,000–4,000 years before present (yr BP). Biomass burning also increased during warm periods, particularly in the Yukon Flats ecoregion from ca. 1,000 to 500 yr BP. Increases in biomass burning concurrent with constant fire return intervals suggest increases in average fire severity (i.e., more biomass burning per fire) during warm periods. Results also indicate increases in biomass burning over the last century across much of Alaska that exceed Holocene maxima, providing important context for ongoing change. Our analysis documents the sensitivity of fire activity to broad‐scale environmental change, including climate warming and biome‐scale shifts in vegetation. The lack of widespread, prolonged fire synchrony suggests regional heterogeneity limited simultaneous fire‐regime change across our study areas during the Holocene. This finding implies broad‐scale resilience of the boreal forest to extensive fire activity, but does not preclude novel responses to 21st‐century changes. If projected increases in fire activity over the 21st century are realized, they would be unprecedented in the context of the last 8,000 yr or more. 

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