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Abstract
Abstract. Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km2). We used these field data together with high-resolution remote sensing data to develop machine learning models for predicting (i.e., upscaling) daytime GHG fluxes across the landscape at 2 m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO2 uptake, it also revealed small but widespread CH4 uptake in upland vegetation types, almost surpassing the high wetland CH4 emissions at the landscape scale. Average N2O fluxes were negligible. CO2 fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH4 fluxes by soil moisture (increases net emissions) and vegetation type, and N2O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modeling of GHG fluxes in the Arctic. They also reveal the dominant role of CO2 fluxes across the tundra landscape but suggest that CH4 uptake in dry upland soils might play a significant role in the regional GHG budget.
Highlights
Over the past millennia, Arctic soils in the treeless tundra biome have played an important role in the global climate system by accumulating large amounts of carbon (C) and nitrogen (N), cooling the climate (Hugelius et al, 2014, 2020; Strauss et al, 2017)
We developed machine learning models, in which we (1) upscaled environmental data using remotely sensed variables as predictors; (2) modeled greenhouse gas (GHG) fluxes using the environmental data as predictors, and (3) upscaled GHG fluxes using the upscaled environmental data maps at a 2 m spatial resolution across the landscape (Fig. 2)
We examined the relationship between the five primary response variables (GPP, Ecosystem respiration (ER), net ecosystem exchange (NEE), CH4 flux, N2O flux) and environmental predictors that describe (i) soil resources and conditions, which are relevant to, for example, the growth of organisms (Nobrega and Grogan, 2008; Happonen et al, 2022); (ii) soil C and N stocks and dissolved organic carbon, which are one of the main sources for the GHG emissions (BradleyCook and Virginia, 2018); (iii) soil temperatures, which regulate enzymatic processes (St Pierre et al, 2019; Mauritz et al, 2017); and (iv) biomass and vegetation type, which describe resource-use strategies, carbon inputs to soils and plant photosynthetic capacity and integrate multiple environmental properties into one variable (Magnani et al, 2022)
Summary
Arctic soils in the treeless tundra biome have played an important role in the global climate system by accumulating large amounts of carbon (C) and nitrogen (N), cooling the climate (Hugelius et al, 2014, 2020; Strauss et al, 2017). Even the current GHG balance of Arctic ecosystems is insufficiently understood due to severe gaps in flux measurement networks and poorly performing coarse-resolution models (Virkkala et al, 2021; Treat et al, 2018c). One of the main uncertainties in understanding the Arctic GHG balance is related to the inadequately quantified magnitudes of all three main GHG fluxes – carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) – which show pronounced spatial variability across the diverse terrestrial environmental gradients in tundra (Virkkala et al, 2018; Pallandt et al, 2021; Voigt et al, 2020). Only a few studies have simultaneously considered the contributions of all three main GHG fluxes to the tundra GHG balance (Voigt et al, 2017b; Kelsey et al, 2016; Brummell et al, 2012; Wagner et al, 2019)
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