Anna Liljedahl

Cold season emissions dominate the Arctic tundra methane budget

Significance Arctic ecosystems are major global sources of methane. We report that emissions during the cold season (September to May) contribute ≥50% of annual sources of methane from Alaskan tundra, based on fluxes obtained from eddy covariance sites and from regional fluxes calculated from aircraft data. The largest emissions were observed at the driest site (<5% inundation). Emissions of methane in the cold season are linked to the extended “zero curtain” period, where soil temperatures are poised near 0 °C, indicating that total emissions are very sensitive to soil climate and related factors, such as snow depth. The dominance of late season emissions, sensitivity to soil conditions, and importance of dry tundra are not currently simulated in most global climate models. Arctic terrestrial ecosystems are major global sources of methane (CH4); hence, it is important to understand the seasonal and climatic controls on CH4 emissions from these systems. Here, we report year-round CH4 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 CH4 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 CH4 emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH4 derived from aircraft data demonstrate the large spatial extent of late season CH4 emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 ± 5 (95% confidence interval) Tg CH4 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 CH4 emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.

Interactions between soil thermal and hydrological dynamics in the response of Alaska ecosystems to fire disturbance

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 model-data 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.

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r2 = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

Large CO2 and CH4 emissions from polygonal tundra during spring thaw in northern Alaska

Author(s): Raz-Yaseef, N; Torn, MS; Wu, Y; Billesbach, DP; Liljedahl, AK; Kneafsey, TJ; Romanovsky, VE; Cook, DR; Wullschleger, SD | Abstract: ©2016. American Geophysical Union. All Rights Reserved. The few prethaw observations of tundra carbon fluxes suggest that there may be large spring releases, but little is known about the scale and underlying mechanisms of this phenomenon. To address these questions, we combined ecosystem eddy flux measurements from two towers near Barrow, Alaska, with mechanistic soil-core thawing experiment. During a 2 week period prior to snowmelt in 2014, large fluxes were measured, reducing net summer uptake of CO2 by 46% and adding 6% to cumulative CH4 emissions. Emission pulses were linked to unique rain-on-snow events enhancing soil cracking. Controlled laboratory experiment revealed that as surface ice thaws, an immediate, large pulse of trapped gases is emitted. These results suggest that the Arctic CO2 and CH4 spring pulse is a delayed release of biogenic gas production from the previous fall and that the pulse can be large enough to offset a significant fraction of the moderate Arctic tundra carbon sink.

Tundra water budget and implications of precipitation underestimation

Abstract Difficulties in obtaining accurate precipitation measurements have limited meaningful hydrologic assessment for over a century due to performance challenges of conventional snowfall and rainfall gauges in windy environments. Here, we compare snowfall observations and bias adjusted snowfall to end‐of‐winter snow accumulation measurements on the ground for 16 years (1999–2014) and assess the implication of precipitation underestimation on the water balance for a low‐gradient tundra wetland near Utqiagvik (formerly Barrow), Alaska (2007–2009). In agreement with other studies, and not accounting for sublimation, conventional snowfall gauges captured 23–56% of end‐of‐winter snow accumulation. Once snowfall and rainfall are bias adjusted, long‐term annual precipitation estimates more than double (from 123 to 274 mm), highlighting the risk of studies using conventional or unadjusted precipitation that dramatically under‐represent water balance components. Applying conventional precipitation information to the water balance analysis produced consistent storage deficits (79 to 152 mm) that were all larger than the largest actual deficit (75 mm), which was observed in the unusually low rainfall summer of 2007. Year‐to‐year variability in adjusted rainfall (±33 mm) was larger than evapotranspiration (±13 mm). Measured interannual variability in partitioning of snow into runoff (29% in 2008 to 68% in 2009) in years with similar end‐of‐winter snow accumulation (180 and 164 mm, respectively) highlights the importance of the previous summers rainfall (25 and 60 mm, respectively) on spring runoff production. Incorrect representation of precipitation can therefore have major implications for Arctic water budget descriptions that in turn can alter estimates of carbon and energy fluxes.

A lake-centric geospatial database to guide research and inform management decisions in an Arctic watershed in northern Alaska experiencing climate and land-use changes

Lakes are dominant and diverse landscape features in the Arctic, but conventional land cover classification schemes typically map them as a single uniform class. Here, we present a detailed lake-centric geospatial database for an Arctic watershed in northern Alaska. We developed a GIS dataset consisting of 4362 lakes that provides information on lake morphometry, hydrologic connectivity, surface area dynamics, surrounding terrestrial ecotypes, and other important conditions describing Arctic lakes. Analyzing the geospatial database relative to fish and bird survey data shows relations to lake depth and hydrologic connectivity, which are being used to guide research and aid in the management of aquatic resources in the National Petroleum Reserve in Alaska. Further development of similar geospatial databases is needed to better understand and plan for the impacts of ongoing climate and land-use changes occurring across lake-rich landscapes in the Arctic.

Regional Patterns and Asynchronous Onset of Ice-Wedge Degradation since the Mid-20th Century in Arctic Alaska

Ice-wedge polygons are widespread and conspicuous surficial expressions of ground-ice in permafrost landscapes. Thawing of ice wedges triggers differential ground subsidence, local ponding, and persistent changes to vegetation and hydrologic connectivity across the landscape. Here we characterize spatio-temporal patterns of ice-wedge degradation since circa 1950 across environmental gradients on Alaska’s North Slope. We used a spectral thresholding approach validated by field observations to map flooded thaw pits in high-resolution images from circa 1950, 1982, and 2012 for 11 study areas (1577–4460 ha). The total area of flooded pits increased since 1950 at 8 of 11 study areas (median change +3.6 ha; 130.3%). There were strong regional differences in the timing and extent of degradation; flooded pits were already extensive by 1950 on the Chukchi coastal plain (alluvial-marine deposits) and subsequent changes there indicate pit stabilization. Degradation began more recently on the central Beaufort coastal plain (eolian sand) and Arctic foothills (yedoma). Our results indicate that ice-wedge degradation in northern Alaska cannot be explained by late-20th century warmth alone. Likely mechanisms for asynchronous onset include landscape-scale differences in surficial materials and ground-ice content, regional climate gradients from west (maritime) to east (continental), and regional differences in the timing and magnitude of extreme warm summers after the Little Ice Age.

The Polar WRF Downscaled Historical and Projected Twenty-First Century Climate for the Coast and Foothills of Arctic Alaska

Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research & Forecast (Polar WRF) model was forced by three sources: The ERA-interim reanalysis data (for 1979-2014), the Community Earth System Model 1.0 (CESM1.0) historical simulation (for 1950-2005), and the CESM1.0 projected (for 2006-2100) simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-hour interval. The ERA-interim forced WRF (ERA-WRF) proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF) holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.

Recent Extreme Runoff Observations From Coastal Arctic Watersheds in Alaska

Arctic coastal watersheds, though rarely monitored, are expected to have increased runoff, as climate models predict more precipitation in the Arctic. This study provides a synthesis of streamflow changes in watersheds of the Alaska Arctic Coastal Plain (AACP) based on available historic discharge data and water balance analysis. A comparison of annual runoff from the Putuligayuk River watershed (471 km2) from the period 1970–1986 (78 ± 24.1 mm/yr) to the period 1999–2015 (122 ± 49.6 mm/yr) shows increasing discharge and interannual variability. From this discontinuous record of 32 years, the three lowest runoff years occurred in 1979, 2007, and 2008, and the three highest runoff years occurred in 2003, 2014, and 2015. Other studied AACP watersheds with shorter discharge records demonstrate similar patterns of dry (2007–2008) and wet (2014–2015) years during common periods of observation. A combination of favorable antecedent surface storage conditions and above-average precipitation is required to generate large volumes of surface runoff. A strong relationship between climate, surface storage, and runoff inherent to AACP watersheds makes these systems highly responsive to sea ice retreat and hydrological intensification. Our new estimates of freshwater flux from the AACP to the Beaufort Sea and Chukchi Sea account for an observed range of runoff variability and provide baseline data for modeling arctic hydrologic systems.

Recursive active contours for hierarchical segmentation of wetlands in high-resolution satellite imagery of Arctic landscapes

We present a semi-automated approach to recognize and hierarchically partition water-body regions of Arctic tundra landscape, such as streams, inundated drained thaw lake basins and ice wedge polygon ponds, in high resolution satellite imagery. The approach integrates the active contours without edges (ACWE) technique and shape-based recognition, and introduces a recursive mode of ACWE application. We build a successive coarse-to-fine hierarchy of image partitions corresponding to the low-gradient Arctic wetlands by recursively partitioning them at the coarser scale into constituent parts. The approach is evaluated using 0.6 m resolution WorldView-2 satellite image of Arctic tundra landscape. The water-body regions segmentation producers accuracy is 97.7 %, and the users accuracy is 92.9 %. Visual inspection of the classification and hierarchical partitioning of the segmented water-body regions has demonstrated their qualitatively accurate recognition and hierarchical partitioning.