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BackgroundThere is an ongoing need for improved understanding of wildfire plume dynamics. AimsTo improve process-level understanding of wildfire plume dynamics including strong (>10 m s-1) fire-generated winds and pyrocumulus (pyroCu) development. MethodsKa-band Doppler radar and two Doppler lidars were used to quantify plume dynamics during a high-intensity prescribed fire and airborne laser scanning (ALS) to quantify the fuel consumption. Key resultsWe document the development of a strongly rotating (>10 m s-1) pyroCu-topped plume reaching 10 km. Plume rotation develops during merging of discrete plume elements and is characterised by inflow and rotational winds an order of magnitude stronger than the ambient flow. Deep pyroCu is initiated after a sequence of plume-deepening events that push the plume top above its condensation level. The pyroCu exhibits a strong central updraft (~35 m s-1) flanked by mechanically and evaporative forced downdrafts. The downdrafts do not reach the surface and have no impact on fire behaviour. ALS data show plume development is linked to large fuel consumption (~20 kg m-2). ConclusionsInteractions between discrete plume elements contributed to plume rotation and large fuel consumption led to strong updrafts triggering deep pyroCu. ImplicationsThese results identify conditions conducive to strong plume rotation and deep pyroCu initiation. 

Abstract Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities. 

Abstract Characterizing pre‐fire fuel load and fuel consumption are critical for assessing fire behavior, fire effects, and smoke emissions. Two approaches for quantifying fuel load are airborne laser scanning (ALS) and the Fuel Characteristic Classification System (FCCS). The implementation of multitemporal ALS (i.e., the use of two or more ALS datasets across time at a given location) in conjunction with empirical models trained with field data can be used to measure fuel and estimate fuel consumption from a fire. FCCS, adapted for use in LANDFIRE (LF), provides 30 m resolution estimates of fuel load across the contiguous United States and can be used to estimate fuel consumption through software programs such as Fuel and Fire Tools (FFT). This study compares the two approaches for two wildfires in the northwestern United States having predominantly sagebrush steppe and ponderosa pine savanna ecosystems. The results showed that the LF FCCS approach yielded higher pre‐fire fuel loads and fuel consumption than the ALS approach and that the coarser scale LF FCCS data did not capture as much heterogeneity as the ALS data. At Tepee, 50.0% of the difference in fuel load and 87.3% of the difference in fuel consumption were associated with distinguishing sparse trees from rangeland. At Keithly, this only accounted for 8.2% and 8.6% of the differences, demonstrating the significance of capturing heterogeneity in rangeland vegetation structure and fire effects. Our results suggest future opportunities to use ALS data to better parametrize fine‐scale fuel load variability that LF FCCS does not capture.