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

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

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

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

Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter

Information on the timing of melt onset over sea ice is important for understanding the Arctic's changing climate. The daily temporal resolution of passive microwave brightness temperatures provides the most widely utilized observations to detect melt onset but are limited to a spatial resolution of 25 km. Wide-swath synthetic aperture radar (SAR) imagery provides a much higher spatial resolution (20–100 m) but melt onset detection remains challenging because of i) insufficient temporal resolution to facilitate accurate melt onset detection, ii) inconsistent viewing geometries and iii) limited image availability across the Arctic. Here, we construct high temporal resolution composite gamma nought backscatter products (1 day, 1–2 day and 2–4 day) using Sentinel-1 and RADARSAT-2 over a close-to-seamless revisit region located in northern Canadian Arctic and Greenland for estimating melt onset over Arctic sea ice in 2016 and 2017. We employ the necessary radiometric terrain flattening and local resolution weighting techniques to generate normalised backscatter over the entire study region, removing restrictions limiting analysis to a single sensor or track's swath width by integrating both ascending and descending passes into the composite products. Results indicate that higher temporal resolution multi-sensor composite gamma nought products (1 day) that make use of the most imagery provide a robust temporal evolution of the backscatter. This allows for more representative estimates of melt onset as it is easier to separate the melt onset threshold from winter variability that is otherwise a considerable challenge for SAR based melt onset algorithms because of inconsistent temporal resolution. Multi-sensor composite gamma naught melt onset detection is in good agreement with melt onset estimates derived from the Advance Scatterometer (ASCAT) backscatter values and passive microwave brightness temperatures over homogenous sea ice regions but very noticeable improvement was found within narrow channels and regions with more heterogeneous sea ice. In anticipation of the availability of data from even more SAR satellites with the launch of the RADARSAT Constellation Mission, the multi-sensor composite gamma nought approach presented here may offer the most robust approach to estimate the timing of melt onset over sea ice across the Arctic using high spatiotemporal resolution SAR