CMS Phase 2 (2012 Selection) Projects
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Bowman (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Continuation of the Carbon Monitoring System Flux Pilot Project | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Kevin Bowman, JPL
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Precursor Projects: | Gunson-Pawson-Potter (2009) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
We propose to evolve the Carbon Monitoring System Flux Pilot Project funded under
Phase 1 into a framework that integrates observational constraints on all major
components of the carbon-cycle-anthropogenic system anthropogenic, terrestrial, oceanic,
atmospheric in a top-down CO2 attribution system constrained by atmospheric satellite
observations. This expanded framework will enable a deeper understanding of the global
carbon cycle and a means of quantifying the effectiveness of climate mitigation policies.
This CMS-FPP is motivated by the increase in tropospheric CO2 from anthropogenic
emissions, which is the single largest driver of observed and predicted climate change
[Forster et al, 2007]. However, roughly half of the CO2 produced from these emissions
has been removed by terrestrial and ocean sinks. Consequently, The future trajectory of
climate forcing will depend on future emissions and on the capacity of the carbon-cycle
to absorb more CO2 [Friedlingstein, 2008]. Recent years have seen an acceleration of
fossil fuel emissions and signs of an onset of carbon-cycle feedbacks [Canadell et al, 2007]. Since 2005, fossil fuel emissions have been regionally redistributed towards
developing countries, which now make up more than half of CO2 emissions (>4 PgC/yr)
[Peters et al, 2012]. While the global carbon budget and its partitioning between
anthropogenic, terrestrial, and oceanic fluxes are reasonably understood, the contribution
of regional drivers to that budget are not [Canadell et al, 2010]. Consequently,
uncertainty in the attribution of CO2 accumulation rate on a year-to-year basis to those
drivers limits our capacity to quantify the effectiveness of climate mitigation policies [Le
Quere et al, 2009].
In order to reduce uncertainty in CO2 attribution, we will simultaneously improve and
augment all major aspects of the current CMS-FPP: new satellites observations, an
additional terrestrial eco-system model, a new fossil fuel assimilation system, updated
ocean assimilation algorithms, and improved atmospheric inversion algorithms. The
CMS-FPP Phase 2 will generate a suite of new and updated products covering 7/2009-
2011 including new global spatially resolved CO2 sources and sinks, new high resolution
global fossil fuel emissions, better estimates of oceanic CO2 air-sea exchange, new
estimates of global above-ground biomass, and refinements in top-down attribution and
uncertainty algorithms. Products generated from bottom-up and top-down estimates will
be made publically available through carbon.nasa.gov and linked to cmsflux.jpl.nasa.gov.
Through these updates, the CMS-FPP will play a crucial and on-going role in assessing
the current capability of space-borne observing systems to improve our knowledge of the
integrated carbon-cycle-anthropogenic system and its impact on climate forcing | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Christopher Badurek, College of New Jersey | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): |
http://cmsflux.jpl.nasa.gov http://ecco2.org | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Haynes, K. D., Baker, I. T., Denning, A. S., Stockli, R., Schaefer, K., Lokupitiya, E. Y., Haynes, J. M. 2019. Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4). Journal of Advances in Modeling Earth Systems. 11(12), 4423-4439. DOI: 10.1029/2018MS001540 Haynes, K. D., Baker, I. T., Denning, A. S., Wolf, S., Wohlfahrt, G., Kiely, G., Minaya, R. C., Haynes, J. M. 2019. Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling. Journal of Advances in Modeling Earth Systems. 11(12), 4440-4465. DOI: 10.1029/2018MS001541 Haynes, K., I. Baker, and S. Denning. 2020. Simple Biosphere Model version 4.2 (SiB4) technical description. Mountain Scholar, Colorado State University, Fort Collins, CO, USA. https://hdl.handle.net/10217/200691 Hogue, S., Marland, E., Andres, R. J., Marland, G., Woodard, D. 2016. Uncertainty in gridded CO 2 emissions estimates. Earth's Future. 4(5), 225-239. DOI: 10.1002/2015EF000343 Brix, H., Menemenlis, D., Hill, C., Dutkiewicz, S., Jahn, O., Wang, D., Bowman, K., Zhang, H. 2015. Using Green's Functions to initialize and adjust a global, eddying ocean biogeochemistry general circulation model. Ocean Modelling. 95, 1-14. DOI: 10.1016/j.ocemod.2015.07.008 Bousserez, N., Henze, D. K., Perkins, A., Bowman, K. W., Lee, M., Liu, J., Deng, F., Jones, D. B. A. 2015. Improved analysis-error covariance matrix for high-dimensional variational inversions: application to source estimation using a 3D atmospheric transport model. Quarterly Journal of the Royal Meteorological Society. 141(690), 1906-1921. DOI: 10.1002/qj.2495 Liu, J., Bowman, K. W., Lee, M., Henze, D. K., Bousserez, N., Brix, H., James Collatz, G., Menemenlis, D., Ott, L., Pawson, S., Jones, D., Nassar, R. 2014. Carbon monitoring system flux estimation and attribution: impact of ACOS-GOSAT XCO2 sampling on the inference of terrestrial biospheric sources and sinks. Tellus B: Chemical and Physical Meteorology. 66(1), 22486. DOI: 10.3402/tellusb.v66.22486 Asefi-Najafabady, S., Rayner, P. J., Gurney, K. R., McRobert, A., Song, Y., Coltin, K., Huang, J., Elvidge, C., Baugh, K. 2014. A multiyear, global gridded fossil fuel CO2emission data product: Evaluation and analysis of results. Journal of Geophysical Research: Atmospheres. 119(17), 10,213-10,231. DOI: 10.1002/2013JD021296 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Kevin Bowman(2017), Carbon Monitoring System Flux for Shipping, Aviation, and Chemical Sources L4 V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/RLT7JTCRJ11M
Kevin Bowman(2017), Carbon Monitoring System Flux for Posterior Fire Carbon L4 V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/N3HM4V0JZVLB Kevin Bowman(2017), Carbon Monitoring System Flux from the Net Ecosystem Exchange L4 V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/4ACY6GOWQ7BB Kevin Bowman(2017), Carbon Monitoring System Flux for Posterior Total Carbon L4 V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/QCBSYYY4CENP Kevin Bowman(2017), Carbon Monitoring System Flux for Prior Total Carbon L4 V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/F0JBNZ5QYWY6 Kevin Bowman(2017), Carbon Monitoring System Carbon Flux for Fire L4 V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/3C1Y3EJB1E7Q Kevin Bowman(2017), Carbon Monitoring System Flux for Fossil Fuel L4 V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/JC6BC3CPEJXQ Kevin Bowman(2017), Carbon Monitoring System Flux for Ocean Carbon L4 V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/96SSC2AOLE3Z Haynes, K.D., I.T. Baker, and A.S. Denning. 2021. SiB4 Modeled Global 0.5-Degree Daily Carbon Fluxes and Pools, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1849 Haynes, K.D., I.T. Baker, and A.S. Denning. 2021. SiB4 Modeled Global 0.5-Degree Hourly Carbon Fluxes and Productivity, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1847 Haynes, K.D., I.T. Baker, and A.S. Denning. 2021. SiB4 Modeled Global 0.5-Degree Monthly Carbon Fluxes and Pools, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1848 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
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4th NACP All-Investigators Meeting Posters (2013):
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Cook (CMS 2011) (2012) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Improving Forest Biomass Mapping Accuracy with Optical-LiDAR Data and Hierarchical Bayesian Spatial Models | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Bruce Cook, NASA GSFC
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
We propose to implement a novel approach for mapping forest biomass and associated
errors using the fusion of airborne LiDAR, passive optical and thermal data and a
Bayesian hierarchical model that accounts for spatial variances between ground
observations and remotely sensed data. This method will be compared with the more
traditional approach of using a variety of plot-scale LiDAR metrics in a generalized,
multiple linear regression model for relatively large region of interest (e.g., county- or
state- scale). Also, we will use fine-resolution LiDAR and passive optical data (<1 m) to
delineate individual trees, identify species class, and derive additional tree-level attributes
(e.g., crown dimensions, crown area weighted heights, stem density) to improve upon
biomass estimates made with aggregated point cloud metrics and inventory data at the
plot-level (the traditional approach).
These three methods will be evaluated and compared at four study sites in the midAtlantic and New England regions of the eastern US: Howland Forest and Holt Research
Forest, ME; Harvard Forest, MA; and the Smithsonian Environmental Research Center
near Edgewater, MD. This study will leverage coincident and co-registered LiDAR,
passive optical, and thermal data that were collected at these sites for NASA s local-scale
biomass pilot project between 2011 and 2012. Remotely sensed data was collected with Goddard s LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager, which PI
Cook developed at NASA-GSFC for studying the complex relationship between
terrestrial ecosystem form and function. Large-area stem maps (3 to 35 ha per site, in
which all stems greater than 1 cm have been measured) exist at each of these study sites,
and these data will be used to verify crown delineations and enable the creation of a fineresolution spectral library. Subsets of the stem map areas will be used to simulate
inventory plots, which will then be used as inputs for the Bayesian spatial latent factor
model. Each of the stem map areas contain a variety of over/understory tree species,
variable topography and range of drainage conditions, which will allow us to validate
each of the methods over a wide range of forest types between and within each of the
four study sites.
Benefits of the proposed Bayesian spatial latent factor prediction model are 1) variables
are selected using an efficient, dimension reduction technique; 2) spatial dependencies
are incorporated into the model to and improve inference; 3) data compression is used to
reduce the computational burden; and 4) sources of uncertainty are acknowledged and
propagated through to prediction. Benefits of using data fusion for biomass mapping is
that LiDAR and passive optical data provide unique information on the 3- dimensional
structure and species composition of the forest, respectively. This synergy has been the
focus of recent research, and has spawned the development of multi-instrument airborne
systems such at the Carnegie Airborne Observatory (CAO), NASA s G-LiHT, and
National Ecological Observatory Network (NEON) system that will begin systematic
data collections in 2012. New algorithms and model variables for mapping forest
biomass, such as the Bayesian latent spatial factor model and individual tree attributes we
propose in this study, are needed to take full advantage of the synergy offered by these
new, complementary datasets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Hans Andersen, U.S. Forest Service Pacific Northwest Research Station | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): |
http://gliht.gsfc.nasa.gov/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Publications: |
Finley, A. O., Banerjee, S., Zhou, Y., Cook, B. D., Babcock, C. 2017. Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables. Remote Sensing of Environment. 190, 149-161. DOI: 10.1016/j.rse.2016.12.004 Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. On nearest-neighbor Gaussian process models for massive spatial data. WIREs Computational Statistics. 8(5), 162-171. DOI: 10.1002/wics.1383 Salazar, E., Hammerling, D., Wang, X., Sanso, B., Finley, A. O., Mearns, L. O. 2016. Observation-based blended projections from ensembles of regional climate models. Climatic Change. 138(1-2), 55-69. DOI: 10.1007/s10584-016-1722-1 Babcock, C., Finley, A. O., Cook, B. D., Weiskittel, A., Woodall, C. W. 2016. Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data. Remote Sensing of Environment. 182, 1-12. DOI: 10.1016/j.rse.2016.04.014 Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets. Journal of the American Statistical Association. 111(514), 800-812. DOI: 10.1080/01621459.2015.1044091 Duncanson, L. I., Dubayah, R. O., Cook, B. D., Rosette, J., Parker, G. 2015. The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sensing of Environment. 168, 102-112. DOI: 10.1016/j.rse.2015.06.021 Rosette, J., Cook, B., Nelson, R., Huang, C., Masek, J., Tucker, C., Sun, G., Huang, W., Montesano, P., Rubio-Gil, J., Ranson, J. 2015. Sensor Compatibility for Biomass Change Estimation Using Remote Sensing Data Sets: Part of NASA's Carbon Monitoring System Initiative. IEEE Geoscience and Remote Sensing Letters. 12(7), 1511-1515. DOI: 10.1109/LGRS.2015.2411262 Goetz, S. J., Hansen, M., Houghton, R. A., Walker, W., Laporte, N., Busch, J. 2015. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environmental Research Letters. 10(12), 123001. DOI: 10.1088/1748-9326/10/12/123001 Finley, A. O., Banerjee, S., Cook, B. D. 2014. Bayesian hierarchical models for spatially misaligned data in R. Methods in Ecology and Evolution. 5(6), 514-523. DOI: 10.1111/2041-210X.12189 White, J. C., Wulder, M. A., Varhola, A., Vastaranta, M., Coops, N. C., Cook, B. D., Pitt, D., Woods, M. 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. The Forestry Chronicle. 89(06), 722-723. DOI: 10.5558/tfc2013-132 White, J.C., M. A. Wulder, A. Varhola, M. Vastaranta, N. C. Coops, B. D. Cook, D. Pitt, and M. Woods. 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using the area-based approach. Information Report FI-X-10. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Pacific Forestry Centre, Victoria, BC. 50 p. http://cfs.nrcan.gc.ca/pubwarehouse/pdfs/34887.pdf Duncanson, L. I., Cook, B. D., Hurtt, G. C., Dubayah, R. O. 2014. An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sensing of Environment. 154, 378-386. DOI: 10.1016/j.rse.2013.07.044 Cook, B., Corp, L., Nelson, R., Middleton, E., Morton, D., McCorkel, J., Masek, J., Ranson, K., Ly, V., Montesano, P. 2013. NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager. Remote Sensing. 5(8), 4045-4066. DOI: 10.3390/rs5084045 Huang, W., Sun, G., Dubayah, R., Cook, B., Montesano, P., Ni, W., Zhang, Z. 2013. Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales. Remote Sensing of Environment. 134, 319-332. DOI: 10.1016/j.rse.2013.03.017 Babcock, C., Matney, J., Finley, A. O., Weiskittel, A., Cook, B. D. 2013. Multivariate Spatial Regression Models for Predicting Individual Tree Structure Variables Using LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6(1), 6-14. DOI: 10.1109/JSTARS.2012.2215582 Finley, A. O., Banerjee, S., Cook, B. D., Bradford, J. B. 2013. Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets. International Journal of Applied Earth Observation and Geoinformation. 22, 147-160. DOI: 10.1016/j.jag.2012.04.007 Montesano, P. M., Cook, B. D., Sun, G., Simard, M., Nelson, R. F., Ranson, K. J., Zhang, Z., Luthcke, S. 2013. Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error. Remote Sensing of Environment. 130, 153-170. DOI: 10.1016/j.rse.2012.11.016 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Babcock, C., A.O. Finley, B.D. Cook, A. Weiskittel, and C.W. Woodall. 2016. CMS: Aboveground Biomass from Penobscot Experimental Forest, Maine, 2012. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1318
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2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
5th NACP All-Investigators Meeting Posters (2015): | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
4th NACP All-Investigators Meeting Posters (2013): | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)
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Dubayah (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | High Resolution Carbon Monitoring and Modeling: A CMS Phase 2 Study | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Ralph Dubayah, University of Maryland
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
The overall goal of our proposed research is the continuing prototype development of a
framework for estimating local-scale carbon stocks and future carbon sequestration
potential for the State of Maryland using remote sensing and ecosystem modeling.
Specifically, we will address the following objectives:
(1) Improve our existing methodology for carbon stock estimation and uncertainty
and assess its efficacy across an expanded range of environmental and forest conditions;
(2) Provide local-scale estimates of carbon stocks and their uncertainties for the entire
state of Maryland representing Eastern U.S. forest types;
(3) Initialize and run a prognostic ecosystem model to estimate carbon stocks and
their changes, and to estimate carbon sequestration potential;
(4) Provide detailed validation of national biomass maps using FIA data and localscale biomass maps.(5) Demonstrate new data acquisition technology (single photon counting) for lowcost, rapid carbon assessments.
Our proposed work will greatly expand our coverage from 2 to 24 Maryland counties and
extends from the tidewater forests of the Chesapeake Bay through the coastal plains and
uplands, to the mountainous forests of Western Maryland and the Appalachians. This
gradient in land use, topographic, edaphic, and climatic conditions enables an appropriate
expansion of methods, models, data, and assessments consistent with the goals of the
second phase of CMS.
Our objectives build from our Phase 1 work and lead to a clear set of tasks for the
proposed effort. These are divided into seven categories of activities traceable to this
framework: (1) Remote sensing data acquisition and processing; (2) Field data collection
and analysis; (3) Algorithm development and image processing; (4) Statistical and
machine learning model development; (5) County biomass and uncertainty map
generation, and end-to-end error analysis; (6) Prognostic ecosystem modeling, and; (7)
national biomass map validations. An additional element of our proposed work is a
coordinated outreach effort to county and state agencies to inform and promote their
activities in CMS and includes a transfer of technology to the State of Vermont. To
promote this outreach we will also implement a new, web-based data visualization, query
and delivery system, Grid^Intel Online (GIO) that allows any user to call up lidar data,
associated imagery, biomass and error estimates for arbitrary map areas.
Deliverables for this project expand upon those from Phase 1. In addition to the
developed framework the project will produce the following CMS products: (1) tiled and
mosaicked canopy height and forest/non-forest maps at 2 m and 30 m resolution for
Maryland; (2) AGBM maps at 30 m resolution with associated uncertainty maps; (3) EDmodel based carbon and carbon-flux maps at 90 m resolution; (4) ED-model maps of
carbon sequestration potential; (5) web-based data visualization and query system; (6)
map of canopy structure and biomass derived from wall-to-wall single photon lidar for
Alleghany county; (7) assessment of main sources of error and proposed strategies for
reducing errors in future deployment of an operational CMS. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Phillip Abbott, Purdue University | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Huang, W., Dolan, K., Swatantran, A., Johnson, K., Tang, H., O'Neil-Dunne, J., Dubayah, R., Hurtt, G. 2019. High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA. Environmental Research Letters. 14(9), 095002. DOI: 10.1088/1748-9326/ab2917 O'Neil-Dunne J, MacFaden S, Royar A, Reis M., Dubayah R. and Swatantran A. (2014) An Object-Based Approach to Statewide Land Cover Mapping. Proceedings of the 2014 ASPRS Annual Conference. Louisville, KY http://www.asprs.org/a/publications/proceedings/Louisville2014/ONeilDunne.pdf Tang, H., Ma, L., Lister, A., O'Neill-Dunne, J., Lu, J., Lamb, R. L., Dubayah, R., Hurtt, G. 2021. High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA. Environmental Research Letters. 16(3), 035011. DOI: 10.1088/1748-9326/abd2ef Tang, H., Swatantran, A., Barrett, T., DeCola, P., Dubayah, R. 2016. Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar. Remote Sensing. 8(9), 771. DOI: 10.3390/rs8090771 Finley, A. O., Banerjee, S., Zhou, Y., Cook, B. D., Babcock, C. 2017. Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables. Remote Sensing of Environment. 190, 149-161. DOI: 10.1016/j.rse.2016.12.004 Datta, A., Banerjee, S., Finley, A. O., Gelfand, A. E. 2016. On nearest-neighbor Gaussian process models for massive spatial data. WIREs Computational Statistics. 8(5), 162-171. DOI: 10.1002/wics.1383 Salazar, E., Hammerling, D., Wang, X., Sanso, B., Finley, A. O., Mearns, L. O. 2016. Observation-based blended projections from ensembles of regional climate models. Climatic Change. 138(1-2), 55-69. DOI: 10.1007/s10584-016-1722-1 Swatantran, A., Tang, H., Barrett, T., DeCola, P., Dubayah, R. 2016. Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar. Scientific Reports. 6(1). DOI: 10.1038/srep28277 Huang, W., Swatantran, A., Johnson, K., Duncanson, L., Tang, H., O'Neil Dunne, J., Hurtt, G., Dubayah, R. 2015. Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA. Carbon Balance and Management. 10(1). DOI: 10.1186/s13021-015-0030-9 Johnson, K. D., Birdsey, R., Cole, J., Swatantran, A., O'Neil-Dunne, J., Dubayah, R., Lister, A. 2015. Integrating LIDAR and forest inventories to fill the trees outside forests data gap. Environmental Monitoring and Assessment. 187(10). DOI: 10.1007/s10661-015-4839-1 Johnson, K. D., Birdsey, R., Finley, A. O., Swantaran, A., Dubayah, R., Wayson, C., Riemann, R. 2014. Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system. Carbon Balance and Management. 9(1). DOI: 10.1186/1750-0680-9-3 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Hurtt, G.C., M. Zhao, R. Sahajpal, A. Armstrong, R. Birdsey, E. Campbell, K. Dolan, R.O. Dubayah, J.P. Fisk, S. Flanagan, C. Huang, W. Huang, K. Johnson, R. Lamb, L. Ma, R. Marks, D. O'Leary III, J. O'Neil-Dunne, A. Swatantran, and H. Tang. 2019. Forest Aboveground Biomass and Carbon Sequestration Potential for Maryland, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1660
Dubayah, R.O., A. Swatantran, W. Huang, L. Duncanson, K. Johnson, H. Tang, J.O. Dunne, and G.C. Hurtt. 2016. CMS: LiDAR-derived Aboveground Biomass, Canopy Height and Cover for Maryland, 2011. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1320 Dubayah, R.O., A. Swatantran, W. Huang, L. Duncanson, K. Johnson, H. Tang, J.O. Dunne, and G.C. Hurtt. 2018. LiDAR Derived Biomass, Canopy Height and Cover for Tri-State (MD, PA, DE) Region, V2. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1538 Tang, H., L. Ma, A.J. Lister, J. O'Neil-Dunne, J. Lu, R. Lamb, R.O. Dubayah, and G.C. Hurtt. 2021. LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1854 O'Neil-Dunne, J., E. Buford, S. Macfaden, and A. Royar. 2022. CMS: Tree Canopy Cover at 0.5-meter resolution, Vermont, 2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/2072 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
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4th NACP All-Investigators Meeting Posters (2013):
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2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)
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French (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||
Project Title: | Development of Regional Fire Emissions Products for NASA's Carbon Monitoring System using the Wildland Fire Emissions Information System | ||||||||||||||||||||||||||||||||||||||
Science Team |
Nancy French, Michigan Tech Research Institute (MTRI)
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||
Abstract: |
Current work under the NASA-CMS Flux Pilot project includes measures of biomass
burning emissions for the quantification of carbon flux from land to the atmosphere. Fire
is recognized as an important mechanism for this exchange. Measures of biomass burning
emissions are included in this pilot project, but the estimates would greatly benefit from
further refinement, and some idea of the uncertainty in biomass burning emissions is
needed. There is a growing community of international, federal, and state-level parties
that desire and in some cases require refinements in methods to quantify emissions
from wildland and prescribed fire (biomass burning). To meet these requirements, these
parties are developing a suite of methods to address their needs. We propose to use tools
developed from collaborations with the US Forest Service and US Environmental Protection Agency, as well as recent research carried out for NASA, to refine the fire
emissions module of the CASA-GFED model currently used by CMS.
For the proposed project, to be conducted in Phase II of the CMS, we are proposing to
assist the NASA-Goddard CASA-GFED team in improving the GFED approach
currently used in the CMS Phase I Flux Pilot project. We will use the Wildland Fire
Emissions Information System (WFEIS), an approach developed under NASA s Carbon
Cycle Science program in collaboration with others in the fire emissions community, to
adjust GFED estimates over North America. WFEIS operates at a 1-km spatial grid scale,
while GFED operates at a 0.5 deg grid scale. The two approaches use the same general
construct, however they use different data sources for the model parameters and make
different assumptions when applying the general model. WFEIS uses a ground-based
method to map biomass (fuel loading) and a more direct method to estimate combustion
completeness (fuel consumption) than GFED. WFEIS was developed as a regional to
landscape-scale method, making it an appropriate tool to refine the GFED estimates of
emissions for areas where the two methods can be implemented.
The proposed activity includes: 1) improvements in quantifying mapped fuels (biomass)
for the US and combustion in deep organic soils of Alaska; 2) development of an
uncertainty measurement methodology for emissions estimation; 3) production of 1-kmscale fire emissions estimates for the US; 4) a comparison of these products to CASAGFED emissions estimates; and 5) refinements of GFED parameters based on the results
found with WFEIS. Specific outputs from this activity will provide important information
for improving our understanding of carbon emissions from wildland fire. | ||||||||||||||||||||||||||||||||||||||
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Participants: |
Michael Billmire, Michigan Tech Research Institute (MTRI) | ||||||||||||||||||||||||||||||||||||||
Project URL(s): |
http://wfeis.mtri.org | ||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
French, N. H. F., McKenzie, D., Erickson, T., Koziol, B., Billmire, M., Endsley, K. A., Yager Scheinerman, N. K., Jenkins, L., Miller, M. E., Ottmar, R., Prichard, S. 2014. Modeling Regional-Scale Wildland Fire Emissions with the Wildland Fire Emissions Information System. Earth Interactions. 18(16), 1-26. DOI: 10.1175/EI-D-14-0002.1 van Leeuwen, T. T., van der Werf, G. R., Hoffmann, A. A., Detmers, R. G., Rucker, G., French, N. H. F., Archibald, S., Carvalho Jr., J. A., Cook, G. D., de Groot, W. J., Hely, C., Kasischke, E. S., Kloster, S., McCarty, J. L., Pettinari, M. L., Savadogo, P., Alvarado, E. C., Boschetti, L., Manuri, S., Meyer, C. P., Siegert, F., Trollope, L. A., Trollope, W. S. W. 2014. Biomass burning fuel consumption rates: a field measurement database. Biogeosciences. 11(24), 7305-7329. DOI: 10.5194/bg-11-7305-2014 McKenzie, D., French, N. H. F., Ottmar, R. D. 2012. National database for calculating fuel available to wildfires. Eos, Transactions American Geophysical Union. 93(6), 57-58. DOI: 10.1029/2012EO060002 | ||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
French, N.H.F., D. McKenzie, T. Erickson, B. Koziol, M. Billmire, K.A. Endsley, N.K.Y. Scheinerman, L. Jenkins, M.E. Miller, R. Ottmar, and S. Prichard. 2016. Annual wildland fire emissions (WFEIS v0.5) for Conterminous US and Alaska, 2001-2013. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1306
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4th NACP All-Investigators Meeting Posters (2013): |
Healey (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | A Global Forest Biomass Inventory Based upon GLAS Lidar Data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Sean Healey, USDA Forest Service
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
The United Nations Food and Agriculture Organization (FAO) compiles and monitors
national-level biomass estimates across the world s forests through the Global Forest
Resources Assessment (FRA). FRA reports represent the current state of knowledge
regarding key forest parameters as expressed by national forest agencies and ministries
worldwide. Data collected in the FRA is important to UN initiatives such as REDD
(Reducing Emissions from Deforestation and Degradation), which depend upon accurate,
precise, and consistent national-level reporting of forest carbon storage. The proposed
work would establish a satellite-based NASA CMS global inventory of aboveground tree
biomass (a primary component of overall biomass) as an official component of FAO s
FRA 2015. Methods for this inventory were developed during the CMS pilot phase
though a partnership between members of the CMS national biomass pilot team and
representatives of the national forest inventory (FIA: US Forest Service s Forest
Inventory and Analysis unit) on the CMS Science Definition Team.
Discrete full waveform lidar footprints from the GLAS (Geoscience Laser Altimeter
System aboard ICESat) are strongly correlated with aboveground tree biomass, and are
here used in a survey/sample context as the basis for the CMS/FAO global biomass
inventory. Based upon CMS pilot results, this approach is likely to provide an improvement in the precision of biomass estimates for countries without established
national forest inventories, and its global consistency should enhance inter-comparability
of biomass stocks across all nations. This inventory would be based upon model-based
estimation, an approach which provides clear estimates of biomass and related
uncertainty, accounting for both the variance of the sample and variance introduced by
modeling biomass at each GLAS shot.
FAO will coordinate global compilation of the ground data needed from national forestry
agencies for calibration of models to be used in this inventory. A series of approximately
10 regional workshops will be held for national forest inventory representatives from
around the world in 2013. At each workshop, time will be dedicated to engage
participating countries in the needed data sharing. Almost all costs associated with this
effort (including travel and lodging for many participants) will be borne by FAO. In
addition to providing country- and global-level forest biomass estimates, this project will
publish relationships between GLAS heights and field-measured biomass, which may be
of use to other CMS efforts using GLAS data to calibrate wall-to-wall maps.
Lastly, there is a forward-looking element which involves forecasting the precision of
this inventory approach using lidar data from the ICESat-2 satellite (launch: 2016).
Collection of ground data by this project will be coordinated with the ICESat-2 Science
Team, which is programming overflights of GLAS shots by MABEL (an ICESat-2
simulation platform) and airborne lidar. Taken together, the components of the proposed
project will: 1) develop a global CMS aboveground forest biomass product; 2) establish it
as a critical monitoring asset within the FAO FRA monitoring process; and 3) assess its
sustainability in view of upcoming NASA missions. The proposed work includes a good
deal of in-kind salary contribution from the Forest Service, and there is a 55/45 balance
of funding to non-federal/federal entities. Sean Healey, FIA s remote sensing
representative to FAO and a member of the CMS Science Definition Team, is nominated
for membership on the CMS Science Team. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Sean Healey, USDA Forest Service | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Birdsey, Richard A.; Dugan, Alexa J.; Healey, Sean P.; Dante-Wood, Karen; Zhang, Fangmin; Mo, Gang; Chen, Jing M.; Hernandez, Alexander J.; Raymond, Crystal L.; McCarter, James. 2019. Assessment of the influence of disturbance, management activities, and environmental factors on carbon stocks of U.S. national forests. Gen. Tech. Rep. RMRS-GTR-402. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 116 pages plus appendices. Healey, S. P., Patterson, P. L., Saatchi, S., Lefsky, M. A., Lister, A. J., Freeman, E. A. 2012. A sample design for globally consistent biomass estimation using lidar data from the Geoscience Laser Altimeter System (GLAS). Carbon Balance and Management. 7(1). DOI: 10.1186/1750-0680-7-10 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Healey, S.P., M.W. Hernandez, D.P. Edwards, M.A. Lefsky, E. Freeman, P.L. Patterson, E.J. Lindquist, and A.J. Lister. 2015. CMS: GLAS LiDAR-derived Global Estimates of Forest Canopy Height, 2004-2008. ORNL DAAC, Oak Ridge, Tennessee, USA DOI: 10.3334/ORNLDAAC/1271
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2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
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2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)
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Houghton (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||
Project Title: | Spatially Explicit Sources and Sinks of Carbon from Deforestation, Reforestation, Growth and Degradation in the Tropics: Development of a Method and a 10 Year Data Set 2000-2010 | ||||||||||||||||||||||||||||||||||||||
Science Team |
Richard (Skee) Houghton, Woodwell Climate Research Center
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||
Abstract: |
Neither of the pilot studies in NASA s Phase 1 of the CMS has explicitly considered
changes in terrestrial carbon storage that result from land use and land-cover change
(LULCC). The biomass pilot study could be extended to estimate changes in
aboveground carbon density, but modeling and ancillary data will be needed to account
for changes in soils, downed wood, and wood products. The flux pilot study has, so far,
concentrated on short-term fluxes of carbon (i.e., photosynthesis, respiration, etc.) and
has paid less attention to the longer-term, structural changes that result from disturbance
and recovery. Yet it is these changes in biomass and soil carbon that define the net
contribution of LULCC to the global carbon budget.
We propose (1) to develop and demonstrate a method for monitoring changes in carbon
density in forests and (2) to produce a map of gross and net fluxes of carbon associated with deforestation, reforestation, growth and degradation for the entire tropics. We will
focus on the changes in carbon density that result from disturbance and recovery.
We propose to use multi-scale changes in forest cover (gains and losses) combined with
lidar-based estimates of aboveground carbon density to inform a carbon-tracking model
that will calculate losses and gains of carbon at a spatial resolution of 250m across the
tropics and at a resolution of 30m for two regions within southeast Asia and the Congo
Basin.
As a part of this research, we will determine the propagation of error for each
method (change in land cover, change in carbon density), including allometry error and
modeling error. The analysis of error will help define how small a disturbance (in area
and in carbon density) can be observed. And, using a carbon tracking model, we will
investigate the effect of this minimum detection on carbon emissions.
The work proposed here will complement the current pilot studies and will track
changes in terrestrial carbon density, in particular the changes that result from
disturbance and recovery of forests. The model will use a combination of MODIS,
Landsat, and GLAS data to determine annual changes in carbon density in aboveground
living and dead biomass, belowground biomass, litter, coarse woody debris, and wood
products. The work will focus on identifying, characterizing, and measuring disturbances
(and recovery) and on calculating the resulting fluxes of carbon.
The products of this work will be (1) a methodological approach incorporating satellite
data, a carbon-tracking model, and error analyses, and (2) multi-scale gridded data sets
showing the distribution of carbon sources and sinks attributable to forest disturbance and
recovery. The method will not be limited to the data inputs used here. Rather the model
will be flexible enough to accommodate other data sets as they evolve. The products will
include the data sets used to calculate carbon sources and sinks (rates and intensities of
disturbance and aboveground carbon densities), the errors in each data set and the
propagation of error through the calculation of net carbon flux.
The work is relevant to societal needs in two ways: first, carbon emissions from
LULCC are an important but poorly constrained component in the global carbon balance;
this work will demonstrate the capacity of satellite-based measurements to reduce the
error of that flux. Second, project-level and national-level emissions are the basis for
evaluating emission reduction strategies, arguably the most effective mechanism for
reducing emissions of carbon from developing countries. | ||||||||||||||||||||||||||||||||||||||
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Participants: |
Alessandro (Ale) Baccini, Boston University | ||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Anderegg, W. R. L., Ballantyne, A. P., Smith, W. K., Majkut, J., Rabin, S., Beaulieu, C., Birdsey, R., Dunne, J. P., Houghton, R. A., Myneni, R. B., Pan, Y., Sarmiento, J. L., Serota, N., Shevliakova, E., Tans, P., Pacala, S. W. 2015. Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proceedings of the National Academy of Sciences. 112(51), 15591-15596. DOI: 10.1073/pnas.1521479112 Ballantyne, A. P., Andres, R., Houghton, R., Stocker, B. D., Wanninkhof, R., Anderegg, W., Cooper, L. A., DeGrandpre, M., Tans, P. P., Miller, J. B., Alden, C., White, J. W. C. 2015. Audit of the global carbon budget: estimate errors and their impact on uptake uncertainty. Biogeosciences. 12(8), 2565-2584. DOI: 10.5194/bg-12-2565-2015 Goetz, S. J., Hansen, M., Houghton, R. A., Walker, W., Laporte, N., Busch, J. 2015. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environmental Research Letters. 10(12), 123001. DOI: 10.1088/1748-9326/10/12/123001 Olofsson, P., Kuemmerle, T., Griffiths, P., Knorn, J., Baccini, A., Gancz, V., Blujdea, V., Houghton, R. A., Abrudan, I. V., Woodcock, C. E. 2011. Carbon implications of forest restitution in post-socialist Romania. Environmental Research Letters. 6(4), 045202. DOI: 10.1088/1748-9326/6/4/045202 Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P., Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A., Sitch, S., Hayes, D. 2011. A Large and Persistent Carbon Sink in the World's Forests. Science. 333(6045), 988-993. DOI: 10.1126/science.1201609 Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., Houghton, R. A. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change. 2(3), 182-185. DOI: 10.1038/nclimate1354 Gloor, M., Gatti, L., Brienen, R., Feldpausch, T. R., Phillips, O. L., Miller, J., Ometto, J. P., Rocha, H., Baker, T., de Jong, B., Houghton, R. A., Malhi, Y., Aragao, L. E. O. C., Guyot, J., Zhao, K., Jackson, R., Peylin, P., Sitch, S., Poulter, B., Lomas, M., Zaehle, S., Huntingford, C., Levy, P., Lloyd, J. 2012. The carbon balance of South America: a review of the status, decadal trends and main determinants. Biogeosciences. 9(12), 5407-5430. DOI: 10.5194/bg-9-5407-2012 Goetz, S. J., Bond-Lamberty, B., Law, B. E., Hicke, J. A., Huang, C., Houghton, R. A., McNulty, S., O'Halloran, T., Harmon, M., Meddens, A. J. H., Pfeifer, E. M., Mildrexler, D., Kasischke, E. S. 2012. Observations and assessment of forest carbon dynamics following disturbance in North America. Journal of Geophysical Research: Biogeosciences. 117(G2). DOI: 10.1029/2011JG001733 Houghton, R. A. 2012. Carbon emissions and the drivers of deforestation and forest degradation in the tropics. Current Opinion in Environmental Sustainability. 4(6), 597-603. DOI: 10.1016/j.cosust.2012.06.006 Houghton, R. A. 2012. Historic Changes in Terrestrial Carbon Storage in: Recarbonization of the Biosphere. Springer Netherlands, 59-82. DOI: 10.1007/978-94-007-4159-1_4 Houghton, R. A., House, J. I., Pongratz, J., van der Werf, G. R., DeFries, R. S., Hansen, M. C., Le Quere, C., Ramankutty, N. 2012. Carbon emissions from land use and land-cover change. Biogeosciences. 9(12), 5125-5142. DOI: 10.5194/bg-9-5125-2012 Erb, K., Kastner, T., Luyssaert, S., Houghton, R. A., Kuemmerle, T., Olofsson, P., Haberl, H. 2013. Bias in the attribution of forest carbon sinks. Nature Climate Change. 3(10), 854-856. DOI: 10.1038/nclimate2004 Houghton, R. A. 2014. The emissions of carbon from deforestation and degradation in the tropics: past trends and future potential. Carbon Management. 4(5), 539-546. DOI: 10.4155/cmt.13.41 Houghton, R.A. 2013. Role of forests and impact of deforestation in the global carbon cycle. Pages 15-38 in: F. Achard & M.C. Hansen (editors). Global Forest Monitoring from Earth Observation. CRC Press, Boca Raton. ISBN 9781466552012 - CAT# K15197 Patra, P. K., Canadell, J. G., Houghton, R. A., Piao, S. L., Oh, N., Ciais, P., Manjunath, K. R., Chhabra, A., Wang, T., Bhattacharya, T., Bousquet, P., Hartman, J., Ito, A., Mayorga, E., Niwa, Y., Raymond, P. A., Sarma, V. V. S. S., Lasco, R. 2013. The carbon budget of South Asia. Biogeosciences. 10(1), 513-527. DOI: 10.5194/bg-10-513-2013 Tyukavina, A., Stehman, S. V., Potapov, P. V., Turubanova, S. A., Baccini, A., Goetz, S. J., Laporte, N. T., Houghton, R. A., Hansen, M. C. 2013. National-scale estimation of gross forest aboveground carbon loss: a case study of the Democratic Republic of the Congo. Environmental Research Letters. 8(4), 044039. DOI: 10.1088/1748-9326/8/4/044039 Valentini, R., Arneth, A., Bombelli, A., Castaldi, S., Cazzolla Gatti, R., Chevallier, F., Ciais, P., Grieco, E., Hartmann, J., Henry, M., Houghton, R. A., Jung, M., Kutsch, W. L., Malhi, Y., Mayorga, E., Merbold, L., Murray-Tortarolo, G., Papale, D., Peylin, P., Poulter, B., Raymond, P. A., Santini, M., Sitch, S., Vaglio Laurin, G., van der Werf, G. R., Williams, C. A., Scholes, R. J. 2014. A full greenhouse gases budget of Africa: synthesis, uncertainties, and vulnerabilities. Biogeosciences. 11(2), 381-407. DOI: 10.5194/bg-11-381-2014 | ||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Baccini, M., W. Walker, M. Farina, and R.A. Houghton. 2016. CMS: Estimated Deforested Area Biomass, Tropical America, Africa, and Asia, 2000. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1337
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Huntzinger (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Reduction in Bottom-Up Land Surface CO2 Flux Uncertainty in NASA's Carbon Monitoring System Flux Project through Systematic Multi-Model Evaluation and Infrastructure Development | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Deborah (Debbie) Huntzinger, Northern Arizona University
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
This study will generate improved global estimates of land-atmosphere carbon exchange
by combining and enhancing the technical infrastructure and observational constraints
within the NASA Carbon Monitoring System (CMS) Flux Project with new “bottom-up”
a priori surface flux estimates. These new surface flux products will be derived from a community of models that represent our best process-based understanding of how carbon
is exchanged between the land and the atmosphere. We will leverage and build off of an
existing NASA funded grant: The Multi-Scale Synthesis and Terrestrial Model
Intercomparison Project (MsTMIP). MsTMIP is a coordinated, large-scale
intercomparison effort that combines common forcing data and a detailed simulation
protocol in order to improve the diagnosis and attribution of carbon sources and sinks
across both global and regional scales.
What MsTMIP does that other intercomparisons have failed to do, is create a framework
that isolates, interprets, and helps inform understanding of how differences in process
parameterizations among current “bottom-up” models impact their flux estimations. As a
result, the MsTMIP framework allows for the isolation and quantification of the intermodel variance in estimates of land-atmosphere carbon exchange due to model structure,
or variations in the types of processes consider in the model and how these process are
represented. This inter-model variance provides a robust assessment of uncertainty in
land surface priors due to varying model physics, a component currently missing from the
CMS-Flux system.
CMS-Flux has the ability to produce ensembles of atmospheric CO2 distributions using
perturbations to transport and surface fluxes. These ensembles can help build
understanding of the relationship between surface flux and atmospheric CO2
concentrations, particularly if the consistency (or inconsistency) between surface flux
representations and atmospheric CO2 measurements can be linked back to representation
of processes within the models. However, to do so effectively CMS-Flux needs to include
a priori flux estimates that are more representative of our current understanding of landatmosphere sources and sinks than what is currently in
the system. In other words, the a priori flux estimates need to be informed by the range of
models used by the scientific community given that there is no consensus on the “best”
model overall. CMS-Flux is currently limited with respect to the land surface bottom-up
priors because: 1) it uses only two closely related land surface models, and as a result has
a restricted representation of the “true” uncertainty in the land surface bottom-up fluxes;
2) the uncertainty in the bottom-up fluxes themselves is not quantified in the system; and,
3) the atmospheric inversion system is disconnected from the TBMs in that one unified
system cannot currently be run.
This proposed effort improves the current CMS-Flux product with four key advances.
First, we propose to leverage the existing NASA funded MsTMIP activity to generate
new a priori “bottom-up” land-surface flux products for the CMS-Flux system. Second,
we will quantify uncertainties in a priori flux estimates. Third, we will develop the
technical infrastructure of CMS-Flux to handle multiple land-surface models as priors.
Four, we will combine the new a priori input products with the enhanced CMS
infrastructure to test the influence of prior flux estimates (and their associated
uncertainty) on posterior flux estimations from the inversion. Finally, the new
infrastructure will also be used to compare existing terrestrial biospheric model estimates
to the atmospheric CO2 constraints within CMS-Flux, providing another means of
evaluating understanding of the processes controlling land-atmosphere carbon exchange.
Combined, this proposed activity will expand the operational-use of CMS-Flux and allow
for more robust posterior flux estimates and their associated uncertainties. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Dominique Bachelet, Oregon State University | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S., Ahlstrom, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly, B., Rodenbeck, C., Tramontana, G., Viovy, N., Wang, Y., Weber, U., Zaehle, S., Zeng, N. 2017. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature. 541(7638), 516-520. DOI: 10.1038/nature20780 Schwalm, C. R., Huntzinger, D. N., Fisher, J. B., Michalak, A. M., Bowman, K., Ciais, P., Cook, R., El-Masri, B., Hayes, D., Huang, M., Ito, A., Jain, A., King, A. W., Lei, H., Liu, J., Lu, C., Mao, J., Peng, S., Poulter, B., Ricciuto, D., Schaefer, K., Shi, X., Tao, B., Tian, H., Wang, W., Wei, Y., Yang, J., Zeng, N. 2015. Toward "optimal" integration of terrestrial biosphere models. Geophysical Research Letters. 42(11), 4418-4428. DOI: 10.1002/2015GL064002 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Fisher, J.B., M. Sikka, D.N. Huntzinger, C.R. Schwalm, J. Liu, Y. Wei, R.B. Cook, A.M. Michalak, K. Schaefer, A.R. Jacobson, M.A. Arain, P. Ciais, B. El-masri, D.J. Hayes, M. Huang, S. Huang, A. Ito, A.K. Jain, H. Lei, C. Lu, F. Maignan, J. Mao, N.C. Parazoo, C. Peng, S. Peng, B. Poulter, D.M. Ricciuto, H. Tian, X. Shi, W. Wang, N. Zeng, F. Zhao, and Q. Zhu. 2016. CMS: Modeled Net Ecosystem Exchange at 3-hourly Time Steps, 2004-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1315
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5th NACP All-Investigators Meeting Posters (2015): | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2013 NASA Terrestrial Ecology Science Team Meeting Poster(s)
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Jacob (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Use of GOSAT, TES, and Suborbital Observations to Constrain North American Methane Emissions in the Carbon Monitoring System | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Daniel Jacob, Harvard University
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
We propose to contribute to the NASA Carbon Monitoring System (CMS) with a fourdimensional variational (4D-var) inverse modeling capability for methane emissions in
North America integrating satellite (GOSAT, TES), aircraft (CalNex, HIPPO,
NOAA/CCGG), and surface-based (TCCON, NOAA/CCGG) observations. Our work
will build on the existing CMS capability at JPL for carbon flux inversions using the
adjoint of the global GEOS-Chem chemical transport model (CTM). Here we will apply
the adjoint of the nested version of GEOS-Chem with 1/2o × 2/3o (~50 × 50 km2)
horizontal resolution over North America and adjacent oceans. The nested model will
enable fine-scale constraints on methane sources through the 4D-var inversion. We will
focus on 2009 2011 when data from both GOSAT and TES are available together with
aircraft campaign data over the US from CalNex (May July 2010) and HIPPO (June
September 2011). Combined use of GOSAT and TES data will enable us to separate
boundary layer and free tropospheric contributions to the methane column through the
inversion. The satellite data will be ingested in the 4D-var inverse model while the
suborbital data will be used for independent analysis of the optimized methane fluxes.
We will conduct a targeted analysis of the CalNex period to constrain methane sources in
California by applying both Lagrangian (STILT) and Eulerian (GEOS-Chem) inverse
modeling approaches to the aircraft and satellite data, testing the effect of different
meteorological data sets and of different a priori constraints. This analysis will provide a
unique opportunity to assess inverse modeling uncertainties related to resolution, data
type (satellite or aircraft), meteorological model, and inversion procedure. We will use
results from our continental-scale inversion of methane fluxes to better understand and
quantify the major sources contributing to methane emissions in North America, and to
provide guidance to the US EPA for improving its national emission inventories. The
inverse modeling capability for methane will be implemented into the existing CMS Flux
Pilot Project at JPL for consistent inversion of CO2 and methane fluxes over North
America using the same 4D-var system. This will provide a powerful facility to monitor
the fluxes of the two most important anthropogenic greenhouse gases. Our work will be
directly responsive to major climate policy initiatives in the US targeting methane
emissions including the Global Climate Change and Clean Air Initiative of the US State
Department and the Global Methane Initiative of the U.S. EPA. P.I. Daniel Jacob and CoI Steve Wofsy will join the CMS Science Team as part of this project. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Ramon Alvarez, Environmental Defense Fund | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Barrera, Y. D., Nehrkorn, T., Hegarty, J., Sargent, M., Benmergui, J., Gottlieb, E., Wofsy, S. C., DeCola, P., Hutyra, L., Jones, T. 2019. Using Lidar Technology To Assess Urban Air Pollution and Improve Estimates of Greenhouse Gas Emissions in Boston. Environmental Science & Technology. 53(15), 8957-8966. DOI: 10.1021/acs.est.9b00650 Wecht, K. J., Jacob, D. J., Sulprizio, M. P., Santoni, G. W., Wofsy, S. C., Parker, R., Bosch, H., Worden, J. 2014. Spatially resolving methane emissions in California: constraints from the CalNex aircraft campaign and from present (GOSAT, TES) and future (TROPOMI, geostationary) satellite observations. Atmospheric Chemistry and Physics. 14(15), 8173-8184. DOI: 10.5194/acp-14-8173-2014 Wecht, K. J., Jacob, D. J., Frankenberg, C., Jiang, Z., Blake, D. R. 2014. Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data. Journal of Geophysical Research: Atmospheres. 119(12), 7741-7756. DOI: 10.1002/2014JD021551 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Daniel Jacob (2016), Carbon Monitoring System (CMS) Methane (CH4) Flux for North America 0.5 degree x 0.667 degree V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] DOI: 10.5067/RF3R3G9I3UVX
Alex Turner & Daniel Jacob(2018), Methane (CH4) Flux for North America L4 Daily V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed [Data Access Date] 10.5067/GLUV19BRB081 |
Kennedy (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Integrating and Expanding a Regional Carbon Monitoring System into the NASA CMS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Robert Kennedy, Oregon State University
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
A key challenge in a carbon monitoring system is scaling thematically rich but highly localized information to the broad spatial scales needed for carbon accounting and management. This is particularly true for wooded ecosystems, where carbon storage potential is high, but actual carbon status is highly determined by local-scale environmental and forest management conditions.
Through a USDA-NIFA funded project entitled Integrated, observation-based carbon monitoring for wooded lands of Washington, Oregon and California , our team is developing a system to integrate Landsat satellite imagery, maps of environmental characteristics, Forest Inventory and Analysis (FIA) plot data, small-footprint lidar data, and aerial photos to characterize key carbon dynamics in forested ecosystems across all ownerships in the states of Washington, Oregon, and California from 1985 to 2010.
Key characteristics of our system include:
' Operational scaling of local-scale dynamics to all forests in Washington, Oregon, and California
' Yearly mapping of forest biomass and change in biomass from 1990 to 2010
' Explicit characterization of cause of change
' Integration of USDA Forest Service Forest Inventory and Analysis (FIA) plot data
' Linkage of small-footprint lidar data with regional scale biomass maps
' Explicit quantification of methodological uncertainties for all estimates
Because our approach addresses key challenges faced by the current NASA Carbon Monitoring System (CMS), we believe it has the potential to complement and aid NASA s mandate for operational carbon monitoring. To help reach that potential, we propose three activities.
-- 1. We will utilize the products from our own carbon monitoring program in forests of Washington, Oregon, and California to evaluate, understand, and improve performance of the NASA CMS products, and compare a variety of national-scale products both to each other and to FIA plot estimates.
-- 2. We will work with collaborators within the USDA FIA to extend our approaches to a different forest system, linking explicitly with the local-scale NASA CMS efforts in eastern forests.
-- 3. Finally, we will bring our data, methods, and lessons-learned to NASA CMS Science Definition Team, and work closely with other SDT members to link our approaches into those analytical and modeling frameworks to further the overarching goals of the CMS.
The following characteristics of our project are relevant to NASA s need to evaluate and improve its CMS:
- Evaluating the utility and characterizing uncertainties in CMS products
- Understanding scaling issues needed to link local to national scale products
- Developing and demonstrating feasibility of alternative approaches to monitoring
- Illustrating capabilities of satellite-based monitoring for science and management | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Van Kane, University of Washington | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Bell, D. M., Gregory, M. J., Kane, V., Kane, J., Kennedy, R. E., Roberts, H. M., Yang, Z. 2018. Multiscale divergence between Landsat- and lidar-based biomass mapping is related to regional variation in canopy cover and composition. Carbon Balance and Management. 13(1). DOI: 10.1186/s13021-018-0104-6 Kennedy, R. E., Ohmann, J., Gregory, M., Roberts, H., Yang, Z., Bell, D. M., Kane, V., Hughes, M. J., Cohen, W. B., Powell, S., Neeti, N., Larrue, T., Hooper, S., Kane, J., Miller, D. L., Perkins, J., Braaten, J., Seidl, R. 2018. An empirical, integrated forest biomass monitoring system. Environmental Research Letters. 13(2), 025004. DOI: 10.1088/1748-9326/aa9d9e Neeti, N., Kennedy, R. 2016. Comparison of national level biomass maps for conterminous US: understanding pattern and causes of differences. Carbon Balance and Management. 11(1). DOI: 10.1186/s13021-016-0060-y | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
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Loboda (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||
Project Title: | The Forest Disturbance Carbon Tracking System -- A CMS Pilot Project | ||||||||||||||||||||||||||||||||||||||
Science Team |
Tatiana Loboda, University of Maryland
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||
Abstract: |
Forest disturbances are a key process that drives significant variations in the terrestrial carbon budget for North America. While many NASA funded projects for the North American Carbon Program, as well as others funded by U.S. land management agencies, were focused on developing approaches to map forest disturbances, and assess the impacts of these disturbances on carbon cycling. However, efforts have not progressed enough to integrate the results from these efforts in order to provide a forest disturbance product that is useful for assessing the impacts of disturbances. The goal for this proposed Carbon Monitoring System (CMS) pilot project is to (1) develop a new regional carbon monitoring product that utilizes satellite remote sensing data to map forest disturbed area on an annual basis at medium resolution; and (2) to use this product as a basis for assessing the impacts of disturbance on forest carbon stocks for specific ecoregions of the United States. The development of the Forest Disturbance Carbon Tracking System (FDCTS) will provide an approach based on using a number of information products derived from remotely sensed data to address the following objectives: (a) Integrate a number of forest disturbance products in systematic fashion to create a map of the spatial and temporal extent of different forest disturbance events and episodes; and (b) Assess the impacts of these disturbances on key forest characteristics that control changes to carbon cycling (tree mortality, damage to branches and foliage, loss of live biomass, harvest removals, and combustion) for specific forest types in two North American forest ecoregions in order to produce a data product that depicts changes to forest carbon stocks on an annual basis for the ecoregions being studied. This CMS pilot project would focus on forest disturbances in two Level II U.S. ecoregions where disturbances have been dominant drivers of the terrestrial carbon cycle over the past decade: (a) the Western Cordillera ecoregion which has experienced major outbreaks of pine bark beetles as well as wildfire; and (b) the Alaska Boreal Interior ecoregion where burning of deep organic layers during fires represents the major impact on forest carbon cycles. As part of this pilot project, for each ecoregion, we would develop disturbance maps and forest impact products for the 2000s on an annual basis. The outputs from thithis CMS pilot project would be medium resolution (30 m) maps of all forest disturbances in the study ecoregions, which also contains data layers on pre-disturbance forest and carbon pool characteristics, the impacts of forest disturbances on carbon pools, and the amount of carbon remaining after the disturbances for specific pools. Within the climate change area, reliable and up-to-date information is needed on the terrestrial sources and sinks for a number of carbon-based greenhouse gases. The results from this pilot project will demonstrate a new approach for integrating multiple data sources to generate a product that quantifies the impacts of forest disturbance on the primary forest carbon pools. This new data product would not only provide the basis for providing inputs into models that quantify the impacts of disturbances on carbon cycling, but also to validate such models. The pilot project not only represents the first step towards creating national and continental-scale forest disturbance products, but also would provide the foundation for developing a system that would be able to quantify the impacts of forest disturbances on an annual basis going back thirty years to the mid-1980s (based on exploiting the Landsat TM/ETM+ data archive). Such an analysis would not only provide scientists, managers, and policy makers with clearer information on the integrated impacts of past disturbances, but the approach could be used to provide improved information on the impacts of forest disturbance on an annual basis. | ||||||||||||||||||||||||||||||||||||||
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Participants: |
Kirsten Barrett, University of Leicester | ||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: | None provided. | ||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Loboda, T.V., and E.E. Hoy. 2017. CMS: Fire Weather Indices for Interior Alaska, 2001-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1509
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5th NACP All-Investigators Meeting Posters (2015): | |||||||||||||||||||||||||||||||||||||||
4th NACP All-Investigators Meeting Posters (2013): |
Lohrenz (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Development of Observational Products and Coupled Models of Land-Ocean-Atmospheric Fluxes in the Mississippi River Watershed and Gulf of Mexico in Support of Carbon Monitoring | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Steven (Steve) Lohrenz, University of Massachusetts
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
Information about carbon fluxes in continental margins and linkages to terrestrial carbon cycles is key focus of NASA s Earth Science Research Program and a central aspect of NASA s Carbon Monitoring System. The uncertainties in coastal carbon fluxes are such that the net uptake of carbon in the coastal margins remains a poorly constrained term in global budgets. In particular, our ability to estimate current air-sea CO2 fluxes in continental margins is limited, and there is even less capability for predicting changes in the CO2 uptake capacity in coastal waters. The need to improve the understanding of coastal carbon dynamics and precision of estimates of coastal carbon fluxes has implications for attribution of land sources and sinks because atmospheric inversions are sensitive to uncertainties in coastal boundaries. Moreover, characterization of trends in carbon inventories reveal an increasing fraction of fossil fuel carbon is remaining in the atmosphere due to reductions in the efficiencies of ocean sinks and other sink processes not considered in current models. The proposed research will employ a combination of models and remotely-sensed and in situ observations to develop georeferenced products and associated uncertainties for land-ocean exchange of carbon, air-sea exchanges of carbon dioxide, and coastal to open ocean exchanges of carbon. Such information is critically needed to better constrain the contribution of coastal margins to carbon sources and sinks and improve capabilities to attribute sources and sinks to different regions as well as reducing uncertainties in estimates. The proposed effort will use a combination of observations and coupled terrestrial and ocean models to examine carbon processes and fluxes from the watershed to the continental margin. A major aspect of this proposed project will be to establish and populate geospatial portals for sharing and analysis of carbon datasets and products. The primary region of study will be the Mississippi River watershed and northern Gulf of Mexico. However, the model domain will also include the continental margins of Florida and the South Atlantic Bight. The region of study provides an excellent setting to carry out this work as there are a large number of supporting datasets and on-going programs that will complement this work. The proposed work is closely aligned with objectives of the NASA Carbon Monitoring System scoping effort and of the North American Carbon Program and will support National Climate Assessment activities. The effort will also contribute to NASA Coastal Carbon Synthesis effort and international efforts to develop a North American carbon budget (CarboNA).
The unique nature of our approach, coupling models of terrestrial and ocean ecosystem dynamics and associated carbon processes, will allow for assessment of how societal and human-related LCLUC, as well as climate change, affects terrestrial carbon sources and sinks, export of materials to coastal margins, and associated carbon processes in the continental margins. Results would also benefit efforts to describe and predict how land cover and land use changes impact coastal water quality, including possible effects of coastal eutrophication, hypoxia, and ocean acidification. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Wei-Jun Cai, University of Delaware | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Chakraborty, S., Lohrenz, S. E., Gundersen, K. 2017. Photophysiological and light absorption properties of phytoplankton communities in the river-dominated margin of the northern
G
ulf of
M
exico. Journal of Geophysical Research: Oceans. 122(6), 4922-4938. DOI: 10.1002/2016JC012092 Lohrenz, S. E., Cai, W., Chakraborty, S., Huang, W., Guo, X., He, R., Xue, Z., Fennel, K., Howden, S., Tian, H. 2018. Satellite estimation of coastal pCO2 and air-sea flux of carbon dioxide in the northern Gulf of Mexico. Remote Sensing of Environment. 207, 71-83. DOI: 10.1016/j.rse.2017.12.039 Tian, H., Ren, W., Yang, J., Tao, B., Cai, W., Lohrenz, S. E., Hopkinson, C. S., Liu, M., Yang, Q., Lu, C., Zhang, B., Banger, K., Pan, S., He, R., Xue, Z. 2015. Climate extremes dominating seasonal and interannual variations in carbon export from the Mississippi River Basin. Global Biogeochemical Cycles. 29(9), 1333-1347. DOI: 10.1002/2014GB005068 Tian, H., Xu, R., Pan, S., Yao, Y., Bian, Z., Cai, W., Hopkinson, C. S., Justic, D., Lohrenz, S., Lu, C., Ren, W., Yang, J. 2020. Long-Term Trajectory of Nitrogen Loading and Delivery From Mississippi River Basin to the Gulf of Mexico. Global Biogeochemical Cycles. 34(5). DOI: 10.1029/2019GB006475 Zhang, B., Tian, H., Lu, C., Chen, G., Pan, S., Anderson, C., Poulter, B. 2017. Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets. Atmospheric Environment. 165, 310-321. DOI: 10.1016/j.atmosenv.2017.07.001 Cai, W., Arthur Chen, C. T., Borges, A. 2013. Carbon dioxide dynamics and fluxes in coastal waters influenced by river plumes in: Biogeochemical Dynamics at Major River-Coastal Interfaces. Cambridge University Press, 155-173. DOI: 10.1017/CBO9781139136853.010 Lohrenz, S. E., Cai, W., Chakraborty, S., Gundersen, K., Murrell, M. C. 2013. Nutrient and carbon dynamics in a large river-dominated coastal ecosystem: the Mississippi-Atchafalaya River system in: Biogeochemical Dynamics at Major River-Coastal Interfaces. Cambridge University Press, 448-472. DOI: 10.1017/CBO9781139136853.023 Huang, W., Cai, W., Castelao, R. M., Wang, Y., Lohrenz, S. E. 2013. Effects of a wind-driven cross-shelf large river plume on biological production and CO2 uptake on the Gulf of Mexico during spring. Limnology and Oceanography. 58(5), 1727-1735. DOI: 10.4319/lo.2013.58.5.1727 Xue, Z., He, R., Fennel, K., Cai, W., Lohrenz, S., Hopkinson, C. 2013. Modeling ocean circulation and biogeochemical variability in the Gulf of Mexico. Biogeosciences. 10(11), 7219-7234. DOI: 10.5194/bg-10-7219-2013 Liu, M., Tian, H., Yang, Q., Yang, J., Song, X., Lohrenz, S. E., Cai, W. 2013. Long-term trends in evapotranspiration and runoff over the drainage basins of the Gulf of Mexico during 1901-2008. Water Resources Research. 49(4), 1988-2012. DOI: 10.1002/wrcr.20180 Chen, G., Tian, H., Zhang, C., Liu, M., Ren, W., Zhu, W., Chappelka, A. H., Prior, S. A., Lockaby, G. B. 2012. Drought in the Southern United States over the 20th century: variability and its impacts on terrestrial ecosystem productivity and carbon storage. Climatic Change. 114(2), 379-397. DOI: 10.1007/s10584-012-0410-z Guo, X., Cai, W., Huang, W., Wang, Y., Chen, F., Murrell, M. C., Lohrenz, S. E., Jiang, L., Dai, M., Hartmann, J., Lin, Q., Culp, R. 2011. Carbon dynamics and community production in the Mississippi River plume. Limnology and Oceanography. 57(1), 1-17. DOI: 10.4319/lo.2012.57.1.0001 Hopkinson, C. S., Cai, W., Hu, X. 2012. Carbon sequestration in wetland dominated coastal systems--a global sink of rapidly diminishing magnitude. Current Opinion in Environmental Sustainability. 4(2), 186-194. DOI: 10.1016/j.cosust.2012.03.005 Huang, W., Wang, Y., Cai, W. 2012. Assessment of sample storage techniques for total alkalinity and dissolved inorganic carbon in seawater. Limnology and Oceanography: Methods. 10(9), 711-717. DOI: 10.4319/lom.2012.10.711 Tian, H., Lu, C., Chen, G., Tao, B., Pan, S., Grosso, S. J. D., Xu, X., Bruhwiler, L., Wofsy, S. C., Kort, E. A., Prior, S. A. 2012. Contemporary and projected biogenic fluxes of methane and nitrous oxide in North American terrestrial ecosystems. Frontiers in Ecology and the Environment. 10(10), 528-536. DOI: 10.1890/120057 Tian, H., Chen, G., Zhang, C., Liu, M., Sun, G., Chappelka, A., Ren, W., Xu, X., Lu, C., Pan, S., Chen, H., Hui, D., McNulty, S., Lockaby, G., Vance, E. 2012. Century-Scale Responses of Ecosystem Carbon Storage and Flux to Multiple Environmental Changes in the Southern United States. Ecosystems. 15(4), 674-694. DOI: 10.1007/s10021-012-9539-x Xu, X. F., Tian, H. Q., Chen, G. S., Liu, M. L., Ren, W., Lu, C. Q., Zhang, C. 2012. Multifactor controls on terrestrial N<sub>2</sub>O flux over North America from 1979 through 2010. Biogeosciences. 9(4), 1351-1366. DOI: 10.5194/bg-9-1351-2012 Zhang, C., Tian, H., Chen, G., Chappelka, A., Xu, X., Ren, W., Hui, D., Liu, M., Lu, C., Pan, S., Lockaby, G. 2012. Impacts of urbanization on carbon balance in terrestrial ecosystems of the Southern United States. Environmental Pollution. 164, 89-101. DOI: 10.1016/j.envpol.2012.01.020 Tian, H., Chen, G., Lu, C., Xu, X., Hayes, D. J., Ren, W., Pan, S., Huntzinger, D. N., Wofsy, S. C. 2014. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Climatic Change. 129(3-4), 413-426. DOI: 10.1007/s10584-014-1072-9 Tao, B., Tian, H., Ren, W., Yang, J., Yang, Q., He, R., Cai, W., Lohrenz, S. 2014. Increasing Mississippi river discharge throughout the 21st century influenced by changes in climate, land use, and atmospheric CO2. Geophysical Research Letters. 41(14), 4978-4986. DOI: 10.1002/2014GL060361 Chen, G., Tian, H., Huang, C., Prior, S. A., Pan, S. 2013. Integrating a process-based ecosystem model with Landsat imagery to assess impacts of forest disturbance on terrestrial carbon dynamics: Case studies in Alabama and Mississippi. Journal of Geophysical Research: Biogeosciences. 118(3), 1208-1224. DOI: 10.1002/jgrg.20098 Wang, Z. A., Wanninkhof, R., Cai, W., Byrne, R. H., Hu, X., Peng, T., Huang, W. 2013. The marine inorganic carbon system along the Gulf of Mexico and Atlantic coasts of the United States: Insights from a transregional coastal carbon study. Limnology and Oceanography. 58(1), 325-342. DOI: 10.4319/lo.2013.58.1.0325 Xue, Z., He, R., Fennel, K., Cai, W., Lohrenz, S., Huang, W., Tian, H., Ren, W., Zang, Z. 2016. Modeling <i>p</i>CO<sub>2</sub> variability in the Gulf of Mexico. Biogeosciences. 13(15), 4359-4377. DOI: 10.5194/bg-13-4359-2016 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Cai, W.-J., Y. Wang, and W.-J. Huang. 2012. Sea Surface pCO2 measurements in the Gulf of Mexico during the Ocean Survey Vessel Bold cruises in 2006.
http://cdiac.ess-dive.lbl.gov/ftp/oceans/UG_GoM_UW_Data/2006.data. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee. DOI: 10.3334/CDIAC/OTG.UG_GOM_UW_2006
Cai, W.-J., Y. Wang, and W.-J. Huang. 2012. Sea Surface pCO2 measurements in the Gulf of Mexico during the Ocean Survey Vessel Bold cruises in 2007.
http://cdiac.ess-dive.lbl.gov/ftp/oceans/UG_GoM_UW_Data/2007.data. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee. DOI: 10.3334/CDIAC/OTG.UG_GOM_UW_2007
Cai, W.-J., Y. Wang and W.-J. Huang. 2014. Sea surface pCO2 survey in the Gulf of Mexico during the R/V Cape Hatteras cruises in 2009 and 2010. http://cdiac.ess-dive.lbl.gov/ftp/oceans/Cape_Hatteras_GM/.
Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee. DOI: 10.3334/CDIAC/OTG.Cape_Hatteras_GM
Tian, H., S.E. Lohrenz, S. Pan, W.J. Cai, and R. He. 2019. Export and Leaching of Carbon and Nitrogen from Mississippi River Basin, 1901-2099. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1699 Yao, Y., and H. Tian. 2021. CMS: Annual Estimates of Global Riverine Nitrous Oxide Emissions, 1900-2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1791 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
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5th NACP All-Investigators Meeting Posters (2015):
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4th NACP All-Investigators Meeting Posters (2013):
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Miller (CMS 2011) (2012) | |||||||||||||||||||||||||||||
Project Title: | In Situ CO2-Based Evaluation of the Carbon Monitoring System Flux Product | ||||||||||||||||||||||||||||
Science Team |
John Miller, NOAA Global Monitoring Laboratory
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||
Abstract: |
The fundamental objective of the NASA Carbon Monitoring System (CMS) flux product is to derive surface CO2 fluxes using satellite-based column CO2 mole fractions. Although the CMS flux product has an existing evaluation strategy, it is limited in scope and has acknowledged shortcomings, especially with regard to tropical carbon fluxes. Here, we propose to use the large number of high-accuracy, high-precision, globally distributed in situ tropospheric CO2 observations (including a unique set of tropical observations) to assess the realism of the optimized CMS fluxes and their stated uncertainties. First, CO2 observations will be compared directly with a posteriori CMS-modeled CO2 mole fractions. To first-order, near surface CO2 surpluses in the modeled CO2 mole fractions can be interpreted as excess positive surface flux, and vice versa. Second, CMS fluxes will be compared to fluxes derived from independent flux optimization systems (using in situ CO2 data). This more direct flux evaluation will be conducted globally using the CarbonTracker data assimilation system. Moreover, CarbonTracker will be run using multiple transport models to help assess the role of transport errors in the mismatch between simulation and observation. Additionally, in tropical South America we will use a state of the art regional flux inversion system to create a second set of fluxes, taking advantage of a two-year data set of fortnightly measurements in Brazil at four vertical profile sites and two additional surface sites. Tropical South America is of particular interest in global satellite-based inversions because of its disproportionate importance for the global carbon cycle combined with the anticipated seasonal biases in tropical satellite-based column CO2 arising from frequent cloud cover and high aerosol loadings. Working with the CMS flux product team, we will use the in situ CO2-based flux evaluations to diagnose shortcomings in the existing CMS flux optimization approach, transport parameterization and input GOSAT/ACOS CO2 columns. Finally, while we do expect OCO-2 to ultimately have better coverage than GOSAT over tropical South America, we still anticipate significant seasonal biases in sensitivity to Amazonian surface fluxes. To address this issue, and guard against biases in eventual CO2 flux optimization, we will produce an in situ CO2-optimized flux map for use as a prior in future CMS flux products. For any top-down CO2 flux estimation system, evaluation and uncertainty characterization is as important as the flux calculation itself, and the research proposed here will leverage the highest precision measurements in the global carbon cycle to assess the quality of the CMS flux product. | ||||||||||||||||||||||||||||
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Participants: |
Sourish Basu, NASA GSFC GMAO / University of Maryland | ||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Alden, C. B., Miller, J. B., Gatti, L. V., Gloor, M. M., Guan, K., Michalak, A. M., Laan-Luijkx, I. T., Touma, D., Andrews, A., Basso, L. S., Correia, C. S. C., Domingues, L. G., Joiner, J., Krol, M. C., Lyapustin, A. I., Peters, W., Shiga, Y. P., Thoning, K., Velde, I. R., Leeuwen, T. T., Yadav, V., Diffenbaugh, N. S. 2016. Regional atmospheric CO
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inversion reveals seasonal and geographic differences in Amazon net biome exchange. Global Change Biology. 22(10), 3427-3443. DOI: 10.1111/gcb.13305 |
Pawson (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | GEOS-CARB: A Framework for Monitoring Carbon Concentrations and Fluxes | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Steven Pawson, NASA GSFC GMAO
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Precursor Projects: | Gunson-Pawson-Potter (2009) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
This proposal is for a continuation of NASA GSFC s activities related to the Carbon Monitoring System, Flux Pilot Study (CMS FPP). The work will enhance and develop the capabilities of NASA s Goddard Earth Observing System (GEOS) set of models and assimilation components to further develop a core capability for CMS-related carbon cycle science and monitoring. The work consists of three components: (i) continuation of past work to compute atmosphere-ocean and atmosphere-land biosphere fluxes, as well as their evaluation using forward modeling in GEOS-5; (ii) enhancements of GEOS-5 for carbon monitoring, including a model study of the intermingling of uncertainties in anthropogenic and land-biospheric carbon emissions, and development of an enhanced assimilation capability to include multiple space-borne CO2 estimates (from AIRS and ACOS-GOSAT); (iii) a focused activity that examines aspects related to top-down (inverse) estimates of carbon fluxes. The latter effort will include a controlled comparison of three inverse estimates, including the one from CMS FPP, that use the same input data but use different methods. It also includes the implementation and application of a Lagrangian particle dispersion model to compute global footprints of GOSAT observations. Further, substantial new developments will be implemented into an existing variation inversion system. The work proposed in GEOS-CARB will implement and adapt various modeling and analysis tools, linking them closely with GEOS-5 systems available in the Global Modeling and Assimilation Office, in order to better exploit NASA s carbon-relevant observations for monitoring and understanding the global carbon cycle. The development work will leave NASA with enhanced modeling and analysis tools for carbon-cycle monitoring using space-based observations. These tools will be used to address some of the research questions that have arisen in the course of CMS FPP, with a strong emphasis on characterizing uncertainty in CO2 flux computations. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
David Baker, CIRA/Colorado State University | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Ott, L. E., Pawson, S., Collatz, G. J., Gregg, W. W., Menemenlis, D., Brix, H., Rousseaux, C. S., Bowman, K. W., Liu, J., Eldering, A., Gunson, M. R., Kawa, S. R. 2015. Assessing the magnitude of CO2flux uncertainty in atmospheric CO2records using products from NASA's Carbon Monitoring Flux Pilot Project. Journal of Geophysical Research: Atmospheres. 120(2), 734-765. DOI: 10.1002/2014JD022411 Gregg, W. W., Casey, N. W., Rousseaux, C. S. 2014. Sensitivity of simulated global ocean carbon flux estimates to forcing by reanalysis products. Ocean Modelling. 80, 24-35. DOI: 10.1016/j.ocemod.2014.05.002 Gregg, W. W., N. W. Casey, and C. S. Rousseaux, 2013, Global surface ocean carbon estimates in a model forced by MERRA, NASA Technical Report Series on Global Modeling and Data Assimilation, NASA TM-2013-104606, Vol. 31, 39 pp. Oda, T., Maksyutov, S. 2011. A very high-resolution (1 kmx1 km) global fossil fuel CO<sub>2</sub> emission inventory derived using a point source database and satellite observations of nighttime lights. Atmospheric Chemistry and Physics. 11(2), 543-556. DOI: 10.5194/acp-11-543-2011 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Lesley Ott (2020), GEOS-Carb CASA-GFED 3-hourly Ecosystem Exchange Fluxes 0.5 degree x 0.625 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/5MQJ64JTBQ40
Lesley Ott (2020), GEOS-Carb CASA-GFED Monthly Fire Fuel NPP Rh NEE Fluxes 0.5 degree x 0.5 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/03147VMJE8J9 Lesley Ott (2020), GEOS-Carb CASA-GFED Daily Fire and Fuel Emissions 0.5 degree x 0.5 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/IYZIZJ8ZFZHU Lesley Ott (2020), GEOS-Carb CASA-GFED Daily Fire and Fuel Emissions 0.5 degree x 0.5 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/7TQL49XLIMBD Lesley Ott (2020), GEOS-Carb CASA-GFED Monthly Fire Fuel NPP Rh NEE Fluxes 0.5 degree x 0.5 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/FZU47Y00Q79U Lesley Ott (2020), GEOS-Carb CASA-GFED 3-hourly Ecosystem Exchange Fluxes 0.5 degree x 0.625 degree, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/VQPRALE26L20 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
5th NACP All-Investigators Meeting Posters (2015): |
Saatchi (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Prototyping MRV Systems Based on Systematic and Spatial Estimates of Carbon Stock and Stock Changes of Forestlands | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Sassan Saatchi, Jet Propulsion Laboratory / Caltech
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Precursor Projects: | Masek-Nemani-Saatchi-Tucker (2009) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Successor Projects: | Saatchi (CMS 2015) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
Under phase I of the Carbon Monitoring System (CMS) Biomass Pilot Project, we developed a map of aboveground carbon stocks at 100-m spatial resolution using a combination of remote sensing products combined with ground inventory data. In phase II, we propose to build upon phase I by developing similar spatial products for carbon stocks in all pools (belowground biomass, dead wood, forest floor, soil organic carbon) for three points in time so that net annual carbon stock changes (fluxes) over time may be estimated spatially over US forestlands.
Additionally, we propose to test a methodology for separating net flux into its component parts of gross emissions and gross removals to enable a better understanding of how forests should be managed to decrease emissions and increase removals. We will use remote sensing products to quantify areas of forest disturbance and change and develop a fully spatial framework for estimating GHG dynamics (i.e., gross emissions and removals). Our proposed methodology will follow the IPCC Good Practice Guidelines for national GHG accounting from the forest/land-use sector.
The expected spatial framework will enable future integration of the proposed activities and products with the CMS Flux Pilot Project. It will also demonstrate a method by which spatial data and models can be integrated with ground data to prototype IPCC recommended Monitoring, Reporting and Verification (MRV) systems for reducing emissions from deforestation and forest degradation and increasing removals from enhancement of forest carbon stocks. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Richard (Rich) Birdsey, Woodwell Climate Research Center | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): |
http://carbon.jpl.nasa.gov/ http://carbon.nasa.gov/ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Zhang, G., Ganguly, S., Nemani, R. R., White, M. A., Milesi, C., Hashimoto, H., Wang, W., Saatchi, S., Yu, Y., Myneni, R. B. 2014. Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sensing of Environment. 151, 44-56. DOI: 10.1016/j.rse.2014.01.025 Junttila, V., Finley, A. O., Bradford, J. B., Kauranne, T. 2013. Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory. Forest Ecology and Management. 292, 75-85. DOI: 10.1016/j.foreco.2012.12.019 Babcock, C., Matney, J., Finley, A. O., Weiskittel, A., Cook, B. D. 2013. Multivariate Spatial Regression Models for Predicting Individual Tree Structure Variables Using LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6(1), 6-14. DOI: 10.1109/JSTARS.2012.2215582 Finley, A. O., Banerjee, S., Cook, B. D., Bradford, J. B. 2013. Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets. International Journal of Applied Earth Observation and Geoinformation. 22, 147-160. DOI: 10.1016/j.jag.2012.04.007 Guhaniyogi, R., Finley, A. O., Banerjee, S., Kobe, R. K. 2013. Modeling Complex Spatial Dependencies: Low-Rank Spatially Varying Cross-Covariances With Application to Soil Nutrient Data. Journal of Agricultural, Biological, and Environmental Statistics. 18(3), 274-298. DOI: 10.1007/s13253-013-0140-3 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Hagen, S., N. Harris, S.S. Saatchi, T. Pearson, C.W. Woodall, S. Ganguly, G.M. Domke, B.H. Braswell, B.F. Walters, J.C. Jenkins, S. Brown, W.A. Salas, A. Fore, Y. Yu, R.R. Nemani, C. Ipsan, and K.R. Brown. 2016. CMS: Forest Carbon Stocks, Emissions, and Net Flux for the Conterminous US: 2005-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1313
Yang, Y., and S.S. Saatchi. 2020. CMS: Terrestrial Carbon Stocks, Emissions, and Fluxes for Conterminous US, 2001-2016. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1837 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
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4th NACP All-Investigators Meeting Posters (2013):
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Shuchman (CMS 2011) (2012) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Development of New Regional Carbon Monitoring Products for the Great Lakes Using Satellite Remote Sensing Data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Robert (Bob) Shuchman, Michigan Technological University
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Successor Projects: | Sayers (CMS 2016) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
The Great Lakes represent approximately 20% of Earth's surface freshwater and are the largest surface area of freshwater on the planet. Understanding the magnitude of the contribution that the Great Lakes make to Earth's carbon budget is important to regional, national, and international carbon monitoring efforts. Quantifying the annual carbon fixation for each of the five Great Lakes as well as determining which of the Lakes are carbon sinks versus sources will be a significant contribution to the overall understanding of the Earth's carbon budget. Despite the large number of in situ based productivity measurements made at selected locations and limited times during the year in the Great Lakes, a strong case can be made that accurate annual lake-wide estimates of primary production do not exist for any of the Great Lakes. Thus, a new approach using satellite data is needed to provide truly lake-wide primary production in these important large ecosystems.
This proposed satellite based program will result in new regional carbon monitoring products that will characterize each Laurentian Great Lake's annual carbon fixation and additionally address whether each Great Lake is a net source or sink of carbon. This will be accomplished through characterization of phytoplankton primary production (PP) using a new Great Lakes Primary Productivity Model (GLPPM). The GLPPM utilizes NASA OceanColor satellite imagery (MODIS, VIIRS). Additionally, aggregating annual PP for all five lakes will give insight into whether the Great Lakes are as a whole is a source or sink of carbon and to determine the significance of the Great Lakes to Earth's total carbon budget. Individual Great Lakes annual carbon production information will also be invaluable input into high resolution regional carbon models.
A key element to the success of this program includes additional field measurements in Lakes Superior, Michigan, Huron and Great Lakes embayments. These in situ observations will be used to better quantify carbon fixation rates that are key to producing accurate carbon estimation products. Additionally the field data will aid in the product accuracy assessment.
In summary, monthly and annual carbon production products for each of the five Great Lakes generated under this program will be provided to stakeholders via an active data sharing program within NOAA/Great Lakes Environmental Research Lab (GLERL) and the Great Lakes Observing System (GLOS). A key to providing this valuable data for decision makers, scientists, the public, and other stakeholders will be rigorous error quantification and accuracy assessment. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Gary Fahnenstiel, Michigan Technological University | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Fahnenstiel, G. L., Sayers, M. J., Shuchman, R. A., Yousef, F., Pothoven, S. A. 2016. Lake-wide phytoplankton production and abundance in the Upper Great Lakes: 2010-2013. Journal of Great Lakes Research. 42(3), 619-629. DOI: 10.1016/j.jglr.2016.02.004 Yousef, F., Charles Kerfoot, W., Shuchman, R., Fahnenstiel, G. 2014. Bio-optical properties and primary production of Lake Michigan: Insights from 13-years of SeaWiFS imagery. Journal of Great Lakes Research. 40(2), 317-324. DOI: 10.1016/j.jglr.2014.02.018 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Gary L. Fahnenstiel, Michael J. Sayers, Robert A. Shuchman, Foad Yousef, & Steven A. Pothoven (2019), Carbon Monitoring System Lake Huron Primary Production Yearly, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/11TMFK7VSHDY
Gary L. Fahnenstiel, Michael J. Sayers, Robert A. Shuchman, Foad Yousef, & Steven A. Pothoven (2019), Carbon Monitoring System Lake Superior Primary Production Monthly, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/FZRE98046VM7 Gary L. Fahnenstiel, Michael J. Sayers, Robert A. Shuchman, Foad Yousef, & Steven A. Pothoven (2019), Carbon Monitoring System Lake Michigan Primary Production Monthly, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/5AZRS4SRGS1R Gary L. Fahnenstiel, Michael J. Sayers, Robert A. Shuchman, Foad Yousef, & Steven A. Pothoven (2019), Carbon Monitoring System Lake Huron Primary Production Monthly, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/CZ39JIR4ZAT4 Gary L. Fahnenstiel, Michael J. Sayers, Robert A. Shuchman, Foad Yousef, & Steven A. Pothoven (2019), Carbon Monitoring System Lake Michigan Primary Production Yearly, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/NCJELM4CS8H8 Gary L. Fahnenstiel, Michael J. Sayers, Robert A. Shuchman, Foad Yousef, & Steven A. Pothoven (2019), Carbon Monitoring System Lake Superior Primary Production Yearly, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/SQ2R9DWW6WDV |
Verdy (CMS 2011) (2012) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Towards a 4D-Var Approach for Estimation of Air-Sea Carbon Dioxide Fluxes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Ariane Verdy, Scripps Institution of Oceanography
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
The challenge- Any Carbon Monitoring System (CMS) must account for fluxes of carbon between the atmosphere and the oceans, the world s largest reservoir of carbon dioxide (CO2). Currently, air-sea CO2 flux estimates are produced by sophisticated physical-biogeochemical models. However, these models still fail to represent significant patterns in the observed fluxes, and these discrepancies are thought to be largely due to errors in the simulation of biogeochemical processes.
Our goal- This proposal capitalizes on two recent developments in oceanography to lay the groundwork for a global ocean CMS with improved biogeochemistry. Satellite measurements of the surface ocean and sensor-based measurements of the interior ocean are rapidly increasing the temporal and spatial coverage of biogeochemical data. Simultaneously, the development of four-dimensional variational assimilation (4D-Var) modeling has combined the forward modeling and traditional static inversion approaches to overcome the primary limitations of both: forward models estimate what could have happened in the ocean rather than what actually happened, and inversions cannot yield predictions. The 4D-Var approach automates the process of adjusting initial conditions and model parameters to produce an optimal fit of the model to physical constraints and all available observations. Our vision is of a state-of-the-art global physical-biogeochemical ocean model that incorporates data from the growing global network of satellites, sensors, and shipboard measurements to improve its estimates of air-sea CO2 fluxes.
Our contribution- We will provide the missing components for 4D-Var physical-biogeochemical assimilation. As we build toward our goal of a global model-observation synthesis, each step of the proposed research will generate independently valuable scientific products:
1. We will test the efficacy of extending the 4D-Var approach to biogeochemistry by using it to optimize both the idealized biogeochemical and physical state of an eddy-resolving model of the California Current Ecosystem (CCE) for 2007-2011. The model will be optimized by adjusting the initial conditions, boundary conditions, external forcing, and parameter values to reduce the misfit between the model and the dense and diverse observations (including in situ measurements of carbon, oxygen, phosphate, pH, and alkalinity) available of the CCE during this time period.
2. We will further develop the biogeochemical component of the model to allow assimilation of satellite-based chlorophyll estimates and to improve the representation of other constraints, and optimize this new implementation of the physical-biogeochemical model to improve our estimate of air-sea CO2 fluxes in the CCE.
3. We will extend the data-processing of hydrographic observations to produce a self-consistent dataset of the quality, richness of properties, and temporal extent that will be required to constrain a global 4D-Var biogeochemical model. GLODAPv2 (GLobal Ocean Data Analysis Project version 2) will be a calibrated unification of existing biogeochemical data products and new data over the period 1972-2011.
As more observations become available, state estimation is undoubtedly the way forward for addressing the objectives of NASA's CMS by bringing together observations and modeling tools to generate accurate high-resolution and time-varying maps of air-sea CO2 fluxes. Together, the development of 4D-Var methods and the observational dataset will enable global model-observation syntheses of the ocean carbon cycle over climate-relevant time scales. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Brendan Carter, NOAA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): |
http://iod.ucsd.edu/~averdy/becco.html http://cdiac.ornl.gov/oceans/glodap/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Verdy, A., Mazloff, M. R., Cornuelle, B. D., Kim, S. Y. 2014. Wind-Driven Sea Level Variability on the California Coast: An Adjoint Sensitivity Analysis. Journal of Physical Oceanography. 44(1), 297-318. DOI: 10.1175/JPO-D-13-018.1 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Key, R.M., A. Olsen, S. van Heuven, S. K. Lauvset, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterström, R. Steinfeldt, E. Jeansson, M. Ishi, F. F. Perez, and T. Suzuki. 2015. Global Ocean Data Analysis Project, Version 2 (GLODAPv2), ORNL/CDIAC-162, NDP-P093. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee.
DOI: 10.3334/CDIAC/OTG.NDP093_GLODAPv2
Ariane Verdy(2017), Ocean Biogeochemistry in the California Current System 2007-2010 L4 Monthly, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), DOI: 10.5067/G854SWM56S7H |
West (CMS 2011) (2012) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project Title: | Estimating Global Inventory-Based Net Carbon Exchange from Agricultural Lands for Use in the NASA Flux Pilot Study | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Science Team |
Tristram (Tris) West, DOE
(Project Lead)
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Solicitation: | NASA: Carbon Monitoring System (2011) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract: |
Inventory-based estimates of C flux have been developed for US agriculture (West et al. 2011), US forests (Zheng et al. 2011 and McKinley et al. 2011), and for North American agriculture and forest lands (Hayes et al. 2012). These estimates combine C uptake, harvest and removal, and C release to generate regional C flux estimates. These estimates differ from carbon biomass or stock estimates which often only represent the net C uptake component of the flux. The inventory-based C flux method of estimation has evolved over the past 5 years, as noted by the recent aforementioned citations, and has been used successfully as input to biogeochemical models, atmospheric transport models, and economic models. Estimates have also been used as independent data sets for comparison with other methods (King et al. 2012). The usefulness of this new method is evident. What is needed now is an expansion of the method for global use. The purpose of this proposed research is to develop a global C budget for agricultural carbon uptake and release, as was done for the US by West et al. (2011). A global US C budget, together with satellite remote sensing of land cover, will provide a gridded global C flux for agricultural lands. This product can be used as input to the NASA Flux Pilot Study and by models currently engaged in the Study. The proposed method combines aforementioned methods of spatially explicit C uptake and release with a NASA-generated global data set on human consumption of agricultural commodities (Imhoff et al. 2004, 2006) for use in the CASA model (Potter et al. 1993, Williams et al. 2012) and other Pilot Study models. Datasets generated will also be commensurate with those used in the DOE Integrated Assessment (IA) program, which allows for future economic projections of land use and human population to be linked with carbon fluxes generated with NASA models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Participants: |
Varaprasad (Prasad) Bandaru, USDA ARS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Project URL(s): | None provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data Products: |
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Publications: |
Wolf, J., West, T. O., Le Page, Y., Kyle, G. P., Zhang, X., Collatz, G. J., Imhoff, M. L. 2015. Biogenic carbon fluxes from global agricultural production and consumption. Global Biogeochemical Cycles. 29(10), 1617-1639. DOI: 10.1002/2015GB005119 West, T. O., Le Page, Y., Huang, M., Wolf, J., Thomson, A. M. 2014. Downscaling global land cover projections from an integrated assessment model for use in regional analyses: results and evaluation for the US from 2005 to 2095. Environmental Research Letters. 9(6), 064004. DOI: 10.1088/1748-9326/9/6/064004 King, A. W., Hayes, D. J., Huntzinger, D. N., West, T. O., Post, W. M. 2012. North American carbon dioxide sources and sinks: magnitude, attribution, and uncertainty. Frontiers in Ecology and the Environment. 10(10), 512-519. DOI: 10.1890/120066 Li, Z., Liu, S., Tan, Z., Bliss, N. B., Young, C. J., West, T. O., Ogle, S. M. 2014. Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States. Ecological Modelling. 277, 1-12. DOI: 10.1016/j.ecolmodel.2014.01.012 Ogle, S. M., Davis, K., Lauvaux, T., Schuh, A., Cooley, D., West, T. O., Heath, L. S., Miles, N. L., Richardson, S., Breidt, F. J., Smith, J. E., McCarty, J. L., Gurney, K. R., Tans, P., Denning, A. S. 2015. An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO 2 concentration data. Environmental Research Letters. 10(3), 034012. DOI: 10.1088/1748-9326/10/3/034012 Post, W. M., Izaurralde, R. C., West, T. O., Liebig, M. A., King, A. W. 2012. Management opportunities for enhancing terrestrial carbon dioxide sinks. Frontiers in Ecology and the Environment. 10(10), 554-561. DOI: 10.1890/120065 Schuh, A. E., Lauvaux, T., West, T. O., Denning, A. S., Davis, K. J., Miles, N., Richardson, S., Uliasz, M., Lokupitiya, E., Cooley, D., Andrews, A., Ogle, S. 2013. Evaluating atmospheric CO2inversions at multiple scales over a highly inventoried agricultural landscape. Global Change Biology. 19(5), 1424-1439. DOI: 10.1111/gcb.12141 West, T. O., Brown, M. E., Duren, R. M., Ogle, S. M., Moss, R. H. 2014. Definition, capabilities and components of a terrestrial carbon monitoring system. Carbon Management. 4(4), 413-422. DOI: 10.4155/CMT.13.36 Zeng, N., Zhao, F., Collatz, G. J., Kalnay, E., Salawitch, R. J., West, T. O., Guanter, L. 2014. Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude. Nature. 515(7527), 394-397. DOI: 10.1038/nature13893 Zhang, X., Sahajpal, R., Manowitz, D. H., Zhao, K., LeDuc, S. D., Xu, M., Xiong, W., Zhang, A., Izaurralde, R. C., Thomson, A. M., West, T. O., Post, W. M. 2014. Multi-scale geospatial agroecosystem modeling: A case study on the influence of soil data resolution on carbon budget estimates. Science of The Total Environment. 479-480, 138-150. DOI: 10.1016/j.scitotenv.2014.01.099 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Archived Data Citations: |
Wolf, J., T.O. West, Y. Le Page, G. Kyle, X. Zhang, G.J. Collatz, and M.L. Imhoff. 2015. CMS: Carbon Fluxes from Global Agricultural Production and Consumption, 2005-2011. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1279
West, T.O. and Le Page, Y. 2014. CMS: Land Cover Projections (5.6-km) from GCAM v3.1 for Conterminous USA, 2005-2095. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1216 |