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1. Climate Influences Wildfire Activity Through Opportunity: An Event-Scale Perspective

   https://doi.org/10.3390/fire9040164  

   Coen, Janice L (April 2026, Fire)

   Williamson, Grant (Ed.)

   Annual area burned correlates with temperature and fuel aridity, yet extreme wildfire outcomes arise from a small fraction of fires and rapid-growth days. This asymmetry indicates that thermodynamic favorability sets background susceptibility but does not determine when extreme growth occurs. This Perspective proposes a cross-scale framework that distinguishes susceptibility from regime-conditioned event-scale realization. At seasonal and regional scales, temperature and humidity influence fuel dryness, ignition likelihood, and fire-season length, explaining substantial interannual variability in area burned. These variables vary smoothly in space and retain signal under aggregation. By contrast, extreme fire growth occurs during short-lived synoptic configurations that organize winds, pressure gradients, and stability into discrete opportunity windows that permit sustained spread. The strongest winds governing rapid spread are intermittent, terrain-structured, and often unresolved in coarse datasets or aggregated indices. Within these windows, terrain interactions, organized flow, and fire–atmosphere feedbacks can amplify growth until circulation patterns shift. Extreme wildfire behavior therefore operates as a gated joint-probability process requiring the coincidence of susceptibility (S), dynamical weather opportunity (W), and ignition (I). Separating susceptibility from realization reconciles strong climate–fire correlations with the dynamical control of event-scale extremes. 

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2. Severe Convective Storms in Limited Instability Organized by Pattern and Distribution

   https://doi.org/10.1175/WAF-D-23-0130.1  

   Campbell, Trevor A; Lackmann, Gary M; Molina, Maria J; Parker, Matthew D (January 2024, Weather and Forecasting)

   Abstract Severe convection occurring in high-shear, low-CAPE (HSLC) environments is a common cool-season threat in the southeastern United States. Previous studies of HSLC convection document the increased operational challenges that these environments present compared to their high-CAPE counterparts, corresponding to higher false-alarm ratios and lower probability of detection for severe watches and warnings. These environments can exhibit rapid destabilization in the hours prior to convection, sometimes associated with the release of potential instability. Here, we use self-organizing maps (SOMs) to objectively identify environmental patterns accompanying HSLC cool-season severe events and associate them with variations in severe weather frequency and distribution. Large-scale patterns exhibit modest variation within the HSLC subclass, featuring strong surface cyclones accompanied by vigorous upper-tropospheric troughs and northward-extending regions of instability, consistent with prior studies. In most patterns, severe weather occurs immediately ahead of a cold front. Other convective ingredients, such as lower-tropospheric vertical wind shear, near-surface equivalent potential temperature (θ_e) advection, and the release of potential instability, varied more significantly across patterns. No single variable used to train SOMs consistently demonstrated differences in the distribution of severe weather occurrence across patterns. Comparison of SOMs based on upper and lower quartiles of severe occurrence demonstrated that the release of potential instability was most consistently associated with higher-impact events in comparison to other convective ingredients. Overall, we find that previously developed HSLC composite parameters reasonably identify high-impact HSLC events. Significance StatementEven when atmospheric instability is not optimal for severe convective storms, in some situations they can still occur, presenting increased challenges to forecasters. These marginal environments may occur at night or during the cool season, when people are less attuned to severe weather threats. Here, we use a sorting algorithm to classify different weather patterns accompanying such storms, and we distinguish which specific patterns and weather system features are most strongly associated with severe storms. Our goals are to increase situational awareness for forecasters and to improve understanding of the processes leading to severe convection in marginal environments. 

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3. Extreme fire spread events burn more severely and homogenize post-fire landscapes in the southwestern US

   McFarland, Jessika; Coop, Jonathan; Balik, Jared; Rodman, Kyle; Parks, Sean; Stevens-Rumann, Camille (August 2024, Ecological Society of America Annual Meeting)

   Extreme single-day fire spread events are associated with warmer and drier fire seasons and are expected to increase in the future. However, our understanding of the post-fire landscape outcomes of such events is limited. Here, we ask whether extreme events burn more severely or produce landscape patterns that are less conducive to forest recovery. To identify extreme single-day fire spread events, we used satellite fire detections to create day of burning (DOB) maps for 633 fire events >400 ha within forested ecoregions of the southwestern United States. We categorized daily rates of spread as extreme (top 2.5% of events; >4901 ha/day) and non-extreme (bottom 97.5%, <4901 ha/day). We contrasted satellite-measured burn severity and a suite of high severity patch landscape metrics between extreme and non-extreme spread events. We found that extreme single-day fire spread events were associated with increased severity, a greater proportion of area burned severely, and a higher percentage of like adjacencies between high severity pixels. Average distances from high severity patch interiors to edges were also greater in extreme single-day spread events. Additionally, area-weighted mean patch size and total core area of high severity patches were higher for fires containing one or more extreme single-day spread events. Larger and more homogenous high-severity patches produced during extreme events have been shown to limit tree seedling establishment, inhibiting forest recovery and facilitating vegetation type conversion. These landscape outcomes are expected to be magnified under future climate as extreme fire spread becomes more prevalent, accelerating fire-driven forest loss across the southwestern US. 

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4. Diagnosing Flavors of Tropospheric Rossby Wave Breaking and Their Associated Dynamical and Sensible Weather Features

   https://doi.org/10.1175/MWR-D-23-0153.1  

   LaChat, Grant; Bowley, Kevin A; Gervais, Melissa (February 2024, Monthly Weather Review)

   Abstract Rossby wave breaking (RWB) can be manifested by the irreversible overturning of isentropes on constant potential vorticity (PV) surfaces. Traditionally, the type of breaking is categorized as anticyclonic (AWB) or cyclonic (CWB) and can be identified using the orientation of streamers of high potential temperature (θ) and lowθair on a PV surface. However, an examination of the differences in RWB structure and their associated tropospheric impacts within these types remains unexplored. In this study, AWB and CWB are identified from overturning isentropes on the dynamic tropopause (DT), defined as the 2 potential vorticity unit (PVU; 1 PVU = 10⁻⁶K kg⁻¹m²s⁻¹) surface, in the ERA5 dataset during December, January, and February 1979–2019. Self-organizing maps (SOM), a machine learning method, is used to cluster the identified RWB events into archetypal patterns, or “flavors,” for each type. AWB and CWB flavors capture variations in theθminima/maxima of each streamer and the localized meridionalθgradient (∇θ) flanking the streamers. Variations in the magnitude and position of ∇θbetween flavors correspond to a diversity of jet structures leading to differences in vertical motion patterns and troposphere-deep circulations. A subset of flavors of AWB (CWB) events are associated with the development of strong surface high (low) pressure systems and the generation of extreme poleward moisture transport. For CWB, many events occurred in similar geographical regions, but the precipitation and moisture patterns were vastly different between flavors. Our findings suggest that the location, type, and severity of the tropospheric impacts from RWB are strongly dictated by RWB flavor. Significance StatementLarge-scale atmospheric waves ∼15 km above Earth’s surface are responsible for the daily weather patterns that we experience. These waves can undergo wave breaking, a process that is analogous to ocean waves breaking along the seashore. Wave breaking events have been linked to extreme weather impacts at the surface including cold and heat waves, strong low pressure systems, and extreme precipitation events. Machine learning is used to identify and analyze different flavors, or patterns, of wave breaking events that result in differing surface weather impacts. Some flavors are able to generate notable channels of moisture that result in extreme high precipitation events. This is a crucial insight as forecasting of extreme weather events could be improved from this work. 

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5. A Hierarchical Framework for Quantifying Seasonal and Daily Wildland Fire Risk in Great Plains Grasslands

   https://doi.org/10.3390/fire9080329  

   Okafor, Izuchukwu Oscar; Liu, Zifei; George, Mayowa Boluwatife (August 2026, Fire)

   Benim, Ali Cemal (Ed.)

   Accurate quantification of wildfire risk is essential for balancing wildfire mitigation and prescribed fire management in grassland ecosystems, yet existing fire danger indices do not explicitly distinguish seasonal fuel dynamics from daily weather variability. This study presents a hierarchical framework for quantifying wildland fire risk by explicitly separating seasonal wildfire potential from daily weather-driven fire activity. The framework introduces the Daily Burned Area Ratio (DBAR) as a quantitative measure of realized wildfire risk and decomposes it into the Seasonal Burned Area Ratio (SBAR) and the Daily Burn Activity Index (DBAI). Wildfire records from the U.S. Forest Service Fire Program Analysis Fire-Occurrence Database, Oklahoma Mesonet weather observations, and remotely sensed vegetation data collected between 1995 and 2020 were used to develop and evaluate the framework in the Flint Hills of Kansas and Oklahoma. SBAR was modeled using grass curing and air temperature to characterize the seasonal baseline of wildfire activity, whereas DBAI was modeled using dead fuel moisture content (DFMC) and wind speed to quantify day-to-day departures from that seasonal baseline. The SBAR model accurately reproduced the characteristic bimodal wildfire regime of the Great Plains, whereas the DBAI model identified DFMC as the dominant control on daily wildfire activity, with wind speed providing an important secondary influence. Compared with the Burning Index (BI) and the Grassland Fire Danger Index (GFDI), the hierarchical framework achieved superior performance in discriminating fire days from non-fire days. Global sensitivity analysis further demonstrated that the framework provides a more balanced representation of the influences of grass curing, relative humidity, air temperature, and wind speed than the conventional indices. By explicitly separating seasonal fuel dynamics from short-term weather variability, the proposed framework provides an ecologically interpretable, locally calibratable, and operationally practical approach to wildfire risk assessment. Because the seasonal and daily components can be calibrated independently, the framework is readily transferable to other grassland ecosystems and provides a flexible foundation for adaptive wildfire and prescribed fire management under changing climatic conditions. 

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