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  • 1
    In: Environmental Research Letters, IOP Publishing, Vol. 16, No. 9 ( 2021-09-01), p. 094023-
    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.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2255379-4
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  • 2
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 10, No. 9 ( 2017-09-22), p. 3499-3517
    Abstract: Abstract. How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: i. dynamic C allocation scheme (named "D-CLM4.5") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP); ii. an alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i), C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.–iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM4.5 generally overestimated gross primary production (GPP) and ecosystem respiration, and underestimated net ecosystem exchange (NEE). In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g C m−2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g C m−2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C–LAI relationship in the model did not match the observed leaf C–LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic Cstem ∕ Cleaf ratios and strongly reduced the overestimation of initial aboveground biomass and aboveground NPP for deciduous forests by D-CLM4.5. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. These include changing how C is allocated in fixed and dynamic schemes based on data from current forest syntheses and different parameterization of allocation schemes for different forest types. Our results highlight the utility of using measurements of aboveground biomass to evaluate and constrain the C allocation scheme in LSMs, and suggest that stem turnover is overestimated by CLM4.5 for these AmeriFlux sites. Understanding the controls of turnover will be critical to improving long-term C processes in LSMs.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2456725-5
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  • 3
    In: Communications Earth & Environment, Springer Science and Business Media LLC, Vol. 2, No. 1 ( 2021-12-02)
    Abstract: Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.
    Type of Medium: Online Resource
    ISSN: 2662-4435
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 3037243-4
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  • 4
    In: Journal of Geophysical Research: Biogeosciences, American Geophysical Union (AGU), Vol. 126, No. 10 ( 2021-10)
    Abstract: ORCHIDEE terrestrial biosphere model radically underestimates dryland mean annual net CO 2 fluxes and their inter‐annual variability (IAV) Optimizing phenology, carbon allocation, and respiration parameters are crucial for capturing net CO 2 flux mean and IAV Models need to be optimized against dryland CO 2 flux data to achieve accurate predictions of dryland's role in global C cycle variability
    Type of Medium: Online Resource
    ISSN: 2169-8953 , 2169-8961
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2021
    detail.hit.zdb_id: 3094167-2
    detail.hit.zdb_id: 2220777-6
    SSG: 16,13
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  • 5
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2018
    In:  Journal of Advances in Modeling Earth Systems Vol. 10, No. 10 ( 2018-10), p. 2471-2494
    In: Journal of Advances in Modeling Earth Systems, American Geophysical Union (AGU), Vol. 10, No. 10 ( 2018-10), p. 2471-2494
    Abstract: Data assimilation was used to initialize biomass and leaf area in the Community Land Model Adaptive inflation was needed to give more weight to observations due to substantial discrepancies between model forecast and observations Data assimilation reduces forecast error in a land surface model
    Type of Medium: Online Resource
    ISSN: 1942-2466 , 1942-2466
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2018
    detail.hit.zdb_id: 2462132-8
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