| 1. |
- Chang, Kuang Yu, et al.
(författare)
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Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1, s. 2266-2266
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Tidskriftsartikel (refereegranskat)abstract
- Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] 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.
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| 2. |
- Martinez-Garcia, Eduardo, et al.
(författare)
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Drought response of the boreal forest carbon sink is driven by understorey-tree composition
- 2024
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Ingår i: Nature Geoscience. - : Springer Nature. - 1752-0894 .- 1752-0908. ; 17, s. 197-204
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Tidskriftsartikel (refereegranskat)abstract
- The boreal forest is an important global carbon sink, but its response to drought remains uncertain. Here, we compiled biometric- and chamber-based flux data from 50 boreal forest stands to assess the impact of the 2018 European summer drought on net ecosystem production (NEP) across a 68 km2 managed landscape in northern Sweden. Our results reveal a non-uniform reduction in NEP (on average by 80 +/- 16 g C m-2 yr-1 or 57 +/- 13%) across the landscape, which was greatest in young stands of 20-50 years (95 +/- 39 g C m-2 yr-1), but gradually decreased towards older stands (54 +/- 57 g C m-2 yr-1). This pattern was attributed to the higher sensitivity of forest-floor understorey to drought and its decreasing contribution to production relative to trees during stand development. This suggests that an age-dependent shift in understorey-tree composition with increasing stand age drives the drought response of the boreal forest NEP. Thus, our study advocates the need for partitioning ecosystem responses to improve empirical and modelling assessments of carbon cycle-climate feedbacks in boreal forests. It further implies that the forest age structure may strongly determine the carbon sink response to the projected increase in drought events across the managed boreal landscape. Carbon sink in young boreal forests is more vulnerable to drought than in mature forests due to the greater contribution and drought sensitivity of understorey relative to trees, according to carbon flux assessments of managed boreal forests in northern Sweden during the 2018 European summer drought.
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| 3. |
- Yuan, Kunxiaojia, et al.
(författare)
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Causality guided machine learning model on wetland CH4 emissions across global wetlands
- 2022
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier. - 0168-1923 .- 1873-2240. ; 324
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Tidskriftsartikel (refereegranskat)abstract
- Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.
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