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Abstract Mass loss of the Greenland Ice Sheet (GrIS) plays a major role in the global sea level rise. The west coast of the GrIS has contributed 1,000 Gt of the 4,488 Gt GrIS mass loss between 2002 and 2021, making it a hotspot for GrIS mass loss. Surface melting is driven by changes in the radiative budget at the surface, which are modulated by clouds. Previous works have shown the impact of North Atlantic transport for influencing cloudiness over the GrIS. Here we used space‐based lidar cloud profile observations to show that a polar low circulation promotes the presence of low clouds over the GrIS west coast that warm radiatively the GrIS surface during the melt season. Polar low circulation transports moisture and low clouds from the sea to the west of Greenland up over the GrIS west coast through the melt season. The concomitance of the increasing presence of low cloud in fall over the Baffin Sea due to seasonal sea‐ice retreat and a maximum occurrence of Polar low circulation in September results in a maximum of low cloud fraction (∼14% at 2.5 km above sea level) over the GrIS west coast in September. These low clouds warm radiatively the GrIS west coast surface up to 80 W/m²locally. This warming contributes to an average increase of 10 W/m²of cloud surface warming in September compared to July on the GrIS west coast. Overall, this study suggests that regional atmospheric processes independent from North Atlantic transport may also influence the GrIS melt. 

Abstract Wildland fire–atmosphere interaction generates complex turbulence patterns, organized across multiple scales, which inform fire-spread behaviour, firebrand transport, and smoke dispersion. Here, we utilize wavelet-based techniques to explore the characteristic temporal scales associated with coherent patterns in the measured temperature and the turbulent fluxes during a prescribed wind-driven (heading) surface fire beneath a forest canopy. We use temperature and velocity measurements from tower-mounted sonic anemometers at multiple heights. Patterns in the wavelet-based energy density of the measured temperature plotted on a time–frequency plane indicate the presence of fire-modulated ramp–cliff structures in the low-to-mid-frequency band (0.01–0.33 Hz), with mean ramp durations approximately 20% shorter and ramp slopes that are an order of magnitude higher compared to no-fire conditions. We then investigate heat- and momentum-flux events near the canopy top through a cross-wavelet coherence analysis. Briefly before the fire-front arrives at the tower base, momentum-flux events are relatively suppressed and turbulent fluxes are chiefly thermally-driven near the canopy top, owing to the tilting of the flame in the direction of the wind. Fire-induced heat-flux events comprising warm updrafts and cool downdrafts are coherent down to periods of a second, whereas ambient heat-flux events operate mainly at higher periods (above 17 s). Later, when the strongest temperature fluctuations are recorded near the surface, fire-induced heat-flux events occur intermittently at shorter scales and cool sweeps start being seen for periods ranging from 8 to 35 s near the canopy top, suggesting a diminishing influence of the flame and increasing background atmospheric variability thereat. The improved understanding of the characteristic time scales associated with fire-induced turbulence features, as the fire-front evolves, will help develop more reliable fire behaviour and scalar transport models. 

In the context of developing novel fuel cell catalysts, we have successfully synthesized in high yields not only ultrathin nanowires with compositions of Pt1Ru1 and Pt3Ru1 but also more complex spoke-like dendritic clusters of Pt1Ru1 and Pt1Ru9 in ambient pressure under relatively straightforward, solution-based reaction conditions, mediated by either CTAB (cetyltrimethylammonium bromide) or oleylamine (OAm), respectively. EXAFS analysis allowed us to determine the homogeneity of as-prepared samples. Based on this analysis, only the Pt3Ru1 sample was found to be relatively homogeneous. All of the other samples yielded results, suggestive of a tendency for the elements to segregate into clusters of ‘like’ atoms. We have also collected complementary HRTEM EDS mapping data, which support the idea of a segregation of elements consistent with the EXAFS results. We attribute the differences in the observed morphologies and elemental distributions within as-prepared samples to the presence of varying surfactants and heating environments, employed in these reactions. Methanol oxidation reaction (MOR) measurements indicated a correlation of specific activity (SA) values not only with intrinsic chemical composition and degree of alloying but also with the reaction process used to generate the nanoscale motifs in the first place. Specifically, the observed performance of samples tested decreased as a function of chemical composition (surfactant used in their synthesis), as follows: Pt3Ru1 (CTAB) > Pt1Ru1 (CTAB) > Pt1Ru1 (OAm) > Pt1Ru9 (OAm). 

The challenges inherent in field validation data, and real-world light detection and ranging (lidar) collections make it difficult to assess the best algorithms for using lidar to characterize forest stand volume. Here, we demonstrate the use of synthetic forest stands and simulated terrestrial laser scanning (TLS) for the purpose of evaluating which machine learning algorithms, scanning configurations, and feature spaces can best characterize forest stand volume. The random forest (RF) and support vector machine (SVM) algorithms generally outperformed k-nearest neighbor (kNN) for estimating plot-level vegetation volume regardless of the input feature space or number of scans. Also, the measures designed to characterize occlusion using spherical voxels generally provided higher predictive performance than measures that characterized the vertical distribution of returns using summary statistics by height bins. Given the difficulty of collecting a large number of scans to train models, and of collecting accurate and consistent field validation data, we argue that synthetic data offer an important means to parameterize models and determine appropriate sampling strategies. 

ABSTRACT Long-term terrestrial ecosystem monitoring is a critical component of documenting outcomes of land management actions, assessing progress towards management objectives, and guiding realistic long-term ecological goals, all through repeated observation and measurement. Traditional monitoring methods have evolved for specific applications in forestry, ecology, and fire and fuels management. While successful monitoring programs have clear goals, trained expertise, and rigorous sampling protocols, new advances in technology and data management can help overcome the most common pitfalls in data quality and repeatability. This paper presents Terrestrial Laser Scanning (TLS), a specific form of LiDAR (Light Detection and Ranging), as an emerging sampling method that can complement and enhance existing monitoring methods. TLS captures in high resolution the 3D structure of a terrestrial ecosystem (forest, grassland, etc.), and is increasingly efficient and affordable (<$30,000). Integrating TLS into ecosystem monitoring can standardize data collection, improve efficiency, and reduce bias and error. Streamlined data processing pipelines can rigorously analyze TLS data and incorporate constant improvements to inform management decisions and planning. The approach described in this paper utilizes portable, push-button TLS equipment that, when calibrated with initial transect sampling, captures detailed forestry, fuels, and ecological features in less than 5 minutes per plot. We also introduce an interagency automated processing pipeline and dashboard viewer for instant, user-friendly analysis, and data retrieval of hundreds of metrics. Forest metrics and inventories produced with these methods offer effective decision-support data for managers to quantify landscape-scale conditions and respond with efficient action. This protocol further supports interagency compatibility for efficient natural resource monitoring across jurisdictional boundaries with uniform data, language, methods, and data analysis. With continued improvement of scanner capabilities and affordability, these data will shape the future of terrestrial ecosystem monitoring as an important means to address the increasingly fast pace of ecological change facing natural resource managers. 

Terrestrial laser scanning (TLS) data can offer a means to estimate subcanopy fuel characteristics to support site characterization, quantification of treatment or fire effects, and inform fire modeling. Using field and TLS data within the New Jersey Pinelands National Reserve (PNR), this study explores the impact of forest phenology and density of shrub height (i.e., shrub fuel bed depth) measurements on estimating average shrub heights at the plot-level using multiple linear regression and metrics derived from ground-classified and normalized point clouds. The results highlight the importance of shrub height sampling density when these data are used to train empirical models and characterize plot-level characteristics. We document larger prediction intervals (PIs), higher root mean square error (RMSE), and lower R-squared with reduction in the number of randomly selected field reference samples available within each plot. At least 10 random shrub heights collected in situ were needed to produce accurate and precise predictions, while 20 samples were ideal. Additionally, metrics derived from leaf-on TLS data generally provided more accurate and precise predictions than those calculated from leaf-off data within the study plots and landscape. This study highlights the importance of reference data sampling density and design and data characteristics when data will be used to train empirical models for extrapolation to new sites or plots. 

Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models. 

Abstract. Snowfall is the major source of mass for the Greenland ice sheet (GrIS) but the spatial and temporalvariability of snowfall and the connections between snowfall and mass balance have so far been inadequatelyquantified. By characterizing local atmospheric circulation and utilizing CloudSat spaceborne radarobservations of snowfall, we provide a detailed spatial analysis of snowfall variability and its relationshipto Greenland mass balance, presenting first-of-their-kind maps of daily spatial variability in snowfallfrom observations across Greenland. For identified regional atmospheric circulation patterns, we show that thespatial distribution and net mass input of snowfall vary significantly with the position and strength ofsurface cyclones. Cyclones west of Greenland driving southerly flow contribute significantly more snowfall thanany other circulation regime, with each daily occurrence of the most extreme southerly circulation patterncontributing an average of 1.66 Gt of snow to the Greenland ice sheet. While cyclones east of Greenland,patterns with the least snowfall, contribute as little as 0.58 Gt each day. Above 2 km on the ice sheet wheresnowfall is inconsistent, extreme southerly patterns are the most significant mass contributors, with up to1.20 Gt of snowfall above this elevation. This analysis demonstrates that snowfall over the interior ofGreenland varies by up to a factor of 5 depending on regional circulation conditions. Using independentobservations of mass changes made by the Gravity Recovery and Climate Experiment (GRACE), we verify that thelargest mass increases are tied to the southerly regime with cyclones west of Greenland. For occurrences of thestrongest southerly pattern, GRACE indicates a net mass increase of 1.29 Gt in the ice sheet accumulation zone(above 2 km elevation) compared to the 1.20 Gt of snowfall observed by CloudSat. This overall agreementsuggests that the analytical approach presented here can be used to directly quantify snowfall masscontributions and their most significant drivers spatially across the GrIS. While previous research hasimplicated this same southerly regime in ablation processes during summer, this paper shows that ablation massloss in this circulation regime is nearly an order of magnitude larger than the mass gain from associatedsnowfall. For daily occurrences of the southerly circulation regime, a mass loss of approximately 11 Gt isobserved across the ice sheet despite snowfall mass input exceeding 1 Gt. By analyzing the spatialvariability of snowfall and mass changes, this research provides new insight into connections between regionalatmospheric circulation and GrIS mass balance. 

Abstract The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was a yearlong expedition supported by the icebreakerR/V Polarstern, following the Transpolar Drift from October 2019 to October 2020. The campaign documented an annual cycle of physical, biological, and chemical processes impacting the atmosphere-ice-ocean system. Of central importance were measurements of the thermodynamic and dynamic evolution of the sea ice. A multi-agency international team led by the University of Colorado/CIRES and NOAA-PSL observed meteorology and surface-atmosphere energy exchanges, including radiation; turbulent momentum flux; turbulent latent and sensible heat flux; and snow conductive flux. There were four stations on the ice, a 10 m micrometeorological tower paired with a 23/30 m mast and radiation station and three autonomous Atmospheric Surface Flux Stations. Collectively, the four stations acquired ~928 days of data. This manuscript documents the acquisition and post-processing of those measurements and provides a guide for researchers to access and use the data products.