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Abstract. Large changes in the Arctic carbon balance are expectedas warming linked to climate change threatens to destabilize ancientpermafrost carbon stocks. The eddy covariance (EC) method is an establishedtechnique to quantify net losses and gains of carbon between the biosphereand atmosphere at high spatiotemporal resolution. Over the past decades, agrowing network of terrestrial EC tower sites has been established acrossthe Arctic, but a comprehensive assessment of the network'srepresentativeness within the heterogeneous Arctic region is still lacking.This creates additional uncertainties when integrating flux data acrosssites, for example when upscaling fluxes to constrain pan-Arctic carbonbudgets and changes therein. This study provides an inventory of Arctic (here > = 60∘ N)EC sites, which has also been made available online(https://cosima.nceas.ucsb.edu/carbon-flux-sites/, last access: 25 January 2022). Our database currentlycomprises 120 EC sites, but only 83 are listed as active, and just 25 ofthese active sites remain operational throughout the winter. To map therepresentativeness of this EC network, we evaluated the similarity betweenenvironmental conditions observed at the tower locations and those withinthe larger Arctic study domain based on 18 bioclimatic and edaphicvariables. This allows us to assess a general level of similarity betweenecosystem conditions within the domain, while not necessarily reflectingchanges in greenhouse gas flux rates directly. We define two metrics basedon this representativeness score: one that measures whether a location isrepresented by an EC tower with similar characteristics (ER1) and a secondfor which we assess if a minimum level of representation for statisticallyrigorous extrapolation is met (ER4). We find that while half of the domainis represented by at least one tower, only a third has enough towers insimilar locations to allow reliable extrapolation. When we consider methanemeasurements or year-round (including wintertime) measurements, the valuesdrop to about 1/5 and 1/10 of the domain, respectively. With themajority of sites located in Fennoscandia and Alaska, these regions wereassigned the highest level of network representativeness, while large partsof Siberia and patches of Canada were classified as underrepresented.Across the Arctic, mountainous regions were particularly poorly representedby the current EC observation network. We tested three different strategies to identify new site locations orupgrades of existing sites that optimally enhance the representativeness ofthe current EC network. While 15 new sites can improve therepresentativeness of the pan-Arctic network by 20 %, upgrading as fewas 10 existing sites to capture methane fluxes or remain active duringwintertime can improve their respective ER1 network coverage by 28 % to 33 %. This targeted network improvement could be shown to be clearlysuperior to an unguided selection of new sites, therefore leading tosubstantial improvements in network coverage based on relatively smallinvestments. 

Abstract Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO 2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO 2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO 2 later in the season. 

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We firstdefineeach of the major C pools and fluxes and providerationalefor their importance to wetland C dynamics. For each approach, we clarifywhatcomponent of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such aswhereandwhenan approach is typically used,whocan conduct the measurements (expertise, training requirements), andhowapproaches are conducted, including considerations on equipment complexity and costs. Finally, we reviewkey covariatesandancillary measurementsthat enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.

 

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. 

Abstract The leaf economics spectrum 1,2 and the global spectrum of plant forms and functions 3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species 2 . Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities 4 . However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability 4,5 . Here we derive a set of ecosystem functions 6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems 7,8 . 

Abstract. Natural wetlands constitute the largest and most uncertain sourceof methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance datafrom 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available athttps://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).