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The continued warming of the Arctic could release vast stores of carbon into the atmosphere from high-latitude ecosystems, especially from thawing permafrost. Increasing uptake of carbon dioxide (CO2) by vegetation during longer growing seasons may partially offset such release of carbon. However, evidence of significant net annual release of carbon from site-level observations and model simulations across tundra ecosystems has been inconclusive. To address this knowledge gap, we combined top-down observations of atmospheric CO2 concentration enhancements from aircraft and a tall tower, which integrate ecosystem exchange over large regions, with bottom-up observed CO2 fluxes from tundra environments and found that the Alaska North Slope is not a consistent net source nor net sink of CO2 to the atmosphere (ranging from −6 to +6 Tg C yr−1 for 2012–2017). Our analysis suggests that significant biogenic CO2 fluxes from unfrozen terrestrial soils, and likely inland waters, during the early cold season (September–December) are major factors in determining the net annual carbon balance of the North Slope, implying strong sensitivity to the rapidly warming freeze-up period. At the regional level, we find no evidence of the previously reported large late-cold-season (January–April) CO2 emissions to the atmosphere during the study period. Despite the importance of the cold-season CO2 emissions to the annual total, the interannual variability in the net CO2 flux is driven by the variability in growing season fluxes. During the growing season, the regional net CO2 flux is also highly sensitive to the distribution of tundra vegetation types throughout the North Slope. This study shows that quantification and characterization of year-round CO2 fluxes from the heterogeneous terrestrial and aquatic ecosystems in the Arctic using both site-level and atmospheric observations are important to accurately project the Earth system response to future warming.

For applications such as spin accumulation sensors for next-generation hard disk drive read heads, and for fundamental research, it is desirable to increase the spin signal in metallic non-local spin valves, which are central devices in spintronics. To this end, here, we report on the integration of high-spin-polarization Co–Fe binary alloy ferromagnetic injectors and detectors in Al-based non-local spin valves. Room-temperature deposition on amorphous substrates from an alloy target is shown to generate smooth, polycrystalline (110-textured), solid-solution body-centered-cubic Co75Fe25 films, which we characterize by energy dispersive x-ray spectroscopy, x-ray diffraction, x-ray reflectivity, atomic force microscopy, and electronic transport. Simple integration into transparent-interface Al non-local spin valves is then shown to realize up to a factor of ∼5 enhancement of the spin signal relative to Co, with full quantitative analysis yielding strikingly temperature-independent current spin polarizations exceeding 60%. We make a detailed quantitative comparison of these values with prior literature, concluding that Co–Fe alloys present a remarkably facile route to higher spin polarization and spin signals in non-local spin valves, with minimal barrier to adoption.

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).

Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small ( km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

The contribution of soil heterotrophic respiration to the boreal–Arctic carbon (CO2) cycle and its potential feedback to climate change remains poorly quantified. We developed a remote-sensing-driven permafrost carbon model at intermediate scale (∼1 km) to investigate how environmental factors affect the magnitude and seasonality of soil heterotrophic respiration in Alaska. The permafrost carbon model simulates snow and soil thermal dynamics and accounts for vertical soil carbon transport and decomposition at depths up to 3 m below the surface. Model outputs include soil temperature profiles and carbon fluxes at 1 km resolution spanning the recent satellite era (2001–2017) across Alaska. Comparisons with eddy covariance tower measurements show that the model captures the seasonality of carbon fluxes, with favorable accuracy in simulating net ecosystem CO2 exchange (NEE) for both tundra (R>0.8, root mean square error (RMSE – 0.34 g C m−2 d−1), and boreal forest (R>0.73; RMSE – 0.51 g C m−2 d−1). Benchmark assessments using two regional in situ data sets indicate that the model captures the complex influence of snow insulation on soil temperature and the temperature sensitivity of cold-season soil heterotrophic respiration. Across Alaska, we find that seasonal snow cover imposes strong controls on the contribution from different soil depths to total soil heterotrophic respiration. Earlier snowmelt in spring promotes deeper soil warming and enhances the contribution of deeper soils to total soil heterotrophic respiration during the later growing season, thereby reducing net ecosystem carbon uptake. Early cold-season soil heterotrophic respiration is closely linked to the number of snow-free days after the land surface freezes (R=-0.48, p), i.e., the delay in snow onset relative to surface freeze onset. Recent trends toward earlier autumn snow onset in northern Alaska promote a longer zero-curtain period and enhanced cold-season respiration. In contrast, southwestern Alaska shows a strong reduction in the number of snow-free days after land surface freeze onset, leading to earlier soil freezing and a large reduction in cold-season soil heterotrophic respiration. Our results also show nonnegligible influences of subgrid variability in surface conditions on the model-simulated CO2 seasonal cycle, especially during the early cold season at 10 km scale. Our results demonstrate the critical role of snow cover affecting the seasonality of soil temperature and respiration and highlight the challenges of incorporating these complex processes into future projections of the boreal–Arctic carbon cycle.

Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002–December 2015) global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (Tmx and Tmn), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003–2010) and AMSR2 (2013–2015) observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE ≤ 0.06), PWV (R ≥ 0.91; RMSE ≤ 4.94 mm), Tmx and Tmn (R ≥ 0.90; RMSE ≤ 3.48 °C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE ≤ 0.06 cm3 cm−3). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at http://files.ntsg.umt.edu/data/LPDR_v2/.<

Artificial spin ices (ASIs) are interacting arrays of lithographically defined nanomagnets in which novel, frustrated magnetic phases can be intentionally designed. A key emergent description of fundamental excitations in ASIs is that of magnetic monopoles—mobile quasiparticles that carry an effective magnetic charge. Here, we demonstrate that the archetypal square ASI lattice can host, in specific regions of its magnetic phase diagram, plasmalike regimes containing a high density of mobile magnetic monopoles. These regimes result from the magnetic field-tunable tension on the Dirac strings connecting mobile monopoles. By passively “listening” to spontaneous monopole noise under conditions of strict thermal equilibrium, we reveal their intrinsic dynamics and show that monopole kinetics are most diffusive (that is, minimally correlated) in the plasma regime. These results open the door to on-demand monopole regimes having continuously field-tunable densities and dynamic properties, thereby providing a new paradigm for probing the physics of effective magnetic charges in synthetic matter.

A new automated method enabling consistent satellite assessment of seasonal lake ice phenology at 5 km resolution was developed for all lake pixels (water coverage ≥ 90 %) in the Northern Hemisphere using 36.5 GHz H-polarized brightness temperature (Tb) observations from the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) sensors. The lake phenology metrics include seasonal timing and duration of annual ice cover. A moving t test (MTT) algorithm allows for automated lake ice retrievals with daily temporal fidelity and 5 km resolution gridding. The resulting ice phenology record shows strong agreement with available ground-based observations from the Global Lake and River Ice Phenology Database (95.4 % temporal agreement) and favorable correlations (R) with alternative ice phenology records from the Interactive Multisensor Snow and Ice Mapping System (R = 0.84 for water clear of ice (WCI) dates; R = 0.41 for complete freeze over (CFO) dates) and Canadian Ice Service (R = 0.86 for WCI dates; R = 0.69 for CFO dates). Analysis of the resulting 12-year (2002–2015) AMSR-E/2 ice record indicates increasingly shorter ice cover duration for 43 out of 71 (60.6 %) Northern Hemisphere lakes examined, with significant (p

The northern terrestrial net ecosystem carbon balance (NECB) is contingent on inputs from vegetation gross primary productivity (GPP) to offset the ecosystem respiration (Reco) of carbon dioxide (CO2) and methane (CH4) emissions, but an effective framework to monitor the regional Arctic NECB is lacking. We modified a terrestrial carbon flux (TCF) model developed for satellite remote sensing applications to evaluate wetland CO2 and CH4 fluxes over pan-Arctic eddy covariance (EC) flux tower sites. The TCF model estimates GPP, CO2 and CH4 emissions using in situ or remote sensing and reanalysis-based climate data as inputs. The TCF model simulations using in situ data explained > 70% of the r2 variability in the 8 day cumulative EC measured fluxes. Model simulations using coarser satellite (MODIS) and reanalysis (MERRA) records accounted for approximately 69% and 75% of the respective r2 variability in the tower CO2 and CH4 records, with corresponding RMSE uncertainties of &leq; 1.3 g C m−2 d−1 (CO2) and 18.2 mg C m−2 d−1 (CH4). Although the estimated annual CH4 emissions were small (< 18 g C m−2 yr−1) relative to Reco (> 180 g C m−2 yr−1), they reduced the across-site NECB by 23% and contributed to a global warming potential of approximately 165 ± 128 g CO2eq m−2 yr−1 when considered over a 100 year time span. This model evaluation indicates a strong potential for using the TCF model approach to document landscape-scale variability in CO2 and CH4 fluxes, and to estimate the NECB for northern peatland and tundra ecosystems.