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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.

 

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.

 

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.

 

Leads play an important role in the exchange of heat, gases, vapour, and particles between seawater and the atmosphere in ice-covered polar oceans. In summer, these processes can be modified significantly by the formation of a meltwater layer at the surface, yet we know little about the dynamics of meltwater layer formation and persistence. During the drift campaign of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), we examined how variation in lead width, re-freezing, and mixing events affected the vertical structure of lead waters during late summer in the central Arctic. At the beginning of the 4-week survey period, a meltwater layer occupied the surface 0.8 m of the lead, and temperature and salinity showed strong vertical gradients. Stable oxygen isotopes indicate that the meltwater consisted mainly of sea ice meltwater rather than snow meltwater. During the first half of the survey period (before freezing), the meltwater layer thickness decreased rapidly as lead width increased and stretched the layer horizontally. During the latter half of the survey period (after freezing of the lead surface), stratification weakened and the meltwater layer became thinner before disappearing completely due to surface ice formation and mixing processes. Removal of meltwater during surface ice formation explained about 43% of the reduction in thickness of the meltwater layer. The remaining approximate 57% could be explained by mixing within the water column initiated by disturbance of the lower boundary of the meltwater layer through wind-induced ice floe drift. These results indicate that rapid, dynamic changes to lead water structure can have potentially significant effects on the exchange of physical and biogeochemical components throughout the atmosphere–lead–underlying seawater system.

 

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. Clouds warm the surface in the longwave (LW), and this warming effect can be quantified through the surface LW cloud radiativeeffect (CRE). The global surface LW CRE has been estimated over more than2 decades using space-based radiometers (2000–2021) and over the 5-year period ending in 2011 using the combination of radar, lidar and space-basedradiometers. Previous work comparing these two types of retrievals has shown that the radiometer-based cloud amount has some bias over icy surfaces. Here we propose new estimates of the global surface LW CRE from space-based lidarobservations over the 2008–2020 time period. We show from 1D atmosphericcolumn radiative transfer calculations that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us toestablish simple parameterizations between surface LW CRE and five cloud properties that are well observed by the Cloud-Aerosol Lidar and InfraredPathfinder Satellite Observations (CALIPSO) space-based lidar: opaque cloud cover and altitude and thin cloud cover, altitude, and emissivity. We evaluate this new surface LWCRE–LIDAR product by comparing it to existingsatellite-derived products globally on instantaneous collocated data atfootprint scale and on global averages as well as to ground-based observations at specific locations. This evaluation shows good correlationsbetween this new product and other datasets. Our estimate appears to be animprovement over others as it appropriately captures the annual variabilityof the surface LW CRE over bright polar surfaces and it provides a datasetmore than 13 years long. 

Greenland ice sheet melt is a large contributor to rising global sea level and melt is dependent on surface air temperature. Arctic temperatures are strongly coupled to clouds but spatial connections between clouds and temperature have yet to be established across Greenland. By mapping spaceborne lidar measurements and surface observations, it is shown that radiatively opaque clouds generally coincide with anomalously warm near‐surface temperatures at Greenland sites. These results indicate that both temperatures over 0^°C as well as positive daily temperature anomalies relate to spatially extensive opaque cloud cover. While prior studies indicate that clouds enhance extreme melt events, this research shows that opaque cloud cover and surface warming are closely related across the Greenland ice sheet, particularly in the ablation region. These findings establish broadly the spatial relationships between opaque clouds and temperatures and demonstrate the importance of direct observations across Greenland.