Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Abstract

Wetland methane (CH4) emissions ($${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

“…In the few instances where we did observe negative lags between FCH4 and temperature, FCH4 peaked slightly before TS or TA. This is also consistent with the findings of Delwiche et al (in press) who observed that for 36% of the wetland sites in the FLUXNET-CH4 database, the timing of peak seasonal FCH4 led the soil temperature peak, and the findings of Chang et al (2021) who observed a negative seasonal FCH4 hysteresis with temperature (for both the shallowest and deepest TS used) at a number of sites. However, as discussed in Section 4.6, further research is needed to better mechanistically constrain the causes of the observed lags, in particular for factors affecting CH 4 production, oxidation, and transport (Chang et al, 2019).…”

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

“…Future work could also explore the use of led or lagged predictors, which could be used to engineer predictors with greater coherence with CH 4 flux (Vitale et al 2018). For example, recent syntheses have demonstrated that the timing and seasonality of CH 4 fluxes lags TS across several FLUXNET-CH4 sites , leading to an apparent hysteretic dependency (Chang et al 2021), and therefore using lagged TS predictors may improve ML gap-filling performance. More sophisticated feature selection methods are possible, such as information theory, which can be used to first identify the predictor and timescale of the lag (or lead), and then curate a more parsimonious predictor set (e.g., Sturtevant et al 2016;Knox et al 2021).…”

A distributed temperature profiling system for vertically and laterally dense acquisition of soil and snow temperature

“…Temperature is a key property for understanding and quantifying a multitude of processes occurring in and across the deep subsurface, soil, snow, vegetation, and atmosphere compartments of our Earth (e.g., Dingman, 2014;García et al, 2018). In addition to being a manifestation of thermal energy modulated by the heterogeneity of a given medium's thermal parameters, temperature influences a myriad of above-and belowground processes, including aboveground biological dynamics, energy-water exchanges, subsurface heat and water fluxes, soil and root biogeochemical processes, and cryospheric processes (e.g., Chang et al, 2021;Davidson and Janssens, 2006;Jorgenson et al, 2010;Natali et al, 2019). The predictive understanding of the abovementioned processes across a large range of gradients in topography, air mass exposure, geology, soil type, and vegeta-tion cover requires reliable measurement of the spatial and temporal distribution of snow and/or soil temperature (e.g., Lundquist et al, 2019;Strachan et al, 2016).…”