A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes

Dietze, Michael C.; Serbin, Shawn P.; Davidson, C.; Desai, Ankur R.; Feng, Xiaohui; Kelly, Ryan; Kooper, Rob; LeBauer, David; Mantooth, Joshua A.; McHenry, Kenton; Wang, Dan

doi:10.1002/2013jg002392

Cited by 109 publications

(173 citation statements)

References 94 publications

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“…A recent study by Dietze et al (2014) likewise identified mis-parameterization of light-use efficiency at low-light levels to play a central role in biospheric model uncertainties across HL regions of North America. The study indicated that a likely source for mis-parameterization is due to greater variance in this parameter across high-latitude sites, even when these contain similar biota.…”

Section: Error Analysismentioning

confidence: 99%

The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO<sub>2</sub> exchange

Luus

Lin

2015

Geosci. Model Dev.

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Abstract. We introduce the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM), a remote-sensingbased approach for generating accurate, high-resolution ( ≥ 1 km 2 , 3 hourly) estimates of net ecosystem CO 2 exchange (NEE). PolarVPRM simulates NEE using polarspecific vegetation classes, and by representing high-latitude influences on NEE, such as the influence of soil temperature on subnivean respiration. We present a description, validation and error analysis (first-order Taylor expansion) of PolarVPRM, followed by an examination of per-pixel trends (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) in model output for the North American terrestrial region north of 55 • N. PolarVPRM was validated against eddy covariance (EC) observations from nine North American sites, of which three were used in model calibration. Comparisons of EC NEE to NEE from three models indicated that PolarVPRM displayed similar or better statistical agreement with eddy covariance observations than existing models showed. Trend analysis (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) indicated that warming air temperatures and drought stress in forests increased growing season rates of respiration, and decreased rates of net carbon uptake by vegetation when air temperatures exceeded optimal temperatures for photosynthesis. Concurrent increases in growing season length at Arctic tundra sites allowed for increases in photosynthetic uptake over time by tundra vegetation. PolarVPRM estimated that the North American high-latitude region changed from a carbon source (2001)(2002)(2003)(2004) to a carbon sink (2005)(2006)(2007)(2008)(2009)(2010) to again a source (2011)(2012) in response to changing environmental conditions.

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Section: Error Analysismentioning

confidence: 99%

The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO<sub>2</sub> exchange

Luus

Lin

2015

Geosci. Model Dev.

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“…Before these experiments, we conducted an uncertainty analysis (LeBauer et al, 2013;Dietze et al, 2014) to choose the model parameters for calibration. The parameters that can be constrained by data are those that contribute to the model uncertainty for that corresponding variable.…”

Section: Emulator Experimentsmentioning

confidence: 99%

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Fer

Kelly²,

Andrews

et al. 2018

Preprint

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Abstract. Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov Chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally-demanding models a n d large data sets. We describe an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature, and introduced novel approaches to the specification of both model and data uncertainties, including bias and autocorrelation corrections on multiple data streams. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard "bruteforce" MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the bruteforce approach, but reduced computation time by more than two orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (bruteforce) Bayesian data assimilation uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data.Our study furthers efforts toward reducing model uncertainties showing that the emulator method makes it possible to efficiently calibrate complex models. This efficient data assimilation method allows us to conduct more calibration experiments in relatively much shorter times, enabling constraining of numerous models using the expanding amount and types of data.

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“…The Ecosystem Demography model v2 (ED2; Medvigy et al, 2009) is another relatively coupled model, with high sensitivity to g s . Dietze et al (2014) estimated that ∼ 10 % of the uncertainty in net primary productivity (NPP) predicted by the ED2 model across North America Biomes was directly due to the stomatal slope parameter (i.e. g 1 ).…”

Section: Implications For Other Modelsmentioning

confidence: 99%

A test of an optimal stomatal conductance scheme within the CABLE land surface model

et al. 2015

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Abstract. Stomatal conductance (g s ) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between g s model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We constrained the key model parameter "g 1 ", which represents plant water use strategy, by PFT, based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the g 1 parameter can be estimated using two long-term average bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal model, in conjunction with PFT parameterisations, resulted in a large reduction in annual fluxes of transpiration (∼ 30 % compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance against upscaled data products was not degraded, but did not noticeably reduce existing model-data biases. We identified assumptions relating to the coupling of the vegetation to the atmosphere and the parameterisation of the minimum stomatal conductance as areas requiring further investigation in both CABLE and potentially other LSMs.We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.

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A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes

Cited by 109 publications

References 94 publications

The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO<sub>2</sub> exchange

The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO<sub>2</sub> exchange

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

A test of an optimal stomatal conductance scheme within the CABLE land surface model

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A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes

Cited by 109 publications

References 94 publications

The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO&lt;sub&gt;2&lt;/sub&gt; exchange

The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO&lt;sub&gt;2&lt;/sub&gt; exchange

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

A test of an optimal stomatal conductance scheme within the CABLE land surface model

Contact Info

Product

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The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO<sub>2</sub> exchange

The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO<sub>2</sub> exchange