Regional uptake and release of crop carbon in the United States

West, Tristram O.; Bandaru, Varaprasad; Brandt, Craig C.; Schuh, A. E.; Ogle, Stephen M.

doi:10.5194/bg-8-2037-2011

Cited by 40 publications

(66 citation statements)

References 15 publications

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“…Assumption 5 is that disturbance events are represented in models in different ways (Grosse et al, 2011;West et al, 2011;Goetz et al, 2012;Hicke et al, 2012). Fire, extreme drought, insect outbreaks, land management, and land cover and land use change influence terrestrial C dynamics via (1) altering rate processes, for example, gross primary productivity (GPP), growth, tree mortality, or heterotrophic respiration; (2) modifying microclimatic environments; or (3) transferring C from one pool to another (e.g., from live to dead pools during storms or release to the atmosphere with fire) (Kloster et al, 2010;Thonicke et al, 2010;Luo and Weng, 2011;Prentice et al, 2011;Weng et al, 2012).…”

Section: Assumptions Of the C Cycle Models And Validity Of This Analysismentioning

confidence: 99%

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

et al. 2017

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Abstract. Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO 2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is timedependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times.Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a threedimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationPublished by Copernicus Publications on behalf of the European Geosciences Union. 146Y. Luo et al.: Land carbon storage dynamics ally feasible so that both C flux-and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.

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Section: Assumptions Of the C Cycle Models And Validity Of This Analysismentioning

confidence: 99%

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

et al. 2017

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“…The regional patterns of CO 2 uptake and release in US croplands based on agricultural statistics (West et al, 2011) are used to adjust the prior biosphere flux used in our inversion. The data include county-level net primary productivity ( .…”

Section: Us Cropland Carbon Budget Based On Inventory Datamentioning

confidence: 99%

“…The consumption of crop products by livestock and humans is about twice the consumption of forest products (West et al, 2011;Hayes et al, 2012). Unlike forest products with a large range of residence times, crop products can be assumed to be consumed within a year of harvest (West et al, 2011). The spatial distributions of crop production and consumption at the county level (West et al, 2011) provide a sufficient resolution for use in atmospheric inverse modeling.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Unlike forest products with a large range of residence times, crop products can be assumed to be consumed within a year of harvest (West et al, 2011). The spatial distributions of crop production and consumption at the county level (West et al, 2011) provide a sufficient resolution for use in atmospheric inverse modeling. The specific objectives of our study are: (1) to investigate the changes in the inverted carbon source and sink distribution after considering the spatial patterns of crop production and consumption, (2) to explore whether these changes improve our understanding of the carbon source and sink distribution within the conterminous US, (3) to evaluate the impact of the crop data on the inverted carbon balance for the conterminous US and other regions of the globe, and (4) to assess the relative importance of atmospheric CO 2 data and the prior flux in determining the spatial pattern of the carbon sink in the conterminous US.…”

Section: Introductionmentioning

confidence: 99%

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Atmospheric inversion of surface carbon flux with consideration of the spatial distribution of US crop production and consumption

Chen

Fung

et al. 2015

Biogeosciences

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Abstract. In order to improve quantification of the spatial distribution of carbon sinks and sources in the conterminous US, we conduct a nested global atmospheric inversion with detailed spatial information on crop production and consumption. County-level cropland net primary productivity, harvested biomass, soil carbon change, and human and livestock consumption data over the conterminous US are used for this purpose. Time-dependent Bayesian synthesis inversions are conducted based on CO 2 observations at 210 stations to infer CO 2 fluxes globally at monthly time steps with a nested focus on 30 regions in North America. Prior land surface carbon fluxes are first generated using a biospheric model, and the inversions are constrained using prior fluxes with and without adjustments for crop production and consumption over the 2002-2007 period. After these adjustments, the inverted regional carbon sink in the US Midwest increases from 0.25 ± 0.03 to 0.42 ± 0.13 Pg C yr −1 , whereas the large sink in the US southeast forest region is weakened from 0.41 ± 0.12 to 0.29 ± 0.12 Pg C yr −1 . These adjustments also reduce the inverted sink in the west region from 0.066 ± 0.04 to 0.040 ± 0.02 Pg C yr −1 because of high crop consumption and respiration by humans and livestock. The general pattern of sink increases in crop production areas and sink decreases (or source increases) in crop consumption areas highlights the importance of considering the lateral carbon transfer in crop products in atmospheric inverse modeling, which provides a reliable atmospheric perspective of the overall carbon balance at the continental scale but is unreliable for separating fluxes from different ecosystems.

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Model‐data comparison of MCI field campaign atmospheric CO₂ mole fractions

et al. 2014

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Atmospheric transport model errors are a major contributor to uncertainty in CO 2 inverse flux estimates. Our study compares CO 2 mole fraction observations from the North American Carbon Program Mid-Continental Intensive (MCI) field campaign and modeled mole fractions from two atmospheric transport models: the global Transport Model 5 from NOAA's CarbonTracker system and the mesoscale Weather Research and Forecasting model. Both models are coupled to identical CO 2 fluxes and lateral boundary conditions from CarbonTracker (CT2009 release). Statistical analyses were performed for two periods of 2007 using observed daily daytime average mole fractions of CO 2 to test the ability of these models to reproduce the observations and to infer possible causes of the discrepancies. TM5-CT2009 overestimates midsummer planetary boundary layer CO 2 for sites in the U.S. corn belt by 10 ppm. Weather Research and Forecasting (WRF)-CT2009 estimates diverge from the observations with similar magnitudes, but the signs of the differences vary from site to site. The modeled mole fractions are highly correlated with the observed seasonal cycle (r ≥ 0.7) but less correlated in the growing season, where weather-related changes in CO 2 dominate the observed variability. Spatial correlations in residuals from TM5-CT2009 are higher than WRF-CT2009 perhaps due to TM5's coarse horizontal resolution and shallow vertical mixing. Vertical mixing appears to have influenced CO 2 residuals from both models. TM5-CT2009 has relatively weak vertical mixing near the surface limiting the connection between local CO 2 surface fluxes and boundary layer. WRF-CT2009 has stronger vertical mixing that may increase the connections between local surface fluxes and the boundary layer.

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Regional uptake and release of crop carbon in the United States

Cited by 40 publications

References 15 publications

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

Atmospheric inversion of surface carbon flux with consideration of the spatial distribution of US crop production and consumption

Model‐data comparison of MCI field campaign atmospheric CO₂ mole fractions

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Regional uptake and release of crop carbon in the United States

Cited by 40 publications

References 15 publications

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

Atmospheric inversion of surface carbon flux with consideration of the spatial distribution of US crop production and consumption

Model‐data comparison of MCI field campaign atmospheric CO2 mole fractions

Contact Info

Product

Resources

About

Model‐data comparison of MCI field campaign atmospheric CO₂ mole fractions