Estimates of net CO<sub>2</sub> flux by application of equilibrium boundary layer concepts to CO<sub>2</sub> and water vapor measurements from a tall tower

Helliker, Brent R.; Berry, Joseph A.; Betts, Alan K.; Bakwin, Peter S.; Davis, Kenneth J.; Denning, Scott; Ehleringer, James R.; Miller, J. B.; Butler, M. P.; Ricciuto, D. M.

doi:10.1029/2004jd004532

Cited by 71 publications

(106 citation statements)

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“…Our vision for the future includes: (1) identifying and funding critical new locations for FLUXNET towers in poorly studied biomes, for instance in tropical croplands; (2) nesting EC towers within the regional footprints of tall towers that sample the CO 2 concentration of the planetary boundary layer (Helliker et al, 2004) to assist upscaling; (3) linking EC towers effectively with the atmospheric column CO 2 measurements generated by the satellites OCO and GOSAT, expected from late 2009 (Feng et al, 2008); (4) continued linkage to optical products from remote sensing, as these provide critical information on canopy structure and phenology (Demarty et al, 2007); (5) use of plant functional traits database to identify more realistic priors and to validate posteriors in the data assimilation process (Luyssaert et al, 2007). Full exploitation of this vision is dependent on model-data fusion.…”

Section: Discussionmentioning

confidence: 99%

Improving land surface models with FLUXNET data

et al. 2009

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Abstract. There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking) LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independentCorrespondence to: M. Williams (mat.williams@ed.ac.uk) and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) -we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: (1) to determine appropriate use of current data and to explore the information gained in using longer time series; (2) to avoid confounding effects of missing process representation on parameter estimation; (3) to assimilate more data types, including those from earth observation; (4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and (5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Discussionmentioning

confidence: 99%

Improving land surface models with FLUXNET data

et al. 2009

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“…Terrestrial NPP has been showed to play a central role in determining the local and global CO2 content of the atmosphere at temporal scales spanning hours [8] to epochs [9].…”

Section: Introductionmentioning

confidence: 99%

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

et al. 2013

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Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models. Here we evaluated the interannual variability in the new 30.5-year long global satellite-derived surface reflectance index data, Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g). Pearson's correlation and multiple linear stepwise regression analyses were applied to quantify the NDVI interannual variability driven by climate anomalies, and to evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVI signal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systems where in some regions and seasons > 40% of the NDVI variance could be explained by precipitation anomalies. Temperature correlations were strongest in northern mid-to high-latitudes in the spring and early summer where up to 70% of the NDVI variance was OPEN ACCESSRemote Sens. 2013, 5 3919 explained by temperature anomalies. We find that, in western and central North America, winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wet season precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

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“…In order to estimate CO 2 exchange over such wide areas, it is necessary to determine the atmospheric CO 2 concentration on a regional scale, broadly defined as 10 4 to 10 6 km 2 (Helliker et al 2004). To meet this requirement, tall towers have been used as monitoring sites since the 1990s (Bakwin et al 1995(Bakwin et al , 1998(Bakwin et al , 2004Haszpra 1999).…”

Section: Introductionmentioning

confidence: 99%

Seasonal and Long-Term Variations in Atmospheric CO2 and 85Kr in Tsukuba, Central Japan

Inoue

Matsueda²,

Igarashi³

et al. 2006

Journal of the Meteorological Society of Japan

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Since February 1992, measurements of atmospheric CO 2 at 200 m above the ground have been taken from a meteorological tower (lat 36.1 N, long 140.1 E, 25 m a.s.l.) in Tsukuba, central Japan. We have examined seasonal /long-term variations in atmospheric CO 2 , based on atmospheric 85 Kr, a noble gas with well-known source and sink terms, monitored continuously at weekly intervals in Tsukuba since May 1995. During the annual cycle, the daily mean values of atmospheric CO 2 in the afternoons (1300-1600 JST) showed maximum in late April, except in 2002, and minimum in late August or early September. The seasonal variations in atmospheric 85 Kr, which is low in summer and high in winter, were caused by the seasonal variations in the large-scale atmospheric circulation, and latitudinal distribution of atmospheric 85 Kr. Compared with seasonal variations in atmospheric 85 Kr, the draw-down (DD) and buildup (BU) for atmospheric CO 2 occurred about 1 week and 3 weeks later, respectively. The peak-totrough amplitude of de-trended seasonal variations in atmospheric CO 2 concentrations ranged from 11 to 14 ppm. A decrease in the seasonal CO 2 minimum occurred with a decrease in the 85 Kr concentration, showing the effect of air mass transport from different origins. Unlike the minimum, the maximum CO 2 did not vary as expected from the air mass transport. The daily mean values of atmospheric CO 2 increased at an average rate of 2.0 ppm yr À1 , with a range from À0.5 to 4 ppm yr À1 . While both CO 2 and

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Estimates of net CO₂ flux by application of equilibrium boundary layer concepts to CO₂ and water vapor measurements from a tall tower

Cited by 71 publications

References 50 publications

Improving land surface models with FLUXNET data

Improving land surface models with FLUXNET data

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

Seasonal and Long-Term Variations in Atmospheric CO2 and 85Kr in Tsukuba, Central Japan

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Estimates of net CO2 flux by application of equilibrium boundary layer concepts to CO2 and water vapor measurements from a tall tower

Cited by 71 publications

References 50 publications

Improving land surface models with FLUXNET data

Improving land surface models with FLUXNET data

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

Seasonal and Long-Term Variations in Atmospheric CO2 and 85Kr in Tsukuba, Central Japan

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Estimates of net CO₂ flux by application of equilibrium boundary layer concepts to CO₂ and water vapor measurements from a tall tower