Biome-Bgc: Terrestrial Ecosystem Process Model, Version 4.1.1

Thornton, P.E.; Running, Steven W.; Hunt, E. Raymond

doi:10.3334/ornldaac/805

Cited by 21 publications

(15 citation statements)

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“…Synthetic data at the times and locations of the MOZAIC profiles and the ground network sites are generated to both investigate the impact of boundary layer height errors and assess the impact the addition of aircraft observations has on flux retrievals. For the forward run, we use fluxes from the BIOME-BGC biosphere model (Thornton et al, 2005) in order to get realistic mixing ratios at the locations of aircraft profiles and the surface stations. These fluxes form our "true flux".…”

Section: Methodsmentioning

confidence: 99%

The constraint of CO2 measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing

Verma¹,

Marshall²,

Gerbig³

et al. 2016

Preprint

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Abstract. Inaccurate representation of atmospheric processes by transport models is a dominant source of uncertainty in inverse analyses and can lead to large discrepancies in the retrieved flux estimates. We investigate the impact of uncertainties in vertical transport as simulated by atmospheric transport models on fluxes retrieved using vertical profiles from aircraft as an observational constraint. Our numerical experiments are based on synthetic data with realistic spatial and temporal sampling of aircraft measurements. The impact of such uncertainties on the flux retrieved using the ground-based network with those retrieved using the aircraft profiles are compared. We find that the posterior flux retrieved using aircraft profiles is less susceptible to errors in boundary layer height as compared to the ground- based network. This highlights the benefit of utilizing atmospheric observations made onboard aircraft over surface measurements for flux estimation using inverse methods. We further use synthetic vertical profiles of CO2 in an inversion to estimate the potential of these measurements, which will be made available through the IAGOS (In-Service Aircraft for a Global Observing System) project in future, in constraining the regional carbon budget. Our results show that the regions tropical Africa and temperate Eurasia, that are under constrained by the existing surface based network, will benefit the most from these measurements, the reduction of posterior flux uncertainty being about 7 to 10&#8201;%.

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Section: Methodsmentioning

confidence: 99%

The constraint of CO2 measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing

Verma¹,

Marshall²,

Gerbig³

et al. 2016

Preprint

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“…In such models, SI-x combined with FSI could provide researchers with predicted shifts in frequency of false springs under emission scenarios. Some models, such as the Ecosystem Demography (ED) and the BIOME-BGC models, already integrate phenology data by functional group (Kim, Moorcroft, Aleinov, Puma, & Kiang, 2015;Moorcroft et al, 2001;Thornton et al, 2005), and by adding last freeze date information, FSI could then be evaluated to predict false spring occurrence with predicted shifts in climate. By including even a simple proxy for false spring risk, models, including ED and BIOME-BGC, could better inform predicted range shifts.…”

Section: The Future Of Fal S E S Pring Re S E Archmentioning

confidence: 99%

Rethinking false spring risk

Chamberlain

Cook

Cortázar-Atauri

et al. 2019

Global Change Biology

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Temperate plants are at risk of being exposed to late spring freezes. These freeze events—often called false springs—are one of the strongest factors determining temperate plants species range limits and can impose high ecological and economic damage. As climate change may alter the prevalence and severity of false springs, our ability to forecast such events has become more critical, and it has led to a growing body of research. Many false spring studies largely simplify the myriad complexities involved in assessing false spring risks and damage. While these studies have helped advance the field and may provide useful estimates at large scales, studies at the individual to community levels must integrate more complexity for accurate predictions of plant damage from late spring freezes. Here, we review current metrics of false spring, and how, when, and where plants are most at risk of freeze damage. We highlight how life stage, functional group, species differences in morphology and phenology, and regional climatic differences contribute to the damage potential of false springs. More studies aimed at understanding relationships among species tolerance and avoidance strategies, climatic regimes, and the environmental cues that underlie spring phenology would improve predictions at all biological levels. An integrated approach to assessing past and future spring freeze damage would provide novel insights into fundamental plant biology and offer more robust predictions as climate change progresses, which are essential for mitigating the adverse ecological and economic effects of false springs.

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“…To further understand how these systems evolve in response to land cover change, we can couple these models with ecosystem models, such as Biome-BGC (Thornton et al, 2005). One limitation to such complex numerical models is the numerous datasets needed for parameterization.…”

Section: Example Of Integrated Modelling 20mentioning

confidence: 99%

Integration of terrestrial observational networks: opportunity for advancing Earth system dynamics modelling

Baatz

Sullivan

et al. 2017

Preprint

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Abstract. Advancing our understanding of Earth System Dynamics (ESD) depends on the development of models and other analytical tools that integrate physical, biological and chemical data. This ambition of increased understanding and model development of ESD based on integrated site observations was at the origin of the creation of the networks of Long Term Ecological Research (LTER), Critical Zone Observatories (CZO), and others. We organized a survey to identify pressing gaps in data availability from these networks, in particular for the future development and evaluation of models that 5 represent ESD processes. With the survey results, we look for gaps between data collection and ESD model development, to draw perspectives for the improvement both in data collection and model integration.From this overview of model applications gathered in the context of LTER-and CZO research, we identified three challenges: 1) Improving integration of observational data in Earth system modelling, 2) developing coupled Earth system models, and 3) identifying complementarity and ways to integrate the existing networks. These challenges lead to 10 perspectives and recommendations for strategic integration of observational networks and links to the ESD modelling community. We propose the further integration of the observational networks, either by 1) making the existing sitedetermined networks also functional topological networks with organising spread and coverage, and/or 2) thematically and geographically restructuring or co-locating the existing networks, and further formalizing these recommendations among these communities. Such integration will enable cross-site comparison and synthesis studies, which will offer significant 15 insights and extraction of organizing principles, classifications, and general rules of coupling processes and environmental conditions. IntroductionIn light of accelerating global change (e.g. IPCC, 2014;Camill, 2010), the scientific and societal imperatives are paramount 20 to improve our understanding of Earth System Dynamics (ESD), which comprise complex interactions among rock, soil, water, air, and living organisms that regulate the natural habitat and determine the availability of life-sustaining resources for human well-being (MEA, 2005). Understanding and modelling of Earth system processes and interactions among Earth system compartments can be enhanced by accessing a wider range of both observational and experimental data (Banwart et al., 2012;Aronova et al., 2010;Reid et al., 2010). For this purpose, observational networks have been developed the last 25 decennia aiming at a temporal and multidisciplinary coverage of continental and global scale ecosystem observations. The Critical Zone Observatory network (Brantley et al. 2015), the International Long Term Ecosystem Research network (Mirtl et al., 2017), the National Ecological Observatory Network of the US , Schimel et al. 2001, the Chinese Ecosystem Research Network (Fu et al., 2010) and the Australian Terrestrial Ecosystem Research N...

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Biome-Bgc: Terrestrial Ecosystem Process Model, Version 4.1.1

Cited by 21 publications

References 0 publications

The constraint of CO<sub>2</sub> measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing

The constraint of CO<sub>2</sub> measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing

Rethinking false spring risk

Integration of terrestrial observational networks: opportunity for advancing Earth system dynamics modelling

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Biome-Bgc: Terrestrial Ecosystem Process Model, Version 4.1.1

Cited by 21 publications

References 0 publications

The constraint of CO&lt;sub&gt;2&lt;/sub&gt; measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing

The constraint of CO&lt;sub&gt;2&lt;/sub&gt; measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing

Rethinking false spring risk

Integration of terrestrial observational networks: opportunity for advancing Earth system dynamics modelling

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Product

Resources

About

The constraint of CO<sub>2</sub> measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing

The constraint of CO<sub>2</sub> measurements made onboard passenger aircraft on surface-atmosphere fluxes: the impact of transport model errors in vertical mixing