Agricultural and Forest Meteorology, Volume 324

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Elsevier BV
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What explains the year-to-year variation in growing season timing of boreal black spruce forests?
Mariam El-Amine | Alexandre Roy | Franziska Koebsch | Jennifer L. Baltzer | Alan Barr | Andrew Black | Hiroki Ikawa | Hiroyasu Iwata | Hideki Kobayashi | Masahito Ueyama | Oliver Sonnentag

Amplified climate warming in high latitudes is expected to affect growing season timing of the vast boreal biome. It is unclear whether the presence of permafrost (perennially frozen ground) might have an influence on changes in growing season timing. This study examined how different environmental variables explained, either directly or indirectly, the variation in growing season timing of boreal forest stands with and without permafrost. We expected that environmental variables explaining the variation in growing season timing differed or had different explanatory power depending on permafrost presence or absence. The growing season was delineated from daily gross primary productivity (GPP) time series derived from 40 site-year data of net ecosystem carbon dioxide exchange measured with eddy covariance techniques over five black spruce (Picea mariana [Mill.])-dominated boreal forest stands in North America. In permafrost-free forest stands, a combination of start in canopy ‘green-up’ in spring and the timing of air and soil temperature increasing above freezing explained the start-of-season (SOSGPP). Results from commonality analysis and structural equation modeling suggest that canopy ‘green-up’ and air temperature directly affected SOSGPP in permafrost-free forest stands. In addition, soil temperature acted as mediator for an indirect effect of air temperature on SOSGPP. In contrast, none of the environmental variables, or their combination, explained the variation in SOSGPP in forest stands with permafrost. The explanatory power of environmental variables was more consistent regarding the end-of-season (EOSGPP). In both, forest stands with and without permafrost, EOSGPP was directly explained by mean soil water content in the fall and the first day of continuous snowpack formation. A better understanding how environmental variables control SOSGPP and EOSGPP in forest stands with and without permafrost will help to refine parameterizations of the boreal biome in Earth system models.

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Causality guided machine learning model on wetland CH4 emissions across global wetlands
Kunxiaojia Yuan | Qing Zhu | Fa Li | William J. Riley | M. S. Torn | Housen Chu | Gavin McNicol | Min Chen | Sara Knox | Kyle Delwiche | Huayi Wu | Dennis Baldocchi | Hengbo Ma | Ankur R. Desai | Jiquan Chen | Torsten Sachs | Masahito Ueyama | Oliver Sonnentag | Manuel Helbig | Eeva‐Stiina Tuittila | Gerald Jurasinski | Franziska Koebsch | David I. Campbell | Hans Peter Schmid | Annalea Lohila | Mathias Goeckede | Mats Nilsson | Thomas Friborg | Joachim Jansen | Donatella Zona | Eugénie Euskirchen | Eric J. Ward | Gil Bohrer | Zhenong Jin | Licheng Liu | Hiroyasu Iwata | Jordan P. Goodrich | Robert B. Jackson

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 +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 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.