Data from: Soil incubation methods lead to large differences in inferred methane production temperature sensitivity

Quantifying the temperature sensitivity of methane (CH4) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q10 values, with a large portion of this variation remaining unexplained. Here we applied observations in Stordalen Mire, a thawing permafrost peatland, and a well-tested process-rich model, ecosys, to interpret incubation observations and investigate controls on inferred CH4 production temperature sensitivity. We developed a Field-Storage-Incubation (FSI) modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season,refrigerated storage, and the laboratory incubation process, followed by model evaluation. We found that CH4 production rates during incubation are regulated by seasonally-dependent substrate availability and active microbial biomass of key microbial functional groups. Applying a model sensitivity analysis, we found that storage duration, storage temperature, and field sampling time significantly affect CH4 production during incubation. Shorter storage duration and lower storage temperature led to larger CH4 production during incubation. Our findings revealed a wide range of inferred Q10 values (1.2 to 3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. Q10 of CH4 production is controlled by many interacting biological, biochemical, and physical processes, which cause the aggregated Q10 values to differ from those of the component processes. Terrestrial ecosystem models that use a constant Q10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.

This dataset includes all the data used to plot figures in the manuscript, including Fig.2-6 and Fig.S2-S11. Each sheet in the aggregated spreadsheet corresponds to one figure in the manuscript. The simulation experiment setup and analyses are thoroughly described in the manuscript. Here we provide a brief summary. The data includes field greenhouse gas observations and laboratory incubation measurements of CH4 production in Stordalen Mire. These datasets were already published and references were provided in the manuscript and spreadsheet. The data also includes simulation data, including modeled cumulative CH4 production, CH4 production rates, substrate concentrations, and active microbial biomass under different incubation temperature, sampling time and storage conditions. This data also includes inferred temperature sensitivity of CH4 production as Q10 values under different scenarios. Please refer to the manuscript for more detailed information.

Please see "Related works" at the bottom of this page and the "References" tab in the spreadsheet for a full list of source datasets and associated publications.

FUNDING:

This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.

We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). Incubation and field observation data were collected under the IsoGenie Project, which was funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.