On the asymptotic giant branch star origin of peculiar spinel grain OC2

Microscopic presolar grains extracted from primitive meteorites have extremely anomalous isotopic compositions revealing the stellar origin of these grains. The composition of presolar spinel grain OC2 is different from that of all other presolar spinel grains. Large excesses of the heavy Mg isotopes are present and thus an origin from an intermediate-mass (IM) asymptotic giant branch (AGB) star was previously proposed for this grain. We discuss the isotopic compositions of presolar spinel grain OC2 and compare them to theoretical predictions. We show that the isotopic composition of O, Mg and Al in OC2 could be the signature of an AGB star of IM and metallicity close to solar experiencing hot bottom burning, or of an AGB star of low mass (LM) and low metallicity suffering very efficient cool bottom processing. Large measurement uncertainty in the Fe isotopic composition prevents us from discriminating which model better represents the parent star of OC2. However, the Cr isotopic composition of the grain favors an origin in an IM-AGB star of metallicity close to solar. Our IM-AGB models produce a self-consistent solution to match the composition of OC2 within the uncertainties related to reaction rates. Within this solution we predict that the 16O(p,g)17F and the 17O(p,a)14N reaction rates should be close to their lower and upper limits, respectively. By finding more grains like OC2 and by precisely measuring their Fe and Cr isotopic compositions, it may be possible in the future to derive constraints on massive AGB models from the study of presolar grains.Comment: 10 pages, 8 figures, accepted for publication on Astronomy & Astrophysic

Substantial hysteresis in emergent temperature sensitivity of global wetland CH₄ emissions

Wetland methane (CH4) emissions (FCH4 ) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, sitelevel studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 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.s

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4_{4} emissions

Wetland methane (CH$_{4}$) emissions (F$_{CH_{4}}$) are important in global carbon budgets and climate change assessments. Currently, F$_{CH_{4}}$ projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent F$_{CH_{4}}$ temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that F$_{CH_{4}}$ are often controlled by factors beyond temperature. Here, we evaluate the relationship between F$_{CH_{4}}$ and temperature using observations from the FLUXNET-CH$_{4}$ database. Measurements collected across the globe show substantial seasonal hysteresis between F$_{CH_{4}}$ and temperature, suggesting larger F$_{CH_{4}}$ 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 CH$_{4}$ production are thus needed to improve global CH$_{4}$ budget assessments

FLUXNET-CH₄ synthesis activity: objectives, observations, and future directions

We describe a new coordination activity and initial results for a global synthesis of eddy covariance CH4 flux measurements

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

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by similar to 17 +/- 11 days, and lagged air and soil temperature by median values of 8 +/- 16 and 5 +/- 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.Peer reviewe

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

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)