Using a Bayesian framework to account for advection in seven years of snowpack CO2 fluxes in a mortality-impacted subalpine forest

Erin Michele Berryman; John M. Frank; William J. Massman; Michael G. Ryan

doi:10.1016/j.agrformet.2017.11.004

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Using a Bayesian framework to account for advection in seven years of snowpack CO2 fluxes in a mortality-impacted subalpine forest

Erin Michele Berryman·John M. Frank·William J. Massman·Michael G. Ryan

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Abstract

In subalpine forests where snowpacks can persist for 50% of the year or longer, wintertime subnivean soil respiration constitutes a significant, but poorly understood, portion of the carbon (C) cycle. The temporal and seasonal dynamics of this flux are difficult to measure compared to snow-free respiration. This makes it challenging to fully address key knowledge gaps in high-elevation forests, including effects of tree mortality on ecosystem C fluxes. Our paper attempts to overcome these challenges by developing a new method for estimating wintertime CO 2 flux from soil underneath winter snowpack based on CO 2 profiles in the snow and using this to examine potential effects of a tree mortality event on wintertime respiration. The chief problem for estimates based on snow CO 2 measurements is that during strong winds, vertical CO 2 profiles can depart significantly from linearity, resulting in CO 2 fluxes that are not steady-state diffusive fluxes. Consequently, these fluxes include an advective component induced by wind and atmospheric pressure, resulting in above-snowpack fluxes that may be temporally decoupled from biological flux production in the soil. These advective fluxes lead to errors in estimates of the total amount of CO 2 produced from the soil surface during the winter, and may complicate the detection of significant seasonal and interannual trends in snowpack CO 2 fluxes. Our goal was twofold: first, to improve methods for estimating wintertime subnivean soil respiration; and second, to use this new method to examine potential effects of forest mortality on respiration. We develop a Bayesian-based model of CO 2 snowpack profiles and accounts for CO 2 storage flux within the snowpack to calculate diffusive and advective fluxes from both the bottom and the top of the snowpack. We apply this model to hourly snowpack CO 2 profile data collected over 7 years at an AmeriFlux eddy covariance (EC) site in a Wyoming subalpine forest that had recently experienced a mass tree mortality event. Hourly advective fluxes from the snowpack surface are less variable over time than hourly EC fluxes. Nonetheless, on a daily time scale, the two methods agree well with each other. We found that soil biological CO 2 production entering the bottom of snowpack peaked 3 years after the onset of the bark beetle outbreak and 1-year after dead trees dropped their needles, presenting the first evidence of a short-term increase in respiration following bark beetle mortality. Our findings emphasize the importance of accounting for the role of wind-driven advection in wintertime respiration and suggest that tree mortality may nearly double wintertime respiration in the first few years.

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Source-reported bounding extent: 41.14329804846665° to 41.65367073153655° latitude; -107.1352452865994° to -106.3276326029913° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Erin Michele Berryman, John M. Frank, William J. Massman, Michael G. Ryan. 2018. Using a Bayesian framework to account for advection in seven years of snowpack CO2 fluxes in a mortality-impacted subalpine forest. https://doi.org/10.1016/j.agrformet.2017.11.004

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