Sensitivity of a subregional scale atmospheric inverse CO2 modeling framework to boundary conditions

Journal of Geophysical Research Atmospheres

Volumn 115, Issue 24, 2010, Pages

  Göckede, Mathias ^a   Turner, David P ^a   Michalak, Anna M ^b   Vickers, Dean ^c   Law, Beverly E ^a  

^a OREGON STATE UNIVERSITY   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c OREGON STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC MOVEMENTS; BIOSPHERICS; FOSSIL FUELS; INVERSE PROBLEMS;

ATMOSPHERIC SIMULATIONS; BAYESIAN OPTIMIZATION; BOTTOM-UP MODELING; DATA SETS; DROUGHT STRESS; EXCHANGE PROCESS; FLUX FIELDS; FLUX MODEL; FOSSIL FUEL EMISSIONS; HIGHLY SENSITIVE; INVERSE MODELING; INVERSION RESULTS; LAGRANGIAN TRANSPORT; LOW EMISSION; MESOSCALE; MIXING RATIOS; MODEL DOMAINS; MODELING FRAMEWORKS; NORTHWESTERN UNITED STATES; SYSTEMATIC CHANGES; TEMPORAL RESOLUTION; TERRESTRIAL BIOSPHERE;

COMPUTER SIMULATION;

ATMOSPHERIC CHEMISTRY; ATMOSPHERIC MODELING; BAYESIAN ANALYSIS; CARBON BUDGET; CARBON DIOXIDE; DROUGHT STRESS; FOSSIL FUEL; LAGRANGIAN ANALYSIS; MIXING RATIO; PHOTOSYNTHESIS; RESPIRATION; SPATIOTEMPORAL ANALYSIS;

OREGON; UNITED STATES;

EID: 78650958285     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2010JD014443     Document Type: Article

References (50)

1. 2 fluxes, 1988-2003, Global Biogeochem. Cycles, 20, GB1002, doi:10.1029/ 2004GB002439
2. Barnes, R. J., and K. H. You (1992), Adding bounds to kriging, Math. Geol., 24(2), 171-176.
3. Cohen, W. B., et al. (2002), Characterizing 23 years (1972-1995) of stand replacement disturbance in western Oregon forests with Landsat imagery, Ecosystems, 5(2), 122-137.
4. Cressie, N. A. C. (1993), Statistics for Spatial Data, 928 pp., Wiley, New York.
5. Daly, C., et al. (2008), Physiographically sensitive mapping of climatolog-ical temperature and precipitation across the conterminous United States, Int. J. Climatol., 28(15), 2031-2064.
6. Dolman, A. J., C. Gerbig, J. Noilhan, C. Sarrat, and F. Miglietta (2009), Detecting regional variability in sources and sinks of carbon dioxide: a synthesis, Biogeosciences, 6(6), 1015-1026.
7. Enting, I. G. (2005), Inverse Problems in Atmospheric Constituent Transport, 412 pp., Cambridge Univ. Press, New York.
8. Friedlingstein, P., et al. (2006), Climate-carbon cycle feedback analysis: Results from the (CMIP)-M-4 model intercomparison, J. Clim., 19(14), 3337-3353.
9. 2 with atmospheric observations over a continent: 2. Analysis of COBRA data using a receptor-oriented framework, J. Geophys. Res., 108(D24), 4757, doi:10.1029/2003JD003770.
10. Gill, P. E., W. Murray, and M. H. Wright (1986), Practical Optimization, 401 pp., Academic, London.
11. 2 budgets at high spatial and temporal resolution, J. Geophys. Res., 115, D15113, doi:10.1029/2009JD012257.
12. 2 fluxes recovered using a geostatistical inverse modeling approach: 2. Results including auxiliary environmental data, J. Geophys. Res., 113, D21115, doi:10.1029/2007JD009733.
13. 2 sources and sinks using atmospheric transport models, Nature, 415(6872), 626-630.
14. Gurney, K. R., et al. (2004), Transcom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks, Global Biogeochem. Cycles, 18, GB1010, doi:10.1029/2003GB002111.
15. Gurney, K. R., et al. (2009a), VULCAN Science Methods Documention, Version 1.1, 38 pp, available online at http://www.purdue.edu/eas/carbon/ vulcan/Vulcan.documentation.v1.1.pdf.
16. 2 emission fluxes for the United States, Environ. Sci. Technol., 43(14), 5535-5541.
17. Intergovernmental Panel on Climate Change (IPCC) (2007), Climate Change 2007: Synthesis Report, 52 pp., available online at http://www. ipcc.ch/pdf/assessment-report/ar4/syr/ar4-syr.pdf.
18. Irvine, J., B. E. Law, P. M. Anthoni, and F. C. Meinzer (2002), Water limitations to carbon exchange in old-growth and young ponderosa pine stands, Tree Physiol., 22(2-3), 189-196.
19. Jolly, W. M., J. M. Graham, A. Michaelis, R. Nemani, and S. W. Running (2005), A flexible, integrated system for generating meteorological surfaces derived from point sources across multiple geographic scales, Environ. Modell. Software, 20(7), 873-882.
20. Karush, W. (1939), Minima of Function of Several Variables with Inequalities and Side Constraints, Univ. of Chicago, Chicago, Ill.
21. Kuhn, H. W., and A. W. Tucker (1951), Nonlinear Programming, in Second Berkeley Symposium, pp. 481-492, Univ. of California Press, Berkeley, Calif.
22. Lauvaux, T., et al. (2008), Mesoscale inversion: First results from the CERES campaign with synthetic data, Atmos. Chem. Phys., 8(13), 3459-3471.
23. Law, B. E. (2005), Carbon dynamics in response to climate and disturbance: Recent progress from multiscale measurements and modeling in AmeriFlux, in Plant Responses to Air Pollution and Global Change, edited by K. Omasa et al., pp. 205-213, Springer, Tokyo.
24. Law, B. E., and R. H. Waring (1994), Combining remote sensing and climatic data to estimate net primary production across Oregon, Ecol. Appl., 4(4), 717-728.
25. Law, B. E., et al. (2001), Spatial and temporal variation in respiration in a young ponderosa pine forest during a summer drought, Agr. Forest Meteorol., 110(1), 27-43.
26. Law, B. E., et al. (2004), Disturbance and climate effects on carbon stocks and fluxes across Western Oregon USA, Global Change Biol., 10(9), 1429-1444.
27. Lin, J. C., et al. (2003), A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model, J. Geophys. Res., 108(D16), 4493, doi:10.1029/ 2002JD003161.
28. Matross, D. M., et al. (2006), Estimating regional carbon exchange in New England and Quebec by combining atmospheric, ground-based and satellite data, Tellus, Ser. B, 58(5), 344-358.
29. Michalak, A. M., and P. K. Kitanidis (2004), Application of geostatistical inverse modeling to contaminant source identification at Dover AFB, Delaware, J. Hydraul. Res., 42, 9-18.
30. Michalak, A. M., L. Bruhwiler, and P. P. Tans (2004), A geostatistical approach to surface flux estimation of atmospheric trace gases, J. Geo-phys. Res., 109, D14109, doi:10.1029/2003JD004422. (Pubitemid 39364465)
31. Michalak, A. M., et al. (2005), Maximum likelihood estimation of covari-ance parameters for Bayesian atmospheric trace gas surface flux inversions, J. Geophys. Res., 110, D24107, doi:10.1029/2005JD005970. (Pubitemid 43169394)
32. 2 fluxes recovered using a geostatistical inverse modeling approach: 1. Results using atmospheric measurements, J. Geophys. Res., 113, D21114, doi:10.1029/2007JD009734.
33. 2 surface fluxes from atmospheric trace gas observations, J. Geophys. Res., 110, D24304, doi:10.1029/2005JD006157. (Pubitemid 43169417)
34. Peters, W., et al. (2007), An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, Proc. Natl. Acad. Sci. U. S. A., 104(48), 18,925-18,930.
35. Peters, W., et al. (2010), Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations, Global Change Biol., 16(4), 1317-1337.
36. 2 flux estimates over Europe from continuous atmospheric measurements: 1. Inverse methodology, Atmos. Chem. Phys., 5, 3173-3186.
37. Prather, M. J., X. Zhu, S. E. Strahan, S. D. Steenrod, and J. M. Rodriguez (2008), Quantifying errors in trace species transport modeling, Proc. Natl. Acad. Sci. U. S. A., 105(50), 19,617-19,621.
38. 2, Global Biogeochem. Cycles, 19, GB2019, doi:10.1029/ 2004GB002366.
39. 2 observations, Tellus, Ser. B, 51(2), 213-232.
40. 2 measurements: A case study at Trinidad Head, California, J. Geophys. Res., 110, G01005, doi:10.1029/2004JG000007.
41. Rödenbeck, C., C. Gerbig, K. Trusilova, and M. Heimann (2009), A two- step scheme for high-resolution regional atmospheric trace gas inversions based on independent models, Atmos. Chem. Phys., 9(14), 5331-5342.
42. Running, S. W. (2008), Ecosystem Disturbance, Carbon, and Climate, Science, 321(5889), 652-653.
43. Schuh, A. E., et al. (2010), A regional high-resolution carbon flux inversion of North America for 2004, Biogeosciences, 7, 1625-1644.
44. Schwarz, P. A., B. E. Law, M. Williams, J. Irvine, M. Kurpius, and D. Moore (2004), Climatic versus biotic constraints on carbon and water fluxes in seasonally drought-affected ponderosa pine ecosystems, Global Biogeochem. Cycles, 18, GB4007, doi:10.1029/2004GB002234. (Pubitemid 40440569)
45. 2 in relation to climate: A cross-biome analysis across multiple time scales, Biogeosciences, 6, 2297-2312.
46. 2 budget, Science, 247(4949), 1431-1438.
47. Tarantola, A. (1987), Inverse Problem Theory. Methods for Data Fitting and Model Parameter Estimation, 613 pp., Elsevier Sci., New York.
48. Van der Werf, G. R., et al. (2006), Interannual variability in global biomass burning emissions from 1997 to 2004, Atmos. Chem. Phys., 6(11), 3423-3441.
49. Wang, Y.-P., C. M. Trudinger, and I. G. Enting (2009), A review of applications of model-data fusion to studies of terrestrial carbon fluxes at different scales, Agric. For. Meteorol., 149(11), 1829-1842.
50. Williams, M., et al. (2009), Improving land surface models with FLUXNET data, Biogeosciences, 6(7), 1341-1359.