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1. Warm Temperature Extremes Across Greenland Connected to Clouds

   https://doi.org/10.1029/2019GL086059  

   Gallagher, Michael R.; Chepfer, Hélène; Shupe, Matthew D.; Guzman, Rodrigo (April 2020, Geophysical Research Letters)

   Abstract 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. 

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2. Midlatitude Cloud Radiative Effect Sensitivity to Cloud Controlling Factors in Observations and Models: Relationship with Southern Hemisphere Jet Shifts and Climate Sensitivity

   https://doi.org/10.1175/JCLI-D-20-0986.1  

   Grise, Kevin M.; Kelleher, Mitchell K. (April 2021, Journal of Climate)

   null (Ed.)

   Abstract An effective method to understand cloud processes and to assess the fidelity with which they are represented in climate models is the cloud controlling factor framework, in which cloud properties are linked with variations in large-scale dynamical and thermodynamical variables. This study examines how midlatitude cloud radiative effects (CRE) over oceans co-vary with four cloud controlling factors: mid-tropospheric vertical velocity, estimated inversion strength (EIS), near-surface temperature advection, and sea surface temperature (SST), and assesses their representation in CMIP6 models with respect to observations and CMIP5 models. CMIP5 and CMIP6 models overestimate the sensitivity of midlatitude CRE to perturbations in vertical velocity, and underestimate the sensitivity of midlatitude shortwave CRE to perturbations in EIS and temperature advection. The largest improvement in CMIP6 models is a reduced sensitivity of CRE to vertical velocity perturbations. As in CMIP5 models, many CMIP6 models simulate a shortwave cloud radiative warming effect associated with a poleward shift in the Southern Hemisphere (SH) midlatitude jet stream, an effect not present in observations. This bias arises because most models’ shortwave CRE are too sensitive to vertical velocity perturbations and not sensitive enough to EIS perturbations, and because most models overestimate the SST anomalies associated with SH jet shifts. The presence of this bias directly impacts the transient surface temperature response to increasing greenhouse gases over the Southern Ocean, but not the global-mean surface temperature. Instead, the models’ climate sensitivity is correlated with their shortwave CRE sensitivity to surface temperature advection perturbations near 40°S, with models with more realistic values of temperature advection sensitivity generally having higher climate sensitivity. 

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3. From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure

   https://doi.org/10.3390/rs10121949  

   Zhou, Tan; Popescu, Sorin; Malambo, Lonesome; Zhao, Kaiguang; Krause, Keith (December 2018, Remote Sensing)

   Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization. 

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4. Examining Southern Ocean Cloud Controlling Factors on Daily Time Scales and Their Connections to Midlatitude Weather Systems

   https://doi.org/10.1175/JCLI-D-18-0840.1  

   Kelleher, Mitchell K.; Grise, Kevin M. (July 2019, Journal of Climate)

   ABSTRACT Clouds and their associated radiative effects are a large source of uncertainty in global climate models. One region with particularly large model biases in shortwave cloud radiative effects (CRE) is the Southern Ocean. Previous research has shown that many dynamical “cloud controlling factors” influence shortwave CRE on monthly time scales and that two important cloud controlling factors over the Southern Ocean are midtropospheric vertical velocity and estimated inversion strength (EIS). Model errors may thus arise from biases in representing cloud controlling factors (atmospheric dynamics) or in representing how clouds respond to those cloud controlling factors (cloud parameterizations), or some combination thereof. This study extends previous work by examining cloud controlling factors over the Southern Ocean on daily time scales in both observations and global climate models. This allows the cloud controlling factors to be examined in the context of transient weather systems. Composites of EIS and midtropospheric vertical velocity are constructed around extratropical cyclones and anticyclones to examine how the different dynamical cloud controlling factors influence shortwave CRE around midlatitude weather systems and to assess how models compare to observations. On average, models tend to produce a realistic cyclone and anticyclone, when compared to observations, in terms of the dynamical cloud controlling factors. The difference between observations and models instead lies in how the models’ shortwave CRE respond to the dynamics. In particular, the models’ shortwave CRE are too sensitive to perturbations in midtropospheric vertical velocity and, thus, they tend to produce clouds that excessively brighten in the frontal region of the cyclone and excessively dim in the center of the anticyclone. 

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5. Evaluation of SPL100 Single Photon Lidar Data

   https://doi.org/10.3390/rs12040722  

   Brown, Rebecca; Hartzell, Preston; Glennie, Craig (February 2020, Remote Sensing)

   Geiger-mode and single photon lidar sensors have recently emerged on the commercial market, advertising greater collection efficiency than the traditional linear mode lidar (LML) systems. Non-linear photon detection is a new technology for the geospatial community, and its performance characteristics for surveying and mapping are not yet well understood. Therefore, the geospatial quality of the data produced by one of these new sensors, the Leica SPL100, is examined by comparing the achieved lidar point cloud accuracy, precision, digital elevation model (DEM) generation, canopy penetration, and multiple return generation to a LML point cloud. We find the SPL100 has a lower ranging precision than linear mode lidar and that the precision is more negatively affected by surface properties such as low intensity and high incidence angle. The accuracy of the SPL100 point cloud, however, was found to be comparable to LML for smooth horizontal surfaces. A 1 m resolution SPL100 DEM was also comparable to a corresponding LML DEM, but the SPL100 was observed to have a reduced ability to resolve multiple returns through vegetation when compared to a LML sensor. In its current state, the SPL100 is likely best suited for applications in which the need for collection efficiency outweighs the need for maximum precision and canopy penetration and modeling. 

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