15 Aug 2023
 | 15 Aug 2023
Status: this preprint is currently under review for the journal BG.

Resolving heterogeneous fluxes from tundra halves the growing season carbon budget

Sarah M. Ludwig, Luke Schiferl, Jacqueline Hung, Susan M. Natali, and Roisin Commane

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, with variation in plant functional types, soil moisture, thaw depth, and microtopography, for example, influencing net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems could change from a net sink to a net source of carbon to the atmosphere in some locations, but the 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 un-mix tower observations into constituent landcover fluxes based on high resolution landcover maps of the tower region. We compared three types of footprint models and used two landcover 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 landcover-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 landcover 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 two and underestimated CH4 emissions by a factor of two when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high resolution landcover maps can be leveraged to derive detailed landcover carbon fluxes from eddy covariance timeseries. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic.

Sarah M. Ludwig et al.

Status: open (until 29 Oct 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Sarah M. Ludwig et al.

Sarah M. Ludwig et al.


Total article views: 359 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
272 75 12 359 18 6 10
  • HTML: 272
  • PDF: 75
  • XML: 12
  • Total: 359
  • Supplement: 18
  • BibTeX: 6
  • EndNote: 10
Views and downloads (calculated since 15 Aug 2023)
Cumulative views and downloads (calculated since 15 Aug 2023)

Viewed (geographical distribution)

Total article views: 352 (including HTML, PDF, and XML) Thereof 352 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 24 Sep 2023
Short summary
Landscapes are often assumed to be homogeneous when using eddy covariance fluxes, which can lead to biases when calculating carbon budgets. In this study we report eddy covariance carbon fluxes from heterogeneous tundra. We used the footprints of each flux observation to un-mix the fluxes coming from components of the landscape. We identified and quantified hot spots of carbon emissions in the landscape. Accurately scaling with landscape heterogeneity yielded half as much regional carbon uptake.