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Abstract Despite their sparse vegetation, dryland regions exert a huge influence over global biogeochemical cycles because they cover more than 40% of the world surface (Schimel 2010 Science 327 418–9). It is thought that drylands dominate the inter-annual variability (IAV) and long-term trend in the global carbon (C) cycle (Poulter et al 2014 Nature 509 600–3, Ahlstrom et al 2015 Science 348 895–9, Zhang et al 2018 Glob. Change Biol . 24 3954–68). Projections of the global land C sink therefore rely on accurate representation of dryland C cycle processes; however, the dynamic global vegetation models (DGVMs) used in future projections have rarely been evaluated against dryland C flux data. Here, we carried out an evaluation of 14 DGVMs (TRENDY v7) against net ecosystem exchange (NEE) data from 12 dryland flux sites in the southwestern US encompassing a range of ecosystem types (forests, shrub- and grasslands). We find that all the models underestimate both mean annual C uptake/release as well as the magnitude of NEE IAV, suggesting that improvements in representing dryland regions may improve global C cycle projections. Across all models, the sensitivity and timing of ecosystem C uptake to plant available moisture was at fault. Spring biases in gross primary production (GPP) dominate the underestimate of mean annual NEE, whereas models’ lack of GPP response to water availability in both spring and summer monsoon are responsible for inability to capture NEE IAV. Errors in GPP moisture sensitivity at high elevation forested sites were more prominent during the spring, while errors at the low elevation shrub and grass-dominated sites were more important during the monsoon. We propose a range of hypotheses for why model GPP does not respond sufficiently to changing water availability that can serve as a guide for future dryland DGVM developments. Our analysis suggests that improvements in modeling C cycle processes across more than a quarter of the Earth’s land surface could be achieved by addressing the moisture sensitivity of dryland C uptake.
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Hot moments in ecosystem fluxes: High GPP anomalies exert outsized influence on the carbon cycle and are differentially driven by moisture availability across biomes
The “hot spot-hot moment” concept is a long-standing and popular framework often invoked to explain spatially or temporally variable rates of biogeochemical cycling. However, this concept has been rarely extended to ecosystem fluxes such as gross primary productivity (GPP), in part due to the lack of a quantitative definition of hot moments that can be applied to large flux datasets. Here, we develop a general statistical framework for quantifying hot moments in GPP and identify their spatial patterns and climatic drivers. Using 308 site-years of eddy covariance data from the FLUXNET2015 dataset spanning 32 U.S. sites, we found hot moments in GPP to comprise a disproportionate percentage of annual carbon (C) uptake relative to the frequency of their occurrence. For example, at five sites over 12% of annual C uptake occurred during the ~2% most extreme half-hourly or hourly observations of GPP. Hot moments were most quantitatively important for the C cycle in short-stature, arid ecosystem such as grasslands, woody savannas, and open shrublands, where these positive anomalies in GPP were caused by increases in moisture availability. In contrast, hot moments were less important for annual C uptake in more mesic ecosystems, where their occurrence was largely determined by high temperature and light availability. Our results point to a need to consider how short-term spikes in environmental conditions exert an outsized influence on annual GPP, and how future shifts in these conditions could impact the terrestrial C cycl
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- Award ID(s):
- 1753845
- PAR ID:
- 10143648
- Date Published:
- Journal Name:
- Environmental Research Letters
- ISSN:
- 1748-9326
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1088/1748-9326/ab7b97
