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Abstract Surface moisture heterogeneity degrades temperature‐humidity (‐) similarity in the atmospheric surface layer, yet the underlying physical mechanisms driving this dissimilarity remain underexplored. This study employs large‐eddy simulations coupled with a land‐surface model to investigate ‐ similarity in the convective boundary layer (CBL) over surfaces with varying scales of surface moisture heterogeneity. Results reveal that as the heterogeneity scale increases, patch‐scale thermally induced circulations develop and interact with cellular turbulent organized structures, significantly altering scalar transport and turbulence dynamics. The patch‐scale thermally induced circulations enhance horizontal advection, modify the production and transport of scalar variances, and lead to a disproportionate increase in the standard deviations of temperature () and humidity (), accompanied by a reduction in ‐ covariance (). As a result, ‐ similarity is substantially reduced throughout the CBL. Spectral analysis reveals that ‐ dissimilarity is most strongly influenced by turbulent motions at scales corresponding to patch lengths. The findings offer insights into the role of surface heterogeneity in shaping scalar similarity in the CBL, with implications for land‐atmosphere interactions and parameterization in numerical models. 

Abstract The architecture of magma plumbing systems plays a fundamental role in volcano eruption and evolution. However, the precise configuration of crustal magma reservoirs and conduits responsible for supplying eruptions are difficult to explore across most active volcanic systems. Consequently, our understanding of their correlation with eruption dynamics is limited. Axial Seamount is an active submarine volcano located along the Juan de Fuca Ridge, with known eruptions in 1998, 2011, and 2015. Here we present high-resolution images of P-wave velocity, attenuation, and estimates of temperature and partial melt beneath the summit of Axial Seamount, derived from multi-parameter full waveform inversion of a 2D multi-channel seismic line. Multiple magma reservoirs, including a newly discovered western magma reservoir, are identified in the upper crust, with the maximum melt fraction of ~15–32% in the upper main magma reservoir (MMR) and lower fractions of 10% to 26% in other satellite reservoirs. In addition, a feeding conduit below the MMR with a melt fraction of ~4–11% and a low-velocity throat beneath the eastern caldera wall connecting the MMR roof with eruptive fissures are imaged. These findings delineate an asymmetric shallow plumbing system beneath Axial Seamount, providing insights into the magma pathways that fed recent eruptions. 

Abstract How convective boundary‐layer (CBL) processes modify fluxes of sensible (SH) and latent (LH) heat and CO₂(F_c) in the atmospheric surface layer (ASL) remains a recalcitrant problem. Here, large eddy simulations for the CBL show that whileSHin the ASL decreases linearly with height regardless of soil moisture conditions,LHandF_cdecrease linearly with height over wet soils but increase with height over dry soils. This varying flux divergence/convergence is regulated by changes in asymmetric flux transport between top‐down and bottom‐up processes. Such flux divergence and convergence indicate that turbulent fluxes measured in the ASL underestimate and overestimate the “true” surface interfacial fluxes, respectively. While the non‐closure of the surface energy balance persists across all soil moisture states, it improves over drier soils due to overestimatedLH. The non‐closure does not imply thatF_cis always underestimated;F_ccan be overestimated over dry soils despite the non‐closure issue. 

Abstract By fitting observed data with predicted seismograms, least‐squares migration (LSM) computes a generalized inverse for a subsurface reflectivity model, which can improve image resolution and reduce artifacts caused by incomplete acquisition. However, the large computational cost of LSM required for simulations and migrations limits its wide applications for large‐scale imaging problems. Using point‐spread function (PSF) deconvolution, we present an efficient and stable high‐resolution imaging method. The PSFs are first computed on a coarse grid using local ray‐based Gaussian beam Born modeling and migration. Then, we interpolate the PSFs onto a fine‐image grid and apply a high‐dimensional Gaussian function to attenuate artifacts far away from the PSF centers. With 2D/3D partition of unity, we decompose the traditional adjoint migration results into local images with the same window size as the PSFs. Then, these local images are deconvolved by the PSFs in the wavenumber domain to reduce the effects of the band‐limited source function and compensate for irregular subsurface illumination. The final assembled image is obtained by applying the inverse of the partitions for the deconvolved local images. Numerical examples for both synthetic and field data demonstrate that the proposed PSF deconvolution can significantly improve image resolution and amplitudes for deep structures, while not being sensitive to velocity errors as the data‐domain LSM. 

Abstract Top‐down entrainment shapes the vertical gradients of sensible heat, latent heat, and CO₂fluxes, influencing the interpretation of eddy covariance (EC) measurements in the unstable atmospheric surface layer (ASL). Using large eddy simulations for convective boundary layer flows, we demonstrate that decreased temperature gradients across the entrainment zone increase entrainment fluxes by enhancing the entrainment velocity, amplifying the asymmetry between top‐down and bottom‐up flux contributions. These changes alter scalar flux profiles, causing flux divergence or convergence and leading to the breakdown of the constant flux layer assumption (CFLA) in the ASL. As a result, EC‐measured fluxes either underestimate or overestimate “true” surface fluxes during divergence or convergence phases, contributing to energy balance non‐closure. The varying degrees of the CFLA breakdown are a fundamental cause for the non‐closure issue. These findings highlight the underappreciated role of entrainment in interpreting EC fluxes, addressing non‐closure, and understanding site‐to‐site variability in flux measurements. 

Abstract. Wildfire smoke is one of the most significant concerns ofhuman and environmental health, associated with its substantial impacts onair quality, weather, and climate. However, biomass burning emissions andsmoke remain among the largest sources of uncertainties in air qualityforecasts. In this study, we evaluate the smoke emissions and plumeforecasts from 12 state-of-the-art air quality forecasting systemsduring the Williams Flats fire in Washington State, US, August 2019, whichwas intensively observed during the Fire Influence on Regional to GlobalEnvironments and Air Quality (FIREX-AQ) field campaign. Model forecasts withlead times within 1 d are intercompared under the same framework basedon observations from multiple platforms to reveal their performanceregarding fire emissions, aerosol optical depth (AOD), surface PM2.5,plume injection, and surface PM2.5 to AOD ratio. The comparison ofsmoke organic carbon (OC) emissions suggests a large range of daily totalsamong the models, with a factor of 20 to 50. Limited representations of thediurnal patterns and day-to-day variations of emissions highlight the needto incorporate new methodologies to predict the temporal evolution andreduce uncertainty of smoke emission estimates. The evaluation of smoke AOD(sAOD) forecasts suggests overall underpredictions in both the magnitude andsmoke plume area for nearly all models, although the high-resolution modelshave a better representation of the fine-scale structures of smoke plumes.The models driven by fire radiativepower (FRP)-based fire emissions or assimilating satellite AODdata generally outperform the others. Additionally, limitations of thepersistence assumption used when predicting smoke emissions are revealed bysubstantial underpredictions of sAOD on 8 August 2019, mainly over thetransported smoke plumes, owing to the underestimated emissions on7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecastsshow both positive and negative overall biases for these models, with mostmembers presenting more considerable diurnal variations of sPM2.5.Overpredictions of sPM2.5 are found for the models driven by FRP-basedemissions during nighttime, suggesting the necessity to improve verticalemission allocation within and above the planetary boundary layer (PBL).Smoke injection heights are further evaluated using the NASA LangleyResearch Center's Differential Absorption High Spectral Resolution Lidar(DIAL-HSRL) data collected during the flight observations. As the firebecame stronger over 3–8 August, the plume height became deeper, with aday-to-day range of about 2–9 km a.g.l. However, narrower ranges arefound for all models, with a tendency of overpredicting the plume heights forthe shallower injection transects and underpredicting for the days showingdeeper injections. The misrepresented plume injection heights lead toinaccurate vertical plume allocations along the transects corresponding totransported smoke that is 1 d old. Discrepancies in model performance forsurface PM2.5 and AOD are further suggested by the evaluation of theirratio, which cannot be compensated for by solely adjusting the smoke emissionsbut are more attributable to model representations of plume injections,besides other possible factors including the evolution of PBL depths andaerosol optical property assumptions. By consolidating multiple forecastsystems, these results provide strategic insight on pathways to improvesmoke forecasts.