No abstract

Arctic terrestrial ecosystems are major global sources of methane (CH 4 ); hence, it is important to understand the seasonal and climatic controls on CH 4 emissions from these systems. Here, we report year-round CH 4 emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for ≥50% of the annual CH 4 flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the "zero curtain" period, when subsurface soil temperatures are poised near 0°C. The zero curtain may persist longer than the growing season, and CH 4 emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH 4 derived from aircraft data demonstrate the large spatial extent of late season CH 4 emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 ± 5 (95% confidence interval) Tg CH 4 y −1 , ∼25% of global emissions from extratropical wetlands, or ∼6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH 4 emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.permafrost | aircraft | fall | winter | warming E missions of methane (CH 4 ) from Arctic terrestrial ecosystems could increase dramatically in response to climate change (1-3), a potentially significant positive feedback on climate warming. High latitudes have warmed at a rate almost two times faster than the Northern Hemisphere mean over the past century, with the most intense warming in the colder seasons (4) [up to 4°C in winter in 30 y (5)]. Poor understanding of controls on CH 4 emissions outside of the summer season (6-10) represents a large source of uncertainty for the Arctic CH 4 budget. Warmer air temperatures and increased snowfall can potentially increase soil temperatures and deepen the seasonal thawed layer, stimulating CH 4 and CO 2 emissions from the vast stores of labile organic matter in the Arctic (11). The overwhelming majority of prior studies of CH 4 fluxes in the Arctic have been carried out during the summer months (12-15). However, the fall, winter, and spring months represent 70-80% of the year in the Arctic and have been shown to have significant emissions of CO 2 (16-18). The few measurements of CH 4 fluxes in the Arctic that extend into the fall (6, 7, 9, 10) show complex patterns of CH 4 emissions, with a number indicating high fluxes (7, 10). Winter and early spring data appear to be absent in Arctic tundra over continuous permafrost.Beginning usually in late August or early September, the...

Abstract. Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth system models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth system models challenge validation and parameterization of hydrothermal models. A recently developed surface-subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to achieve the goals of constructing a process-rich model based on plausible parameters and to identify fine-scale controls of ALT in ice-wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze-thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g., troughs). The results suggest that properly constructed and calibrated onedimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.

The discovery of the very acidic nature of fog and clouds has created much interest in sampling, analysing, and elucidating the chemistry of fog, principally because an understanding of the chemical transformations leading to acid fog may provide important clues to the origin of acid rain. Recently, the knowledge of the chemistry of fog has expanded to include carbonyl compounds, volatile organic acids, and alkyl sulphonates. We have discovered that a variety of pesticides and their toxic alteration products are present in fog, and that they occasionally reach high concentrations relative to reported rainwater concentrations. In our experiments, we were able to measure the air-water distribution coefficients of pesticides between the liquid fog and the interstitial gas phase. These measurements reveal that some chemicals are enriched several thousandfold in the suspended liquid fog droplets compared to equilibrium distributions expected from Henry's Law coefficients for pure aqueous solutions.

[1] Soil temperature and moisture are important factors that control many ecosystem processes. However, interactions between soil thermal and hydrological processes are not adequately understood in cold regions, where the frozen soil, fire disturbance, and soil drainage play important roles in controlling interactions among these processes. These interactions were investigated with a new ecosystem model framework, the dynamic organic soil version of the Terrestrial Ecosystem Model, that incorporates an efficient and stable numerical scheme for simulating soil thermal and hydrological dynamics within soil profiles that contain a live moss horizon, fibrous and amorphous organic horizons, and mineral soil horizons. The performance of the model was evaluated for a tundra burn site that had both preburn and postburn measurements, two black spruce fire chronosequences (representing space-for-time substitutions in well and intermediately drained conditions), and a poorly drained black spruce site. Although space-for-time substitutions present challenges in modeldata comparison, the model demonstrates substantial ability in simulating the dynamics of evapotranspiration, soil temperature, active layer depth, soil moisture, and water table depth in response to both climate variability and fire disturbance. Several differences between model simulations and field measurements identified key challenges for evaluating/improving model performance that include (1) proper representation of discrepancies between air temperature and ground surface temperature; (2) minimization of precipitation biases in the driving data sets; (3) improvement of the measurement accuracy of soil moisture in surface organic horizons; and (4) proper specification of organic horizon depth/properties, and soil thermal conductivity.

Lakes are prevalent in the Arctic and thus play a key role in regional hydrology. Since many Arctic lakes are shallow and ice grows thick (historically 2 m or greater), seasonal ice commonly freezes to the lake bed (bedfast ice) by winter's end. Bedfast ice fundamentally alters lake energy balance and meltout processes compared to deeper lakes that exceed the maximum ice thickness (floating ice) and maintain perennial liquid water below floating ice. Our analysis of lakes in northern Alaska indicated that ice-out of bedfast ice lakes occurred on average 17 days earlier (22 June) than ice-out on adjacent floating ice lakes (9 July). Earlier ice-free conditions in bedfast ice lakes caused higher open-water evaporation, 28% on average, relative to floating ice lakes and this divergence increased in lakes closer to the coast and in cooler summers. Water isotopes ( 18 O and 2 H) indicated similar differences in evaporation between these lake types. Our analysis suggests that ice regimes created by the combination of lake depth relative to ice thickness and associated ice-out timing currently cause a strong hydrologic divergence among Arctic lakes. Thus, understanding the distribution and dynamics of lakes by ice regime is essential for predicting regional hydrology. An observed regime shift in lakes to floating ice conditions due to thinner ice growth may initially offset lake drying because of lower evaporative loss from this lake type. This potential negative feedback caused by winter processes occurs in spite of an overall projected increase in evapotranspiration as the Arctic climate warms.

Abstract. Projected increases in air temperature and precipitation due to climate change in Arctic wetlands could dramatically affect ecosystem function. As a consequence, it is important to define controls on evapotranspiration, the major pathway of water loss from these systems. We quantified the multi-year controls on midday Arctic coastal wetland evapotranspiration, measured with the eddy covariance method at two vegetated, drained thaw lake basins near Barrow, Alaska. Variations in near-surface soil moisture and atmospheric vapor pressure deficits were found to have nonlinear effects on midday evapotranspiration rates. Vapor pressure deficits (VPD) near 0.3 kPa appeared to be an important hydrological threshold, allowing latent heat flux to persistently exceed sensible heat flux. Dry (compared to wet) soils increased bulk surface resistance (water-limited). Wet soils favored ground heat flux and therefore limited the energy available to sensible and latent heat flux (energy-limited). Thus, midday evapotranspiration was suppressed from both dry and wet soils but through different mechanisms. We also found that wet soils (ponding excluded) combined with large VPD, resulted in an increased bulk surface resistance and therefore suppressing evapotranspiration below its potential rate (Priestley-Taylor α < 1.26). This was likely caused by the limited ability of mosses to transfer moisture during large atmospheric demands. Ultimately, in addition to net radiation, the various controlling factors on midday evapotranspiration (i.e., near-surface soil moisture, atmospheric vapor pressure, Correspondence to: A. K. Liljedahl (akliljedahl@alaska.edu) and the limited ability of saturated mosses to transfer water during high VPD) resulted in an average evapotranspiration rate of up to 75 % of the potential evapotranspiration rate. These multiple limitations on midday evapotranspiration rates have the potential to moderate interannual variation of total evapotranspiration and reduce excessive water loss in a warmer climate. Combined with the prevailing maritime winds and projected increases in precipitation, these curbing mechanisms will likely prevent extensive future soil drying and hence maintain the presence of coastal wetlands.

The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km2) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70 and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system.