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Abstract. Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic.
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Flux Observations of Carbon from an Automotive Laboratory (FOCAL) - Greenhouse Gas (GHG) Fluxes from North Slope, Alaska (AK), 2022 - 2024
The main impetus for this study is to answer the following questions: (i) What are the net late summer and autumn seasonal fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) across the North Slope and coastal Arctic Ocean? Using the van we will be able to measure fluxes in the late summer and autumn along the Dalton highway and connecting roads. While this will not be across the North Slope of Alaska from a longitudinal prospective, we will be able to sample most of the different ecotopes found there and sample into the foothills region. From this and surface ecotope maps we will still be able to derive a net flux and compare with other estimates in the literature. As a bonus, we will plan to acquire data on the transit drive between Boston and Alaska providing a cross-continental snapshot of emissions to compare with the emissions from permafrost. (ii) What are the primary surface land classes and associated mechanisms that contribute to these fluxes? Again, along the Dalton highway we will have access to sampling from rivers, wet sedge, mesic sedge, lakes, tussock tundra, dwarf shrub, among others. Though in extent we will sample less area, we will have better precision from the areas we do sample. We will also be able to operate in a fixed mode (parked vehicle) to increase our precision even further for isotopic measurements which are tied to mechanistic information. (iii) What remotely-sensed products are the best proxy for the physical and biological processes that regulate the net flux of methane and carbon dioxide and how do these vary regionally? This question we will be able to address in the same manner as proposed using our modeling capabilities and available remote products from satellites. (iv) How do the answers to the above questions vary depending on the scale of measurements used from local measurements up to regional scale inversion models? We will be able to answer this question in much the same way as proposed, but will only be able to scale from local to landscape scales. This dataset covers the Flux Observations of Carbon from an Automotive Laboratory (FOCAL) 2024 campaign. It includes two continuous eddy covariance towers set up on the North Slope south of Prudhoe Bay in 2023 and 2024. It also includes discontinuous eddy covariance measurements from 4 sites using a mobile tower van laboratory. The motivation of the campaign was to focus on late season carbon dioxide, methane, and nitrous oxide fluxes from various ecotypes along a North - South transect of the North Slope, AK. By measuring gas fluxes from different sites as the season progressed from summer to early winter we hoped to better understand how the permafrost is changing in terms of gas emissions. We also hoped to better quantify late season emissions which have been shown to be an important source of methane emissions from the Arctic region. We used eddy covariance to measure the gas fluxes combining sonic anemometers for the 3D wind with spectrometers tuned to the different gases of interest. The two towers used commercial instrumentation from LiCOR and Cambridge Scientific and the mobile tower used custom built spectrometers and a Gil Windmaster.
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- PAR ID:
- 10627726
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Format(s):
- Medium: X Other: text/xml
- Institution:
- Harvard University
- Sponsoring Org:
- National Science Foundation
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