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Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020-2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr−1 (2015-2020) to 56 ± 12 cm yr−1 (2020-2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr−1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr−1 and 39 ± 13 cm yr−1, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

Arctic shorelines are vulnerable to climate change impacts as sea level rises, permafrost thaws, storms intensify, and sea ice thins. Seventy-five years of aerial and satellite observations have established coastal erosion as an increasing Arctic hazard. However, other hazards at play--for instance, the cumulative impact that sea-level rise and permafrost thaw subsidence will have on permafrost shorelines--have received less attention, preventing assessments of these processes' impacts compared to and combined with coastal erosion. Alaska's Arctic Coastal Plain (ACP) is ideal for such assessments because of the high-density observations of topography, coastal retreat rates, and permafrost characteristics, and importance to Indigenous communities and oilfield infrastructure. Here, we produce 21st-century projections of Arctic shoreline position that include erosion, permafrost subsidence, and sea-level rise. Focusing on the ACP, we merge 5 m topography, satellite-derived coastal lake depth estimates, and empirical assessments of land subsidence due to permafrost thaw with projections of coastal erosion and sea-level rise for medium and high emissions scenarios from the Intergovernmental Panel on Climate Change's AR6 Report. We find that by 2100, erosion and inundation will together transform the ACP, leading to 6-8x more land loss than coastal erosion alone and disturbing 8-11x more organic carbon. Without mitigating measures, by 2100, coastal change could damage 40 to 65% of infrastructure in present-day ACP coastal villages and 10 to 20% of oilfield infrastructure. Our findings highlight the risks that compounding climate hazards pose to coastal communities and underscore the need for adaptive planning for Arctic coastlines in the 21st century. [ABSTRACT FROM AUTHOR]

Seasonal snowpack is an important predictor of the water resources available in the following spring and early-summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy; the Kullback–Leibler divergence from the Gaussian distribution for field-observed snow depth data from the North Slope, Alaska; and three representative SWE distributions in the western USA from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks on slopes and on other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western USA if the land is completely covered by snow. The wind-induced snowdrift effect can potentially be partitioned from the observed snow spatial distribution guided by its Gaussianity. [ABSTRACT FROM AUTHOR]

Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues.

Motivation As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth. Results We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data. Availability and implementation All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl. [ABSTRACT FROM AUTHOR]

Coastal erosion mobilizes large quantities of organic matter (OM) to the Arctic Ocean where it may fuel greenhouse gas emissions and marine production. While the biodegradability of permafrost‐derived dissolved organic carbon (DOC) has been extensively studied in inland soils and freshwaters, few studies have examined dissolved OM (DOM) leached from eroding coastal permafrost in seawater. To address this knowledge gap, we sampled three horizons from bluff exposures near Drew Point, Alaska: seasonally thawed active layer soils, permafrost containing Holocene terrestrial and/or lacustrine OM, and permafrost containing late‐Pleistocene marine‐derived OM. Samples were leached in seawater to compare DOC yields, DOM composition (chromophoric DOM, Fourier transform ion cyclotron resonance mass spectrometry), and biodegradable DOC (BDOC). Holocene terrestrial permafrost leached the most DOC compared to active layer soils and Pleistocene marine permafrost. However, DOC from Pleistocene marine permafrost was the most biodegradable (33 ± 6% over 90 days), followed by DOC from active layer soils (23 ± 5%) and Holocene terrestrial permafrost (14 ± 3%). Permafrost leachates contained relatively more aliphatic and peptide‐like formulae, whereas active layer leachates contained relatively more aromatic formulae. BDOC was positively correlated with nitrogen‐containing and aliphatic formulae, and negatively correlated with polyphenolic and condensed aromatic formulae. Using estimates of eroding OM, we scale our results to estimate DOC and BDOC inputs to the Alaska Beaufort Sea. While DOC inputs from coastal erosion are relatively small compared to rivers, our results suggest that erosion may be an important source of BDOC to the Beaufort Sea when river inputs are low. Plain Language Summary: Arctic coastlines are rapidly eroding into the ocean. Soils along these coastlines contain large quantities of organic matter (OM) that dissolves in seawater and may be consumed by microbes. If this dissolved organic matter (DOM) is biodegradable, it can be an important energy source to coastal food webs and/or quickly decomposed to greenhouse gases. We used laboratory experiments to examine the chemical composition of the DOM that is released from eroding soils into seawater and measured its biodegradability. We compared results for three different layers of soil at Drew Point, Alaska, including near‐surface soils that thaw seasonally and deeper soils that are perennially frozen (permafrost). Our results show that different layers, which contain OM of different sources and ages, have distinct chemical characteristics that impact biodegradability. While rivers supply more OM to the Alaska Beaufort Sea than coastal erosion, our results show that DOM released from all soil layers is highly biodegradable, and that DOM from deep permafrost is the most biodegradable. We deomonstrate that coastal erosion can be an important source of OM to Arctic coastal ecosystems, particularly in locations and seasons (e.g., late summer) that receive fewer river inputs. Key Points: Eroding soils and permafrost along the Alaska Beaufort Sea coast leach biodegradable dissolved organic carbon into seawaterBiodegradability was higher in leachates from Late‐Pleistocene relict marine permafrost than Holocene terrestrial soils and permafrostPermafrost leached aliphatic and peptide‐like molecular formulae that were not present or less abundant in active layer soils [ABSTRACT FROM AUTHOR]