An official website of the United States government
Abstract. Methane (CH4) emissions from the boreal and arcticregion are globally significant and highly sensitive to climate change.There is currently a wide range in estimates of high-latitude annualCH4 fluxes, where estimates based on land cover inventories andempirical CH4 flux data or process models (bottom-up approaches)generally are greater than atmospheric inversions (top-down approaches). Alimitation of bottom-up approaches has been the lack of harmonizationbetween inventories of site-level CH4 flux data and the land coverclasses present in high-latitude spatial datasets. Here we present acomprehensive dataset of small-scale, surface CH4 flux data from 540terrestrial sites (wetland and non-wetland) and 1247 aquatic sites (lakesand ponds), compiled from 189 studies. The Boreal–Arctic Wetland and LakeMethane Dataset (BAWLD-CH4) was constructed in parallel with acompatible land cover dataset, sharing the same land cover classes to enablerefined bottom-up assessments. BAWLD-CH4 includes information onsite-level CH4 fluxes but also on study design (measurement method,timing, and frequency) and site characteristics (vegetation, climate,hydrology, soil, and sediment types, permafrost conditions, lake size anddepth, and our determination of land cover class). The different land coverclasses had distinct CH4 fluxes, resulting from definitions that wereeither based on or co-varied with key environmental controls. Fluxes ofCH4 from terrestrial ecosystems were primarily influenced by watertable position, soil temperature, and vegetation composition, while CH4fluxes from aquatic ecosystems were primarily influenced by watertemperature, lake size, and lake genesis. Models could explain more of thebetween-site variability in CH4 fluxes for terrestrial than aquaticecosystems, likely due to both less precise assessments of lake CH4fluxes and fewer consistently reported lake site characteristics. Analysisof BAWLD-CH4 identified both land cover classes and regions within theboreal and arctic domain, where future studies should be focused, alongsidemethodological approaches. Overall, BAWLD-CH4 provides a comprehensivedataset of CH4 emissions from high-latitude ecosystems that are usefulfor identifying research opportunities, for comparison against new fielddata, and model parameterization or validation. BAWLD-CH4 can bedownloaded from https://doi.org/10.18739/A2DN3ZX1R (Kuhn et al., 2021).
more »
« less
Vegetation type is an important predictor of the arctic summer land surface energy budget
Abstract Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm −2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.
more »
« less
- PAR ID:
- 10399364
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract. The objective of this study was to upscale airborne flux measurements ofsensible heat and latent heat and to develop high-resolution flux maps. Inorder to support the evaluation of coupled atmospheric–land-surface models weinvestigated spatial patterns of energy fluxes in relation to land-surfaceproperties. We used airborne eddy-covariance measurements acquired by the Polar 5research aircraft in June–July 2012 to analyze surface fluxes.Footprint-weighted surface properties were then related to 21 529 sensibleheat flux observations and 25 608 latent heat flux observations using bothremote sensing and modeled data. A boosted regression tree technique wasused to estimate environmental response functions between spatially andtemporally resolved flux observations and corresponding biophysical andmeteorological drivers. In order to improve the spatial coverage and spatialrepresentativeness of energy fluxes we used relationships extracted acrossheterogeneous Arctic landscapes to infer high-resolution surface energy fluxmaps, thus directly upscaling the observational data. These maps of projectedsensible heat and latent heat fluxes were used to assess energy partitioningin northern ecosystems and to determine the dominant energy exchangeprocesses in permafrost areas. This allowed us to estimate energy fluxes forspecific types of land cover, taking into account meteorological conditions.Airborne and modeled fluxes were then compared with measurements from aneddy-covariance tower near Atqasuk. Our results are an important contribution for the advanced, scale-dependentquantification of surface energy fluxes and they provide new insights into theprocesses affecting these fluxes for the main vegetation types inhigh-latitude permafrost areas.more » « less
-
Abstract. Rapid warming across the Arctic is the primary driver of widespread permafrost thaw, with far-reaching consequences for local ecosystem resilience, the regional carbon budget, and the global climate system. Because permafrost characteristics and vulnerability are tightly linked to land cover, particularly vegetation type and surface properties, understanding these dynamics requires accurate and detailed land cover information. Spatial variation in vegetation cover influences energy balance, snow insulation, and soil moisture, factors that directly affect permafrost stability. Consequently, high-resolution land cover products are essential for assessing the ecological impacts of permafrost thaw and for improving the representation of permafrost-related processes in predictive models. However, many global land cover datasets fail to capture the spatial heterogeneity and fine-scale ecological features that influence permafrost dynamics, while more detailed regional products often lack coverage across broader, continental extents. This gap presents a challenge for large-scale assessments of permafrost vulnerability under accelerating climate change. To create a spatially cohesive land cover map that accurately represents the distribution of ecosystems across the Arctic-Boreal region, we integrated existing global and regional land cover datasets using a workflow including machine learning techniques. This approach seamlessly combines diverse data sources, enhancing representation and accuracy. The resulting map represents high-latitude land cover types at a 1-km spatial resolution, better capturing the spatial heterogeneity of the landscape compared to coarser resolution land surface products, with a total of 35 land cover classes, including 20 forest types (e.g., Larch, Birch, Mixed forests), 6 shrubland classes, and wetlands subdivided into bog, fen, and marsh. To achieve this, we used a global land cover map, the European Space Agency Climate Change Initiative Land Cover data (ESA CCI-LC), as the base map and integrated regional maps across the circumpolar region with finer-resolution land cover information to capture the diversity of land cover types. This approach ensured consistent classification across geopolitical boundaries while incorporating representative vegetation communities at a region-specific level. We show that regional land cover products can be successfully fused to yield a higher-resolution thematic content at the circumpolar scale in comparison to existing global products. The hybrid land cover product can be freely access via https://doi.org/10.5281/zenodo.15231293 (Briones et al 2025).more » « less
-
During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m−1 K−1 to 0.47 W m−1 K−1 with a median from all data of 0.39 W m−1 K−1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m−2, but the average residual during winter is −6 W m−2, which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.more » « less
-
During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux.The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m_1 K_1 to 0.47Wm_1 K_1 with a median from all data of 0.39Wm_1 K_1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20Wm_2, but the average residual during winter is _6 Wm_2, which is within the uncertainties.The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41467-022-34049-3
