Publications by authors named "Natasha MacBean"
Article Synopsis
- Water potential is crucial for the functioning of leaves, roots, and microbes, influencing water movement in ecosystems, but is often not measured in practice.
- Current data on soil and plant water potential is limited, making it difficult to understand moisture stress impacts and introducing uncertainty into models.
- The authors propose advancements in sensor technology and data networks to improve monitoring, which could enhance our understanding of ecohydrological processes and decrease modeling uncertainties.
Article Synopsis
- The amount of carbon dioxide (CO₂) in the air is going up, which helps plants grow better and use water more efficiently.
- This growth can lead to more plants and soil that store carbon, which might help slow down climate change.
- However, figuring out how plants and soil react to this extra CO₂ is complicated, and while there's strong evidence of increased carbon storage, it's hard to know exactly how much it helps and what other factors are at play.
The response of terrestrial carbon uptake to increasing atmospheric [CO ], that is the CO fertilization effect (CFE), remains a key area of uncertainty in carbon cycle science. Here we provide a perspective on how satellite observations could be better used to understand and constrain CFE. We then highlight data assimilation (DA) as an effective way to reconcile different satellite datasets and systematically constrain carbon uptake trends in Earth System Models.
View Article and Find Full Text PDFA correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.
View Article and Find Full Text PDFAccurate terrestrial biosphere model (TBM) simulations of gross carbon uptake (gross primary productivity - GPP) are essential for reliable future terrestrial carbon sink projections. However, uncertainties in TBM GPP estimates remain. Newly-available satellite-derived sun-induced chlorophyll fluorescence (SIF) data offer a promising direction for addressing this issue by constraining regional-to-global scale modelled GPP.
View Article and Find Full Text PDFThe maximum photosynthetic carboxylation rate (V ) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.
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