Snow Property Controls on Modeled Ku-Band Altimeter Estimates of First-Year Sea Ice Thickness: Case Studies From the Canadian and Norwegian Arctic

Uncertainty in snow properties impacts the accuracy of Arctic sea ice thickness estimates from radar altimetry. On first-year sea ice (FYI), spatiotemporal variations in snow properties can cause the Ku-band main radar scattering horizon to appear above the snow/sea ice interface. This can increase the estimated sea ice freeboard by several centimeters, leading to FYI thickness overestimations. This article examines the expected changes in Ku-band main scattering horizon and its impact on FYI thickness estimates, with variations in snow temperature, salinity, and density derived from ten naturally occurring Arctic FYI Cases encompassing saline/nonsaline, warm/cold, simple/complexly layered snow (4–45 cm) overlying FYI (48–170 cm). Using a semi-empirical modeling approach, snow properties from these Cases are used to derive layer-wise brine volume and dielectric constant estimates, to simulate the Ku-band main scattering horizon and delays in radar propagation speed. Differences between modeled and observed FYI thickness are calculated to assess sources of error. Under both cold and warm conditions, saline snow covers are shown to shift the main scattering horizon above from the snow/sea ice interface, causing thickness retrieval errors. Overestimates in FYI thicknesses of up to 65% are found for warm, saline snow overlaying thin sea ice. Our simulations exhibited a distinct shift in the main scattering horizon when the snow layer densities became greater than 440 kg/m 3 , especially under warmer snow conditions. Our simulations suggest a mean Ku-band propagation delay for snow of 39%, which is higher than 25%, suggested in previous studies

Divergent Serpentoviruses in Free-Ranging Invasive Pythons and Native Colubrids in Southern Florida, United States

Burmese python (Python bivittatus) is an invasive snake that has significantly affected ecosystems in southern Florida, United States. Aside from direct predation and competition, invasive species can also introduce nonnative pathogens that can adversely affect native species. The subfamily Serpentovirinae (order Nidovirales) is composed of positive-sense RNA viruses primarily found in reptiles. Some serpentoviruses, such as shingleback nidovirus, are associated with mortalities in wild populations, while others, including ball python nidovirus and green tree python nidovirus can be a major cause of disease and mortality in captive animals. To determine if serpentoviruses were present in invasive Burmese pythons in southern Florida, oral swabs were collected from both free-ranging and long-term captive snakes. Swabs were screened for the presence of serpentovirus by reverse transcription PCR and sequenced. A total serpentovirus prevalence of 27.8% was detected in 318 python samples. Of the initial swabs from 172 free-ranging pythons, 42 (24.4%) were positive for multiple divergent viral sequences comprising four clades across the sampling range. Both sex and snout-vent length were statistically significant factors in virus prevalence, with larger male snakes having the highest prevalence. Sampling location was statistically significant in circulating virus sequence. Mild clinical signs and lesions consistent with serpentovirus infection were observed in a subset of sampled pythons. Testing of native snakes (n = 219, 18 species) in part of the python range found no evidence of python virus spillover; however, five individual native snakes (2.3%) representing three species were PCR positive for unique, divergent serpentoviruses. Calculated pairwise uncorrected distance analysis indicated the newly discovered virus sequences likely represent three novel genera in the subfamily Serpentovirinae. This study is the first to characterize serpentovirus in wild free-ranging pythons or in any free-ranging North America reptile. Though the risk these viruses pose to the invasive and native species is unknown, the potential for spillover to native herpetofauna warrants further investigation

Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

Snow thickness measurements over relatively smooth Arctic first-year sea ice, obtained near Cambridge Bay, Nunavut, Canada (2014, 2016 and 2017) and near Elson Lagoon, Alaska, USA (2003 and 2006), are analyzed to quantify physical length-scales and their relevant scaling behaviors. We use the Multi-Fractal Temporally Weighted Detrended Fluctuation Analysis (MF-TWDFA) method to detect two major physical length-scales from both the study areas. Our results suggest that physical processes underlying the formation of snow dunes are consistent and that the wind is the main process shaping the redistribution and variability of snow thickness. One scale, around 10 m, appears to be related to the formation of the snow "dunes", while the other scale, between 30 m and 60 m, is likely associated with the various interactions of the snow dunes including merging, calving and lateral linking showing self-organized characteristics. We suggest that a simple cellular automata model can be used to generate the variability of snow thickness on smooth Arctic first-year sea ice

Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice

Wind-driven redistribution of snow on sea ice alters its topography and microstructure, yet the impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution over Arctic sea ice on radar waveforms and backscatter signatures obtained from a surface-based, fully polarimetric Ka- and Ku-band radar at incidence angles between 0∘ (nadir) and 50∘. Two wind events in November 2019 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coefficients at nadir are observed, coincident with surface topography changes measured by a terrestrial laser scanner. At both frequencies, redistribution caused snow densification at the surface and the uppermost layers, increasing the scattering at the air–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previous air–snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air–snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite altimeters. With increasing incidence angles, the relative scattering contribution of the air–snow interface decreases, and the snow–sea ice interface scattering increases. Relative to pre-wind event conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scan area at all incidence angles and polarizations, in response to increasing wind speed and changes in wind direction. Our results show that snow redistribution and wind compaction need to be accounted for to interpret airborne and satellite radar measurements of snow-covered sea ice

Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice

Wind-driven redistribution of snow on sea ice alters its topography and microstructure, yet the impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution over Arctic sea ice on radar waveforms and backscatter signatures obtained from a surface-based, fully polarimetric Ka- and Ku-band radar at incidence angles between 0∘ (nadir) and 50∘. Two wind events in November 2019 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coefficients at nadir are observed, coincident with surface topography changes measured by a terrestrial laser scanner. At both frequencies, redistribution caused snow densification at the surface and the uppermost layers, increasing the scattering at the air–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previous air–snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air–snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite altimeters. With increasing incidence angles, the relative scattering contribution of the air–snow interface decreases, and the snow–sea ice interface scattering increases. Relative to pre-wind event conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scan area at all incidence angles and polarizations, in response to increasing wind speed and changes in wind direction. Our results show that snow redistribution and wind compaction need to be accounted for to interpret airborne and satellite radar measurements of snow-covered sea ice

Burmese pythons in Florida: A synthesis of biology, impacts, and management tools

Burmese pythons (Python molurus bivittatus) are native to southeastern Asia, however, there is an established invasive population inhabiting much of southern Florida throughout the Greater Everglades Ecosystem. Pythons have severely impacted native species and ecosystems in Florida and represent one of the most intractable invasive-species management issues across the globe. The difficulty stems from a unique combination of inaccessible habitat and the cryptic and resilient nature of pythons that thrive in the subtropical environment of southern Florida, rendering them extremely challenging to detect. Here we provide a comprehensive review and synthesis of the science relevant to managing invasive Burmese pythons. We describe existing control tools and review challenges to productive research, identifying key knowledge gaps that would improve future research and decision making for python control. (119 pp

An analysis of the spatial and temporal character of the chinook in southern Alberta

Bibliography: p. 123-128